diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 41fdee0554..2834540916 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -199,11 +199,21 @@ jobs:
         source pygraphistry/bin/activate
         ./bin/typecheck.sh
 
+    - name: Full dbscan tests (rich featurize)
+      run: |
+        source pygraphistry/bin/activate
+        ./bin/test-dbscan.sh
+        
     - name: Full feature tests (rich featurize)
       run: |
         source pygraphistry/bin/activate
         ./bin/test-features.sh
 
+    - name: Full search tests (rich featurize)
+      run: |
+        source pygraphistry/bin/activate
+        ./bin/test-text.sh
+
     - name: Full umap tests (rich featurize)
       run: |
         source pygraphistry/bin/activate
diff --git a/.gitignore b/.gitignore
index 9fb2c10c4c..f8a1ee9544 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,7 @@
+# vim temporary files
+*.swp
+*.swo
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 57e82f8602..69fd423074 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -7,9 +7,19 @@ This project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.htm
 
 ## [Development]
 
+### Added
+* AI: moves public `g.g_dgl` from KG `embed` method to private method `g._kg_dgl`
+* AI: moves public `g.DGL_graph` to private attribute `g._dgl_graph`
+* AI: BREAKING CHANGES: to return matrices during transform, set the flag:  `X, y = g.transform(df, return_graph=False)` default behavior is ~ `g2 = g.transform(df)` returning a Plottable instance. 
+
 ## [0.28.7 - 2022-12-22]
 
 ### Added
+* AI: all `transform_*` methods return graphistry Plottable instances, using an infer_graph method. To return matrices, set the `return_graph=False` flag. 
+* AI: adds `g.get_matrix(**kwargs)` general method to retrieve (sub)-feature/target matrices
+* AI: DBSCAN -- `g.featurize().dbscan()` and `g.umap().dbscan()` with options to use UMAP embedding, feature matrix, or subset of feature matrix via `g.dbscan(cols=[...])`
+* AI: Demo cleanup using ModelDict & new features, refactoring demos using `dbscan` and `transform` methods.
+* Tests: dbscan tests
 * AI: Easy import of featurization kwargs for `g.umap(**kwargs)` and `g.featurize(**kwargs)`
 * AI: `g.get_features_by_cols` returns featurized submatrix with `col_part` in their columns
 * AI: `g.conditional_graph` and `g.conditional_probs` assessing conditional probs and graph
diff --git a/README.md b/README.md
index 00ed8c5e2f..4756c86654 100644
--- a/README.md
+++ b/README.md
@@ -358,12 +358,12 @@ Automatically and intelligently transform text, numbers, booleans, and other for
     g = g.umap()  # UMAP, GNNs, use features if already provided, otherwise will compute
 
     # other pydata libraries
-    X = g._node_features  # g._get_feature('nodes')
-    y = g._node_target  # g._get_target('nodes')
+    X = g._node_features  # g._get_feature('nodes') or g.get_matrix()
+    y = g._node_target  # g._get_target('nodes') or g.get_matrix(target=True)
     from sklearn.ensemble import RandomForestRegressor
-    model = RandomForestRegressor().fit(X, y) #assumes train/test split
-    new_df = pandas.read_csv(...)
-    X_new, _ = g.transform(new_df, None, kind='nodes')
+    model = RandomForestRegressor().fit(X, y)  # assumes train/test split
+    new_df = pandas.read_csv(...)  # mini batch
+    X_new, _ = g.transform(new_df, None, kind='nodes', return_graph=False)
     preds = model.predict(X_new)
     ```
 
@@ -371,17 +371,17 @@ Automatically and intelligently transform text, numbers, booleans, and other for
 
    ```python
     # graphistry
-    from graphistry.features import search_model, topic_model, ngrams_model, ModelDict, default_featurize_parameters
+    from graphistry.features import search_model, topic_model, ngrams_model, ModelDict, default_featurize_parameters, default_umap_parameters
 
     g = graphistry.nodes(df)
     g2 = g.umap(X=[..], y=[..], **search_model)  
 
-    # set custom encoding model with any feature kwargs
+    # set custom encoding model with any feature/umap/dbscan kwargs
     new_model = ModelDict(message='encoding new model parameters is easy', **default_featurize_parameters)
     new_model.update(dict(
                       y=[...],
                       kind='edges', 
-                      model_name='sbert/hf/a_cool_transformer_model', 
+                      model_name='sbert/cool_transformer_model', 
                       use_scaler_target='kbins', 
                       n_bins=11, 
                       strategy='normal'))
@@ -389,7 +389,6 @@ Automatically and intelligently transform text, numbers, booleans, and other for
 
     g3 = g.umap(X=[..], **new_model)
     # compare g2 vs g3 or add to different pipelines
-    # ...
     ```
 
 
@@ -397,13 +396,13 @@ See `help(g.featurize)` for more options
 
 ### [sklearn-based UMAP](https://umap-learn.readthedocs.io/en/latest/), [cuML-based UMAP](https://docs.rapids.ai/api/cuml/stable/api.html?highlight=umap#cuml.UMAP)
 
-* Reduce dimensionality and plot a similarity graph from feature vectors:
+* Reduce dimensionality by plotting a similarity graph from feature vectors:
 
     ```python
       # automatic feature engineering, UMAP
       g = graphistry.nodes(df).umap()
       
-      # plot the similarity graph even though there was no explicit edge_dataframe passed in -- it is created during UMAP.
+      # plot the similarity graph without any explicit edge_dataframe passed in -- it is created during UMAP.
       g.plot()
     ```
 
@@ -411,8 +410,20 @@ See `help(g.featurize)` for more options
 
     ```python
       new_df = pd.read_csv(...)
-      embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes')
+      embeddings, X_new, _ = g.transform_umap(new_df, None, kind='nodes', return_graph=False)
     ```
+* Infer a new graph from new data using the old umap coordinates to run inference without having to train a new umap model.
+
+    ```python
+      new_df = pd.read_csv(...)
+      g2 = g.transform_umap(new_df, return_graph=True)   # return_graph=True is default
+      g2.plot()  # 
+      
+      # or if you want the new minibatch to cluster to closest points in previous fit:
+      g3 = g.transform_umap(new_df, return_graph=True, merge_policy=True)
+      g3.plot()  # useful to see how new data connects to old -- play with `sample` and `n_neighbors` to control how much of old to include
+    ```
+    
 
 * UMAP supports many options, such as supervised mode, working on a subset of columns, and passing arguments to underlying `featurize()` and UMAP implementations (see `help(g.umap)`):
 
@@ -451,11 +462,11 @@ See `help(g.umap)` for more options
 
     from [your_training_pipeline] import train, model
     # Train
-    g = graphistry.nodes(df).build_gnn(y=`target`) 
+    g = graphistry.nodes(df).build_gnn(y_nodes=`target`) 
     G = g.DGL_graph
     train(G, model)
     # predict on new data
-    X_new, _ = g.transform(new_df, None, kind='nodes' or 'edges') # no targets
+    X_new, _ = g.transform(new_df, None, kind='nodes' or 'edges', return_graph=False) # no targets
     predictions = model.predict(G_new, X_new)
     ```
 
@@ -480,12 +491,21 @@ GNN support is rapidly evolving, please contact the team directly or on Slack fo
                         #encode text as paraphrase embeddings, supports any sbert model
                         model_name = "paraphrase-MiniLM-L6-v2")
                         
+      # or use convienence `ModelDict` to store parameters
+      
+      from graphistry.features import search_model
+      g2 = g.featurize(X = ['text_col_1', .., 'text_col_n'], kind='nodes', **search_model)
+     
+      # query using the power of transformers to find richly relevant results                   
+      
       results_df, query_vector = g2.search('my natural language query', ...)
       
-      print(results_df[['_distance', 'text_col_1', ..., 'text_col_n']])  #sorted by relevancy
+      print(results_df[['_distance', 'text_col', ..]])  #sorted by relevancy
+      
+      # or see graph of matching entities and original edges
       
-      # or see graph of matching entities and similarity edges (or optional original edges)
       g2.search_graph('my natural language query', ...).plot()
+      
     ```
 
     
@@ -521,7 +541,7 @@ See `help(g.search_graph)` for options
                       relation=['relationship_1', 'relationship_4', ..], 
                       destination=['entity_l', 'entity_m', ..], 
                       threshold=0.9,  # score threshold
-                      return_dataframe=False)  # set to `True` to return dataframe, or just access via `g5._edges`
+                      return_dataframe=False)  # set to `True` to return dataframe, or just access via `g4._edges`
     ```
 
 * Detect Anamolous Behavior (example use cases such as Cyber, Fraud, etc)
@@ -552,8 +572,42 @@ See `help(g.search_graph)` for options
       g2.predict_links_all(threshold=0.95).plot()
     ```
 
-See `help(g.embed)`, `help(g.predict_links)` , `help(g.predict_links_all)` for options
+See `help(g.embed)`, `help(g.predict_links)` , or `help(g.predict_links_all)` for options
+
+### DBSCAN 
+
+* Enrich UMAP embeddings or featurization dataframe with GPU or CPU DBSCAN
+
+    ```python
+      g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+      
+      # cluster by UMAP embeddings
+      kind = 'nodes' | 'edges'
+      g2 = g.umap(kind=kind).dbscan(kind=kind)
+      print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
+
+      # dbscan in `umap` or `featurize` via flag
+      g2 = g.umap(dbscan=True, min_dist=0.2, min_samples=1)
+      
+      # or via chaining,
+      g2 = g.umap().dbscan(min_dist=1.2, min_samples=2, **kwargs)
+      
+      # cluster by feature embeddings
+      g2 = g.featurize().dbscan(**kwargs)
+      
+      # cluster by a given set of feature column attributes, inhereted from `g.get_matrix(cols)`
+      g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], **kwargs)
+      
+      # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
+      g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
+      g2.plot() # color by `_dbscan`
+      
+      new_df = pd.read_csv(..)
+      # transform on new data according to fit dbscan model
+      g3 = g2.transform_dbscan(new_df)
+    ```
 
+See `help(g.dbscan)` or `help(g.transform_dbscan)` for options
 
 ### Quickly configurable
 
diff --git a/bin/test-dbscan.sh b/bin/test-dbscan.sh
new file mode 100755
index 0000000000..8e39b18fb7
--- /dev/null
+++ b/bin/test-dbscan.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+set -ex
+
+# Run from project root
+# - Args get passed to pytest phase
+# Non-zero exit code on fail
+
+# Assume [umap-learn,test]
+
+python -m pytest --version
+
+python -B -m pytest -vv \
+    graphistry/tests/test_compute_cluster.py
+
+#chmod +x bin/test-dbscan.sh
\ No newline at end of file
diff --git a/bin/test-text.sh b/bin/test-text.sh
new file mode 100755
index 0000000000..949bd68735
--- /dev/null
+++ b/bin/test-text.sh
@@ -0,0 +1,15 @@
+#!/bin/bash
+set -ex
+
+# Run from project root
+# - Args get passed to pytest phase
+# Non-zero exit code on fail
+
+# Assume [umap-learn,test]
+
+python -m pytest --version
+
+python -B -m pytest -vv \
+    graphistry/tests/test_text_utils.py
+
+# chmod +x bin/test-text.sh
\ No newline at end of file
diff --git a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
index 47e010af9a..8fd5a0b3a2 100644
--- a/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
+++ b/demos/ai/Introduction/Ask-HackerNews-Demo.ipynb
@@ -5,7 +5,7 @@
    "id": "c39da4a9",
    "metadata": {},
    "source": [
-    "# Hello PyGraphistry[ai] - HackerNews visual semantic search with UMAP & BERT and \n",
+    "# Hello PyGraphistry[ai] - HackerNews visual semantic search with UMAP & BERT.\n",
     "\n",
     "`PyGraphistry[ai]` can quickly create visual graph search interfaces for structured text. It automates much of the work in cleaning, connecting, encoding, searching, and visualing graph data. The result is increasing the *time to graph* and overall results in as little as one line of code.\n",
     "\n",
@@ -28,18 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "385ea5a4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# depends on where you have your data/ folder\n",
-    "#mkdir data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "8e7f75b3",
    "metadata": {},
    "outputs": [],
@@ -49,17 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "cbd6050e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cd .. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "id": "503a96d2",
    "metadata": {},
    "outputs": [],
@@ -81,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "id": "a73d67a0",
    "metadata": {},
    "outputs": [],
@@ -92,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "46f3b61b",
    "metadata": {},
    "outputs": [],
@@ -103,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "0c0bcb74",
    "metadata": {},
    "outputs": [],
@@ -114,7 +93,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "25a407dc",
    "metadata": {},
    "outputs": [],
@@ -124,32 +103,290 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "e8edc98e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['title', 'url', 'text', 'dead', 'by', 'score', 'time', 'timestamp',\n",
+       "       'type', 'id', 'parent', 'descendants', 'ranking', 'deleted'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.columns"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "9536276e",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>url</th>\n",
+       "      <th>text</th>\n",
+       "      <th>dead</th>\n",
+       "      <th>by</th>\n",
+       "      <th>score</th>\n",
+       "      <th>time</th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>type</th>\n",
+       "      <th>id</th>\n",
+       "      <th>parent</th>\n",
+       "      <th>descendants</th>\n",
+       "      <th>ranking</th>\n",
+       "      <th>deleted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I'm a software engineer going blind, how should I prepare?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I&amp;#x27;m a 24 y&amp;#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&amp;#x27;v...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>zachrip</td>\n",
+       "      <td>3270</td>\n",
+       "      <td>1587332026</td>\n",
+       "      <td>2020-04-19 21:33:46+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>22918980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>473.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Am I the longest-serving programmer – 57 years and counting?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>genedangelo</td>\n",
+       "      <td>2634</td>\n",
+       "      <td>1590890024</td>\n",
+       "      <td>2020-05-31 01:53:44+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>23366546</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>531.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is S3 down?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I&amp;#x27;m getting&lt;p&gt;{\\n  &amp;quot;errorCode&amp;quot; : &amp;quot;InternalError&amp;quot;\\n}&lt;p&gt;When I attempt to use the AWS Console to view s3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>iamdeedubs</td>\n",
+       "      <td>2589</td>\n",
+       "      <td>1488303958</td>\n",
+       "      <td>2017-02-28 17:45:58+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>13755673</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1055.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>What tech job would let me get away with the least real work possible?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Hey HN,&lt;p&gt;I&amp;#x27;ll probably get a lot of flak for this. Sorry.&lt;p&gt;I&amp;#x27;m an average developer looking for ways to work as little as humanely possible.&lt;p&gt;The pandemic made me realize that I do no...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>lmueongoqx</td>\n",
+       "      <td>2022</td>\n",
+       "      <td>1617784863</td>\n",
+       "      <td>2021-04-07 08:41:03+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>26721951</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1091.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>What books changed the way you think about almost everything?</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>I was reflecting today about how often I think about Freakonomics. I don&amp;#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>anderspitman</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>1549387905</td>\n",
+       "      <td>2019-02-05 17:31:45+00:00</td>\n",
+       "      <td>story</td>\n",
+       "      <td>19087418</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1165.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                    title  \\\n",
+       "0              I'm a software engineer going blind, how should I prepare?   \n",
+       "1            Am I the longest-serving programmer – 57 years and counting?   \n",
+       "2                                                             Is S3 down?   \n",
+       "3  What tech job would let me get away with the least real work possible?   \n",
+       "4           What books changed the way you think about almost everything?   \n",
+       "\n",
+       "   url  \\\n",
+       "0  NaN   \n",
+       "1  NaN   \n",
+       "2  NaN   \n",
+       "3  NaN   \n",
+       "4  NaN   \n",
+       "\n",
+       "                                                                                                                                                                                                      text  \\\n",
+       "0  I&#x27;m a 24 y&#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&#x27;v...   \n",
+       "1  In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...   \n",
+       "2                                                                          I&#x27;m getting<p>{\\n  &quot;errorCode&quot; : &quot;InternalError&quot;\\n}<p>When I attempt to use the AWS Console to view s3   \n",
+       "3  Hey HN,<p>I&#x27;ll probably get a lot of flak for this. Sorry.<p>I&#x27;m an average developer looking for ways to work as little as humanely possible.<p>The pandemic made me realize that I do no...   \n",
+       "4  I was reflecting today about how often I think about Freakonomics. I don&#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...   \n",
+       "\n",
+       "  dead            by  score        time                  timestamp   type  \\\n",
+       "0  NaN       zachrip   3270  1587332026  2020-04-19 21:33:46+00:00  story   \n",
+       "1  NaN   genedangelo   2634  1590890024  2020-05-31 01:53:44+00:00  story   \n",
+       "2  NaN    iamdeedubs   2589  1488303958  2017-02-28 17:45:58+00:00  story   \n",
+       "3  NaN    lmueongoqx   2022  1617784863  2021-04-07 08:41:03+00:00  story   \n",
+       "4  NaN  anderspitman   2009  1549387905  2019-02-05 17:31:45+00:00  story   \n",
+       "\n",
+       "         id  parent  descendants  ranking  deleted  \n",
+       "0  22918980     NaN        473.0      NaN      NaN  \n",
+       "1  23366546     NaN        531.0      NaN      NaN  \n",
+       "2  13755673     NaN       1055.0      NaN      NaN  \n",
+       "3  26721951     NaN       1091.0      NaN      NaN  \n",
+       "4  19087418     NaN       1165.0      NaN      NaN  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df.head()  # see the dataset"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "3a479ce6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>I'm a software engineer going blind, how should I prepare?</td>\n",
+       "      <td>I&amp;#x27;m a 24 y&amp;#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&amp;#x27;v...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Am I the longest-serving programmer – 57 years and counting?</td>\n",
+       "      <td>In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Is S3 down?</td>\n",
+       "      <td>I&amp;#x27;m getting&lt;p&gt;{\\n  &amp;quot;errorCode&amp;quot; : &amp;quot;InternalError&amp;quot;\\n}&lt;p&gt;When I attempt to use the AWS Console to view s3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>What tech job would let me get away with the least real work possible?</td>\n",
+       "      <td>Hey HN,&lt;p&gt;I&amp;#x27;ll probably get a lot of flak for this. Sorry.&lt;p&gt;I&amp;#x27;m an average developer looking for ways to work as little as humanely possible.&lt;p&gt;The pandemic made me realize that I do no...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>What books changed the way you think about almost everything?</td>\n",
+       "      <td>I was reflecting today about how often I think about Freakonomics. I don&amp;#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                    title  \\\n",
+       "0              I'm a software engineer going blind, how should I prepare?   \n",
+       "1            Am I the longest-serving programmer – 57 years and counting?   \n",
+       "2                                                             Is S3 down?   \n",
+       "3  What tech job would let me get away with the least real work possible?   \n",
+       "4           What books changed the way you think about almost everything?   \n",
+       "\n",
+       "                                                                                                                                                                                                      text  \n",
+       "0  I&#x27;m a 24 y&#x2F;o full stack engineer (I know some of you are rolling your eyes right now, just highlighting that I have experience on frontend apps as well as backend architecture). I&#x27;v...  \n",
+       "1  In May of 1963, I started my first full-time job as a computer programmer for Mitchell Engineering Company, a supplier of steel buildings.  At Mitchell, I developed programs in Fortran II on an IB...  \n",
+       "2                                                                          I&#x27;m getting<p>{\\n  &quot;errorCode&quot; : &quot;InternalError&quot;\\n}<p>When I attempt to use the AWS Console to view s3  \n",
+       "3  Hey HN,<p>I&#x27;ll probably get a lot of flak for this. Sorry.<p>I&#x27;m an average developer looking for ways to work as little as humanely possible.<p>The pandemic made me realize that I do no...  \n",
+       "4  I was reflecting today about how often I think about Freakonomics. I don&#x27;t study it religiously. I read it one time more than 10 years ago. I can only remember maybe a single specific anecdot...  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df[good_cols].head()"
    ]
@@ -164,10 +401,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "748116ab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------------------------------------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Encoding 3000 records using SentenceTransformer took 1.53 minutes\n"
+     ]
+    }
+   ],
    "source": [
     "from time import time\n",
     "t0 = time()\n",
@@ -183,23 +442,22 @@
     "# set to False if you want to reload last trained instance\n",
     "process = True\n",
     "\n",
+    "print('-'*80)\n",
     "if process:\n",
     "    # Umap will create a similarity graph from the features which we can view as a graph\n",
-    "    g2 = g.umap(X=['title', 'text'], # the features to encode (can add/remove 'text', etc)\n",
+    "    g2 = g.umap(X=['title'], # the features to encode (can add/remove 'text', etc)\n",
     "                y=['score'], # for demonstrative purposes, we include a target -- though this one is not really conditioned on textual features in a straightforward way\n",
     "                model_name='msmarco-distilbert-base-v2', #'paraphrase-MiniLM-L6-v2', etc, from sbert/Huggingface, the text encoding model\n",
     "                min_words = 0, # when 0 forces all X=[..] as textually encoded, higher values would ascertain if a column is textual or not depending on average number of words per column\n",
     "                use_ngrams=False, # set to True if you want ngram features instead (does not make great plots but useful for other situations)\n",
-    "                use_scaler_target='zscale', # for regressive targets\n",
+    "                use_scaler_target='standard', # for regressive targets\n",
     "                use_scaler=None, # there are many more settings see `g.featurize?` and `g.umap?` for further options\n",
     "               )\n",
     "    g2.save_search_instance('data/hn.search')\n",
-    "    print('-'*80)\n",
     "    print(f'Encoding {df.shape[0]} records using {str(g2._node_encoder.text_model)[:19]} took {(time()-t0)/60:.2f} minutes')\n",
     "else:\n",
     "    # or load the search instance\n",
     "    g2 = g.load_search_instance('data/hn.search')\n",
-    "    print('-'*80)\n",
     "    print(f'Loaded saved instance')\n",
     "    \n",
     "################################################################\n"
@@ -207,10 +465,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "d6de7795",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c37e7656-a104-481f-a3bd-c13e15c036cd\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7fb76b13a32f4872a52f160603a73c5f&type=arrow&viztoken=3c2c8be8-c38f-4b43-95c0-1bfdda8802e2&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280727&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c37e7656-a104-481f-a3bd-c13e15c036cd\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# see all the data\n",
     "g2.plot()"
@@ -218,36 +504,541 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "22ed4eec",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title_0</th>\n",
+       "      <th>title_1</th>\n",
+       "      <th>title_2</th>\n",
+       "      <th>title_3</th>\n",
+       "      <th>title_4</th>\n",
+       "      <th>title_5</th>\n",
+       "      <th>title_6</th>\n",
+       "      <th>title_7</th>\n",
+       "      <th>title_8</th>\n",
+       "      <th>title_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>title_758</th>\n",
+       "      <th>title_759</th>\n",
+       "      <th>title_760</th>\n",
+       "      <th>title_761</th>\n",
+       "      <th>title_762</th>\n",
+       "      <th>title_763</th>\n",
+       "      <th>title_764</th>\n",
+       "      <th>title_765</th>\n",
+       "      <th>title_766</th>\n",
+       "      <th>title_767</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1654</th>\n",
+       "      <td>0.604488</td>\n",
+       "      <td>0.101652</td>\n",
+       "      <td>-0.063497</td>\n",
+       "      <td>0.307542</td>\n",
+       "      <td>0.844374</td>\n",
+       "      <td>0.197561</td>\n",
+       "      <td>0.896631</td>\n",
+       "      <td>0.631857</td>\n",
+       "      <td>0.315805</td>\n",
+       "      <td>-0.578581</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.042918</td>\n",
+       "      <td>-0.322729</td>\n",
+       "      <td>-0.277031</td>\n",
+       "      <td>-0.319512</td>\n",
+       "      <td>-0.165631</td>\n",
+       "      <td>-0.584383</td>\n",
+       "      <td>0.261868</td>\n",
+       "      <td>0.429799</td>\n",
+       "      <td>-0.303072</td>\n",
+       "      <td>-0.377494</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>-1.103507</td>\n",
+       "      <td>-0.835217</td>\n",
+       "      <td>-1.237520</td>\n",
+       "      <td>0.549196</td>\n",
+       "      <td>0.397246</td>\n",
+       "      <td>0.199831</td>\n",
+       "      <td>-1.196874</td>\n",
+       "      <td>0.290311</td>\n",
+       "      <td>-1.171076</td>\n",
+       "      <td>0.513540</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.006427</td>\n",
+       "      <td>-0.731422</td>\n",
+       "      <td>-0.750713</td>\n",
+       "      <td>-0.486637</td>\n",
+       "      <td>0.841622</td>\n",
+       "      <td>-0.198652</td>\n",
+       "      <td>0.195885</td>\n",
+       "      <td>-0.570250</td>\n",
+       "      <td>0.050978</td>\n",
+       "      <td>-0.436235</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2708</th>\n",
+       "      <td>0.326092</td>\n",
+       "      <td>0.045712</td>\n",
+       "      <td>0.308224</td>\n",
+       "      <td>0.803963</td>\n",
+       "      <td>-0.063246</td>\n",
+       "      <td>-0.123905</td>\n",
+       "      <td>-0.731468</td>\n",
+       "      <td>0.227643</td>\n",
+       "      <td>0.261804</td>\n",
+       "      <td>-0.048012</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.206501</td>\n",
+       "      <td>0.034402</td>\n",
+       "      <td>0.796114</td>\n",
+       "      <td>-0.237042</td>\n",
+       "      <td>0.117702</td>\n",
+       "      <td>0.649347</td>\n",
+       "      <td>-0.299433</td>\n",
+       "      <td>0.995765</td>\n",
+       "      <td>-0.009557</td>\n",
+       "      <td>-0.119748</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.926326</td>\n",
+       "      <td>-0.392618</td>\n",
+       "      <td>0.035194</td>\n",
+       "      <td>-0.161504</td>\n",
+       "      <td>-0.326212</td>\n",
+       "      <td>-0.166737</td>\n",
+       "      <td>0.070937</td>\n",
+       "      <td>0.950549</td>\n",
+       "      <td>-0.228309</td>\n",
+       "      <td>-0.056017</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.551132</td>\n",
+       "      <td>0.639072</td>\n",
+       "      <td>-0.468963</td>\n",
+       "      <td>-0.290477</td>\n",
+       "      <td>0.117795</td>\n",
+       "      <td>-0.803580</td>\n",
+       "      <td>0.804826</td>\n",
+       "      <td>0.423588</td>\n",
+       "      <td>-0.092650</td>\n",
+       "      <td>-0.687976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>0.384588</td>\n",
+       "      <td>-0.832760</td>\n",
+       "      <td>0.033876</td>\n",
+       "      <td>0.215492</td>\n",
+       "      <td>0.593188</td>\n",
+       "      <td>-0.432190</td>\n",
+       "      <td>-0.283562</td>\n",
+       "      <td>0.400813</td>\n",
+       "      <td>0.045255</td>\n",
+       "      <td>-0.430429</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.028084</td>\n",
+       "      <td>-0.152095</td>\n",
+       "      <td>-0.226646</td>\n",
+       "      <td>0.208703</td>\n",
+       "      <td>0.187091</td>\n",
+       "      <td>0.133619</td>\n",
+       "      <td>0.486250</td>\n",
+       "      <td>0.575210</td>\n",
+       "      <td>0.730881</td>\n",
+       "      <td>-0.129466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1264</th>\n",
+       "      <td>-0.098364</td>\n",
+       "      <td>-0.276086</td>\n",
+       "      <td>0.550586</td>\n",
+       "      <td>0.542078</td>\n",
+       "      <td>0.321339</td>\n",
+       "      <td>-0.601650</td>\n",
+       "      <td>-0.540975</td>\n",
+       "      <td>-0.333377</td>\n",
+       "      <td>0.094011</td>\n",
+       "      <td>0.031201</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.266734</td>\n",
+       "      <td>-1.171137</td>\n",
+       "      <td>0.190349</td>\n",
+       "      <td>-1.094334</td>\n",
+       "      <td>-0.939085</td>\n",
+       "      <td>0.294115</td>\n",
+       "      <td>-0.118376</td>\n",
+       "      <td>-0.473456</td>\n",
+       "      <td>-0.321870</td>\n",
+       "      <td>0.111786</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1171</th>\n",
+       "      <td>0.702274</td>\n",
+       "      <td>-0.091761</td>\n",
+       "      <td>0.348669</td>\n",
+       "      <td>-0.431706</td>\n",
+       "      <td>1.191116</td>\n",
+       "      <td>0.006005</td>\n",
+       "      <td>-1.105823</td>\n",
+       "      <td>-0.625805</td>\n",
+       "      <td>-0.168052</td>\n",
+       "      <td>0.075096</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.838124</td>\n",
+       "      <td>-0.305236</td>\n",
+       "      <td>0.398299</td>\n",
+       "      <td>0.156232</td>\n",
+       "      <td>0.146867</td>\n",
+       "      <td>0.339570</td>\n",
+       "      <td>-0.152106</td>\n",
+       "      <td>-0.456346</td>\n",
+       "      <td>-0.393480</td>\n",
+       "      <td>0.293989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>-0.406301</td>\n",
+       "      <td>-0.531044</td>\n",
+       "      <td>-0.563821</td>\n",
+       "      <td>-0.012661</td>\n",
+       "      <td>0.380232</td>\n",
+       "      <td>0.187900</td>\n",
+       "      <td>0.169093</td>\n",
+       "      <td>0.475025</td>\n",
+       "      <td>-0.772457</td>\n",
+       "      <td>0.188258</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.478902</td>\n",
+       "      <td>-0.781922</td>\n",
+       "      <td>0.135231</td>\n",
+       "      <td>0.847367</td>\n",
+       "      <td>0.451199</td>\n",
+       "      <td>0.420809</td>\n",
+       "      <td>0.683643</td>\n",
+       "      <td>-0.713218</td>\n",
+       "      <td>0.390578</td>\n",
+       "      <td>-0.141390</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2342</th>\n",
+       "      <td>0.128966</td>\n",
+       "      <td>0.168480</td>\n",
+       "      <td>0.055048</td>\n",
+       "      <td>-0.287427</td>\n",
+       "      <td>-0.069591</td>\n",
+       "      <td>-0.533780</td>\n",
+       "      <td>-0.401158</td>\n",
+       "      <td>-0.270016</td>\n",
+       "      <td>-0.398377</td>\n",
+       "      <td>0.062334</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.068983</td>\n",
+       "      <td>-0.483162</td>\n",
+       "      <td>-0.373780</td>\n",
+       "      <td>-0.411517</td>\n",
+       "      <td>0.044580</td>\n",
+       "      <td>0.602551</td>\n",
+       "      <td>0.423918</td>\n",
+       "      <td>0.028719</td>\n",
+       "      <td>-0.160396</td>\n",
+       "      <td>0.211980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2782</th>\n",
+       "      <td>-0.144229</td>\n",
+       "      <td>0.703746</td>\n",
+       "      <td>-0.852380</td>\n",
+       "      <td>-0.084720</td>\n",
+       "      <td>-0.654991</td>\n",
+       "      <td>-0.374648</td>\n",
+       "      <td>0.142915</td>\n",
+       "      <td>-0.072289</td>\n",
+       "      <td>-0.082889</td>\n",
+       "      <td>0.965485</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.068612</td>\n",
+       "      <td>0.432348</td>\n",
+       "      <td>-0.718999</td>\n",
+       "      <td>-0.465670</td>\n",
+       "      <td>1.038647</td>\n",
+       "      <td>0.308591</td>\n",
+       "      <td>-0.369232</td>\n",
+       "      <td>0.004829</td>\n",
+       "      <td>-0.020801</td>\n",
+       "      <td>0.027217</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 768 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       title_0   title_1   title_2   title_3   title_4   title_5   title_6  \\\n",
+       "1654  0.604488  0.101652 -0.063497  0.307542  0.844374  0.197561  0.896631   \n",
+       "1538 -1.103507 -0.835217 -1.237520  0.549196  0.397246  0.199831 -1.196874   \n",
+       "2708  0.326092  0.045712  0.308224  0.803963 -0.063246 -0.123905 -0.731468   \n",
+       "62    0.926326 -0.392618  0.035194 -0.161504 -0.326212 -0.166737  0.070937   \n",
+       "1481  0.384588 -0.832760  0.033876  0.215492  0.593188 -0.432190 -0.283562   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "1264 -0.098364 -0.276086  0.550586  0.542078  0.321339 -0.601650 -0.540975   \n",
+       "1171  0.702274 -0.091761  0.348669 -0.431706  1.191116  0.006005 -1.105823   \n",
+       "589  -0.406301 -0.531044 -0.563821 -0.012661  0.380232  0.187900  0.169093   \n",
+       "2342  0.128966  0.168480  0.055048 -0.287427 -0.069591 -0.533780 -0.401158   \n",
+       "2782 -0.144229  0.703746 -0.852380 -0.084720 -0.654991 -0.374648  0.142915   \n",
+       "\n",
+       "       title_7   title_8   title_9  ...  title_758  title_759  title_760  \\\n",
+       "1654  0.631857  0.315805 -0.578581  ...  -0.042918  -0.322729  -0.277031   \n",
+       "1538  0.290311 -1.171076  0.513540  ...   0.006427  -0.731422  -0.750713   \n",
+       "2708  0.227643  0.261804 -0.048012  ...  -0.206501   0.034402   0.796114   \n",
+       "62    0.950549 -0.228309 -0.056017  ...  -0.551132   0.639072  -0.468963   \n",
+       "1481  0.400813  0.045255 -0.430429  ...   0.028084  -0.152095  -0.226646   \n",
+       "...        ...       ...       ...  ...        ...        ...        ...   \n",
+       "1264 -0.333377  0.094011  0.031201  ...  -0.266734  -1.171137   0.190349   \n",
+       "1171 -0.625805 -0.168052  0.075096  ...   0.838124  -0.305236   0.398299   \n",
+       "589   0.475025 -0.772457  0.188258  ...  -0.478902  -0.781922   0.135231   \n",
+       "2342 -0.270016 -0.398377  0.062334  ...   1.068983  -0.483162  -0.373780   \n",
+       "2782 -0.072289 -0.082889  0.965485  ...   0.068612   0.432348  -0.718999   \n",
+       "\n",
+       "      title_761  title_762  title_763  title_764  title_765  title_766  \\\n",
+       "1654  -0.319512  -0.165631  -0.584383   0.261868   0.429799  -0.303072   \n",
+       "1538  -0.486637   0.841622  -0.198652   0.195885  -0.570250   0.050978   \n",
+       "2708  -0.237042   0.117702   0.649347  -0.299433   0.995765  -0.009557   \n",
+       "62    -0.290477   0.117795  -0.803580   0.804826   0.423588  -0.092650   \n",
+       "1481   0.208703   0.187091   0.133619   0.486250   0.575210   0.730881   \n",
+       "...         ...        ...        ...        ...        ...        ...   \n",
+       "1264  -1.094334  -0.939085   0.294115  -0.118376  -0.473456  -0.321870   \n",
+       "1171   0.156232   0.146867   0.339570  -0.152106  -0.456346  -0.393480   \n",
+       "589    0.847367   0.451199   0.420809   0.683643  -0.713218   0.390578   \n",
+       "2342  -0.411517   0.044580   0.602551   0.423918   0.028719  -0.160396   \n",
+       "2782  -0.465670   1.038647   0.308591  -0.369232   0.004829  -0.020801   \n",
+       "\n",
+       "      title_767  \n",
+       "1654  -0.377494  \n",
+       "1538  -0.436235  \n",
+       "2708  -0.119748  \n",
+       "62    -0.687976  \n",
+       "1481  -0.129466  \n",
+       "...         ...  \n",
+       "1264   0.111786  \n",
+       "1171   0.293989  \n",
+       "589   -0.141390  \n",
+       "2342   0.211980  \n",
+       "2782   0.027217  \n",
+       "\n",
+       "[3000 rows x 768 columns]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# get the encoded features, and use in downstream models (clf.fit(x, y), etc)\n",
     "x=g2._get_feature('nodes')\n",
+    "# same as \n",
+    "x = g2._node_features\n",
+    "# same as\n",
+    "x = g2.get_matrix()\n",
     "x"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "67b15408",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1654</th>\n",
+       "      <td>-0.38835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1538</th>\n",
+       "      <td>-0.33530</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2708</th>\n",
+       "      <td>-0.71150</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>2.70326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1481</th>\n",
+       "      <td>-0.31601</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1264</th>\n",
+       "      <td>-0.19060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1171</th>\n",
+       "      <td>-0.13273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>0.44605</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2342</th>\n",
+       "      <td>-0.62951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2782</th>\n",
+       "      <td>-0.72115</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3000 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        score\n",
+       "1654 -0.38835\n",
+       "1538 -0.33530\n",
+       "2708 -0.71150\n",
+       "62    2.70326\n",
+       "1481 -0.31601\n",
+       "...       ...\n",
+       "1264 -0.19060\n",
+       "1171 -0.13273\n",
+       "589   0.44605\n",
+       "2342 -0.62951\n",
+       "2782 -0.72115\n",
+       "\n",
+       "[3000 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# likewise with the (scaled) targets\n",
     "y = g2._get_target('nodes')\n",
+    "# same as \n",
+    "y = g2._node_target\n",
+    "# same as\n",
+    "y = g2.get_matrix(target=True)\n",
     "y"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "f43b7806",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"a5c69c70-ac12-454b-87f9-47a383660a94\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=588279365f0d42299c8480bbea46da57&type=arrow&viztoken=1a60b723-1c53-4097-88fa-5a33a5ce2d73&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280731&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#a5c69c70-ac12-454b-87f9-47a383660a94\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# visualize the results where we prune edges using the `filter_weighted_edges` method\n",
     "# this keeps all weights that are (more similar) 0.5 and above. The initial layout is the same (given by umap in 2d)\n",
@@ -265,10 +1056,73 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "e79eabfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1647</th>\n",
+       "      <td>Is it normal to fall out of love with coding?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1412</th>\n",
+       "      <td>What landing page do you love?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2854</th>\n",
+       "      <td>Hackers falling in love</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2770</th>\n",
+       "      <td>What do you love/hate about terminals? Would you change them?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>Have you found something you love to do? If yes how?</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                              title\n",
+       "1647                  Is it normal to fall out of love with coding?\n",
+       "1412                                 What landing page do you love?\n",
+       "2854                                        Hackers falling in love\n",
+       "2770  What do you love/hate about terminals? Would you change them?\n",
+       "1182           Have you found something you love to do? If yes how?"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# direct keyword search when fuzzy=False and a set of columns are given, does not require featurization\n",
     "g.search('love', fuzzy=False, cols=['title'])[0][['title']]"
@@ -276,12 +1130,250 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
+   "id": "c9a8e3bb-faf0-432f-be9e-b173528af866",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2532</th>\n",
+       "      <td>How did you find your passion?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1182</th>\n",
+       "      <td>Have you found something you love to do? If yes how?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2509</th>\n",
+       "      <td>After almost 30 years the romance is over - What now?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2669</th>\n",
+       "      <td>Is it better to be good at many things or great at one thing?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1164</th>\n",
+       "      <td>My wife needs something to do from home to make money...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2469</th>\n",
+       "      <td>Does success in work bring you happiness?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2177</th>\n",
+       "      <td>As an adult introvertish nerd what makes you happy?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1650</th>\n",
+       "      <td>Anxiety is limiting my enjoyment of a wonderful career. Can you relate?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1853</th>\n",
+       "      <td>What do you wish you had done/known in your 30s?</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1360</th>\n",
+       "      <td>Turning 40 soon – seeking personal and professional life advice</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                                        title\n",
+       "2532                                           How did you find your passion?\n",
+       "1182                     Have you found something you love to do? If yes how?\n",
+       "2509                    After almost 30 years the romance is over - What now?\n",
+       "2669            Is it better to be good at many things or great at one thing?\n",
+       "1164                 My wife needs something to do from home to make money...\n",
+       "2469                                Does success in work bring you happiness?\n",
+       "2177                      As an adult introvertish nerd what makes you happy?\n",
+       "1650  Anxiety is limiting my enjoyment of a wonderful career. Can you relate?\n",
+       "1853                         What do you wish you had done/known in your 30s?\n",
+       "1360          Turning 40 soon – seeking personal and professional life advice"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.search('love')[0][['title']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "id": "85cf9c06",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "*********************************\n",
+      "Is true love possible?\n",
+      "******************************\n",
+      "1182             Have you found something you love to do? If yes how?\n",
+      "2043        Why aren't there many credible online bachelors programs?\n",
+      "2509            After almost 30 years the romance is over - What now?\n",
+      "2469                        Does success in work bring you happiness?\n",
+      "2532                                   How did you find your passion?\n",
+      "1569             What do you wish you had known before you turned 40?\n",
+      "2669    Is it better to be good at many things or great at one thing?\n",
+      "1853                 What do you wish you had done/known in your 30s?\n",
+      "1198            What are the books you wish your colleagues had read?\n",
+      "400                                        What Lived Up to the Hype?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "How to create deep learning models?\n",
+      "******************************\n",
+      "35                     How to get started with machine learning?\n",
+      "959          How to Seriously Start with Machine Learning and AI\n",
+      "2172         Why TensorFlow instead of Theano for deep learning?\n",
+      "1833               How do you manage multiple learning projects?\n",
+      "1739           How to incorporate machine learning into day job?\n",
+      "208                Good ways to capture institutional knowledge?\n",
+      "1726                       What do you use Machine Learning for?\n",
+      "2219                How do I start with test driven development?\n",
+      "1988                            How to develop a growth mindset?\n",
+      "704     Best introductory video courses on ML and Deep Learning?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Best tech careers\n",
+      "******************************\n",
+      "1328      What tech that's right around the corner are you most excited about?\n",
+      "366                                              Joining Big Tech in One’s 40s\n",
+      "3       What tech job would let me get away with the least real work possible?\n",
+      "247             What is the most exciting development in your field right now?\n",
+      "2428                                Companies of one, what is your tech stack?\n",
+      "748                                 Companies of one, what is your tech stack?\n",
+      "981       Who here has built a profitable startup while keeping their day job?\n",
+      "831                       What company environment has enabled your best work?\n",
+      "801         What are some of the best job boards you have seen (any industry)?\n",
+      "259                      What was your experience starting a tech consultancy?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "How do I make more money?\n",
+      "******************************\n",
+      "1302                                    How do you earn your money?\n",
+      "975                             Why can't I make as much as I make?\n",
+      "500        Ways to generate income when you're at home without pay?\n",
+      "1164       My wife needs something to do from home to make money...\n",
+      "1034     How do you motivate yourself to keep working on a project?\n",
+      "2496      Should I find a job or try to build a profitable project?\n",
+      "844     How do you decide when you've done enough work for the day?\n",
+      "402                      How to optimize your career for happiness?\n",
+      "1870         How to get out of Tech and still make a decent living?\n",
+      "1987                         How do you stay productive after work?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Advances in particle physics\n",
+      "******************************\n",
+      "1277                  What are the greatest discoveries in the last few years?\n",
+      "1200               Will there ever be a resurgence of interest in symbolic AI?\n",
+      "438     What tech were you convinced would take the world by storm but didn't?\n",
+      "850               Any scientifically proven techniques to boost concentration?\n",
+      "738                 Has any progress been made on large format E-ink displays?\n",
+      "2528                       Why aren't there any real alternatives to Electron?\n",
+      "1029                     I'm looking for a good book on the fundamentals of CS\n",
+      "2399              What is the emerging state of the art in fuzzing techniques?\n",
+      "522              What are some interesting projects to reuse your old devices?\n",
+      "650                            What things do you wish you discovered earlier?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Best apps and gadgets\n",
+      "******************************\n",
+      "2638    What are the best web tools to build basic web apps as of October 2016?\n",
+      "817          Best-architected open-source business applications worth studying?\n",
+      "2769                                              What Android apps do you use?\n",
+      "1032               What are the best technologies you've worked with this year?\n",
+      "1439                    What is the best enterprise software you use every day?\n",
+      "2826                       Inspirational money making web apps made by hackers.\n",
+      "211           What's your favorite way of getting a web app up quickly in 2018?\n",
+      "2773       Best tech for a web site 2018? (PHP, Rails, Django, Node, Go, etc.)?\n",
+      "1923        What is good business advice for independent mobile app developers?\n",
+      "2658            What is the best way to promote your new fancy web application?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "Graph Neural Networks\n",
+      "******************************\n",
+      "1827                       What was your experience using a graph database?\n",
+      "1825                                  Why GraphQL APIs but no Datalog APIs?\n",
+      "1155    Why are relational DBs are the standard instead of graph-based DBs?\n",
+      "1540               If you've used a graph database, would you use it again?\n",
+      "799                    Were you happy moving your API from REST to GraphQL?\n",
+      "919           What's the best algorithms and data structures online course?\n",
+      "2907    What are the best resources for learning about algorithmic trading?\n",
+      "1436                   Looking for a book on algorithms and data structures\n",
+      "377                 What are some examples of good database schema designs?\n",
+      "2498                                        Building a game for AI Research\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "recommend impactful books\n",
+      "******************************\n",
+      "113     Great fiction books that have had a positive impact on your life?\n",
+      "2507                       What book impacted your life the most and how?\n",
+      "104        What books have made the biggest impact on your mental models?\n",
+      "2737                 Which books have helped you the most professionally?\n",
+      "523        Which non-technology book has influenced you the most and why?\n",
+      "1099                           Recommendations of good cybercrime novels?\n",
+      "1933         Recommend books that give you insight into other professions\n",
+      "2837              What are the best books for professional effectiveness?\n",
+      "1764               What is one book you would recommend everyone to read?\n",
+      "815           What makes a good technical leader – any recommended books?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n",
+      "*********************************\n",
+      "lamenting about life\n",
+      "******************************\n",
+      "1218                         Words of encouragement for someone lost in life?\n",
+      "1337                                              What do you regret in life?\n",
+      "2155                               The Internet is getting lame. What's next?\n",
+      "741                                                             I'm So Lonely\n",
+      "2165                                                   Coping with Loneliness\n",
+      "514                                               When you feel stuck in life\n",
+      "1526    What should I say to my manager when my performance starts suffering?\n",
+      "969                              How to cope with the death of a dear person?\n",
+      "2195                                 I'm a solopreneur and I feel demoralised\n",
+      "520      Failed interview, feeling unemployable and depressed – what do I do?\n",
+      "Name: title, dtype: object\n",
+      "------------------------------------------------------------\n"
+     ]
+    }
+   ],
    "source": [
     "# Query semantically instead of strict keyword matching\n",
     "\n",
@@ -317,10 +1409,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "302a0b53",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"ac49af37-83dc-4064-bfdd-60c2f9df03da\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8ba787b703f6488eae2354fec63fd13a&type=arrow&viztoken=b52fd61f-d1bb-4fd2-9221-d2a727c6a5ef&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280734&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#ac49af37-83dc-4064-bfdd-60c2f9df03da\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "gr = g2.search_graph('How to create deep learning models', thresh=15, top_n=50, scale=0.25, broader=False) \n",
     "gr.plot()"
@@ -328,20 +1448,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "543f7b83",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"ed95c853-575d-4f9d-a6af-8eff8ab5f1ef\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=adbbbef85cda47e39047ec024c6b307a&type=arrow&viztoken=859108a5-9d2e-4cf1-b56c-9a13893cfbf4&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280736&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#ed95c853-575d-4f9d-a6af-8eff8ab5f1ef\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2.search_graph('Graph Neural Networks', thresh=50, top_n=50, scale=0.1, broader=False).plot()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "6f2f9157",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"44a0ba1f-8cb1-49ec-b152-3369e7f5537c\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1c9439fa209b428ebf8a8687e1b7d5cc&type=arrow&viztoken=1cbd5b1b-bd0d-438e-8228-007fa3135a61&usertag=8a6d667e-pygraphistry-0.28.6+137.g985f699.dirty&splashAfter=1674280738&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#44a0ba1f-8cb1-49ec-b152-3369e7f5537c\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2.search_graph('fraud detection algorithms', thresh=50, top_n=50, scale=0.1, broader=False).plot()  # works better if you encode 'text' column as well"
    ]
@@ -356,12 +1532,360 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "09b941fe",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title_0</th>\n",
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+       "      <th>title_2</th>\n",
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+       "      <th>title_5</th>\n",
+       "      <th>title_6</th>\n",
+       "      <th>title_7</th>\n",
+       "      <th>title_8</th>\n",
+       "      <th>title_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>title_758</th>\n",
+       "      <th>title_759</th>\n",
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+       "      <th>title_766</th>\n",
+       "      <th>title_767</th>\n",
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+       "      <th>1517</th>\n",
+       "      <td>0.979180</td>\n",
+       "      <td>0.041072</td>\n",
+       "      <td>-0.593098</td>\n",
+       "      <td>0.214304</td>\n",
+       "      <td>0.962338</td>\n",
+       "      <td>-0.713328</td>\n",
+       "      <td>-0.989697</td>\n",
+       "      <td>0.145388</td>\n",
+       "      <td>0.075608</td>\n",
+       "      <td>0.488215</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.696708</td>\n",
+       "      <td>0.139317</td>\n",
+       "      <td>-0.846404</td>\n",
+       "      <td>-0.025490</td>\n",
+       "      <td>-0.120986</td>\n",
+       "      <td>-0.055843</td>\n",
+       "      <td>0.637930</td>\n",
+       "      <td>0.143514</td>\n",
+       "      <td>0.585826</td>\n",
+       "      <td>-0.303448</td>\n",
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+       "    <tr>\n",
+       "      <th>1605</th>\n",
+       "      <td>0.688653</td>\n",
+       "      <td>0.019184</td>\n",
+       "      <td>-0.227892</td>\n",
+       "      <td>-0.239211</td>\n",
+       "      <td>0.189795</td>\n",
+       "      <td>0.341207</td>\n",
+       "      <td>-0.120877</td>\n",
+       "      <td>-1.296878</td>\n",
+       "      <td>0.206908</td>\n",
+       "      <td>0.103815</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.236452</td>\n",
+       "      <td>-0.384294</td>\n",
+       "      <td>0.394059</td>\n",
+       "      <td>0.244530</td>\n",
+       "      <td>-0.364924</td>\n",
+       "      <td>0.675864</td>\n",
+       "      <td>-0.271245</td>\n",
+       "      <td>-0.173634</td>\n",
+       "      <td>-0.298026</td>\n",
+       "      <td>-0.017564</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1451</th>\n",
+       "      <td>-0.184770</td>\n",
+       "      <td>0.235804</td>\n",
+       "      <td>-0.400443</td>\n",
+       "      <td>-0.211511</td>\n",
+       "      <td>0.114818</td>\n",
+       "      <td>0.160413</td>\n",
+       "      <td>-0.131262</td>\n",
+       "      <td>0.500900</td>\n",
+       "      <td>-0.275231</td>\n",
+       "      <td>0.190890</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.091999</td>\n",
+       "      <td>-0.233226</td>\n",
+       "      <td>-0.072699</td>\n",
+       "      <td>-0.713460</td>\n",
+       "      <td>0.423684</td>\n",
+       "      <td>1.398612</td>\n",
+       "      <td>-0.203436</td>\n",
+       "      <td>0.473697</td>\n",
+       "      <td>-0.219005</td>\n",
+       "      <td>-0.128714</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1372</th>\n",
+       "      <td>-0.983680</td>\n",
+       "      <td>0.548434</td>\n",
+       "      <td>-0.584351</td>\n",
+       "      <td>0.353703</td>\n",
+       "      <td>0.117870</td>\n",
+       "      <td>-0.098879</td>\n",
+       "      <td>1.095775</td>\n",
+       "      <td>-0.385954</td>\n",
+       "      <td>-0.541251</td>\n",
+       "      <td>0.007578</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.047079</td>\n",
+       "      <td>0.485933</td>\n",
+       "      <td>-0.285741</td>\n",
+       "      <td>-0.035659</td>\n",
+       "      <td>-0.101807</td>\n",
+       "      <td>0.110145</td>\n",
+       "      <td>1.122281</td>\n",
+       "      <td>-0.237854</td>\n",
+       "      <td>-0.532332</td>\n",
+       "      <td>0.939817</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>964</th>\n",
+       "      <td>-0.431375</td>\n",
+       "      <td>-0.915085</td>\n",
+       "      <td>-0.580861</td>\n",
+       "      <td>0.395472</td>\n",
+       "      <td>0.406366</td>\n",
+       "      <td>-0.131193</td>\n",
+       "      <td>1.074949</td>\n",
+       "      <td>-0.996813</td>\n",
+       "      <td>-0.183665</td>\n",
+       "      <td>-0.006735</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.946300</td>\n",
+       "      <td>0.433078</td>\n",
+       "      <td>-0.190154</td>\n",
+       "      <td>0.137894</td>\n",
+       "      <td>-0.198106</td>\n",
+       "      <td>-0.261280</td>\n",
+       "      <td>-0.695857</td>\n",
+       "      <td>0.226295</td>\n",
+       "      <td>-0.670496</td>\n",
+       "      <td>-0.423368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1104</th>\n",
+       "      <td>0.096140</td>\n",
+       "      <td>-0.056172</td>\n",
+       "      <td>0.063150</td>\n",
+       "      <td>0.234838</td>\n",
+       "      <td>0.117753</td>\n",
+       "      <td>-0.346006</td>\n",
+       "      <td>-0.744430</td>\n",
+       "      <td>-0.151107</td>\n",
+       "      <td>0.143060</td>\n",
+       "      <td>0.241910</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.500574</td>\n",
+       "      <td>-0.478188</td>\n",
+       "      <td>0.296040</td>\n",
+       "      <td>-0.612845</td>\n",
+       "      <td>-0.324935</td>\n",
+       "      <td>-0.439464</td>\n",
+       "      <td>0.469710</td>\n",
+       "      <td>0.539491</td>\n",
+       "      <td>0.906302</td>\n",
+       "      <td>-0.175188</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>37</th>\n",
+       "      <td>0.281097</td>\n",
+       "      <td>-0.608663</td>\n",
+       "      <td>0.423599</td>\n",
+       "      <td>0.420787</td>\n",
+       "      <td>1.051380</td>\n",
+       "      <td>-0.027290</td>\n",
+       "      <td>0.602898</td>\n",
+       "      <td>-0.284727</td>\n",
+       "      <td>0.099539</td>\n",
+       "      <td>-1.925934</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.300906</td>\n",
+       "      <td>-0.111235</td>\n",
+       "      <td>1.123893</td>\n",
+       "      <td>0.459886</td>\n",
+       "      <td>-0.218124</td>\n",
+       "      <td>0.590245</td>\n",
+       "      <td>0.296381</td>\n",
+       "      <td>-0.609109</td>\n",
+       "      <td>-0.147541</td>\n",
+       "      <td>-1.250704</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>-0.131974</td>\n",
+       "      <td>-0.062444</td>\n",
+       "      <td>-0.837820</td>\n",
+       "      <td>0.162044</td>\n",
+       "      <td>-0.451466</td>\n",
+       "      <td>0.319139</td>\n",
+       "      <td>0.052473</td>\n",
+       "      <td>-0.631871</td>\n",
+       "      <td>-0.020183</td>\n",
+       "      <td>-0.478724</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.101025</td>\n",
+       "      <td>1.011868</td>\n",
+       "      <td>-0.704747</td>\n",
+       "      <td>-0.454947</td>\n",
+       "      <td>-0.227243</td>\n",
+       "      <td>0.961758</td>\n",
+       "      <td>0.686837</td>\n",
+       "      <td>0.510259</td>\n",
+       "      <td>0.270457</td>\n",
+       "      <td>-1.069947</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
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+       "      <td>0.634322</td>\n",
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+       "      <td>0.055874</td>\n",
+       "      <td>0.055352</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10 rows × 768 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       title_0   title_1   title_2   title_3   title_4   title_5   title_6  \\\n",
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+       "\n",
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+       "1372 -0.385954 -0.541251  0.007578  ...   0.047079   0.485933  -0.285741   \n",
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+       "1681 -0.007839 -0.371513  0.509530  ...  -0.222305  -1.502060  -0.571068   \n",
+       "\n",
+       "      title_761  title_762  title_763  title_764  title_765  title_766  \\\n",
+       "1517  -0.025490  -0.120986  -0.055843   0.637930   0.143514   0.585826   \n",
+       "1605   0.244530  -0.364924   0.675864  -0.271245  -0.173634  -0.298026   \n",
+       "1451  -0.713460   0.423684   1.398612  -0.203436   0.473697  -0.219005   \n",
+       "1372  -0.035659  -0.101807   0.110145   1.122281  -0.237854  -0.532332   \n",
+       "964    0.137894  -0.198106  -0.261280  -0.695857   0.226295  -0.670496   \n",
+       "1104  -0.612845  -0.324935  -0.439464   0.469710   0.539491   0.906302   \n",
+       "37     0.459886  -0.218124   0.590245   0.296381  -0.609109  -0.147541   \n",
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+       "1681  -1.054443  -0.434218  -0.145071   0.131197  -0.685201   0.055874   \n",
+       "\n",
+       "      title_767  \n",
+       "1517  -0.303448  \n",
+       "1605  -0.017564  \n",
+       "1451  -0.128714  \n",
+       "1372   0.939817  \n",
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+       "37    -1.250704  \n",
+       "228   -1.069947  \n",
+       "1340   0.432614  \n",
+       "1681   0.055352  \n",
+       "\n",
+       "[10 rows x 768 columns]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "x, y = g2.transform(df.sample(10), df.sample(10), kind='nodes')  # or edges if given or already produced through umap-ing the nodes, \n",
+    "sdf = df.sample(10)\n",
+    "x, y = g2.transform(sdf, sdf, return_graph=False)  # or edges if given or already produced through umap-ing the ny_nodes=\n",
     "                                                                #and if neither, set `embedding=True` for random embedding of size `n_topics`\n",
     "x"
    ]
@@ -376,13 +1900,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "e68126cd",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "              x         y\n",
+       "2178  12.067898  5.299919\n",
+       "1227  11.831199  4.594526\n",
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+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "emb, x, y = g2.transform_umap(df.sample(10), df.sample(10))\n",
-    "emb"
+    "emb, x, y = g2.transform_umap(df.sample(10), df.sample(10), return_graph=False)\n",
+    "emb.head()"
    ]
   },
   {
@@ -395,80 +1988,446 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "d148348e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# this inherets all the arguments from the g.featurize api for both nodes and edges, see g.build_gnn? for details\n",
-    "g3 = g25.build_gnn()  # we use the filtered edges graphistry instance as it has higher fidelity similarity scores on edges\n",
+    "g3 = g25.build_gnn(y_nodes='score')  # we use the filtered edges graphistry instance as it has higher fidelity similarity scores on edges\n",
     "                        # ie, less edges"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "5989c286",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# notice the difference in edge dataframes between g2/5 and g3\n",
-    "g25._edges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "59af921c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# versus\n",
-    "g3._edges"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "af1cd73e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Edges come from data supplied by umap on nodes\n",
-    "g3._edge_encoder.feature_names_in"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "764e7ba7",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "<p>5 rows × 3001 columns</p>\n",
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+      ],
+      "text/plain": [
+       "     0    1    2    3    4    5    6    7    8    9  ...  2991  2992  2993  \\\n",
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+       "\n",
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+       "8   0.0   0.0   0.0   0.0   0.0   0.0  0.653791  \n",
+       "\n",
+       "[5 rows x 3001 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3._edge_features.head()"
+    "g3.get_matrix(kind='edges').head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "fc1955b1",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+       "<p>5 rows × 3001 columns</p>\n",
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+      "text/plain": [
+       "         0    1    2    3    4    5    6    7    8    9  ...  2991  2992  \\\n",
+       "7043   0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   \n",
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+       "\n",
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+       "\n",
+       "[5 rows x 3001 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Since edges are featurized, we can transform on \"unseen/batch\" ones\n",
-    "# y_edges will be none since we don't have a label for the implicit edges. One could supply it via enrichment (like clustering, annotation etc)\n",
     "edge_data = g3._edges.sample(10)\n",
     "\n",
-    "x_edges, _ = g3.transform(edge_data, None, kind='edges')\n",
-    "x_edges"
+    "# y_edges will be None since we don't have a label for the implicit edges. One could supply it via enrichment (like clustering, annotation etc)\n",
+    "x_edges, _ = g3.transform(edge_data, None, kind='edges', return_graph=False)\n",
+    "x_edges.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "59d403f9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(num_nodes=3000, num_edges=19100,\n",
+       "      ndata_schemes={'feature': Scheme(shape=(768,), dtype=torch.float32), 'target': Scheme(shape=(1,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
+       "      edata_schemes={'feature': Scheme(shape=(3001,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)})"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# once built, we can get the DGL graph itself\n",
     "G = g3.DGL_graph\n",
@@ -477,10 +2436,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "8380122a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'feature': tensor([[ 0.6045,  0.1017, -0.0635,  ...,  0.4298, -0.3031, -0.3775],\n",
+       "        [-1.1035, -0.8352, -1.2375,  ..., -0.5702,  0.0510, -0.4362],\n",
+       "        [ 0.3261,  0.0457,  0.3082,  ...,  0.9958, -0.0096, -0.1197],\n",
+       "        ...,\n",
+       "        [-0.4063, -0.5310, -0.5638,  ..., -0.7132,  0.3906, -0.1414],\n",
+       "        [ 0.1290,  0.1685,  0.0550,  ...,  0.0287, -0.1604,  0.2120],\n",
+       "        [-0.1442,  0.7037, -0.8524,  ...,  0.0048, -0.0208,  0.0272]]), 'target': tensor([[-0.3883],\n",
+       "        [-0.3353],\n",
+       "        [-0.7115],\n",
+       "        ...,\n",
+       "        [ 0.4461],\n",
+       "        [-0.6295],\n",
+       "        [-0.7211]], dtype=torch.float64), 'train_mask': tensor([True, True, True,  ..., True, True, True]), 'test_mask': tensor([False, False, False,  ..., False, False, False])}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# the features, targets, and masks\n",
     "G.ndata"
@@ -488,10 +2470,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "63beefab",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([19100, 3001])"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# `build_gnn()` will turn edges gotten from umap into bonafide feature matrices, \n",
     "# and make features out of explicit edges with `build_gnn(X_edges=[...], ..)`\n",
@@ -500,12 +2493,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "45d3a37a",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1898c24c0>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# see the edge features which are shape (n_edges, n_nodes + weight)\n",
     "# notice that had we used filter_weighted_edges to create a new graphistry instance and then .build_gnn() we would get\n",
@@ -516,10 +2530,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "6c150a8e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x1854ba370>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# see the way edges are related across the first 500 edges.\n",
     "plt.figure(figsize=(15,8))\n",
@@ -528,21 +2565,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "9ab619bf",
    "metadata": {},
    "outputs": [],
    "source": [
     "# to see how to train a GNN, see the cyber or influence tutorial"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f170fa3a",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/demos/ai/Introduction/simple-power-of-umap.ipynb b/demos/ai/Introduction/simple-power-of-umap.ipynb
new file mode 100644
index 0000000000..c61a4c8c04
--- /dev/null
+++ b/demos/ai/Introduction/simple-power-of-umap.ipynb
@@ -0,0 +1,7538 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "af4a54b9-1959-4fda-a00c-534be66e09a4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from sklearn.datasets import load_breast_cancer, load_diabetes, load_digits\n",
+    "\n",
+    "from collections import Counter\n",
+    "\n",
+    "import graphistry\n",
+    "from graphistry.features import ModelDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b28209f4-6809-4cb1-b6a8-e0d65cbbe07d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username=os.environ['USERNAME'], password=os.environ['GRAPHISTRY_PASSWORD']) "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49752d24-00a6-480f-be5e-62134c9598d8",
+   "metadata": {},
+   "source": [
+    "# Explore Data in a Whole New Way\n",
+    "PyGraphistry is a GPU Graph AI visualization tool that unlocks graphing in your data. \n",
+    "\n",
+    "In the past loading, transforming and interacting with large multivariate datasets took a vast amount of time to set up pipelines and processes. Graphistry makes time-to-graph + AI + interactivity 100x faster. \n",
+    "\n",
+    "We will explore how to see data, explore relationships, create new graphs from batches using sci-kits like api, and build a GNN model one could use in downstream DGL models. \n",
+    "\n",
+    "We will quickly analyze breast cancer, diabetes and digits datasets from sklearn.data\n",
+    "\n",
+    "Add your favorite dataset and explore it with graph AI and Visual exploration! "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d434a151",
+   "metadata": {},
+   "source": [
+    "## Tumor: Malignant or Benign "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ec079ad1-12d8-419c-9f35-d4ecad825635",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = load_breast_cancer()\n",
+    "\n",
+    "good_features = list(data['feature_names'])\n",
+    "\n",
+    "df = pd.DataFrame(data['data'], columns=good_features)\n",
+    "df['target'] = data['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "df071dc0-df4c-4701-8794-57864c37fc0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean radius</th>\n",
+       "      <th>mean texture</th>\n",
+       "      <th>mean perimeter</th>\n",
+       "      <th>mean area</th>\n",
+       "      <th>mean smoothness</th>\n",
+       "      <th>mean compactness</th>\n",
+       "      <th>mean concavity</th>\n",
+       "      <th>mean concave points</th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>mean fractal dimension</th>\n",
+       "      <th>...</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>17.99</td>\n",
+       "      <td>10.38</td>\n",
+       "      <td>122.80</td>\n",
+       "      <td>1001.0</td>\n",
+       "      <td>0.11840</td>\n",
+       "      <td>0.27760</td>\n",
+       "      <td>0.3001</td>\n",
+       "      <td>0.14710</td>\n",
+       "      <td>0.2419</td>\n",
+       "      <td>0.07871</td>\n",
+       "      <td>...</td>\n",
+       "      <td>17.33</td>\n",
+       "      <td>184.60</td>\n",
+       "      <td>2019.0</td>\n",
+       "      <td>0.1622</td>\n",
+       "      <td>0.6656</td>\n",
+       "      <td>0.7119</td>\n",
+       "      <td>0.2654</td>\n",
+       "      <td>0.4601</td>\n",
+       "      <td>0.11890</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>20.57</td>\n",
+       "      <td>17.77</td>\n",
+       "      <td>132.90</td>\n",
+       "      <td>1326.0</td>\n",
+       "      <td>0.08474</td>\n",
+       "      <td>0.07864</td>\n",
+       "      <td>0.0869</td>\n",
+       "      <td>0.07017</td>\n",
+       "      <td>0.1812</td>\n",
+       "      <td>0.05667</td>\n",
+       "      <td>...</td>\n",
+       "      <td>23.41</td>\n",
+       "      <td>158.80</td>\n",
+       "      <td>1956.0</td>\n",
+       "      <td>0.1238</td>\n",
+       "      <td>0.1866</td>\n",
+       "      <td>0.2416</td>\n",
+       "      <td>0.1860</td>\n",
+       "      <td>0.2750</td>\n",
+       "      <td>0.08902</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>19.69</td>\n",
+       "      <td>21.25</td>\n",
+       "      <td>130.00</td>\n",
+       "      <td>1203.0</td>\n",
+       "      <td>0.10960</td>\n",
+       "      <td>0.15990</td>\n",
+       "      <td>0.1974</td>\n",
+       "      <td>0.12790</td>\n",
+       "      <td>0.2069</td>\n",
+       "      <td>0.05999</td>\n",
+       "      <td>...</td>\n",
+       "      <td>25.53</td>\n",
+       "      <td>152.50</td>\n",
+       "      <td>1709.0</td>\n",
+       "      <td>0.1444</td>\n",
+       "      <td>0.4245</td>\n",
+       "      <td>0.4504</td>\n",
+       "      <td>0.2430</td>\n",
+       "      <td>0.3613</td>\n",
+       "      <td>0.08758</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.42</td>\n",
+       "      <td>20.38</td>\n",
+       "      <td>77.58</td>\n",
+       "      <td>386.1</td>\n",
+       "      <td>0.14250</td>\n",
+       "      <td>0.28390</td>\n",
+       "      <td>0.2414</td>\n",
+       "      <td>0.10520</td>\n",
+       "      <td>0.2597</td>\n",
+       "      <td>0.09744</td>\n",
+       "      <td>...</td>\n",
+       "      <td>26.50</td>\n",
+       "      <td>98.87</td>\n",
+       "      <td>567.7</td>\n",
+       "      <td>0.2098</td>\n",
+       "      <td>0.8663</td>\n",
+       "      <td>0.6869</td>\n",
+       "      <td>0.2575</td>\n",
+       "      <td>0.6638</td>\n",
+       "      <td>0.17300</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>20.29</td>\n",
+       "      <td>14.34</td>\n",
+       "      <td>135.10</td>\n",
+       "      <td>1297.0</td>\n",
+       "      <td>0.10030</td>\n",
+       "      <td>0.13280</td>\n",
+       "      <td>0.1980</td>\n",
+       "      <td>0.10430</td>\n",
+       "      <td>0.1809</td>\n",
+       "      <td>0.05883</td>\n",
+       "      <td>...</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>152.20</td>\n",
+       "      <td>1575.0</td>\n",
+       "      <td>0.1374</td>\n",
+       "      <td>0.2050</td>\n",
+       "      <td>0.4000</td>\n",
+       "      <td>0.1625</td>\n",
+       "      <td>0.2364</td>\n",
+       "      <td>0.07678</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 31 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
+       "0        17.99         10.38          122.80     1001.0          0.11840   \n",
+       "1        20.57         17.77          132.90     1326.0          0.08474   \n",
+       "2        19.69         21.25          130.00     1203.0          0.10960   \n",
+       "3        11.42         20.38           77.58      386.1          0.14250   \n",
+       "4        20.29         14.34          135.10     1297.0          0.10030   \n",
+       "\n",
+       "   mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
+       "0           0.27760          0.3001              0.14710         0.2419   \n",
+       "1           0.07864          0.0869              0.07017         0.1812   \n",
+       "2           0.15990          0.1974              0.12790         0.2069   \n",
+       "3           0.28390          0.2414              0.10520         0.2597   \n",
+       "4           0.13280          0.1980              0.10430         0.1809   \n",
+       "\n",
+       "   mean fractal dimension  ...  worst texture  worst perimeter  worst area  \\\n",
+       "0                 0.07871  ...          17.33           184.60      2019.0   \n",
+       "1                 0.05667  ...          23.41           158.80      1956.0   \n",
+       "2                 0.05999  ...          25.53           152.50      1709.0   \n",
+       "3                 0.09744  ...          26.50            98.87       567.7   \n",
+       "4                 0.05883  ...          16.67           152.20      1575.0   \n",
+       "\n",
+       "   worst smoothness  worst compactness  worst concavity  worst concave points  \\\n",
+       "0            0.1622             0.6656           0.7119                0.2654   \n",
+       "1            0.1238             0.1866           0.2416                0.1860   \n",
+       "2            0.1444             0.4245           0.4504                0.2430   \n",
+       "3            0.2098             0.8663           0.6869                0.2575   \n",
+       "4            0.1374             0.2050           0.4000                0.1625   \n",
+       "\n",
+       "   worst symmetry  worst fractal dimension  target  \n",
+       "0          0.4601                  0.11890       0  \n",
+       "1          0.2750                  0.08902       0  \n",
+       "2          0.3613                  0.08758       0  \n",
+       "3          0.6638                  0.17300       0  \n",
+       "4          0.2364                  0.07678       0  \n",
+       "\n",
+       "[5 rows x 31 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6c6f61c-0923-4860-8045-18dfeffe69fa",
+   "metadata": {},
+   "source": [
+    "# UMAP\n",
+    "\n",
+    "Reduce the data into a 2 dimensional graph -- the edges come from similarity in features. \n",
+    "\n",
+    "UMAP is a powerful way to see the parts of the dataset. One can not simply confirm if a predictive model will 'separate' the data off of visuals alone. UMAP provides the tools to explore relationships that can help feed insights and potential treatment strategies. \n",
+    "\n",
+    "What can you find in the data?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "28ae2a26-c7e3-457a-8a50-78031797fad2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "# let's split data and train on half the data\n",
+    "df_train, df_test, df_train_target, df_test_target = train_test_split(df, df[['target']], train_size=0.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a3527e13-7e14-437f-b423-6643e15b9a08",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (284, 0) in UMAP fit, as it is not one dimensionalOMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 15.2 s, sys: 745 ms, total: 15.9 s\n",
+      "Wall time: 16 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "g = graphistry.nodes(df_train)\n",
+    "# fit on specific features by calling out via X=...\n",
+    "# plots are sensitive to scaling, we use_scaler='robust' for good umap separation \n",
+    "# (thought None does better in RF below)\n",
+    "\n",
+    "g2 = g.umap(X=good_features, use_scaler='robust') # y = 'target'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "65758e38",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"4088c62d-d851-456b-bb57-45a0c5f5fddd\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=ecada95bde3d4c03b8145244407f0bcf&type=arrow&viztoken=c5e19343-a80c-45bf-8180-9ae11779e2b9&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276769&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#4088c62d-d851-456b-bb57-45a0c5f5fddd\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b773839a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "## DBSCAN as pivot \n",
+    "\n",
+    "Think of DBSCAN as a way to pivot by clustering in features of interest. \n",
+    "By setting `cols` one may pick out features from the matrix and have dbscan only focus on those.\n",
+    "Coloring by the `_dbscan` label in the UI finds clusters across those variables. \n",
+    "\n",
+    "Contrasting UMAP coordinates versus dbscan labels is a useful way to see total behavior against some part/pivot of interest. In the following, we see k-clusters in the `worst` (case sensitive column selection) meta variable. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "97d11165",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>worst radius</th>\n",
+       "      <th>worst texture</th>\n",
+       "      <th>worst perimeter</th>\n",
+       "      <th>worst area</th>\n",
+       "      <th>worst smoothness</th>\n",
+       "      <th>worst compactness</th>\n",
+       "      <th>worst concavity</th>\n",
+       "      <th>worst concave points</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "      <th>worst fractal dimension</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>-0.90044</td>\n",
+       "      <td>0.64410</td>\n",
+       "      <td>-0.82082</td>\n",
+       "      <td>-0.72995</td>\n",
+       "      <td>0.79066</td>\n",
+       "      <td>-0.23246</td>\n",
+       "      <td>-0.47392</td>\n",
+       "      <td>-0.33095</td>\n",
+       "      <td>0.16828</td>\n",
+       "      <td>0.58154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>1.93428</td>\n",
+       "      <td>0.11740</td>\n",
+       "      <td>1.87355</td>\n",
+       "      <td>2.41938</td>\n",
+       "      <td>-0.39366</td>\n",
+       "      <td>0.16706</td>\n",
+       "      <td>0.24503</td>\n",
+       "      <td>0.85155</td>\n",
+       "      <td>-0.35829</td>\n",
+       "      <td>-0.69391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>-0.16282</td>\n",
+       "      <td>0.68958</td>\n",
+       "      <td>-0.12018</td>\n",
+       "      <td>-0.13056</td>\n",
+       "      <td>-0.18349</td>\n",
+       "      <td>-0.14923</td>\n",
+       "      <td>-0.35499</td>\n",
+       "      <td>-0.34987</td>\n",
+       "      <td>0.52576</td>\n",
+       "      <td>0.09121</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>-0.52379</td>\n",
+       "      <td>-0.17240</td>\n",
+       "      <td>-0.52187</td>\n",
+       "      <td>-0.45097</td>\n",
+       "      <td>-0.07006</td>\n",
+       "      <td>-0.67788</td>\n",
+       "      <td>-0.26686</td>\n",
+       "      <td>-0.28479</td>\n",
+       "      <td>-0.60145</td>\n",
+       "      <td>-0.66861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>2.14419</td>\n",
+       "      <td>0.08144</td>\n",
+       "      <td>2.01526</td>\n",
+       "      <td>2.80297</td>\n",
+       "      <td>0.61384</td>\n",
+       "      <td>1.22533</td>\n",
+       "      <td>1.65922</td>\n",
+       "      <td>1.05014</td>\n",
+       "      <td>0.31320</td>\n",
+       "      <td>0.93080</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>0.00981</td>\n",
+       "      <td>-0.07615</td>\n",
+       "      <td>0.05505</td>\n",
+       "      <td>0.00421</td>\n",
+       "      <td>-0.15680</td>\n",
+       "      <td>-0.01070</td>\n",
+       "      <td>-0.29445</td>\n",
+       "      <td>-0.29685</td>\n",
+       "      <td>-0.84461</td>\n",
+       "      <td>0.23179</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>-0.24326</td>\n",
+       "      <td>-0.50449</td>\n",
+       "      <td>-0.19812</td>\n",
+       "      <td>-0.21323</td>\n",
+       "      <td>0.61051</td>\n",
+       "      <td>0.06005</td>\n",
+       "      <td>0.39452</td>\n",
+       "      <td>-0.32773</td>\n",
+       "      <td>-0.04267</td>\n",
+       "      <td>0.13888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>-0.10790</td>\n",
+       "      <td>-0.39450</td>\n",
+       "      <td>-0.09593</td>\n",
+       "      <td>-0.12810</td>\n",
+       "      <td>0.54712</td>\n",
+       "      <td>-0.04518</td>\n",
+       "      <td>-0.27551</td>\n",
+       "      <td>-0.17208</td>\n",
+       "      <td>0.47907</td>\n",
+       "      <td>0.26341</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>-0.71604</td>\n",
+       "      <td>-0.91486</td>\n",
+       "      <td>-0.68511</td>\n",
+       "      <td>-0.59497</td>\n",
+       "      <td>-0.31693</td>\n",
+       "      <td>-0.73187</td>\n",
+       "      <td>-0.69935</td>\n",
+       "      <td>-0.56084</td>\n",
+       "      <td>-1.40338</td>\n",
+       "      <td>-0.34172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>1.02207</td>\n",
+       "      <td>0.18932</td>\n",
+       "      <td>0.96880</td>\n",
+       "      <td>1.17836</td>\n",
+       "      <td>0.45038</td>\n",
+       "      <td>2.22889</td>\n",
+       "      <td>1.31041</td>\n",
+       "      <td>0.92953</td>\n",
+       "      <td>2.40982</td>\n",
+       "      <td>2.09875</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>284 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     worst radius  worst texture  worst perimeter  worst area  \\\n",
+       "66       -0.90044        0.64410         -0.82082    -0.72995   \n",
+       "70        1.93428        0.11740          1.87355     2.41938   \n",
+       "490      -0.16282        0.68958         -0.12018    -0.13056   \n",
+       "67       -0.52379       -0.17240         -0.52187    -0.45097   \n",
+       "300       2.14419        0.08144          2.01526     2.80297   \n",
+       "..            ...            ...              ...         ...   \n",
+       "544       0.00981       -0.07615          0.05505     0.00421   \n",
+       "48       -0.24326       -0.50449         -0.19812    -0.21323   \n",
+       "519      -0.10790       -0.39450         -0.09593    -0.12810   \n",
+       "173      -0.71604       -0.91486         -0.68511    -0.59497   \n",
+       "34        1.02207        0.18932          0.96880     1.17836   \n",
+       "\n",
+       "     worst smoothness  worst compactness  worst concavity  \\\n",
+       "66            0.79066           -0.23246         -0.47392   \n",
+       "70           -0.39366            0.16706          0.24503   \n",
+       "490          -0.18349           -0.14923         -0.35499   \n",
+       "67           -0.07006           -0.67788         -0.26686   \n",
+       "300           0.61384            1.22533          1.65922   \n",
+       "..                ...                ...              ...   \n",
+       "544          -0.15680           -0.01070         -0.29445   \n",
+       "48            0.61051            0.06005          0.39452   \n",
+       "519           0.54712           -0.04518         -0.27551   \n",
+       "173          -0.31693           -0.73187         -0.69935   \n",
+       "34            0.45038            2.22889          1.31041   \n",
+       "\n",
+       "     worst concave points  worst symmetry  worst fractal dimension  \n",
+       "66               -0.33095         0.16828                  0.58154  \n",
+       "70                0.85155        -0.35829                 -0.69391  \n",
+       "490              -0.34987         0.52576                  0.09121  \n",
+       "67               -0.28479        -0.60145                 -0.66861  \n",
+       "300               1.05014         0.31320                  0.93080  \n",
+       "..                    ...             ...                      ...  \n",
+       "544              -0.29685        -0.84461                  0.23179  \n",
+       "48               -0.32773        -0.04267                  0.13888  \n",
+       "519              -0.17208         0.47907                  0.26341  \n",
+       "173              -0.56084        -1.40338                 -0.34172  \n",
+       "34                0.92953         2.40982                  2.09875  \n",
+       "\n",
+       "[284 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X = g2.get_matrix('worst')\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "1829f1ff",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "-----------------------------------------\n",
+      "DBSCAN found 284 clusters with 0 outliers\n",
+      "--fit on feature embeddings of size (284, 10)\n",
+      "-----------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"f34140df-4de6-4685-b075-ba7574526085\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=87ca648df87547a1b7c624e835306990&type=arrow&viztoken=50ccb834-a917-48f3-acc2-e4d3b5468fb1&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276772&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#f34140df-4de6-4685-b075-ba7574526085\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# suppose we want to cluster and color by these variables,\n",
+    "g2.dbscan(cols='worst', min_dist=0.3, verbose=True).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4354d70",
+   "metadata": {},
+   "source": [
+    "Suppose you wanted to study part of the features matrix, like all entries in 'symmetry'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d866936a-2ace-4f77-af35-a2cea7174427",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean symmetry</th>\n",
+       "      <th>symmetry error</th>\n",
+       "      <th>worst symmetry</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>66</th>\n",
+       "      <td>-0.18831</td>\n",
+       "      <td>0.59780</td>\n",
+       "      <td>0.16828</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>70</th>\n",
+       "      <td>-0.59833</td>\n",
+       "      <td>-0.62800</td>\n",
+       "      <td>-0.35829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>490</th>\n",
+       "      <td>-0.71374</td>\n",
+       "      <td>-0.32327</td>\n",
+       "      <td>0.52576</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67</th>\n",
+       "      <td>-0.79879</td>\n",
+       "      <td>0.46603</td>\n",
+       "      <td>-0.60145</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>300</th>\n",
+       "      <td>0.03948</td>\n",
+       "      <td>0.05559</td>\n",
+       "      <td>0.31320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>544</th>\n",
+       "      <td>-0.48292</td>\n",
+       "      <td>-0.48524</td>\n",
+       "      <td>-0.84461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>-0.31587</td>\n",
+       "      <td>-0.57035</td>\n",
+       "      <td>-0.04267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>1.03569</td>\n",
+       "      <td>-0.05285</td>\n",
+       "      <td>0.47907</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>173</th>\n",
+       "      <td>-0.64692</td>\n",
+       "      <td>1.70007</td>\n",
+       "      <td>-1.40338</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>0.66515</td>\n",
+       "      <td>-0.19286</td>\n",
+       "      <td>2.40982</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>284 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     mean symmetry  symmetry error  worst symmetry\n",
+       "66        -0.18831         0.59780         0.16828\n",
+       "70        -0.59833        -0.62800        -0.35829\n",
+       "490       -0.71374        -0.32327         0.52576\n",
+       "67        -0.79879         0.46603        -0.60145\n",
+       "300        0.03948         0.05559         0.31320\n",
+       "..             ...             ...             ...\n",
+       "544       -0.48292        -0.48524        -0.84461\n",
+       "48        -0.31587        -0.57035        -0.04267\n",
+       "519        1.03569        -0.05285         0.47907\n",
+       "173       -0.64692         1.70007        -1.40338\n",
+       "34         0.66515        -0.19286         2.40982\n",
+       "\n",
+       "[284 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# with new features you can add it too graphistry API to run further modeling\n",
+    "X = g2.get_matrix('symmetry')\n",
+    "small_study = X.columns   # save for later so we can call out only these features during fit\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "a938ae8d-e280-4299-af38-1b945d38a22b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# add the target back so we can color by in UI\n",
+    "X['target'] = df_train.target"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f6eeb6de-b418-4b84-92c7-b28fa241ce97",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (284, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(X)\n",
+    "gq = g.umap(X=small_study, verbose=False).dbscan()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "baf3980c-df0a-479a-8ee0-4fc57109881e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"bdea0371-a81f-4fc7-8a4f-c8dd7474db9e\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=3ebc44b8230c497fbcfca34b241acb7a&type=arrow&viztoken=d9e835ae-875b-4e8d-b2c7-b672f8867f19&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276776&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#bdea0371-a81f-4fc7-8a4f-c8dd7474db9e\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gq.plot()  # finds that `symmetry` features are also a good indicator of target on their own"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8780fe0-ca07-4412-ba2a-1ddde26d46db",
+   "metadata": {},
+   "source": [
+    "# Transform Test Data into Graph\n",
+    "Can add batch onto closest neighbors of existing graph (from fit above). Otherwise, if merge_policy=True\n",
+    "it will create a new graph from the batch. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "8de9e1bf-290d-4901-96ef-4dd8ac9b5887",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 1.89 +/- 1.10\n",
+      " Max distance threshold; epsilon = 1.00\n",
+      " Finding 7 nearest neighbors\n",
+      " 68.76 neighbors per node within epsilon 1.00\n",
+      " 1995 total edges after dropping duplicates\n",
+      " ** Final graph has 348 nodes\n",
+      " - Batch has 285 nodes\n",
+      " - Brought in 63 nodes\n",
+      "--------------------------------------------------\n",
+      "CPU times: user 7.17 s, sys: 54.4 ms, total: 7.23 s\n",
+      "Wall time: 7.28 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# with merge_policy=True, will cluster minibatch to closests elements of existing graph -- useful if you want to find\n",
+    "# centroids in the old variables (imagine labeling goldenset with other targets, this would find which parts of minibatch are likely similar to known annotations)\n",
+    "g3 = g2.transform_umap(df_test, min_dist=1, merge_policy=True,\n",
+    "                       fit_umap_embedding=True, n_neighbors=7,\n",
+    "                       sample=None, \n",
+    "                       return_graph=True, \n",
+    "                       verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6c032e7c-0fc9-4364-9feb-eddb0d8e3c6b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"0b352144-c542-4e0b-9884-e57428b483f7\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7dc2ed5313264581bd51a4bdd7055861&type=arrow&viztoken=20a494c1-f454-41c1-a326-bb90c29b62c7&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276786&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#0b352144-c542-4e0b-9884-e57428b483f7\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "084f07f9-db66-4018-8d01-74a189a3c7ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over features embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 7.13 +/- 4.26\n",
+      " Max distance threshold; epsilon = 6.00\n",
+      " Finding 7 nearest neighbors\n",
+      " 138.57 neighbors per node within epsilon 6.00\n",
+      " 1891 total edges after dropping duplicates\n",
+      " ** Final graph has 285 nodes\n",
+      "--------------------------------------------------\n",
+      "CPU times: user 3.09 s, sys: 24.4 ms, total: 3.12 s\n",
+      "Wall time: 3.13 s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# with merge_policy=False (default), it clusters just by the minibatch df_test here\n",
+    "g4 = g2.transform_umap(df_test, min_dist=6, merge_policy=False,\n",
+    "                       fit_umap_embedding=False, n_neighbors=7,\n",
+    "                       sample=None, \n",
+    "                       return_graph=True, \n",
+    "                       verbose=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "7f9d3612-b2e0-4500-b5ba-4c11c3577dc8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"7d25c37c-b006-4c95-9edf-031d3205d129\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=1ffd8d2ef0a84c2b800af0f74e2b26a8&type=arrow&viztoken=01484534-2663-4549-9aef-ac586e4f8b09&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276792&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#7d25c37c-b006-4c95-9edf-031d3205d129\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g4.dbscan(0.2).plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ab6b089-9c3c-4ad9-bd9f-c3e4b14c87e1",
+   "metadata": {},
+   "source": [
+    "# Regressive Targets\n",
+    "Diabetes dataset with risk scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "d175607a-e108-45e5-aa10-05e306532589",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data2 = load_diabetes()\n",
+    "diabetes_features = list(data2['feature_names'])\n",
+    "diabetes_df = pd.DataFrame(data2['data'], columns=diabetes_features)\n",
+    "# we add target to dataframe as we want all the data for visualization (think coloring by histogram in target)\n",
+    "diabetes_df['target'] = data2['target']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e1bbc28b-9d10-4ff3-8c28-3308b552bbd8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>age</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>bmi</th>\n",
+       "      <th>bp</th>\n",
+       "      <th>s1</th>\n",
+       "      <th>s2</th>\n",
+       "      <th>s3</th>\n",
+       "      <th>s4</th>\n",
+       "      <th>s5</th>\n",
+       "      <th>s6</th>\n",
+       "      <th>target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.038076</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.061696</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>-0.044223</td>\n",
+       "      <td>-0.034821</td>\n",
+       "      <td>-0.043401</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.019908</td>\n",
+       "      <td>-0.017646</td>\n",
+       "      <td>151.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.001882</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.051474</td>\n",
+       "      <td>-0.026328</td>\n",
+       "      <td>-0.008449</td>\n",
+       "      <td>-0.019163</td>\n",
+       "      <td>0.074412</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.068330</td>\n",
+       "      <td>-0.092204</td>\n",
+       "      <td>75.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.085299</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.044451</td>\n",
+       "      <td>-0.005671</td>\n",
+       "      <td>-0.045599</td>\n",
+       "      <td>-0.034194</td>\n",
+       "      <td>-0.032356</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.002864</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>141.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.089063</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.011595</td>\n",
+       "      <td>-0.036656</td>\n",
+       "      <td>0.012191</td>\n",
+       "      <td>0.024991</td>\n",
+       "      <td>-0.036038</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>0.022692</td>\n",
+       "      <td>-0.009362</td>\n",
+       "      <td>206.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.005383</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.036385</td>\n",
+       "      <td>0.021872</td>\n",
+       "      <td>0.003935</td>\n",
+       "      <td>0.015596</td>\n",
+       "      <td>0.008142</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>-0.031991</td>\n",
+       "      <td>-0.046641</td>\n",
+       "      <td>135.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>437</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>0.019662</td>\n",
+       "      <td>0.059744</td>\n",
+       "      <td>-0.005697</td>\n",
+       "      <td>-0.002566</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>-0.002592</td>\n",
+       "      <td>0.031193</td>\n",
+       "      <td>0.007207</td>\n",
+       "      <td>178.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>438</th>\n",
+       "      <td>-0.005515</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>-0.067642</td>\n",
+       "      <td>0.049341</td>\n",
+       "      <td>0.079165</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.034309</td>\n",
+       "      <td>-0.018118</td>\n",
+       "      <td>0.044485</td>\n",
+       "      <td>104.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>439</th>\n",
+       "      <td>0.041708</td>\n",
+       "      <td>0.050680</td>\n",
+       "      <td>-0.015906</td>\n",
+       "      <td>0.017282</td>\n",
+       "      <td>-0.037344</td>\n",
+       "      <td>-0.013840</td>\n",
+       "      <td>-0.024993</td>\n",
+       "      <td>-0.011080</td>\n",
+       "      <td>-0.046879</td>\n",
+       "      <td>0.015491</td>\n",
+       "      <td>132.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>0.039062</td>\n",
+       "      <td>0.001215</td>\n",
+       "      <td>0.016318</td>\n",
+       "      <td>0.015283</td>\n",
+       "      <td>-0.028674</td>\n",
+       "      <td>0.026560</td>\n",
+       "      <td>0.044528</td>\n",
+       "      <td>-0.025930</td>\n",
+       "      <td>220.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>441</th>\n",
+       "      <td>-0.045472</td>\n",
+       "      <td>-0.044642</td>\n",
+       "      <td>-0.073030</td>\n",
+       "      <td>-0.081414</td>\n",
+       "      <td>0.083740</td>\n",
+       "      <td>0.027809</td>\n",
+       "      <td>0.173816</td>\n",
+       "      <td>-0.039493</td>\n",
+       "      <td>-0.004220</td>\n",
+       "      <td>0.003064</td>\n",
+       "      <td>57.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>442 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          age       sex       bmi        bp        s1        s2        s3  \\\n",
+       "0    0.038076  0.050680  0.061696  0.021872 -0.044223 -0.034821 -0.043401   \n",
+       "1   -0.001882 -0.044642 -0.051474 -0.026328 -0.008449 -0.019163  0.074412   \n",
+       "2    0.085299  0.050680  0.044451 -0.005671 -0.045599 -0.034194 -0.032356   \n",
+       "3   -0.089063 -0.044642 -0.011595 -0.036656  0.012191  0.024991 -0.036038   \n",
+       "4    0.005383 -0.044642 -0.036385  0.021872  0.003935  0.015596  0.008142   \n",
+       "..        ...       ...       ...       ...       ...       ...       ...   \n",
+       "437  0.041708  0.050680  0.019662  0.059744 -0.005697 -0.002566 -0.028674   \n",
+       "438 -0.005515  0.050680 -0.015906 -0.067642  0.049341  0.079165 -0.028674   \n",
+       "439  0.041708  0.050680 -0.015906  0.017282 -0.037344 -0.013840 -0.024993   \n",
+       "440 -0.045472 -0.044642  0.039062  0.001215  0.016318  0.015283 -0.028674   \n",
+       "441 -0.045472 -0.044642 -0.073030 -0.081414  0.083740  0.027809  0.173816   \n",
+       "\n",
+       "           s4        s5        s6  target  \n",
+       "0   -0.002592  0.019908 -0.017646   151.0  \n",
+       "1   -0.039493 -0.068330 -0.092204    75.0  \n",
+       "2   -0.002592  0.002864 -0.025930   141.0  \n",
+       "3    0.034309  0.022692 -0.009362   206.0  \n",
+       "4   -0.002592 -0.031991 -0.046641   135.0  \n",
+       "..        ...       ...       ...     ...  \n",
+       "437 -0.002592  0.031193  0.007207   178.0  \n",
+       "438  0.034309 -0.018118  0.044485   104.0  \n",
+       "439 -0.011080 -0.046879  0.015491   132.0  \n",
+       "440  0.026560  0.044528 -0.025930   220.0  \n",
+       "441 -0.039493 -0.004220  0.003064    57.0  \n",
+       "\n",
+       "[442 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "diabetes_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "1f07666e-edf3-4585-b5a1-cc28dac9b04a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "train_diabetes, test_diabetes, train_targets_diabetes, test_targets_diabetes = train_test_split(diabetes_df, diabetes_df.target, train_size=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bb779dee-8df3-4096-adbe-49a76fcb7667",
+   "metadata": {},
+   "source": [
+    "This time let's add target during umap fit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "994c5a46-6e9f-49b2-a5fa-49095d473cd4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g = graphistry.nodes(train_diabetes)\n",
+    "g5 = g.umap(X=diabetes_features, y = 'target',  \n",
+    "            use_scaler=None, # 'robust',\n",
+    "            use_scaler_target=None, #'standard'\n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "15933c92-6f40-40be-b5ab-bf300e67e359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"cf309073-feb6-4be3-824e-087153c244ba\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c425858369544c40b69b1d2b5ec306df&type=arrow&viztoken=68547c11-3ab6-472c-a55c-5e255abe9bc8&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276797&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#cf309073-feb6-4be3-824e-087153c244ba\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g5.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "da4eef58-517c-4908-bff1-7bb5c4872617",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "----------------------------------------\n",
+      "DBSCAN found 11 clusters with 0 outliers\n",
+      "--fit on umap embeddings of size (221, 2)\n",
+      "----------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "#predict on unseen data\n",
+    "# notice you don't need to add y=test_diabetes.target, graphistry knows what column from fit\n",
+    "g_pred = g5.dbscan(verbose=True).transform_dbscan(test_diabetes, y=test_diabetes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "7a14df9a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c481fd86-a911-4490-bb51-5853ec9db4ad\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4741be8722424a1f9b443b0e382fcb9f&type=arrow&viztoken=139232b3-b3ec-463b-b41a-5ab52a94d8a2&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276803&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c481fd86-a911-4490-bb51-5853ec9db4ad\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# will predict dbscan label from fit\n",
+    "g_pred.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a1d000d",
+   "metadata": {},
+   "source": [
+    "## Add your Favorite Model \n",
+    "\n",
+    "We will use Optuna to demonstrate a sample pipeline with HPO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "6477f87a-82c7-471f-acfa-1bdec88eb655",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\u001b[32m[I 2023-01-20 20:53:08,954]\u001b[0m A new study created in memory with name: Diabetes\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,092]\u001b[0m Trial 0 finished with value: 0.3958282204544128 and parameters: {'n_estimators': 125, 'max_depth': 131, 'min_samples_split': 62, 'scaler': 'standard'}. Best is trial 0 with value: 0.3958282204544128.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,399]\u001b[0m Trial 1 finished with value: 0.42954660431688363 and parameters: {'n_estimators': 222, 'max_depth': 185, 'min_samples_split': 6, 'scaler': 'quantile'}. Best is trial 1 with value: 0.42954660431688363.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,594]\u001b[0m Trial 2 finished with value: 0.38437314665755473 and parameters: {'n_estimators': 197, 'max_depth': 190, 'min_samples_split': 65, 'scaler': 'quantile'}. Best is trial 1 with value: 0.42954660431688363.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,685]\u001b[0m Trial 3 finished with value: 0.4333005750494967 and parameters: {'n_estimators': 68, 'max_depth': 152, 'min_samples_split': 13, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:09,922]\u001b[0m Trial 4 finished with value: 0.4318469685060231 and parameters: {'n_estimators': 215, 'max_depth': 130, 'min_samples_split': 50, 'scaler': 'standard'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,143]\u001b[0m Trial 5 finished with value: 0.4056859590113563 and parameters: {'n_estimators': 220, 'max_depth': 40, 'min_samples_split': 55, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,264]\u001b[0m Trial 6 finished with value: 0.35028481664246147 and parameters: {'n_estimators': 121, 'max_depth': 160, 'min_samples_split': 91, 'scaler': 'robust'}. Best is trial 3 with value: 0.4333005750494967.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,511]\u001b[0m Trial 7 finished with value: 0.44191045092853387 and parameters: {'n_estimators': 174, 'max_depth': 113, 'min_samples_split': 26, 'scaler': 'robust'}. Best is trial 7 with value: 0.44191045092853387.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,771]\u001b[0m Trial 8 finished with value: 0.4570822140378319 and parameters: {'n_estimators': 229, 'max_depth': 171, 'min_samples_split': 27, 'scaler': None}. Best is trial 8 with value: 0.4570822140378319.\u001b[0m\u001b[32m[I 2023-01-20 20:53:10,884]\u001b[0m Trial 9 finished with value: 0.4421353551018513 and parameters: {'n_estimators': 109, 'max_depth': 100, 'min_samples_split': 47, 'scaler': None}. Best is trial 8 with value: 0.4570822140378319.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,152]\u001b[0m Trial 10 finished with value: 0.4596624150532256 and parameters: {'n_estimators': 245, 'max_depth': 66, 'min_samples_split': 29, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,432]\u001b[0m Trial 11 finished with value: 0.456070643241286 and parameters: {'n_estimators': 246, 'max_depth': 61, 'min_samples_split': 29, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:11,845]\u001b[0m Trial 12 finished with value: 0.4557118546030289 and parameters: {'n_estimators': 249, 'max_depth': 10, 'min_samples_split': 31, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,191]\u001b[0m Trial 13 finished with value: 0.45365489784188473 and parameters: {'n_estimators': 178, 'max_depth': 67, 'min_samples_split': 19, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,489]\u001b[0m Trial 14 finished with value: 0.454454400785531 and parameters: {'n_estimators': 159, 'max_depth': 81, 'min_samples_split': 39, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:12,829]\u001b[0m Trial 15 finished with value: 0.38346335477400595 and parameters: {'n_estimators': 197, 'max_depth': 34, 'min_samples_split': 80, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,417]\u001b[0m Trial 16 finished with value: 0.45348976701727883 and parameters: {'n_estimators': 239, 'max_depth': 157, 'min_samples_split': 2, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,541]\u001b[0m Trial 17 finished with value: 0.44613313430354085 and parameters: {'n_estimators': 59, 'max_depth': 100, 'min_samples_split': 39, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:13,918]\u001b[0m Trial 18 finished with value: 0.45506726860199953 and parameters: {'n_estimators': 193, 'max_depth': 5, 'min_samples_split': 18, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:14,383]\u001b[0m Trial 19 finished with value: 0.4561936629751806 and parameters: {'n_estimators': 227, 'max_depth': 78, 'min_samples_split': 35, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:14,577]\u001b[0m Trial 20 finished with value: 0.39086287030920086 and parameters: {'n_estimators': 97, 'max_depth': 44, 'min_samples_split': 72, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,019]\u001b[0m Trial 21 finished with value: 0.4500610108687939 and parameters: {'n_estimators': 228, 'max_depth': 81, 'min_samples_split': 38, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,473]\u001b[0m Trial 22 finished with value: 0.45960740078894025 and parameters: {'n_estimators': 209, 'max_depth': 85, 'min_samples_split': 23, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:15,930]\u001b[0m Trial 23 finished with value: 0.4559439244521766 and parameters: {'n_estimators': 208, 'max_depth': 112, 'min_samples_split': 23, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m\u001b[32m[I 2023-01-20 20:53:16,283]\u001b[0m Trial 24 finished with value: 0.45533500184764475 and parameters: {'n_estimators': 178, 'max_depth': 54, 'min_samples_split': 12, 'scaler': None}. Best is trial 10 with value: 0.4596624150532256.\u001b[0m"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.model_selection import cross_val_score\n",
+    "import optuna\n",
+    "\n",
+    "def objective(trail):\n",
+    "    n_estimators = trail.suggest_int('n_estimators', 50, 250)\n",
+    "    max_depth = trail.suggest_int('max_depth', 2, 200)\n",
+    "    min_samples_split = trail.suggest_int('min_samples_split', 2, 100)\n",
+    "    \n",
+    "    use_scaler = trail.suggest_categorical('scaler', [None, 'standard', 'robust', 'quantile'])\n",
+    "    X_train, y_train = g5.scale(use_scaler=use_scaler)\n",
+    "    X_test, y_test = g5.scale(test_diabetes, test_diabetes, use_scaler=use_scaler)\n",
+    "                \n",
+    "    rlf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, min_samples_split=min_samples_split)\n",
+    "    score = rlf.fit(X_train, y_train).score(X_test, y_test)\n",
+    "    return score\n",
+    "\n",
+    "    \n",
+    "study = optuna.create_study(study_name='Diabetes', direction='maximize')\n",
+    "\n",
+    "study.optimize(objective, n_trials=25) # normally have more trials, here to demonstrate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "560a471b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'n_estimators': 245, 'max_depth': 66, 'min_samples_split': 29, 'scaler': None}"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "study.best_params"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0a859318",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "        <script type=\"text/javascript\">\n",
+       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
+       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
+       "        if (typeof require !== 'undefined') {\n",
+       "        require.undef(\"plotly\");\n",
+       "        define('plotly', function(require, exports, module) {\n",
+       "            /**\n",
+       "* plotly.js v2.8.3\n",
+       "* Copyright 2012-2021, Plotly, Inc.\n",
+       "* All rights reserved.\n",
+       "* Licensed under the MIT license\n",
+       "*/\n",
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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <feross@feross.org> <http://feross.org>\n",
+       " * @license  MIT\n",
+       " */function i(t,e){if(t===e)return 0;for(var r=t.length,n=e.length,i=0,a=Math.min(r,n);i<a;++i)if(t[i]!==e[i]){r=t[i],n=e[i];break}return r<n?-1:n<r?1:0}function a(t){return r.Buffer&&\"function\"==typeof r.Buffer.isBuffer?r.Buffer.isBuffer(t):!(null==t||!t._isBuffer)}var o=t(\"util/\"),s=Object.prototype.hasOwnProperty,l=Array.prototype.slice,c=\"foo\"===function(){}.name;function u(t){return Object.prototype.toString.call(t)}function f(t){return!a(t)&&(\"function\"==typeof r.ArrayBuffer&&(\"function\"==typeof ArrayBuffer.isView?ArrayBuffer.isView(t):!!t&&(t instanceof DataView||!!(t.buffer&&t.buffer instanceof ArrayBuffer))))}var h=e.exports=y,p=/\\s*function\\s+([^\\(\\s]*)\\s*/;function d(t){if(o.isFunction(t)){if(c)return t.name;var e=t.toString().match(p);return e&&e[1]}}function m(t,e){return\"string\"==typeof t?t.length<e?t:t.slice(0,e):t}function g(t){if(c||!o.isFunction(t))return o.inspect(t);var e=d(t);return\"[Function\"+(e?\": \"+e:\"\")+\"]\"}function 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+       "/*!\n",
+       " * The buffer module from node.js, for the browser.\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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t[0]=s*(h*v-p*g)-f*(l*v-c*g)+m*(l*p-c*h),t[1]=-(n*(h*v-p*g)-f*(i*v-a*g)+m*(i*p-a*h)),t[2]=n*(l*v-c*g)-s*(i*v-a*g)+m*(i*c-a*l),t[3]=-(n*(l*p-c*h)-s*(i*p-a*h)+f*(i*c-a*l)),t[4]=-(o*(h*v-p*g)-u*(l*v-c*g)+d*(l*p-c*h)),t[5]=r*(h*v-p*g)-u*(i*v-a*g)+d*(i*p-a*h),t[6]=-(r*(l*v-c*g)-o*(i*v-a*g)+d*(i*c-a*l)),t[7]=r*(l*p-c*h)-o*(i*p-a*h)+u*(i*c-a*l),t[8]=o*(f*v-p*m)-u*(s*v-c*m)+d*(s*p-c*f),t[9]=-(r*(f*v-p*m)-u*(n*v-a*m)+d*(n*p-a*f)),t[10]=r*(s*v-c*m)-o*(n*v-a*m)+d*(n*c-a*s),t[11]=-(r*(s*p-c*f)-o*(n*p-a*f)+u*(n*c-a*s)),t[12]=-(o*(f*g-h*m)-u*(s*g-l*m)+d*(s*h-l*f)),t[13]=r*(f*g-h*m)-u*(n*g-i*m)+d*(n*h-i*f),t[14]=-(r*(s*g-l*m)-o*(n*g-i*m)+d*(n*l-i*s)),t[15]=r*(s*h-l*f)-o*(n*h-i*f)+u*(n*l-i*s),t}},{}],196:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return 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t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],203:[function(t,e,r){e.exports=function(t,e){return t[0]=e[0],t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=e[1],t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=e[2],t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],204:[function(t,e,r){e.exports=function(t,e){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=e[0],t[13]=e[1],t[14]=e[2],t[15]=1,t}},{}],205:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=n,t[6]=r,t[7]=0,t[8]=0,t[9]=-r,t[10]=n,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],206:[function(t,e,r){e.exports=function(t,e){var r=Math.sin(e),n=Math.cos(e);return 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n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":209}],213:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],214:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],215:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],216:[function(t,e,r){e.exports=function(t,e,r,n){var i=Math.tan(e.upDegrees*Math.PI/180),a=Math.tan(e.downDegrees*Math.PI/180),o=Math.tan(e.leftDegrees*Math.PI/180),s=Math.tan(e.rightDegrees*Math.PI/180),l=2/(o+s),c=2/(i+a);return t[0]=l,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=c,t[6]=0,t[7]=0,t[8]=-(o-s)*l*.5,t[9]=(i-a)*c*.5,t[10]=n/(r-n),t[11]=-1,t[12]=0,t[13]=0,t[14]=n*r/(r-n),t[15]=0,t}},{}],217:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],218:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],219:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],220:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],221:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],222:[function(t,e,r){e.exports=function(t){return\"mat4(\"+t[0]+\", \"+t[1]+\", \"+t[2]+\", \"+t[3]+\", \"+t[4]+\", \"+t[5]+\", \"+t[6]+\", \"+t[7]+\", \"+t[8]+\", \"+t[9]+\", \"+t[10]+\", \"+t[11]+\", \"+t[12]+\", \"+t[13]+\", \"+t[14]+\", \"+t[15]+\")\"}},{}],223:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],224:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],225:[function(t,e,r){\"use strict\";var n=t(\"css-font\"),i=t(\"pick-by-alias\"),a=t(\"regl\"),o=t(\"gl-util/context\"),s=t(\"es6-weak-map\"),l=t(\"color-normalize\"),c=t(\"font-atlas\"),u=t(\"typedarray-pool\"),f=t(\"parse-rect\"),h=t(\"is-plain-obj\"),p=t(\"parse-unit\"),d=t(\"to-px\"),m=t(\"detect-kerning\"),g=t(\"object-assign\"),v=t(\"font-measure\"),y=t(\"flatten-vertex-data\"),x=t(\"bit-twiddle\").nextPow2,b=new s,_=!1;if(document.body){var w=document.body.appendChild(document.createElement(\"div\"));w.style.font=\"italic small-caps bold condensed 16px/2 cursive\",getComputedStyle(w).fontStretch&&(_=!0),document.body.removeChild(w)}var T=function(t){!function(t){return\"function\"==typeof t&&t._gl&&t.prop&&t.texture&&t.buffer}(t)?this.gl=o(t):(t={regl:t},this.gl=t.regl._gl),this.shader=b.get(this.gl),this.shader?this.regl=this.shader.regl:this.regl=t.regl||a({gl:this.gl}),this.charBuffer=this.regl.buffer({type:\"uint8\",usage:\"stream\"}),this.sizeBuffer=this.regl.buffer({type:\"float\",usage:\"stream\"}),this.shader||(this.shader=this.createShader(),b.set(this.gl,this.shader)),this.batch=[],this.fontSize=[],this.font=[],this.fontAtlas=[],this.draw=this.shader.draw.bind(this),this.render=function(){this.regl._refresh(),this.draw(this.batch)},this.canvas=this.gl.canvas,this.update(h(t)?t:{})};T.prototype.createShader=function(){var t=this.regl,e=t({blend:{enable:!0,color:[0,0,0,1],func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},stencil:{enable:!1},depth:{enable:!1},count:t.prop(\"count\"),offset:t.prop(\"offset\"),attributes:{charOffset:{offset:4,stride:8,buffer:t.this(\"sizeBuffer\")},width:{offset:0,stride:8,buffer:t.this(\"sizeBuffer\")},char:t.this(\"charBuffer\"),position:t.this(\"position\")},uniforms:{atlasSize:function(t,e){return[e.atlas.width,e.atlas.height]},atlasDim:function(t,e){return[e.atlas.cols,e.atlas.rows]},atlas:function(t,e){return e.atlas.texture},charStep:function(t,e){return e.atlas.step},em:function(t,e){return e.atlas.em},color:t.prop(\"color\"),opacity:t.prop(\"opacity\"),viewport:t.this(\"viewportArray\"),scale:t.this(\"scale\"),align:t.prop(\"align\"),baseline:t.prop(\"baseline\"),translate:t.this(\"translate\"),positionOffset:t.prop(\"positionOffset\")},primitive:\"points\",viewport:t.this(\"viewport\"),vert:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tattribute float width, charOffset, char;\\n\\t\\t\\tattribute vec2 position;\\n\\t\\t\\tuniform float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 255.;\\n\\t\\t\\t}\",frag:\"\\n\\t\\t\\tprecision highp float;\\n\\t\\t\\tuniform float fontSize, charStep, opacity;\\n\\t\\t\\tuniform vec2 atlasSize;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform sampler2D atlas;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\n\\t\\t\\tfloat lightness(vec4 color) {\\n\\t\\t\\t\\treturn color.r * 0.299 + color.g * 0.587 + color.b * 0.114;\\n\\t\\t\\t}\\n\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 uv = gl_FragCoord.xy - charCoord + charStep * .5;\\n\\t\\t\\t\\tfloat halfCharStep = floor(charStep * .5 + .5);\\n\\n\\t\\t\\t\\t// invert y and shift by 1px (FF expecially needs that)\\n\\t\\t\\t\\tuv.y = charStep - uv.y;\\n\\n\\t\\t\\t\\t// ignore points outside of character bounding box\\n\\t\\t\\t\\tfloat halfCharWidth = ceil(charWidth * .5);\\n\\t\\t\\t\\tif (floor(uv.x) > halfCharStep + halfCharWidth ||\\n\\t\\t\\t\\t\\tfloor(uv.x) < halfCharStep - halfCharWidth) return;\\n\\n\\t\\t\\t\\tuv += charId * charStep;\\n\\t\\t\\t\\tuv = uv / atlasSize;\\n\\n\\t\\t\\t\\tvec4 color = fontColor;\\n\\t\\t\\t\\tvec4 mask = texture2D(atlas, uv);\\n\\n\\t\\t\\t\\tfloat maskY = lightness(mask);\\n\\t\\t\\t\\t// float colorY = lightness(color);\\n\\t\\t\\t\\tcolor.a *= maskY;\\n\\t\\t\\t\\tcolor.a *= opacity;\\n\\n\\t\\t\\t\\t// color.a += .1;\\n\\n\\t\\t\\t\\t// antialiasing, see yiq color space y-channel formula\\n\\t\\t\\t\\t// color.rgb += (1. - color.rgb) * (1. - mask.rgb);\\n\\n\\t\\t\\t\\tgl_FragColor = color;\\n\\t\\t\\t}\"});return{regl:t,draw:e,atlas:{}}},T.prototype.update=function(t){var e=this;if(\"string\"==typeof t)t={text:t};else if(!t)return;null!=(t=i(t,{position:\"position positions coord coords coordinates\",font:\"font fontFace fontface typeface cssFont css-font family fontFamily\",fontSize:\"fontSize fontsize size font-size\",text:\"text texts chars characters value values symbols\",align:\"align alignment textAlign textbaseline\",baseline:\"baseline textBaseline textbaseline\",direction:\"dir direction textDirection\",color:\"color colour fill fill-color fillColor textColor textcolor\",kerning:\"kerning kern\",range:\"range dataBox\",viewport:\"vp viewport viewBox viewbox viewPort\",opacity:\"opacity alpha transparency visible visibility opaque\",offset:\"offset positionOffset padding shift indent indentation\"},!0)).opacity&&(Array.isArray(t.opacity)?this.opacity=t.opacity.map((function(t){return parseFloat(t)})):this.opacity=parseFloat(t.opacity)),null!=t.viewport&&(this.viewport=f(t.viewport),this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null==this.viewport&&(this.viewport={x:0,y:0,width:this.gl.drawingBufferWidth,height:this.gl.drawingBufferHeight},this.viewportArray=[this.viewport.x,this.viewport.y,this.viewport.width,this.viewport.height]),null!=t.kerning&&(this.kerning=t.kerning),null!=t.offset&&(\"number\"==typeof t.offset&&(t.offset=[t.offset,0]),this.positionOffset=y(t.offset)),t.direction&&(this.direction=t.direction),t.range&&(this.range=t.range,this.scale=[1/(t.range[2]-t.range[0]),1/(t.range[3]-t.range[1])],this.translate=[-t.range[0],-t.range[1]]),t.scale&&(this.scale=t.scale),t.translate&&(this.translate=t.translate),this.scale||(this.scale=[1/this.viewport.width,1/this.viewport.height]),this.translate||(this.translate=[0,0]),this.font.length||t.font||(t.font=T.baseFontSize+\"px sans-serif\");var r,a=!1,o=!1;if(t.font&&(Array.isArray(t.font)?t.font:[t.font]).forEach((function(t,r){if(\"string\"==typeof t)try{t=n.parse(t)}catch(e){t=n.parse(T.baseFontSize+\"px \"+t)}else t=n.parse(n.stringify(t));var i=n.stringify({size:T.baseFontSize,family:t.family,stretch:_?t.stretch:void 0,variant:t.variant,weight:t.weight,style:t.style}),s=p(t.size),l=Math.round(s[0]*d(s[1]));if(l!==e.fontSize[r]&&(o=!0,e.fontSize[r]=l),!(e.font[r]&&i==e.font[r].baseString||(a=!0,e.font[r]=T.fonts[i],e.font[r]))){var c=t.family.join(\", \"),u=[t.style];t.style!=t.variant&&u.push(t.variant),t.variant!=t.weight&&u.push(t.weight),_&&t.weight!=t.stretch&&u.push(t.stretch),e.font[r]={baseString:i,family:c,weight:t.weight,stretch:t.stretch,style:t.style,variant:t.variant,width:{},kerning:{},metrics:v(c,{origin:\"top\",fontSize:T.baseFontSize,fontStyle:u.join(\" \")})},T.fonts[i]=e.font[r]}})),(a||o)&&this.font.forEach((function(r,i){var a=n.stringify({size:e.fontSize[i],family:r.family,stretch:_?r.stretch:void 0,variant:r.variant,weight:r.weight,style:r.style});if(e.fontAtlas[i]=e.shader.atlas[a],!e.fontAtlas[i]){var o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),h=0;h<s.length;h++)s[h]=t.text;t.text=s}if(null!=t.text||a){if(this.textOffsets=[0],Array.isArray(t.text)){this.count=t.text[0].length,this.counts=[this.count];for(var b=1;b<t.text.length;b++)this.textOffsets[b]=this.textOffsets[b-1]+t.text[b-1].length,this.count+=t.text[b].length,this.counts.push(t.text[b].length);this.text=t.text.join(\"\")}else this.text=t.text,this.count=this.text.length,this.counts=[this.count];r=[],this.font.forEach((function(t,n){T.atlasContext.font=t.baseString;for(var i=e.fontAtlas[n],a=0;a<e.text.length;a++){var o=e.text.charAt(a);if(null==i.ids[o]&&(i.ids[o]=i.chars.length,i.chars.push(o),r.push(o)),null==t.width[o]&&(t.width[o]=T.atlasContext.measureText(o).width/T.baseFontSize,e.kerning)){var s=[];for(var l in t.width)s.push(l+o,o+l);g(t.kerning,m(t.family,{pairs:s}))}}}))}if(t.position)if(t.position.length>2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A<this.counts.length;A++){var S=this.counts[A];if(w)for(var E=0;E<S;E++)k[M++]=t.position[2*A],k[M++]=t.position[2*A+1];else for(var L=0;L<S;L++)k[M++]=t.position[A][0],k[M++]=t.position[A][1]}this.position.call?this.position({type:\"float\",data:k}):this.position=this.regl.buffer({type:\"float\",data:k}),u.freeFloat(k)}else this.position.destroy&&this.position.destroy(),this.position={constant:t.position};if(t.text||a){var C=u.mallocUint8(this.count),P=u.mallocFloat(2*this.count);this.textWidth=[];for(var I=0,O=0;I<this.counts.length;I++){for(var z=this.counts[I],D=this.font[I]||this.font[0],R=this.fontAtlas[I]||this.fontAtlas[0],F=0;F<z;F++){var B=this.text.charAt(O),N=this.text.charAt(O-1);if(C[O]=R.ids[B],P[2*O]=D.width[B],F){var j=P[2*O-2],U=P[2*O],V=P[2*O-1]+.5*j+.5*U;if(this.kerning){var H=D.kerning[N+B];H&&(V+=.001*H)}P[2*O+1]=V}else P[2*O+1]=.5*P[2*O];O++}this.textWidth.push(P.length?.5*P[2*O-2]+P[2*O-1]:0)}t.align||(t.align=this.align),this.charBuffer({data:C,type:\"uint8\",usage:\"stream\"}),this.sizeBuffer({data:P,type:\"float\",usage:\"stream\"}),u.freeUint8(C),u.freeFloat(P),r.length&&this.font.forEach((function(t,r){var n=e.fontAtlas[r],i=n.step,a=Math.floor(T.maxAtlasSize/i),o=Math.min(a,n.chars.length),s=Math.ceil(n.chars.length/o),l=x(o*i),u=x(s*i);n.width=l,n.height=u,n.rows=s,n.cols=o,n.em&&n.texture({data:c({canvas:T.atlasCanvas,font:n.fontString,chars:n.chars,shape:[l,u],step:[i,i]})})}))}if(t.align&&(this.align=t.align,this.alignOffset=this.textWidth.map((function(t,r){var n=Array.isArray(e.align)?e.align.length>1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,i+=\"number\"==typeof t?t-n.baseline:-n[t],i*=-1}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof t.color&&isNaN(t.color)){var q;if(\"number\"==typeof t.color[0]&&t.color.length>this.counts.length){var G=t.color.length;q=u.mallocUint8(G);for(var Y=(t.color.subarray||t.color.slice).bind(t.color),W=0;W<G;W+=4)q.set(l(Y(W,W+4),\"uint8\"),W)}else{var X=t.color.length;q=u.mallocUint8(4*X);for(var Z=0;Z<X;Z++)q.set(l(t.color[Z]||0,\"uint8\"),4*Z)}this.color=q}else this.color=l(t.color,\"uint8\");if(t.position||t.text||t.color||t.baseline||t.align||t.font||t.offset||t.opacity)if(this.color.length>4||this.baselineOffset.length>1||this.align&&this.align.length>1||this.fontAtlas.length>1||this.positionOffset.length>2){var J=Math.max(.5*this.position.length||0,.25*this.color.length||0,this.baselineOffset.length||0,this.alignOffset.length||0,this.font.length||0,this.opacity.length||0,.5*this.positionOffset.length||0);this.batch=Array(J);for(var 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this.count?this.batch=[{count:this.count,offset:0,color:this.color||[0,0,0,255],opacity:Array.isArray(this.opacity)?this.opacity[0]:this.opacity,baseline:this.baselineOffset[0],align:this.alignOffset?this.alignOffset[0]:0,atlas:this.fontAtlas[0],positionOffset:this.positionOffset}]:this.batch=[]},T.prototype.destroy=function(){},T.prototype.kerning=!0,T.prototype.position={constant:new Float32Array(2)},T.prototype.translate=null,T.prototype.scale=null,T.prototype.font=null,T.prototype.text=\"\",T.prototype.positionOffset=[0,0],T.prototype.opacity=1,T.prototype.color=new 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r=e.parse(t[1],1,Bt);if(!r)return null;var n=[],i=null;e.expectedType&&\"value\"!==e.expectedType.kind&&(i=e.expectedType);for(var a=1;a<t.length;a+=2){var o=1===a?-1/0:t[a],s=t[a+1],l=a,c=a+1;if(\"number\"!=typeof o)return e.error('Input/output pairs for \"step\" expressions must be defined using literal numeric values (not computed expressions) for the input values.',l);if(n.length&&n[n.length-1][0]>=o)return e.error('Input/output pairs for \"step\" expressions must be arranged with input values in strictly ascending order.',l);var u=e.parse(s,c,i);if(!u)return null;i=i||u.type,n.push([o,u])}return new He(i,r,n)},He.prototype.evaluate=function(t){var e=this.labels,r=this.outputs;if(1===e.length)return r[0].evaluate(t);var n=this.input.evaluate(t);if(n<=e[0])return r[0].evaluate(t);var i=e.length;return n>=e[i-1]?r[i-1].evaluate(t):r[Ve(e,n)].evaluate(t)},He.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},He.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},He.prototype.serialize=function(){for(var t=[\"step\",this.input.serialize()],e=0;e<this.labels.length;e++)e>0&&t.push(this.labels[e]),t.push(this.outputs[e].serialize());return t};var Ge=Object.freeze({__proto__:null,number:qe,color:function(t,e,r){return new te(qe(t.r,e.r,r),qe(t.g,e.g,r),qe(t.b,e.b,r),qe(t.a,e.a,r))},array:function(t,e,r){return t.map((function(t,n){return qe(t,e[n],r)}))}}),Ye=6/29,We=3*Ye*Ye,Xe=Math.PI/180,Ze=180/Math.PI;function Je(t){return t>.008856451679035631?Math.pow(t,1/3):t/We+4/29}function Ke(t){return t>Ye?t*t*t:We*(t-4/29)}function Qe(t){return 255*(t<=.0031308?12.92*t:1.055*Math.pow(t,1/2.4)-.055)}function $e(t){return(t/=255)<=.04045?t/12.92:Math.pow((t+.055)/1.055,2.4)}function tr(t){var e=$e(t.r),r=$e(t.g),n=$e(t.b),i=Je((.4124564*e+.3575761*r+.1804375*n)/.95047),a=Je((.2126729*e+.7151522*r+.072175*n)/1);return{l:116*a-16,a:500*(i-a),b:200*(a-Je((.0193339*e+.119192*r+.9503041*n)/1.08883)),alpha:t.a}}function er(t){var e=(t.l+16)/116,r=isNaN(t.a)?e:e+t.a/500,n=isNaN(t.b)?e:e-t.b/200;return e=1*Ke(e),r=.95047*Ke(r),n=1.08883*Ke(n),new te(Qe(3.2404542*r-1.5371385*e-.4985314*n),Qe(-.969266*r+1.8760108*e+.041556*n),Qe(.0556434*r-.2040259*e+1.0572252*n),t.alpha)}function rr(t,e,r){var n=e-t;return t+r*(n>180||n<-180?n-360*Math.round(n/360):n)}var nr={forward:tr,reverse:er,interpolate:function(t,e,r){return{l:qe(t.l,e.l,r),a:qe(t.a,e.a,r),b:qe(t.b,e.b,r),alpha:qe(t.alpha,e.alpha,r)}}},ir={forward:function(t){var e=tr(t),r=e.l,n=e.a,i=e.b,a=Math.atan2(i,n)*Ze;return{h:a<0?a+360:a,c:Math.sqrt(n*n+i*i),l:r,alpha:t.a}},reverse:function(t){var e=t.h*Xe,r=t.c;return er({l:t.l,a:Math.cos(e)*r,b:Math.sin(e)*r,alpha:t.alpha})},interpolate:function(t,e,r){return{h:rr(t.h,e.h,r),c:qe(t.c,e.c,r),l:qe(t.l,e.l,r),alpha:qe(t.alpha,e.alpha,r)}}},ar=Object.freeze({__proto__:null,lab:nr,hcl:ir}),or=function(t,e,r,n,i){this.type=t,this.operator=e,this.interpolation=r,this.input=n,this.labels=[],this.outputs=[];for(var a=0,o=i;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1];this.labels.push(l),this.outputs.push(c)}};function sr(t,e,r,n){var i=n-r,a=t-r;return 0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}or.interpolationFactor=function(t,e,n,i){var a=0;if(\"exponential\"===t.name)a=sr(e,t.base,n,i);else if(\"linear\"===t.name)a=sr(e,1,n,i);else if(\"cubic-bezier\"===t.name){var o=t.controlPoints;a=new r(o[0],o[1],o[2],o[3]).solve(sr(e,1,n,i))}return a},or.parse=function(t,e){var r=t[0],n=t[1],i=t[2],a=t.slice(3);if(!Array.isArray(n)||0===n.length)return e.error(\"Expected an interpolation type expression.\",1);if(\"linear\"===n[0])n={name:\"linear\"};else if(\"exponential\"===n[0]){var o=n[1];if(\"number\"!=typeof o)return e.error(\"Exponential interpolation requires a numeric base.\",1,1);n={name:\"exponential\",base:o}}else{if(\"cubic-bezier\"!==n[0])return e.error(\"Unknown interpolation type \"+String(n[0]),1,0);var s=n.slice(1);if(4!==s.length||s.some((function(t){return\"number\"!=typeof t||t<0||t>1})))return e.error(\"Cubic bezier interpolation requires four numeric arguments with values between 0 and 1.\",1);n={name:\"cubic-bezier\",controlPoints:s}}if(t.length-1<4)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if((t.length-1)%2!=0)return e.error(\"Expected an even number of arguments.\");if(!(i=e.parse(i,2,Bt)))return null;var l=[],c=null;\"interpolate-hcl\"===r||\"interpolate-lab\"===r?c=Ut:e.expectedType&&\"value\"!==e.expectedType.kind&&(c=e.expectedType);for(var u=0;u<a.length;u+=2){var f=a[u],h=a[u+1],p=u+3,d=u+4;if(\"number\"!=typeof f)return e.error('Input/output pairs for 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a=Ve(e,n),o=e[a],s=e[a+1],l=or.interpolationFactor(this.interpolation,n,o,s),c=r[a].evaluate(t),u=r[a+1].evaluate(t);return\"interpolate\"===this.operator?Ge[this.type.kind.toLowerCase()](c,u,l):\"interpolate-hcl\"===this.operator?ir.reverse(ir.interpolate(ir.forward(c),ir.forward(u),l)):nr.reverse(nr.interpolate(nr.forward(c),nr.forward(u),l))},or.prototype.eachChild=function(t){t(this.input);for(var e=0,r=this.outputs;e<r.length;e+=1){t(r[e])}},or.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))},or.prototype.serialize=function(){var t;t=\"linear\"===this.interpolation.name?[\"linear\"]:\"exponential\"===this.interpolation.name?1===this.interpolation.base?[\"linear\"]:[\"exponential\",this.interpolation.base]:[\"cubic-bezier\"].concat(this.interpolation.controlPoints);for(var e=[this.operator,t,this.input.serialize()],r=0;r<this.labels.length;r++)e.push(this.labels[r],this.outputs[r].serialize());return e};var lr=function(t,e){this.type=t,this.args=e};lr.parse=function(t,e){if(t.length<2)return e.error(\"Expectected at least one argument.\");var r=null,n=e.expectedType;n&&\"value\"!==n.kind&&(r=n);for(var i=[],a=0,o=t.slice(1);a<o.length;a+=1){var s=o[a],l=e.parse(s,1+i.length,r,void 0,{typeAnnotation:\"omit\"});if(!l)return null;r=r||l.type,i.push(l)}var c=n&&i.some((function(t){return Jt(n,t.type)}));return new lr(c?Ht:r,i)},lr.prototype.evaluate=function(t){for(var e,r=null,n=0,i=0,a=this.args;i<a.length;i+=1){if(n++,(r=a[i].evaluate(t))&&r instanceof ie&&!r.available&&(e||(e=r.name),r=null,n===this.args.length&&(r=e)),null!==r)break}return r},lr.prototype.eachChild=function(t){this.args.forEach(t)},lr.prototype.outputDefined=function(){return this.args.every((function(t){return t.outputDefined()}))},lr.prototype.serialize=function(){var t=[\"coalesce\"];return this.eachChild((function(e){t.push(e.serialize())})),t};var cr=function(t,e){this.type=e.type,this.bindings=[].concat(t),this.result=e};cr.prototype.evaluate=function(t){return this.result.evaluate(t)},cr.prototype.eachChild=function(t){for(var e=0,r=this.bindings;e<r.length;e+=1){t(r[e][1])}t(this.result)},cr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found \"+(t.length-1)+\" instead.\");for(var r=[],n=1;n<t.length-1;n+=2){var i=t[n];if(\"string\"!=typeof i)return e.error(\"Expected string, but found \"+typeof i+\" instead.\",n);if(/[^a-zA-Z0-9_]/.test(i))return e.error(\"Variable names must contain only alphanumeric characters or '_'.\",n);var a=e.parse(t[n+1],n+1);if(!a)return null;r.push([i,a])}var o=e.parse(t[t.length-1],t.length-1,e.expectedType,r);return o?new cr(r,o):null},cr.prototype.outputDefined=function(){return this.result.outputDefined()},cr.prototype.serialize=function(){for(var t=[\"let\"],e=0,r=this.bindings;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t.push(i,a.serialize())}return t.push(this.result.serialize()),t};var ur=function(t,e,r){this.type=t,this.index=e,this.input=r};ur.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Bt),n=e.parse(t[2],2,Wt(e.expectedType||Ht));if(!r||!n)return null;var i=n.type;return new ur(i.itemType,r,n)},ur.prototype.evaluate=function(t){var e=this.index.evaluate(t),r=this.input.evaluate(t);if(e<0)throw new ue(\"Array index out of bounds: \"+e+\" < 0.\");if(e>=r.length)throw new ue(\"Array index out of bounds: \"+e+\" > \"+(r.length-1)+\".\");if(e!==Math.floor(e))throw new ue(\"Array index must be an integer, but found \"+e+\" instead.\");return r[e]},ur.prototype.eachChild=function(t){t(this.index),t(this.input)},ur.prototype.outputDefined=function(){return!1},ur.prototype.serialize=function(){return[\"at\",this.index.serialize(),this.input.serialize()]};var fr=function(t,e){this.type=jt,this.needle=t,this.haystack=e};fr.parse=function(t,e){if(3!==t.length)return e.error(\"Expected 2 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);return r&&n?Kt(r.type,[jt,Nt,Bt,Ft,Ht])?new fr(r,n):e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\"):null},fr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!r)return!1;if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");return r.indexOf(e)>=0},fr.prototype.eachChild=function(t){t(this.needle),t(this.haystack)},fr.prototype.outputDefined=function(){return!0},fr.prototype.serialize=function(){return[\"in\",this.needle.serialize(),this.haystack.serialize()]};var hr=function(t,e,r){this.type=Bt,this.needle=t,this.haystack=e,this.fromIndex=r};hr.parse=function(t,e){if(t.length<=2||t.length>=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Ht),n=e.parse(t[2],2,Ht);if(!r||!n)return null;if(!Kt(r.type,[jt,Nt,Bt,Ft,Ht]))return e.error(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Bt);return i?new hr(r,n,i):null}return new hr(r,n)},hr.prototype.evaluate=function(t){var e=this.needle.evaluate(t),r=this.haystack.evaluate(t);if(!Qt(e,[\"boolean\",\"string\",\"number\",\"null\"]))throw new ue(\"Expected first argument to be of type boolean, string, number or null, but found \"+Xt(se(e))+\" instead.\");if(!Qt(r,[\"string\",\"array\"]))throw new ue(\"Expected second argument to be of type array or string, but found \"+Xt(se(r))+\" instead.\");if(this.fromIndex){var n=this.fromIndex.evaluate(t);return r.indexOf(e,n)}return r.indexOf(e)},hr.prototype.eachChild=function(t){t(this.needle),t(this.haystack),this.fromIndex&&t(this.fromIndex)},hr.prototype.outputDefined=function(){return!1},hr.prototype.serialize=function(){if(null!=this.fromIndex&&void 0!==this.fromIndex){var t=this.fromIndex.serialize();return[\"index-of\",this.needle.serialize(),this.haystack.serialize(),t]}return[\"index-of\",this.needle.serialize(),this.haystack.serialize()]};var pr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};pr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of 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m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new pr(r,n,d,i,a,m):null},pr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(se(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},pr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},pr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},pr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i<a.length;i+=1){var o=a[i];void 0===(f=n[this.cases[o]])?(n[this.cases[o]]=r.length,r.push([this.cases[o],[o]])):r[f][1].push(o)}for(var s=function(e){return\"number\"===t.inputType.kind?Number(e):e},l=0,c=r;l<c.length;l+=1){var u=c[l],f=u[0],h=u[1];1===h.length?e.push(s(h[0])):e.push(h.map(s)),e.push(this.outputs[outputIndex$1].serialize())}return e.push(this.otherwise.serialize()),e};var dr=function(t,e,r){this.type=t,this.branches=e,this.otherwise=r};dr.parse=function(t,e){if(t.length<4)return e.error(\"Expected at least 3 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=0)return e.error(\"Expected an odd number of arguments.\");var r;e.expectedType&&\"value\"!==e.expectedType.kind&&(r=e.expectedType);for(var n=[],i=1;i<t.length-1;i+=2){var a=e.parse(t[i],i,jt);if(!a)return null;var o=e.parse(t[i+1],i+1,r);if(!o)return null;n.push([a,o]),r=r||o.type}var s=e.parse(t[t.length-1],t.length-1,r);return s?new dr(r,n,s):null},dr.prototype.evaluate=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];if(i.evaluate(t))return a.evaluate(t)}return this.otherwise.evaluate(t)},dr.prototype.eachChild=function(t){for(var e=0,r=this.branches;e<r.length;e+=1){var n=r[e],i=n[0],a=n[1];t(i),t(a)}t(this.otherwise)},dr.prototype.outputDefined=function(){return this.branches.every((function(t){t[0];return 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or?a.interpolation:void 0;return Ir(new Zr(n?\"camera\":\"composite\",t.value,a.labels,o))}Zr.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},Zr.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)},Zr.prototype.interpolationFactor=function(t,e,r){return this.interpolationType?or.interpolationFactor(this.interpolationType,t,e,r):0};var Kr=function(t,e){this._parameters=t,this._specification=e,It(this,function t(e,r){var n,i,a,o=\"color\"===r.type,s=e.stops&&\"object\"==typeof e.stops[0][0],l=s||void 0!==e.property,c=s||!l,u=e.type||(Rr(r)?\"exponential\":\"interval\");if(o&&((e=It({},e)).stops&&(e.stops=e.stops.map((function(t){return[t[0],te.parse(t[1])]}))),e.default?e.default=te.parse(e.default):e.default=te.parse(r.default)),e.colorSpace&&\"rgb\"!==e.colorSpace&&!ar[e.colorSpace])throw new Error(\"Unknown color space: 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n&&r<n.minimum?[new Ct(e,r,r+\" is less than the minimum value \"+n.minimum)]:\"maximum\"in n&&r>n.maximum?[new Ct(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function en(t){var e,r,n,i=t.valueSpec,a=Ot(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Fr(t.value.stops)&&\"array\"===Fr(t.value.stops[0])&&\"object\"===Fr(t.value.stops[0][0]),u=Qr({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Ct(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;e=e.concat($r({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:f})),\"array\"===Fr(r)&&0===r.length&&e.push(new Ct(t.key,r,\"array must have at least one stop\"));return e},default:function(t){return kn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Ct(t.key,t.value,'missing required property \"property\"')),\"identity\"===a||t.value.stops||u.push(new Ct(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===a&&t.valueSpec.expression&&!Rr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!zr(t.valueSpec)?u.push(new Ct(t.key,t.value,\"property functions not supported\")):s&&!Dr(t.valueSpec)&&u.push(new Ct(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==a&&!c||void 0!==t.value.property||u.push(new Ct(t.key,t.value,'\"property\" property is required')),u;function f(t){var e=[],a=t.value,s=t.key;if(\"array\"!==Fr(a))return[new Ct(s,a,\"array expected, \"+Fr(a)+\" found\")];if(2!==a.length)return[new Ct(s,a,\"array length 2 expected, length \"+a.length+\" found\")];if(c){if(\"object\"!==Fr(a[0]))return[new Ct(s,a,\"object expected, \"+Fr(a[0])+\" found\")];if(void 0===a[0].zoom)return[new Ct(s,a,\"object stop key must have zoom\")];if(void 0===a[0].value)return[new Ct(s,a,\"object stop key must have value\")];if(n&&n>Ot(a[0].zoom))return[new Ct(s,a[0].zoom,\"stop zoom values must appear in ascending order\")];Ot(a[0].zoom)!==n&&(n=Ot(a[0].zoom),r=void 0,o={}),e=e.concat(Qr({key:s+\"[0]\",value:a[0],valueSpec:{zoom:{}},style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:tn,value:h}}))}else e=e.concat(h({key:s+\"[0]\",value:a[0],valueSpec:{},style:t.style,styleSpec:t.styleSpec},a));return Yr(zt(a[1]))?e.concat([new Ct(s+\"[1]\",a[1],\"expressions are not allowed in function stops.\")]):e.concat(kn({key:s+\"[1]\",value:a[1],valueSpec:i,style:t.style,styleSpec:t.styleSpec}))}function h(t,n){var s=Fr(t.value),l=Ot(t.value),c=null!==t.value?t.value:n;if(e){if(s!==e)return[new Ct(t.key,c,s+\" stop domain type must match previous stop domain type \"+e)]}else e=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new Ct(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==a){var u=\"number expected, \"+s+\" found\";return zr(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Ct(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l<r?[new Ct(t.key,c,\"stop domain values must appear in ascending order\")]:(r=l,\"categorical\"===a&&l in o?[new Ct(t.key,c,\"stop domain values must be unique\")]:(o[l]=!0,[])):[new Ct(t.key,c,\"integer expected, found \"+l)]}}function rn(t){var e=(\"property\"===t.expressionContext?Jr:Wr)(zt(t.value),t.valueSpec);if(\"error\"===e.result)return e.value.map((function(e){return new Ct(\"\"+t.key+e.key,t.value,e.message)}));var r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new Ct(t.key,t.value,'Invalid data expression for \"'+t.propertyKey+'\". Output values must be contained as literals within the expression.')];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!Be(r))return[new Ct(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!Ne(r,[\"zoom\",\"feature-state\"]))return[new Ct(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!Fe(r))return[new Ct(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function nn(t){var e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Ot(r))&&i.push(new Ct(e,r,\"expected one of [\"+n.values.join(\", \")+\"], 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on={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function sn(t){if(null==t)return{filter:function(){return!0},needGeometry:!1};an(t)||(t=cn(t));var e=Wr(t,on);if(\"error\"===e.result)throw new Error(e.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return{filter:function(t,r,n){return e.value.evaluate(t,r,{},n)},needGeometry:function t(e){if(!Array.isArray(e))return!1;if(\"within\"===e[0])return!0;for(var r=1;r<e.length;r++)if(t(e[r]))return!0;return!1}(t)}}function ln(t,e){return t<e?-1:t>e?1:0}function cn(t){if(!t)return!0;var e,r=t[0];return t.length<=1?\"any\"!==r:\"==\"===r?un(t[1],t[2],\"==\"):\"!=\"===r?pn(un(t[1],t[2],\"==\")):\"<\"===r||\">\"===r||\"<=\"===r||\">=\"===r?un(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(cn))):\"all\"===r?[\"all\"].concat(t.slice(1).map(cn)):\"none\"===r?[\"all\"].concat(t.slice(1).map(cn).map(pn)):\"in\"===r?fn(t[1],t.slice(2)):\"!in\"===r?pn(fn(t[1],t.slice(2))):\"has\"===r?hn(t[1]):\"!has\"===r?pn(hn(t[1])):\"within\"!==r||t}function un(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function fn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(ln)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function hn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function pn(t){return[\"!\",t]}function dn(t){return an(zt(t.value))?rn(It({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):function t(e){var r=e.value,n=e.key;if(\"array\"!==Fr(r))return[new Ct(n,r,\"array expected, \"+Fr(r)+\" found\")];var i,a=e.styleSpec,o=[];if(r.length<1)return[new Ct(n,r,\"filter array must have at least 1 element\")];switch(o=o.concat(nn({key:n+\"[0]\",value:r[0],valueSpec:a.filter_operator,style:e.style,styleSpec:e.styleSpec})),Ot(r[0])){case\"<\":case\"<=\":case\">\":case\">=\":r.length>=2&&\"$type\"===Ot(r[1])&&o.push(new Ct(n,r,'\"$type\" cannot be use with operator \"'+r[0]+'\"'));case\"==\":case\"!=\":3!==r.length&&o.push(new Ct(n,r,'filter array for operator \"'+r[0]+'\" must have 3 elements'));case\"in\":case\"!in\":r.length>=2&&\"string\"!==(i=Fr(r[1]))&&o.push(new Ct(n+\"[1]\",r[1],\"string expected, \"+i+\" 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r=t.key,n=t.style,i=t.styleSpec,a=t.value,o=t.objectKey,s=i[e+\"_\"+t.layerType];if(!s)return[];var l=o.match(/^(.*)-transition$/);if(\"paint\"===e&&l&&s[l[1]]&&s[l[1]].transition)return kn({key:r,value:a,valueSpec:i.transition,style:n,styleSpec:i});var c,u=t.valueSpec||s[o];if(!u)return[new Ct(r,a,'unknown property \"'+o+'\"')];if(\"string\"===Fr(a)&&zr(u)&&!u.tokens&&(c=/^{([^}]+)}$/.exec(a)))return[new Ct(r,a,'\"'+o+'\" does not support interpolation syntax\\nUse an identity property function instead: `{ \"type\": \"identity\", \"property\": '+JSON.stringify(c[1])+\" }`.\")];var f=[];return\"symbol\"===t.layerType&&(\"text-field\"===o&&n&&!n.glyphs&&f.push(new Ct(r,a,'use of \"text-field\" requires a style \"glyphs\" property')),\"text-font\"===o&&Br(zt(a))&&\"identity\"===Ot(a.type)&&f.push(new Ct(r,a,'\"text-font\" does not support identity 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null!=hi.applyArabicShaping&&null!=hi.processBidirectionalText&&null!=hi.processStyledBidirectionalText},getPluginURL:function(){return oi}},pi=function(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Gn,this.transition={})};pi.prototype.isSupportedScript=function(t){return function(t,e){for(var r=0,n=t;r<n.length;r+=1){if(!Qn(n[r].charCodeAt(0),e))return!1}return!0}(t,hi.isLoaded())},pi.prototype.crossFadingFactor=function(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)},pi.prototype.getCrossfadeParameters=function(){var t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}};var di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(Br(t))return new Kr(t,e);if(Yr(t)){var r=Jr(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=te.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var mi=function(t){this.property=t,this.value=new di(t,void 0)};mi.prototype.transitioned=function(t,e){return new vi(this.property,this.value,e,u({},t.transition,this.transition),t.now)},mi.prototype.untransitioned=function(){return new vi(this.property,this.value,null,{},0)};var gi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)};gi.prototype.getValue=function(t){return x(this._values[t].value.value)},gi.prototype.setValue=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].value=new di(this._values[t].property,null===e?void 0:x(e))},gi.prototype.getTransition=function(t){return x(this._values[t].transition)},gi.prototype.setTransition=function(t,e){this._values.hasOwnProperty(t)||(this._values[t]=new mi(this._values[t].property)),this._values[t].transition=x(e)||void 0},gi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i);var a=this.getTransition(n);void 0!==a&&(t[n+\"-transition\"]=a)}return t},gi.prototype.transitioned=function(t,e){for(var r=new yi(this._properties),n=0,i=Object.keys(this._values);n<i.length;n+=1){var a=i[n];r._values[a]=this._values[a].transitioned(t,e._values[a])}return r},gi.prototype.untransitioned=function(){for(var t=new yi(this._properties),e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e];t._values[n]=this._values[n].untransitioned()}return t};var vi=function(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)};vi.prototype.possiblyEvaluate=function(t,e,r){var n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n<this.begin)return a.possiblyEvaluate(t,e,r);var o=(n-this.begin)/(this.end-this.begin);return this.property.interpolate(a.possiblyEvaluate(t,e,r),i,function(t){if(t<=0)return 0;if(t>=1)return 1;var e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}return i};var yi=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};yi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n},yi.prototype.hasTransition=function(){for(var t=0,e=Object.keys(this._values);t<e.length;t+=1){var r=e[t];if(this._values[r].prior)return!0}return!1};var xi=function(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)};xi.prototype.getValue=function(t){return x(this._values[t].value)},xi.prototype.setValue=function(t,e){this._values[t]=new di(this._values[t].property,null===e?void 0:x(e))},xi.prototype.serialize=function(){for(var t={},e=0,r=Object.keys(this._values);e<r.length;e+=1){var n=r[e],i=this.getValue(n);void 0!==i&&(t[n]=i)}return t},xi.prototype.possiblyEvaluate=function(t,e,r){for(var n=new _i(this._properties),i=0,a=Object.keys(this._values);i<a.length;i+=1){var o=a[i];n._values[o]=this._values[o].possiblyEvaluate(t,e,r)}return n};var bi=function(t,e,r){this.property=t,this.value=e,this.parameters=r};bi.prototype.isConstant=function(){return\"constant\"===this.value.kind},bi.prototype.constantOr=function(t){return\"constant\"===this.value.kind?this.value.value:t},bi.prototype.evaluate=function(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)};var _i=function(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)};_i.prototype.get=function(t){return this._values[t]};var wi=function(t){this.specification=t};wi.prototype.possiblyEvaluate=function(t,e){return t.expression.evaluate(e)},wi.prototype.interpolate=function(t,e,r){var n=Ge[this.specification.type];return n?n(t,e,r):t};var Ti=function(t,e){this.specification=t,this.overrides=e};Ti.prototype.possiblyEvaluate=function(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new bi(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new bi(this,t.expression,e)},Ti.prototype.interpolate=function(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new bi(this,{kind:\"constant\",value:void 0},t.parameters);var n=Ge[this.specification.type];return n?new bi(this,{kind:\"constant\",value:n(t.value.value,e.value.value,r)},t.parameters):t},Ti.prototype.evaluate=function(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)};var ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0===t.value)return new bi(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new bi(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){var s=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new bi(this,{kind:\"constant\",value:s},e)}return new bi(this,t.expression,e)},e.prototype.evaluate=function(t,e,r,n,i,a){if(\"source\"===t.kind){var o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value},e.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Ti),Ai=function(t){this.specification=t};Ai.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new pi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new pi(Math.floor(e.zoom),e)),t.expression.evaluate(new pi(Math.floor(e.zoom+1),e)),e)}},Ai.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Ai.prototype.interpolate=function(t){return t};var Mi=function(t){this.specification=t};Mi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Mi.prototype.interpolate=function(){return!1};var Si=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new mi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};Nn(\"DataDrivenProperty\",Ti),Nn(\"DataConstantProperty\",wi),Nn(\"CrossFadedDataDrivenProperty\",ki),Nn(\"CrossFadedProperty\",Ai),Nn(\"ColorRampProperty\",Mi);var Ei=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(e=e,this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new xi(r.layout)),r.paint)){for(var n in this._transitionablePaint=new gi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new _i(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(In,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return g(t,\"-transition\")?this._transitionablePaint.getTransition(t.slice(0,-\"-transition\".length)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Pn,n,t,e,r))return!1}if(g(t,\"-transition\"))return this._transitionablePaint.setTransition(t.slice(0,-\"-transition\".length),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t<this.minzoom)||(!!(this.maxzoom&&t>=this.maxzoom)||\"none\"===this.visibility)},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),y(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 0===i&&(i={}),(!i||!1!==i.validate)&&On(this,t.call(Ln,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Lt,style:{glyphs:!0,sprite:!0}}))},e.prototype.is3D=function(){return!1},e.prototype.isTileClipped=function(){return!1},e.prototype.hasOffscreenPass=function(){return!1},e.prototype.resize=function(){},e.prototype.isStateDependent=function(){for(var t in this.paint._values){var e=this.paint.get(t);if(e instanceof bi&&zr(e.property.specification)&&((\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent))return!0}return!1},e}(Et),Li={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array},Ci=function(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8},Pi=function(){this.isTransferred=!1,this.capacity=-1,this.resize(0)};function Ii(t,e){void 0===e&&(e=1);var r=0,n=0;return{members:t.map((function(t){var i,a=(i=t.type,Li[i].BYTES_PER_ELEMENT),o=r=Oi(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:Oi(r,Math.max(n,e)),alignment:e}}function Oi(t,e){return Math.ceil(t/e)*e}Pi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Pi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Pi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Pi.prototype.clear=function(){this.length=0},Pi.prototype.resize=function(t){this.reserve(t),this.length=t},Pi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Pi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Pi);zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2i4\",zi);var Di=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Pi);Di.prototype.bytesPerElement=8,Nn(\"StructArrayLayout4i8\",Di);var Ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Ri.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i4i12\",Ri);var Fi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Pi);Fi.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2i4ub8\",Fi);var Bi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var f=9*t,h=18*t;return this.uint16[f+0]=e,this.uint16[f+1]=r,this.uint16[f+2]=n,this.uint16[f+3]=i,this.uint16[f+4]=a,this.uint16[f+5]=o,this.uint16[f+6]=s,this.uint16[f+7]=l,this.uint8[h+16]=c,this.uint8[h+17]=u,t},e}(Pi);Bi.prototype.bytesPerElement=18,Nn(\"StructArrayLayout8ui2ub18\",Bi);var Ni=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f){var h=this.length;return this.resize(h+1),this.emplace(h,t,e,r,n,i,a,o,s,l,c,u,f)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=f,this.int16[p+11]=h,t},e}(Pi);Ni.prototype.bytesPerElement=24,Nn(\"StructArrayLayout4i4ui4i24\",Ni);var ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Pi);ji.prototype.bytesPerElement=12,Nn(\"StructArrayLayout3f12\",ji);var Ui=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Pi);Ui.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1ul4\",Ui);var Vi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,f=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[f+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Pi);Vi.prototype.bytesPerElement=20,Nn(\"StructArrayLayout6i1ul2ui20\",Vi);var Hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Pi);Hi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2i2i2i12\",Hi);var qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Pi);qi.prototype.bytesPerElement=16,Nn(\"StructArrayLayout2f1f2i16\",qi);var Gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Pi);Gi.prototype.bytesPerElement=12,Nn(\"StructArrayLayout2ub2f12\",Gi);var Yi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Pi);Yi.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3ui6\",Yi);var Wi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g){var v=this.length;return this.resize(v+1),this.emplace(v,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v){var y=24*t,x=12*t,b=48*t;return this.int16[y+0]=e,this.int16[y+1]=r,this.uint16[y+2]=n,this.uint16[y+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[y+10]=l,this.uint16[y+11]=c,this.uint16[y+12]=u,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=m,this.uint32[x+10]=g,this.int16[y+22]=v,t},e}(Pi);Wi.prototype.bytesPerElement=48,Nn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Wi);var Xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=c,this.uint16[L+9]=u,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=m,this.uint16[L+15]=g,this.uint16[L+16]=v,this.uint16[L+17]=y,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Pi);Xi.prototype.bytesPerElement=68,Nn(\"StructArrayLayout8i15ui1ul4f68\",Xi);var Zi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Pi);Zi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout1f4\",Zi);var Ji=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Pi);Ji.prototype.bytesPerElement=6,Nn(\"StructArrayLayout3i6\",Ji);var Ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Pi);Ki.prototype.bytesPerElement=8,Nn(\"StructArrayLayout1ul2ui8\",Ki);var Qi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Pi);Qi.prototype.bytesPerElement=4,Nn(\"StructArrayLayout2ui4\",Qi);var $i=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Pi);$i.prototype.bytesPerElement=2,Nn(\"StructArrayLayout1ui2\",$i);var ta=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Pi);ta.prototype.bytesPerElement=8,Nn(\"StructArrayLayout2f8\",ta);var ea=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Pi);ea.prototype.bytesPerElement=16,Nn(\"StructArrayLayout4f16\",ea);var ra=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new i(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Ci);ra.prototype.size=20;var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ra(this,t)},e}(Vi);Nn(\"CollisionBoxArray\",na);var ia=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},glyphStartIndex:{configurable:!0},numGlyphs:{configurable:!0},vertexStartIndex:{configurable:!0},lineStartIndex:{configurable:!0},lineLength:{configurable:!0},segment:{configurable:!0},lowerSize:{configurable:!0},upperSize:{configurable:!0},lineOffsetX:{configurable:!0},lineOffsetY:{configurable:!0},writingMode:{configurable:!0},placedOrientation:{configurable:!0},hidden:{configurable:!0},crossTileID:{configurable:!0},associatedIconIndex:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.glyphStartIndex.get=function(){return this._structArray.uint16[this._pos2+2]},r.numGlyphs.get=function(){return this._structArray.uint16[this._pos2+3]},r.vertexStartIndex.get=function(){return this._structArray.uint32[this._pos4+2]},r.lineStartIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.lineLength.get=function(){return this._structArray.uint32[this._pos4+4]},r.segment.get=function(){return this._structArray.uint16[this._pos2+10]},r.lowerSize.get=function(){return this._structArray.uint16[this._pos2+11]},r.upperSize.get=function(){return this._structArray.uint16[this._pos2+12]},r.lineOffsetX.get=function(){return this._structArray.float32[this._pos4+7]},r.lineOffsetY.get=function(){return this._structArray.float32[this._pos4+8]},r.writingMode.get=function(){return this._structArray.uint8[this._pos1+36]},r.placedOrientation.get=function(){return this._structArray.uint8[this._pos1+37]},r.placedOrientation.set=function(t){this._structArray.uint8[this._pos1+37]=t},r.hidden.get=function(){return this._structArray.uint8[this._pos1+38]},r.hidden.set=function(t){this._structArray.uint8[this._pos1+38]=t},r.crossTileID.get=function(){return this._structArray.uint32[this._pos4+10]},r.crossTileID.set=function(t){this._structArray.uint32[this._pos4+10]=t},r.associatedIconIndex.get=function(){return this._structArray.int16[this._pos2+22]},Object.defineProperties(e.prototype,r),e}(Ci);ia.prototype.size=48;var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ia(this,t)},e}(Wi);Nn(\"PlacedSymbolArray\",aa);var oa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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c=t.readVarint();n=7&c,a=c>>3}if(a--,1===n||2===n)o+=t.readSVarint(),s+=t.readSVarint(),1===n&&(e&&l.push(e),e=[]),e.push(new i(o,s));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&l.push(e),l},ws.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos<e;){if(n<=0){var u=t.readVarint();r=7&u,n=u>>3}if(n--,1===r||2===r)(i+=t.readSVarint())<o&&(o=i),i>s&&(s=i),(a+=t.readSVarint())<l&&(l=a),a>c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},ws.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=ws.types[this.type];function u(t){for(var e=0;e<t.length;e++){var r=t[e],n=180-360*(r.y+s)/a;t[e]=[360*(r.x+o)/a-180,360/Math.PI*Math.atan(Math.exp(n*Math.PI/180))-90]}}switch(this.type){case 1:var f=[];for(n=0;n<l.length;n++)f[n]=l[n][0];u(l=f);break;case 2:for(n=0;n<l.length;n++)u(l[n]);break;case 3:for(l=function(t){var e=t.length;if(e<=1)return[t];for(var r,n,i=[],a=0;a<e;a++){var o=ks(t[a]);0!==o&&(void 0===n&&(n=o<0),n===o<0?(r&&i.push(r),r=[t[a]]):r.push(t[a]))}r&&i.push(r);return i}(l),n=0;n<l.length;n++)for(i=0;i<l[n].length;i++)u(l[n][i])}1===l.length?l=l[0]:c=\"Multi\"+c;var h={type:\"Feature\",geometry:{type:c,coordinates:l},properties:this.properties};return\"id\"in this&&(h.id=this.id),h};var As=Ms;function Ms(t,e){this.version=1,this.name=null,this.extent=4096,this.length=0,this._pbf=t,this._keys=[],this._values=[],this._features=[],t.readFields(Ss,this,e),this.length=this._features.length}function Ss(t,e,r){15===t?e.version=r.readVarint():1===t?e.name=r.readString():5===t?e.extent=r.readVarint():2===t?e._features.push(r.pos):3===t?e._keys.push(r.readString()):4===t&&e._values.push(function(t){var e=null,r=t.readVarint()+t.pos;for(;t.pos<r;){var n=t.readVarint()>>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}function Es(t,e,r){if(3===t){var n=new As(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Ms.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new _s(this._pbf,e,this.extent,this._keys,this._values)};var Ls={VectorTile:function(t,e){this.layers=t.readFields(Es,{},e)},VectorTileFeature:_s,VectorTileLayer:As},Cs=Ls.VectorTileFeature.types,Ps=Math.pow(2,13);function Is(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Ps)+o,i*Ps*2,a*Ps*2,Math.round(s))}var Os=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new Ri,this.indexArray=new Yi,this.programConfigurations=new Ua(bs,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};function zs(t,e){return t.x===e.x&&(t.x<0||t.x>8192)||t.y===e.y&&(t.y<0||t.y>8192)}function Ds(t){return t.every((function(t){return t.x<0}))||t.every((function(t){return t.x>8192}))||t.every((function(t){return t.y<0}))||t.every((function(t){return t.y>8192}))}Os.prototype.populate=function(t,e,r){this.features=[],this.hasPattern=ds(\"fill-extrusion\",this.layers,e);for(var n=0,i=t;n<i.length;n+=1){var a=i[n],o=a.feature,s=a.id,l=a.index,c=a.sourceLayerIndex,u=this.layers[0]._featureFilter.needGeometry,f={type:o.type,id:s,properties:o.properties,geometry:u?Ya(o):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),f,r)){var h={id:s,sourceLayerIndex:c,index:l,geometry:u?f.geometry:Ya(o),properties:o.properties,type:o.type,patterns:{}};void 0!==o.id&&(h.id=o.id),this.hasPattern?this.features.push(ms(\"fill-extrusion\",this.layers,h,this.zoom,e)):this.addFeature(h,h.geometry,l,r,{}),e.featureIndex.insert(o,h.geometry,l,c,this.index,!0)}}},Os.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.features;n<i.length;n+=1){var a=i[n],o=a.geometry;this.addFeature(a,o,a.index,e,r)}},Os.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},Os.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},Os.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},Os.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,bs),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},Os.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},Os.prototype.addFeature=function(t,e,r,n,i){for(var a=0,o=hs(e,500);a<o.length;a+=1){for(var s=o[a],l=0,c=0,u=s;c<u.length;c+=1){l+=u[c].length}for(var f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray),h=0,p=s;h<p.length;h+=1){var d=p[h];if(0!==d.length&&!Ds(d))for(var m=0,g=0;g<d.length;g++){var v=d[g];if(g>=1){var y=d[g-1];if(!zs(v,y)){f.vertexLength+4>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));var x=v.sub(y)._perp()._unit(),b=y.dist(v);m+b>32768&&(m=0),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,v.x,v.y,x.x,x.y,0,1,m),m+=b,Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,0,m),Is(this.layoutVertexArray,y.x,y.y,x.x,x.y,0,1,m);var _=f.vertexLength;this.indexArray.emplaceBack(_,_+2,_+1),this.indexArray.emplaceBack(_+1,_+2,_+3),f.vertexLength+=4,f.primitiveLength+=2}}}}if(f.vertexLength+l>pa.MAX_VERTEX_ARRAY_LENGTH&&(f=this.segments.prepareSegment(l,this.layoutVertexArray,this.indexArray)),\"Polygon\"===Cs[t.type]){for(var w=[],T=[],k=f.vertexLength,A=0,M=s;A<M.length;A+=1){var S=M[A];if(0!==S.length){S!==s[0]&&T.push(w.length/2);for(var E=0;E<S.length;E++){var L=S[E];Is(this.layoutVertexArray,L.x,L.y,0,0,1,1,0),w.push(L.x),w.push(L.y)}}}for(var C=Do(w,T),P=0;P<C.length;P+=3)this.indexArray.emplaceBack(k+C[P],k+C[P+2],k+C[P+1]);f.primitiveLength+=C.length/3,f.vertexLength+=l}}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},Nn(\"FillExtrusionBucket\",Os,{omit:[\"layers\",\"features\"]});var Rs={paint:new Si({\"fill-extrusion-opacity\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ki(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new Ti(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new wi(Lt[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})},Fs=function(t){function e(e){t.call(this,e,Rs)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.createBucket=function(t){return new Os(t)},e.prototype.queryRadius=function(){return so(this.paint.get(\"fill-extrusion-translate\"))},e.prototype.is3D=function(){return!0},e.prototype.queryIntersectsFeature=function(t,e,r,n,a,o,s,l){var c=lo(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),f=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),h=function(t,e,r,n){for(var a=[],o=0,s=t;o<s.length;o+=1){var l=s[o],c=[l.x,l.y,n,1];xo(c,c,e),a.push(new i(c[0]/c[3],c[1]/c[3]))}return a}(c,l,0,0),p=function(t,e,r,n){for(var a=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,f=n[8]*r,h=n[9]*r,p=n[10]*r,d=n[11]*r,m=0,g=t;m<g.length;m+=1){for(var v=g[m],y=[],x=[],b=0,_=v;b<_.length;b+=1){var w=_[b],T=w.x,k=w.y,A=n[0]*T+n[4]*k+n[12],M=n[1]*T+n[5]*k+n[13],S=n[2]*T+n[6]*k+n[14],E=n[3]*T+n[7]*k+n[15],L=S+c,C=E+u,P=A+f,I=M+h,O=S+p,z=E+d,D=new i((A+s)/C,(M+l)/C);D.z=L/C,y.push(D);var R=new i(P/z,I/z);R.z=O/z,x.push(R)}a.push(y),o.push(x)}return[a,o]}(n,f,u,l);return function(t,e,r){var n=1/0;Ka(r,e)&&(n=Ns(r,e[0]));for(var i=0;i<e.length;i++)for(var a=e[i],o=t[i],s=0;s<a.length-1;s++){var l=a[s],c=a[s+1],u=o[s],f=o[s+1],h=[l,c,f,u,l];Za(r,h)&&(n=Math.min(n,Ns(r,h)))}return n!==1/0&&n}(p[0],p[1],h)},e}(Ei);function Bs(t,e){return t.x*e.x+t.y*e.y}function Ns(t,e){if(1===t.length){for(var r,n=0,i=e[n++];!r||i.equals(r);)if(!(r=e[n++]))return 1/0;for(;n<e.length;n++){var a=e[n],o=t[0],s=r.sub(i),l=a.sub(i),c=o.sub(i),u=Bs(s,s),f=Bs(s,l),h=Bs(l,l),p=Bs(c,s),d=Bs(c,l),m=u*h-f*f,g=(h*p-f*d)/m,v=(u*d-f*p)/m,y=1-g-v,x=i.z*y+r.z*g+a.z*v;if(isFinite(x))return x}return 1/0}for(var b=1/0,_=0,w=e;_<w.length;_+=1){var T=w[_];b=Math.min(b,T.z)}return b}var js=Ii([{name:\"a_pos_normal\",components:2,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint8\"}],4).members,Us=Ls.VectorTileFeature.types,Vs=Math.cos(Math.PI/180*37.5),Hs=Math.pow(2,14)/.5,qs=function(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.layoutVertexArray=new Fi,this.indexArray=new Yi,this.programConfigurations=new Ua(js,t.layers,t.zoom),this.segments=new pa,this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id}))};qs.prototype.populate=function(t,e,r){this.hasPattern=ds(\"line\",this.layers,e);for(var n=this.layers[0].layout.get(\"line-sort-key\"),i=[],a=0,o=t;a<o.length;a+=1){var s=o[a],l=s.feature,c=s.id,u=s.index,f=s.sourceLayerIndex,h=this.layers[0]._featureFilter.needGeometry,p={type:l.type,id:c,properties:l.properties,geometry:h?Ya(l):[]};if(this.layers[0]._featureFilter.filter(new pi(this.zoom),p,r)){h||(p.geometry=Ya(l));var d=n?n.evaluate(p,{},r):void 0,m={id:c,properties:l.properties,type:l.type,sourceLayerIndex:f,index:u,geometry:p.geometry,patterns:{},sortKey:d};i.push(m)}}n&&i.sort((function(t,e){return t.sortKey-e.sortKey}));for(var g=0,v=i;g<v.length;g+=1){var y=v[g],x=y,b=x.geometry,_=x.index,w=x.sourceLayerIndex;if(this.hasPattern){var T=ms(\"line\",this.layers,y,this.zoom,e);this.patternFeatures.push(T)}else this.addFeature(y,b,_,r,{});var k=t[_].feature;e.featureIndex.insert(k,b,_,w,this.index)}},qs.prototype.update=function(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)},qs.prototype.addFeatures=function(t,e,r){for(var n=0,i=this.patternFeatures;n<i.length;n+=1){var a=i[n];this.addFeature(a,a.geometry,a.index,e,r)}},qs.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length},qs.prototype.uploadPending=function(){return!this.uploaded||this.programConfigurations.needsUpload},qs.prototype.upload=function(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,js),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0},qs.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())},qs.prototype.addFeature=function(t,e,r,n,i){for(var a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\"),u=0,f=e;u<f.length;u+=1){var h=f[u];this.addLine(h,t,o,s,l,c)}this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)},qs.prototype.addLine=function(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,e.properties&&e.properties.hasOwnProperty(\"mapbox_clip_start\")&&e.properties.hasOwnProperty(\"mapbox_clip_end\")){this.clipStart=+e.properties.mapbox_clip_start,this.clipEnd=+e.properties.mapbox_clip_end;for(var o=0;o<t.length-1;o++)this.totalDistance+=t[o].dist(t[o+1]);this.updateScaledDistance()}for(var s=\"Polygon\"===Us[e.type],l=t.length;l>=2&&t[l-1].equals(t[l-2]);)l--;for(var c=0;c<l-1&&t[c].equals(t[c+1]);)c++;if(!(l<(s?3:2))){\"bevel\"===r&&(i=1.05);var u,f=this.overscaling<=16?122880/(512*this.overscaling):0,h=this.segments.prepareSegment(10*l,this.layoutVertexArray,this.indexArray),p=void 0,d=void 0,m=void 0,g=void 0;this.e1=this.e2=-1,s&&(u=t[l-2],g=t[c].sub(u)._unit()._perp());for(var v=c;v<l;v++)if(!(d=v===l-1?s?t[c+1]:void 0:t[v+1])||!t[v].equals(d)){g&&(m=g),u&&(p=u),u=t[v],g=d?d.sub(u)._unit()._perp():m;var y=(m=m||g).add(g);0===y.x&&0===y.y||y._unit();var x=m.x*g.x+m.y*g.y,b=y.x*g.x+y.y*g.y,_=0!==b?1/b:1/0,w=2*Math.sqrt(2-2*b),T=b<Vs&&p&&d,k=m.x*g.y-m.y*g.x>0;if(T&&v>c){var A=u.dist(p);if(A>2*f){var M=u.sub(u.sub(p)._mult(f/A)._round());this.updateDistance(p,M),this.addCurrentVertex(M,m,0,0,h),p=M}}var S=p&&d,E=S?r:s?\"butt\":n;if(S&&\"round\"===E&&(_<a?E=\"miter\":_<=2&&(E=\"fakeround\")),\"miter\"===E&&_>i&&(E=\"bevel\"),\"bevel\"===E&&(_>2&&(E=\"flipbevel\"),_<i&&(E=\"miter\")),p&&this.updateDistance(p,u),\"miter\"===E)y._mult(_),this.addCurrentVertex(u,y,0,0,h);else if(\"flipbevel\"===E){if(_>100)y=g.mult(-1);else{var L=_*m.add(g).mag()/m.sub(g).mag();y._perp()._mult(L*(k?-1:1))}this.addCurrentVertex(u,y,0,0,h),this.addCurrentVertex(u,y.mult(-1),0,0,h)}else if(\"bevel\"===E||\"fakeround\"===E){var C=-Math.sqrt(_*_-1),P=k?C:0,I=k?0:C;if(p&&this.addCurrentVertex(u,m,P,I,h),\"fakeround\"===E)for(var O=Math.round(180*w/Math.PI/20),z=1;z<O;z++){var D=z/O;if(.5!==D){var R=D-.5;D+=D*R*(D-1)*((1.0904+x*(x*(3.55645-1.43519*x)-3.2452))*R*R+(.848013+x*(.215638*x-1.06021)))}var F=g.sub(m)._mult(D)._add(m)._unit()._mult(k?-1:1);this.addHalfVertex(u,F.x,F.y,!1,k,0,h)}d&&this.addCurrentVertex(u,g,-P,-I,h)}else if(\"butt\"===E)this.addCurrentVertex(u,y,0,0,h);else if(\"square\"===E){var B=p?1:-1;this.addCurrentVertex(u,y,B,B,h)}else\"round\"===E&&(p&&(this.addCurrentVertex(u,m,0,0,h),this.addCurrentVertex(u,m,1,1,h,!0)),d&&(this.addCurrentVertex(u,g,-1,-1,h,!0),this.addCurrentVertex(u,g,0,0,h)));if(T&&v<l-1){var N=u.dist(d);if(N>2*f){var j=u.add(d.sub(u)._mult(f/N)._round());this.updateDistance(u,j),this.addCurrentVertex(j,g,0,0,h),u=j}}}}},qs.prototype.addCurrentVertex=function(t,e,r,n,i,a){void 0===a&&(a=!1);var o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>Hs/2&&0===this.totalDistance&&(this.distance=0,this.addCurrentVertex(t,e,r,n,i,a))},qs.prototype.addHalfVertex=function(t,e,r,n,i,a,o){var s=t.x,l=t.y,c=.5*this.scaledDistance;this.layoutVertexArray.emplaceBack((s<<1)+(n?1:0),(l<<1)+(i?1:0),Math.round(63*e)+128,Math.round(63*r)+128,1+(0===a?0:a<0?-1:1)|(63&c)<<2,c>>6);var u=o.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,u),o.primitiveLength++),i?this.e2=u:this.e1=u},qs.prototype.updateScaledDistance=function(){this.scaledDistance=this.totalDistance>0?(this.clipStart+(this.clipEnd-this.clipStart)*this.distance/this.totalDistance)*(Hs-1):this.distance},qs.prototype.updateDistance=function(t,e){this.distance+=t.dist(e),this.updateScaledDistance()},Nn(\"LineBucket\",qs,{omit:[\"layers\",\"patternFeatures\"]});var Gs=new Si({\"line-cap\":new 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e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.possiblyEvaluate=function(e,r){return r=new pi(Math.floor(r.zoom),{now:r.now,fadeDuration:r.fadeDuration,zoomHistory:r.zoomHistory,transition:r.transition}),t.prototype.possiblyEvaluate.call(this,e,r)},e.prototype.evaluate=function(e,r,n,i){return r=u({},r,{zoom:Math.floor(r.zoom)}),t.prototype.evaluate.call(this,e,r,n,i)},e}(Ti))(Ys.paint.properties[\"line-width\"].specification);Ws.useIntegerZoom=!0;var Xs=function(t){function e(e){t.call(this,e,Ys)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._handleSpecialPaintPropertyUpdate=function(t){\"line-gradient\"===t&&this._updateGradient()},e.prototype._updateGradient=function(){var 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o=t[a],s=[],l=0;l<o.length;l++){var c=o[l-1],u=o[l],f=o[l+1],h=0===l?n:u.sub(c)._unit()._perp(),p=l===o.length-1?n:f.sub(u)._unit()._perp(),d=h._add(p)._unit(),m=d.x*p.x+d.y*p.y;d._mult(1/m),s.push(d._mult(e)._add(u))}r.push(s)}return r}(n,u*s)),function(t,e,r){for(var n=0;n<e.length;n++){var i=e[n];if(t.length>=3)for(var a=0;a<i.length;a++)if(io(t,i[a]))return!0;if(Qa(t,i,r))return!0}return!1}(l,n,c)},e.prototype.isTileClipped=function(){return!0},e}(Ei);function Zs(t,e){return e>0?e+2*t:t}var 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nl=function(t,e,r,n,i){var a,o,s=8*i-n-1,l=(1<<s)-1,c=l>>1,u=-7,f=r?i-1:0,h=r?-1:1,p=t[e+f];for(f+=h,a=p&(1<<-u)-1,p>>=-u,u+=s;u>0;a=256*a+t[e+f],f+=h,u-=8);for(o=a&(1<<-u)-1,a>>=-u,u+=n;u>0;o=256*o+t[e+f],f+=h,u-=8);if(0===a)a=1-c;else{if(a===l)return o?NaN:1/0*(p?-1:1);o+=Math.pow(2,n),a-=c}return(p?-1:1)*o*Math.pow(2,a-n)},il=function(t,e,r,n,i,a){var o,s,l,c=8*a-i-1,u=(1<<c)-1,f=u>>1,h=23===i?Math.pow(2,-24)-Math.pow(2,-77):0,p=n?0:a-1,d=n?1:-1,m=e<0||0===e&&1/e<0?1:0;for(e=Math.abs(e),isNaN(e)||e===1/0?(s=isNaN(e)?1:0,o=u):(o=Math.floor(Math.log(e)/Math.LN2),e*(l=Math.pow(2,-o))<1&&(o--,l*=2),(e+=o+f>=1?h/l:h*Math.pow(2,1-f))*l>=2&&(o++,l/=2),o+f>=u?(s=0,o=u):o+f>=1?(s=(e*l-1)*Math.pow(2,i),o+=f):(s=e*Math.pow(2,f-1)*Math.pow(2,i),o=0));i>=8;t[r+p]=255&s,p+=d,s/=256,i-=8);for(o=o<<i|s,c+=i;c>0;t[r+p]=255&o,p+=d,o/=256,c-=8);t[r+p-d]|=128*m},al=ol;function ol(t){this.buf=ArrayBuffer.isView&&ArrayBuffer.isView(t)?t:new 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e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed32());return t},readPackedFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readFixed64());return t},readPackedSFixed64:function(t){if(this.type!==ol.Bytes)return t.push(this.readSFixed64());var e=ll(this);for(t=t||[];this.pos<e;)t.push(this.readSFixed64());return t},skip:function(t){var e=7&t;if(e===ol.Varint)for(;this.buf[this.pos++]>127;);else if(e===ol.Bytes)this.pos=this.readVarint()+this.pos;else if(e===ol.Fixed32)this.pos+=4;else{if(e!==ol.Fixed64)throw new Error(\"Unimplemented type: \"+e);this.pos+=8}},writeTag:function(t,e){this.writeVarint(t<<3|e)},realloc:function(t){for(var e=this.length||16;e<this.pos+t;)e*=2;if(e!==this.length){var r=new Uint8Array(e);r.set(this.buf),this.buf=r,this.length=e}},finish:function(){return 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r=(7&t)<<4;if(e.buf[e.pos++]|=r|((t>>>=3)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;if(e.buf[e.pos++]=127&t|((t>>>=7)?128:0),!t)return;e.buf[e.pos++]=127&t}(n,e)}(t,this):(this.realloc(4),this.buf[this.pos++]=127&t|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=127&(t>>>=7)|(t>127?128:0),t<=127||(this.buf[this.pos++]=t>>>7&127))))},writeSVarint:function(t){this.writeVarint(t<0?2*-t-1:2*t)},writeBoolean:function(t){this.writeVarint(Boolean(t))},writeString:function(t){t=String(t),this.realloc(4*t.length),this.pos++;var e=this.pos;this.pos=function(t,e,r){for(var n,i,a=0;a<e.length;a++){if((n=e.charCodeAt(a))>55295&&n<57344){if(!i){n>56319||a+1===e.length?(t[r++]=239,t[r++]=191,t[r++]=189):i=n;continue}if(n<56320){t[r++]=239,t[r++]=191,t[r++]=189,i=n;continue}n=i-55296<<10|n-56320|65536,i=null}else i&&(t[r++]=239,t[r++]=191,t[r++]=189,i=null);n<128?t[r++]=n:(n<2048?t[r++]=n>>6|192:(n<65536?t[r++]=n>>12|224:(t[r++]=n>>18|240,t[r++]=n>>12&63|128),t[r++]=n>>6&63|128),t[r++]=63&n|128)}return r}(this.buf,t,this.pos);var r=this.pos-e;r>=128&&ul(e,r,this),this.pos=e-1,this.writeVarint(r),this.pos+=r},writeFloat:function(t){this.realloc(4),il(this.buf,t,this.pos,!0,23,4),this.pos+=4},writeDouble:function(t){this.realloc(8),il(this.buf,t,this.pos,!0,52,8),this.pos+=8},writeBytes:function(t){var e=t.length;this.writeVarint(e),this.realloc(e);for(var r=0;r<e;r++)this.buf[this.pos++]=t[r]},writeRawMessage:function(t,e){this.pos++;var r=this.pos;t(e,this);var n=this.pos-r;n>=128&&ul(r,n,this),this.pos=r-1,this.writeVarint(n),this.pos+=n},writeMessage:function(t,e,r){this.writeTag(t,ol.Bytes),this.writeRawMessage(e,r)},writePackedVarint:function(t,e){e.length&&this.writeMessage(t,fl,e)},writePackedSVarint:function(t,e){e.length&&this.writeMessage(t,hl,e)},writePackedBoolean:function(t,e){e.length&&this.writeMessage(t,ml,e)},writePackedFloat:function(t,e){e.length&&this.writeMessage(t,pl,e)},writePackedDouble:function(t,e){e.length&&this.writeMessage(t,dl,e)},writePackedFixed32:function(t,e){e.length&&this.writeMessage(t,gl,e)},writePackedSFixed32:function(t,e){e.length&&this.writeMessage(t,vl,e)},writePackedFixed64:function(t,e){e.length&&this.writeMessage(t,yl,e)},writePackedSFixed64:function(t,e){e.length&&this.writeMessage(t,xl,e)},writeBytesField:function(t,e){this.writeTag(t,ol.Bytes),this.writeBytes(e)},writeFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFixed32(e)},writeSFixed32Field:function(t,e){this.writeTag(t,ol.Fixed32),this.writeSFixed32(e)},writeFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeFixed64(e)},writeSFixed64Field:function(t,e){this.writeTag(t,ol.Fixed64),this.writeSFixed64(e)},writeVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeVarint(e)},writeSVarintField:function(t,e){this.writeTag(t,ol.Varint),this.writeSVarint(e)},writeStringField:function(t,e){this.writeTag(t,ol.Bytes),this.writeString(e)},writeFloatField:function(t,e){this.writeTag(t,ol.Fixed32),this.writeFloat(e)},writeDoubleField:function(t,e){this.writeTag(t,ol.Fixed64),this.writeDouble(e)},writeBooleanField:function(t,e){this.writeVarintField(t,Boolean(e))}};function Tl(t,e,r){1===t&&r.readMessage(kl,e)}function kl(t,e,r){if(3===t){var n=r.readMessage(Al,{}),i=n.id,a=n.bitmap,o=n.width,s=n.height,l=n.left,c=n.top,u=n.advance;e.push({id:i,bitmap:new So({width:o+6,height:s+6},a),metrics:{width:o,height:s,left:l,top:c,advance:u}})}}function Al(t,e,r){1===t?e.id=r.readVarint():2===t?e.bitmap=r.readBytes():3===t?e.width=r.readVarint():4===t?e.height=r.readVarint():5===t?e.left=r.readSVarint():6===t?e.top=r.readSVarint():7===t&&(e.advance=r.readVarint())}function Ml(t){for(var e=0,r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];e+=a.w*a.h,r=Math.max(r,a.w)}t.sort((function(t,e){return e.h-t.h}));for(var o=[{x:0,y:0,w:Math.max(Math.ceil(Math.sqrt(e/.95)),r),h:1/0}],s=0,l=0,c=0,u=t;c<u.length;c+=1)for(var f=u[c],h=o.length-1;h>=0;h--){var p=o[h];if(!(f.w>p.w||f.h>p.h)){if(f.x=p.x,f.y=p.y,l=Math.max(l,f.y+f.h),s=Math.max(s,f.x+f.w),f.w===p.w&&f.h===p.h){var d=o.pop();h<o.length&&(o[h]=d)}else 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Eo({width:o||1,height:s||1});for(var c in t){var u=t[c],f=r[c].paddedRect;Eo.copy(u.data,l,{x:0,y:0},{x:f.x+1,y:f.y+1},u.data)}for(var h in e){var p=e[h],d=n[h].paddedRect,m=d.x+1,g=d.y+1,v=p.data.width,y=p.data.height;Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g},p.data),Eo.copy(p.data,l,{x:0,y:y-1},{x:m,y:g-1},{width:v,height:1}),Eo.copy(p.data,l,{x:0,y:0},{x:m,y:g+y},{width:v,height:1}),Eo.copy(p.data,l,{x:v-1,y:0},{x:m-1,y:g},{width:1,height:y}),Eo.copy(p.data,l,{x:0,y:0},{x:m+v,y:g},{width:1,height:y})}this.image=l,this.iconPositions=r,this.patternPositions=n};Ll.prototype.addImages=function(t,e,r){for(var n in t){var i=t[n],a={x:0,y:0,w:i.data.width+2,h:i.data.height+2};r.push(a),e[n]=new Sl(a,i),i.hasRenderCallback&&this.haveRenderCallbacks.push(n)}},Ll.prototype.patchUpdatedImages=function(t,e){for(var r in t.dispatchRenderCallbacks(this.haveRenderCallbacks),t.updatedImages)this.patchUpdatedImage(this.iconPositions[r],t.getImage(r),e),this.patchUpdatedImage(this.patternPositions[r],t.getImage(r),e)},Ll.prototype.patchUpdatedImage=function(t,e,r){if(t&&e&&t.version!==e.version){t.version=e.version;var n=t.tl,i=n[0],a=n[1];r.update(e.data,void 0,{x:i,y:a})}},Nn(\"ImagePosition\",Sl),Nn(\"ImageAtlas\",Ll);var Cl={horizontal:1,vertical:2,horizontalOnly:3};var Pl=function(){this.scale=1,this.fontStack=\"\",this.imageName=null};Pl.forText=function(t,e){var r=new Pl;return r.scale=t||1,r.fontStack=e,r},Pl.forImage=function(t){var e=new Pl;return e.imageName=t,e};var Il=function(){this.text=\"\",this.sectionIndex=[],this.sections=[],this.imageSectionID=null};function Ol(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=Il.fromFeature(t,i);f===Cl.vertical&&v.verticalizePunctuation();var y=hi.processBidirectionalText,x=hi.processStyledBidirectionalText;if(y&&1===v.sections.length){g=[];for(var b=0,_=y(v.toString(),jl(v,c,a,e,n,p,d));b<_.length;b+=1){var w=_[b],T=new Il;T.text=w,T.sections=v.sections;for(var k=0;k<w.length;k++)T.sectionIndex.push(0);g.push(T)}}else if(x){g=[];for(var A=0,M=x(v.text,v.sectionIndex,jl(v,c,a,e,n,p,d));A<M.length;A+=1){var S=M[A],E=new Il;E.text=S[0],E.sectionIndex=S[1],E.sections=v.sections,g.push(E)}}else g=function(t,e){for(var r=[],n=t.text,i=0,a=0,o=e;a<o.length;a+=1){var s=o[a];r.push(t.substring(i,s)),i=s}return i<n.length&&r.push(t.substring(i,n.length)),r}(v,jl(v,c,a,e,n,p,d));var L=[],C={positionedLines:L,text:v.toString(),top:u[1],bottom:u[1],left:u[0],right:u[0],writingMode:f,iconsInText:!1,verticalizable:!1};return function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=0,p=-17,d=0,m=0,g=\"right\"===s?1:\"left\"===s?0:.5,v=0,y=0,x=i;y<x.length;y+=1){var b=x[y];b.trim();var _=b.getMaxScale(),w=24*(_-1),T={positionedGlyphs:[],lineOffset:0};t.positionedLines[v]=T;var k=T.positionedGlyphs,A=0;if(b.length()){for(var M=0;M<b.length();M++){var S=b.getSection(M),E=b.getSectionIndex(M),L=b.getCharCode(M),C=0,P=null,I=null,O=null,z=24,D=!(l===Cl.horizontal||!u&&!Zn(L)||u&&(zl[L]||(Y=L,Yn.Arabic(Y)||Yn[\"Arabic Supplement\"](Y)||Yn[\"Arabic Extended-A\"](Y)||Yn[\"Arabic Presentation Forms-A\"](Y)||Yn[\"Arabic Presentation Forms-B\"](Y))));if(S.imageName){var R=n[S.imageName];if(!R)continue;O=S.imageName,t.iconsInText=t.iconsInText||!0,I=R.paddedRect;var F=R.displaySize;S.scale=24*S.scale/f,P={width:F[0],height:F[1],left:1,top:-3,advance:D?F[1]:F[0]};var B=24-F[1]*S.scale;C=w+B,z=P.advance;var N=D?F[0]*S.scale-24*_:F[1]*S.scale-24*_;N>0&&N>A&&(A=N)}else{var j=r[S.fontStack],U=j&&j[L];if(U&&U.rect)I=U.rect,P=U.metrics;else{var V=e[S.fontStack],H=V&&V[L];if(!H)continue;P=H.metrics}C=24*(_-S.scale)}D?(t.verticalizable=!0,k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=z*S.scale+c):(k.push({glyph:L,imageName:O,x:h,y:p+C,vertical:D,scale:S.scale,fontStack:S.fontStack,sectionIndex:E,metrics:P,rect:I}),h+=P.advance*S.scale+c)}if(0!==k.length){var q=h-c;d=Math.max(q,d),Vl(k,0,k.length-1,g,A)}h=0;var G=a*_+A;T.lineOffset=Math.max(A,w),p+=G,m=Math.max(G,m),++v}else p+=a,++v}var Y;var W=p- -17,X=Ul(o),Z=X.horizontalAlign,J=X.verticalAlign;(function(t,e,r,n,i,a,o,s,l){var c=(e-r)*i,u=0;u=a!==o?-s*n- -17:(-n*l+.5)*o;for(var f=0,h=t;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];g.x+=c,g.y+=u}})(t.positionedLines,g,Z,J,d,m,a,W,i.length),t.top+=-J*W,t.bottom=t.top+W,t.left+=-Z*d,t.right=t.left+d}(C,e,r,n,g,o,s,l,f,c,h,m),!function(t){for(var e=0,r=t;e<r.length;e+=1){if(0!==r[e].positionedGlyphs.length)return!1}return!0}(L)&&C}Il.fromFeature=function(t,e){for(var r=new Il,n=0;n<t.sections.length;n++){var i=t.sections[n];i.image?r.addImageSection(i):r.addTextSection(i,e)}return r},Il.prototype.length=function(){return this.text.length},Il.prototype.getSection=function(t){return this.sections[this.sectionIndex[t]]},Il.prototype.getSectionIndex=function(t){return this.sectionIndex[t]},Il.prototype.getCharCode=function(t){return this.text.charCodeAt(t)},Il.prototype.verticalizePunctuation=function(){this.text=function(t){for(var e=\"\",r=0;r<t.length;r++){var n=t.charCodeAt(r+1)||null,i=t.charCodeAt(r-1)||null;(!n||!Jn(n)||rl[t[r+1]])&&(!i||!Jn(i)||rl[t[r-1]])&&rl[t[r]]?e+=rl[t[r]]:e+=t[r]}return e}(this.text)},Il.prototype.trim=function(){for(var t=0,e=0;e<this.text.length&&zl[this.text.charCodeAt(e)];e++)t++;for(var 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e=this.data,r=this.compare,n=this.length>>1,i=e[t];t<n;){var a=1+(t<<1),o=e[a],s=a+1;if(s<this.length&&r(e[s],o)<0&&(a=s,o=e[s]),r(o,i)>=0)break;e[t]=o,t=a}e[t]=i};var pc=Number.POSITIVE_INFINITY;function dc(t,e){return e[1]!==pc?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function mc(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}function gc(t,e,r,n,a,o,s,l,c,u,f,h,p,d,m){var g=function(t,e,r,n,a,o,s,l){for(var c=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,u=[],f=0,h=e.positionedLines;f<h.length;f+=1)for(var p=h[f],d=0,m=p.positionedGlyphs;d<m.length;d+=1){var g=m[d];if(g.rect){var v=g.rect||{},y=4,x=!0,b=1,_=0,w=(a||l)&&g.vertical,T=g.metrics.advance*g.scale/2;if(l&&e.verticalizable){var k=24*(g.scale-1),A=(24-g.metrics.width*g.scale)/2;_=p.lineOffset/2-(g.imageName?-A:k)}if(g.imageName){var M=s[g.imageName];x=M.sdf,y=1/(b=M.pixelRatio)}var S=a?[g.x+T,g.y]:[0,0],E=a?[0,0]:[g.x+T+r[0],g.y+r[1]-_],L=[0,0];w&&(L=E,E=[0,0]);var C=(g.metrics.left-y)*g.scale-T+E[0],P=(-g.metrics.top-y)*g.scale+E[1],I=C+v.w*g.scale/b,O=P+v.h*g.scale/b,z=new i(C,P),D=new i(I,P),R=new i(C,O),F=new i(I,O);if(w){var B=new i(-T,T- -17),N=-Math.PI/2,j=12-T,U=g.imageName?j:0,V=new i(22-j,-U),H=new(Function.prototype.bind.apply(i,[null].concat(L)));z._rotateAround(N,B)._add(V)._add(H),D._rotateAround(N,B)._add(V)._add(H),R._rotateAround(N,B)._add(V)._add(H),F._rotateAround(N,B)._add(V)._add(H)}if(c){var q=Math.sin(c),G=Math.cos(c),Y=[G,-q,q,G];z._matMult(Y),D._matMult(Y),R._matMult(Y),F._matMult(Y)}var W=new i(0,0),X=new i(0,0);u.push({tl:z,tr:D,bl:R,br:F,tex:v,writingMode:e.writingMode,glyphOffset:S,sectionIndex:g.sectionIndex,isSDF:x,pixelOffsetTL:W,pixelOffsetBR:X,minFontScaleX:0,minFontScaleY:0})}}return u}(0,r,l,a,o,s,n,t.allowVerticalPlacement),v=t.textSizeData,y=null;\"source\"===v.kind?(y=[128*a.layout.get(\"text-size\").evaluate(s,{})])[0]>32640&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'):\"composite\"===v.kind&&((y=[128*d.compositeTextSizes[0].evaluate(s,{},m),128*d.compositeTextSizes[1].evaluate(s,{},m)])[0]>32640||y[1]>32640)&&_(t.layerIds[0]+': Value for \"text-size\" is >= 255. Reduce your \"text-size\".'),t.addSymbols(t.text,g,y,l,o,s,u,e,c.lineStartIndex,c.lineLength,p,m);for(var x=0,b=f;x<b.length;x+=1){h[b[x]]=t.text.placedSymbolArray.length-1}return 4*g.length}function vc(t){for(var e in t)return t[e];return null}function yc(t,e,r,n){var i=t.compareText;if(e in i){for(var a=i[e],o=a.length-1;o>=0;o--)if(n.dist(a[o])<r)return!0}else i[e]=[];return i[e].push(n),!1}var xc=Ls.VectorTileFeature.types,bc=[{name:\"a_fade_opacity\",components:1,type:\"Uint8\",offset:0}];function _c(t,e,r,n,i,a,o,s,l,c,u,f,h){var p=s?Math.min(32640,Math.round(s[0])):0,d=s?Math.min(32640,Math.round(s[1])):0;t.emplaceBack(e,r,Math.round(32*n),Math.round(32*i),a,o,(p<<1)+(l?1:0),d,16*c,16*u,256*f,256*h)}function wc(t,e,r){t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r),t.emplaceBack(e.x,e.y,r)}function Tc(t){for(var e=0,r=t.sections;e<r.length;e+=1){if($n(r[e].text))return!0}return!1}var kc=function(t){this.layoutVertexArray=new Ni,this.indexArray=new Yi,this.programConfigurations=t,this.segments=new pa,this.dynamicLayoutVertexArray=new ji,this.opacityVertexArray=new Ui,this.placedSymbolArray=new aa};kc.prototype.isEmpty=function(){return 0===this.layoutVertexArray.length&&0===this.indexArray.length&&0===this.dynamicLayoutVertexArray.length&&0===this.opacityVertexArray.length},kc.prototype.upload=function(t,e,r,n){this.isEmpty()||(r&&(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Js.members),this.indexBuffer=t.createIndexBuffer(this.indexArray,e),this.dynamicLayoutVertexBuffer=t.createVertexBuffer(this.dynamicLayoutVertexArray,Ks.members,!0),this.opacityVertexBuffer=t.createVertexBuffer(this.opacityVertexArray,bc,!0),this.opacityVertexBuffer.itemSize=1),(r||n)&&this.programConfigurations.upload(t))},kc.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.dynamicLayoutVertexBuffer.destroy(),this.opacityVertexBuffer.destroy())},Nn(\"SymbolBuffers\",kc);var Ac=function(t,e,r){this.layoutVertexArray=new t,this.layoutAttributes=e,this.indexArray=new r,this.segments=new pa,this.collisionVertexArray=new Gi};Ac.prototype.upload=function(t){this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,this.layoutAttributes),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.collisionVertexBuffer=t.createVertexBuffer(this.collisionVertexArray,Qs.members,!0)},Ac.prototype.destroy=function(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.collisionVertexBuffer.destroy())},Nn(\"CollisionBuffers\",Ac);var Mc=function(t){this.collisionBoxArray=t.collisionBoxArray,this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((function(t){return t.id})),this.index=t.index,this.pixelRatio=t.pixelRatio,this.sourceLayerIndex=t.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ho([]),this.placementViewportMatrix=ho([]);var e=this.layers[0]._unevaluatedLayout._values;this.textSizeData=Gl(this.zoom,e[\"text-size\"]),this.iconSizeData=Gl(this.zoom,e[\"icon-size\"]);var r=this.layers[0].layout,n=r.get(\"symbol-sort-key\"),i=r.get(\"symbol-z-order\");this.sortFeaturesByKey=\"viewport-y\"!==i&&void 0!==n.constantOr(1);var a=\"viewport-y\"===i||\"auto\"===i&&!this.sortFeaturesByKey;this.sortFeaturesByY=a&&(r.get(\"text-allow-overlap\")||r.get(\"icon-allow-overlap\")||r.get(\"text-ignore-placement\")||r.get(\"icon-ignore-placement\")),\"point\"===r.get(\"symbol-placement\")&&(this.writingModes=r.get(\"text-writing-mode\").map((function(t){return Cl[t]}))),this.stateDependentLayerIds=this.layers.filter((function(t){return t.isStateDependent()})).map((function(t){return t.id})),this.sourceID=t.sourceID};Mc.prototype.createArrays=function(){this.text=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^text/.test(t)}))),this.icon=new kc(new Ua(Js.members,this.layers,this.zoom,(function(t){return/^icon/.test(t)}))),this.glyphOffsetArray=new la,this.lineVertexArray=new ca,this.symbolInstances=new sa},Mc.prototype.calculateGlyphDependencies=function(t,e,r,n,i){for(var a=0;a<t.length;a++)if(e[t.charCodeAt(a)]=!0,(r||n)&&i){var o=rl[t.charAt(a)];o&&(e[o.charCodeAt(0)]=!0)}},Mc.prototype.populate=function(t,e,r){var n=this.layers[0],i=n.layout,a=i.get(\"text-font\"),o=i.get(\"text-field\"),s=i.get(\"icon-image\"),l=(\"constant\"!==o.value.kind||o.value.value instanceof ne&&!o.value.value.isEmpty()||o.value.value.toString().length>0)&&(\"constant\"!==a.value.kind||a.value.value.length>0),c=\"constant\"!==s.value.kind||!!s.value.value||Object.keys(s.parameters).length>0,u=i.get(\"symbol-sort-key\");if(this.features=[],l||c){for(var f=e.iconDependencies,h=e.glyphDependencies,p=e.availableImages,d=new pi(this.zoom),m=0,g=t;m<g.length;m+=1){var v=g[m],y=v.feature,x=v.id,b=v.index,_=v.sourceLayerIndex,w=n._featureFilter.needGeometry,T={type:y.type,id:x,properties:y.properties,geometry:w?Ya(y):[]};if(n._featureFilter.filter(d,T,r)){w||(T.geometry=Ya(y));var k=void 0;if(l){var A=n.getValueAndResolveTokens(\"text-field\",T,r,p),M=ne.factory(A);Tc(M)&&(this.hasRTLText=!0),(!this.hasRTLText||\"unavailable\"===ui()||this.hasRTLText&&hi.isParsed())&&(k=el(M,n,T))}var S=void 0;if(c){var E=n.getValueAndResolveTokens(\"icon-image\",T,r,p);S=E instanceof ie?E:ie.fromString(E)}if(k||S){var L=this.sortFeaturesByKey?u.evaluate(T,{},r):void 0,C={id:x,text:k,icon:S,index:b,sourceLayerIndex:_,geometry:Ya(y),properties:y.properties,type:xc[y.type],sortKey:L};if(this.features.push(C),S&&(f[S.name]=!0),k){var P=a.evaluate(T,{},r).join(\",\"),I=\"map\"===i.get(\"text-rotation-alignment\")&&\"point\"!==i.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(Cl.vertical)>=0;for(var O=0,z=k.sections;O<z.length;O+=1){var D=z[O];if(D.image)f[D.image.name]=!0;else{var R=Wn(k.toString()),F=D.fontStack||P,B=h[F]=h[F]||{};this.calculateGlyphDependencies(D.text,B,I,this.allowVerticalPlacement,R)}}}}}}\"line\"===i.get(\"symbol-placement\")&&(this.features=function(t){var e={},r={},n=[],i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){var a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){var a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){var n=r?e[0][e[0].length-1]:e[0][0];return t+\":\"+n.x+\":\"+n.y}for(var c=0;c<t.length;c++){var u=t[c],f=u.geometry,h=u.text?u.text.toString():null;if(h){var p=l(h,f),d=l(h,f,!0);if(p in r&&d in e&&r[p]!==e[d]){var m=s(p,d,f),g=o(p,d,n[m].geometry);delete e[p],delete r[d],r[l(h,n[g].geometry,!0)]=g,n[m].geometry=null}else p in r?o(p,d,f):d in e?s(p,d,f):(a(c),e[p]=i-1,r[d]=i-1)}else a(c)}return n.filter((function(t){return t.geometry}))}(this.features)),this.sortFeaturesByKey&&this.features.sort((function(t,e){return t.sortKey-e.sortKey}))}},Mc.prototype.update=function(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))},Mc.prototype.isEmpty=function(){return 0===this.symbolInstances.length&&!this.hasRTLText},Mc.prototype.uploadPending=function(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload},Mc.prototype.upload=function(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0},Mc.prototype.destroyDebugData=function(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()},Mc.prototype.destroy=function(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()},Mc.prototype.addToLineVertexArray=function(t,e){var r=this.lineVertexArray.length;if(void 0!==t.segment){for(var n=t.dist(e[t.segment+1]),i=t.dist(e[t.segment]),a={},o=t.segment+1;o<e.length;o++)a[o]={x:e[o].x,y:e[o].y,tileUnitDistanceFromAnchor:n},o<e.length-1&&(n+=e[o+1].dist(e[o]));for(var s=t.segment||0;s>=0;s--)a[s]={x:e[s].x,y:e[s].y,tileUnitDistanceFromAnchor:i},s>0&&(i+=e[s-1].dist(e[s]));for(var l=0;l<e.length;l++){var c=a[l];this.lineVertexArray.emplaceBack(c.x,c.y,c.tileUnitDistanceFromAnchor)}}return{lineStartIndex:r,lineLength:this.lineVertexArray.length-r}},Mc.prototype.addSymbols=function(t,e,r,n,i,a,o,s,l,c,u,f){for(var h=t.indexArray,p=t.layoutVertexArray,d=t.segments.prepareSegment(4*e.length,p,h,a.sortKey),m=this.glyphOffsetArray.length,g=d.vertexLength,v=this.allowVerticalPlacement&&o===Cl.vertical?Math.PI/2:0,y=a.text&&a.text.sections,x=0;x<e.length;x++){var b=e[x],_=b.tl,w=b.tr,T=b.bl,k=b.br,A=b.tex,M=b.pixelOffsetTL,S=b.pixelOffsetBR,E=b.minFontScaleX,L=b.minFontScaleY,C=b.glyphOffset,P=b.isSDF,I=b.sectionIndex,O=d.vertexLength,z=C[1];_c(p,s.x,s.y,_.x,z+_.y,A.x,A.y,r,P,M.x,M.y,E,L),_c(p,s.x,s.y,w.x,z+w.y,A.x+A.w,A.y,r,P,S.x,M.y,E,L),_c(p,s.x,s.y,T.x,z+T.y,A.x,A.y+A.h,r,P,M.x,S.y,E,L),_c(p,s.x,s.y,k.x,z+k.y,A.x+A.w,A.y+A.h,r,P,S.x,S.y,E,L),wc(t.dynamicLayoutVertexArray,s,v),h.emplaceBack(O,O+1,O+2),h.emplaceBack(O+1,O+2,O+3),d.vertexLength+=4,d.primitiveLength+=2,this.glyphOffsetArray.emplaceBack(C[0]),x!==e.length-1&&I===e[x+1].sectionIndex||t.programConfigurations.populatePaintArrays(p.length,a,a.index,{},f,y&&y[I])}t.placedSymbolArray.emplaceBack(s.x,s.y,m,this.glyphOffsetArray.length-m,g,l,c,s.segment,r?r[0]:0,r?r[1]:0,n[0],n[1],o,0,!1,0,u)},Mc.prototype._addCollisionDebugVertex=function(t,e,r,n,i,a){return e.emplaceBack(0,0),t.emplaceBack(r.x,r.y,n,i,Math.round(a.x),Math.round(a.y))},Mc.prototype.addCollisionDebugVertices=function(t,e,r,n,a,o,s){var l=a.segments.prepareSegment(4,a.layoutVertexArray,a.indexArray),c=l.vertexLength,u=a.layoutVertexArray,f=a.collisionVertexArray,h=s.anchorX,p=s.anchorY;this._addCollisionDebugVertex(u,f,o,h,p,new i(t,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,e)),this._addCollisionDebugVertex(u,f,o,h,p,new i(r,n)),this._addCollisionDebugVertex(u,f,o,h,p,new i(t,n)),l.vertexLength+=4;var d=a.indexArray;d.emplaceBack(c,c+1),d.emplaceBack(c+1,c+2),d.emplaceBack(c+2,c+3),d.emplaceBack(c+3,c),l.primitiveLength+=4},Mc.prototype.addDebugCollisionBoxes=function(t,e,r,n){for(var i=t;i<e;i++){var a=this.collisionBoxArray.get(i),o=a.x1,s=a.y1,l=a.x2,c=a.y2;this.addCollisionDebugVertices(o,s,l,c,n?this.textCollisionBox:this.iconCollisionBox,a.anchorPoint,r)}},Mc.prototype.generateCollisionDebugBuffers=function(){this.hasDebugData()&&this.destroyDebugData(),this.textCollisionBox=new Ac(Hi,$s.members,Qi),this.iconCollisionBox=new Ac(Hi,$s.members,Qi);for(var t=0;t<this.symbolInstances.length;t++){var e=this.symbolInstances.get(t);this.addDebugCollisionBoxes(e.textBoxStartIndex,e.textBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.verticalTextBoxStartIndex,e.verticalTextBoxEndIndex,e,!0),this.addDebugCollisionBoxes(e.iconBoxStartIndex,e.iconBoxEndIndex,e,!1),this.addDebugCollisionBoxes(e.verticalIconBoxStartIndex,e.verticalIconBoxEndIndex,e,!1)}},Mc.prototype._deserializeCollisionBoxesForSymbol=function(t,e,r,n,i,a,o,s,l){for(var c={},u=e;u<r;u++){var f=t.get(u);c.textBox={x1:f.x1,y1:f.y1,x2:f.x2,y2:f.y2,anchorPointX:f.anchorPointX,anchorPointY:f.anchorPointY},c.textFeatureIndex=f.featureIndex;break}for(var h=n;h<i;h++){var p=t.get(h);c.verticalTextBox={x1:p.x1,y1:p.y1,x2:p.x2,y2:p.y2,anchorPointX:p.anchorPointX,anchorPointY:p.anchorPointY},c.verticalTextFeatureIndex=p.featureIndex;break}for(var d=a;d<o;d++){var m=t.get(d);c.iconBox={x1:m.x1,y1:m.y1,x2:m.x2,y2:m.y2,anchorPointX:m.anchorPointX,anchorPointY:m.anchorPointY},c.iconFeatureIndex=m.featureIndex;break}for(var g=s;g<l;g++){var v=t.get(g);c.verticalIconBox={x1:v.x1,y1:v.y1,x2:v.x2,y2:v.y2,anchorPointX:v.anchorPointX,anchorPointY:v.anchorPointY},c.verticalIconFeatureIndex=v.featureIndex;break}return c},Mc.prototype.deserializeCollisionBoxes=function(t){this.collisionArrays=[];for(var e=0;e<this.symbolInstances.length;e++){var r=this.symbolInstances.get(e);this.collisionArrays.push(this._deserializeCollisionBoxesForSymbol(t,r.textBoxStartIndex,r.textBoxEndIndex,r.verticalTextBoxStartIndex,r.verticalTextBoxEndIndex,r.iconBoxStartIndex,r.iconBoxEndIndex,r.verticalIconBoxStartIndex,r.verticalIconBoxEndIndex))}},Mc.prototype.hasTextData=function(){return this.text.segments.get().length>0},Mc.prototype.hasIconData=function(){return this.icon.segments.get().length>0},Mc.prototype.hasDebugData=function(){return this.textCollisionBox&&this.iconCollisionBox},Mc.prototype.hasTextCollisionBoxData=function(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0},Mc.prototype.hasIconCollisionBoxData=function(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Mc.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i<n;i+=4)t.indexArray.emplaceBack(i,i+1,i+2),t.indexArray.emplaceBack(i+1,i+2,i+3)},Mc.prototype.getSortedSymbolIndexes=function(t){if(this.sortedAngle===t&&void 0!==this.symbolInstanceIndexes)return this.symbolInstanceIndexes;for(var e=Math.sin(t),r=Math.cos(t),n=[],i=[],a=[],o=0;o<this.symbolInstances.length;++o){a.push(o);var s=this.symbolInstances.get(o);n.push(0|Math.round(e*s.anchorX+r*s.anchorY)),i.push(s.featureIndex)}return a.sort((function(t,e){return n[t]-n[e]||i[e]-i[t]})),a},Mc.prototype.addToSortKeyRanges=function(t,e){var r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})},Mc.prototype.sortFeatures=function(t){var e=this;if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r<n.length;r+=1){var i=n[r],a=this.symbolInstances.get(i);this.featureSortOrder.push(a.featureIndex),[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t,r,n){t>=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Nn(\"SymbolBucket\",Mc,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Mc.MAX_GLYPHS=65535,Mc.addDynamicAttributes=wc;var Sc=new Si({\"symbol-placement\":new wi(Lt.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new wi(Lt.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new wi(Lt.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Ti(Lt.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new wi(Lt.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new wi(Lt.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new wi(Lt.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new wi(Lt.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new wi(Lt.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Ti(Lt.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new wi(Lt.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new wi(Lt.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Ti(Lt.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Ti(Lt.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new wi(Lt.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new wi(Lt.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Ti(Lt.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Ti(Lt.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new wi(Lt.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new wi(Lt.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new wi(Lt.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Ti(Lt.layout_symbol[\"text-field\"]),\"text-font\":new Ti(Lt.layout_symbol[\"text-font\"]),\"text-size\":new Ti(Lt.layout_symbol[\"text-size\"]),\"text-max-width\":new Ti(Lt.layout_symbol[\"text-max-width\"]),\"text-line-height\":new wi(Lt.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Ti(Lt.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Ti(Lt.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Ti(Lt.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new wi(Lt.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Ti(Lt.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new wi(Lt.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new wi(Lt.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Ti(Lt.layout_symbol[\"text-rotate\"]),\"text-padding\":new wi(Lt.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new wi(Lt.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Ti(Lt.layout_symbol[\"text-transform\"]),\"text-offset\":new Ti(Lt.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new wi(Lt.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new wi(Lt.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new wi(Lt.layout_symbol[\"text-optional\"])}),Ec={paint:new Si({\"icon-opacity\":new Ti(Lt.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Ti(Lt.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Ti(Lt.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Ti(Lt.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Ti(Lt.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new wi(Lt.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new wi(Lt.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Ti(Lt.paint_symbol[\"text-opacity\"]),\"text-color\":new Ti(Lt.paint_symbol[\"text-color\"],{runtimeType:Ut,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Ti(Lt.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Ti(Lt.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Ti(Lt.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new wi(Lt.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new wi(Lt.paint_symbol[\"text-translate-anchor\"])}),layout:Sc},Lc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ft,this.defaultValue=t};Lc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},Lc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},Lc.prototype.outputDefined=function(){return!1},Lc.prototype.serialize=function(){return null},Nn(\"FormatSectionOverride\",Lc,{omit:[\"defaultValue\"]});var Cc=function(t){function e(e){t.call(this,e,Ec)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a<o.length;a+=1){var s=o[a];i.indexOf(s)<0&&i.push(s)}this.layout._values[\"text-writing-mode\"]=i}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()},e.prototype.getValueAndResolveTokens=function(t,e,r,n){var i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||Yr(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,(function(e,r){return r in t?String(t[r]):\"\"}))}(e.properties,i)},e.prototype.createBucket=function(t){return new Mc(t)},e.prototype.queryRadius=function(){return 0},e.prototype.queryIntersectsFeature=function(){return!1},e.prototype._setPaintOverrides=function(){for(var t=0,r=Ec.paint.overridableProperties;t<r.length;t+=1){var n=r[t];if(e.hasPaintOverride(this.layout,n)){var i=this.paint.get(n),a=new Lc(i),o=new Gr(a,i.property.specification),s=null;s=\"constant\"===i.value.kind||\"source\"===i.value.kind?new Xr(\"source\",o):new Zr(\"composite\",o,i.value.zoomStops,i.value._interpolationType),this.paint._values[n]=new bi(i.property,s,i.parameters)}}},e.prototype._handleOverridablePaintPropertyUpdate=function(t,r,n){return!(!this.layout||r.isDataDriven()||n.isDataDriven())&&e.hasPaintOverride(this.layout,t)},e.hasPaintOverride=function(t,e){var r=t.get(\"text-field\"),n=Ec.paint.properties[e],i=!1,a=function(t){for(var e=0,r=t;e<r.length;e+=1){var a=r[e];if(n.overrides&&n.overrides.hasOverride(a))return void(i=!0)}};if(\"constant\"===r.value.kind&&r.value.value instanceof ne)a(r.value.value.sections);else if(\"source\"===r.value.kind){var o=function(t){if(!i)if(t instanceof ce&&se(t.value)===Gt){var e=t.value;a(e.sections)}else t instanceof pe?a(t.sections):t.eachChild(o)},s=r.value;s._styleExpression&&o(s._styleExpression.expression)}return i},e}(Ei),Pc={paint:new Si({\"background-color\":new wi(Lt.paint_background[\"background-color\"]),\"background-pattern\":new Ai(Lt.paint_background[\"background-pattern\"]),\"background-opacity\":new wi(Lt.paint_background[\"background-opacity\"])})},Ic=function(t){function e(e){t.call(this,e,Pc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei),Oc={paint:new Si({\"raster-opacity\":new wi(Lt.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new wi(Lt.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new wi(Lt.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new wi(Lt.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new wi(Lt.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new wi(Lt.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new wi(Lt.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new wi(Lt.paint_raster[\"raster-fade-duration\"])})},zc=function(t){function e(e){t.call(this,e,Oc)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(Ei);var Dc=function(t){function e(e){t.call(this,e,{}),this.implementation=e}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.is3D=function(){return\"3d\"===this.implementation.renderingMode},e.prototype.hasOffscreenPass=function(){return void 0!==this.implementation.prerender},e.prototype.recalculate=function(){},e.prototype.updateTransitions=function(){},e.prototype.hasTransition=function(){},e.prototype.serialize=function(){},e.prototype.onAdd=function(t){this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},e.prototype.onRemove=function(t){this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},e}(Ei),Rc={circle:_o,heatmap:Po,hillshade:Oo,fill:xs,\"fill-extrusion\":Fs,line:Xs,symbol:Cc,background:Ic,raster:zc};var Fc=self.HTMLImageElement,Bc=self.HTMLCanvasElement,Nc=self.HTMLVideoElement,jc=self.ImageData,Uc=self.ImageBitmap,Vc=function(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)};Vc.prototype.update=function(t,e,r){var n=t.width,i=t.height,a=!(this.size&&this.size[0]===n&&this.size[1]===i||r),o=this.context,s=o.gl;if(this.useMipmap=Boolean(e&&e.useMipmap),s.bindTexture(s.TEXTURE_2D,this.texture),o.pixelStoreUnpackFlipY.set(!1),o.pixelStoreUnpack.set(1),o.pixelStoreUnpackPremultiplyAlpha.set(this.format===s.RGBA&&(!e||!1!==e.premultiply)),a)this.size=[n,i],t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texImage2D(s.TEXTURE_2D,0,this.format,this.format,s.UNSIGNED_BYTE,t):s.texImage2D(s.TEXTURE_2D,0,this.format,n,i,0,this.format,s.UNSIGNED_BYTE,t.data);else{var l=r||{x:0,y:0},c=l.x,u=l.y;t instanceof Fc||t instanceof Bc||t instanceof Nc||t instanceof jc||Uc&&t instanceof Uc?s.texSubImage2D(s.TEXTURE_2D,0,c,u,s.RGBA,s.UNSIGNED_BYTE,t):s.texSubImage2D(s.TEXTURE_2D,0,c,u,n,i,s.RGBA,s.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&s.generateMipmap(s.TEXTURE_2D)},Vc.prototype.bind=function(t,e,r){var n=this.context.gl;n.bindTexture(n.TEXTURE_2D,this.texture),r!==n.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=n.LINEAR),t!==this.filter&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,t),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,e),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,e),this.wrap=e)},Vc.prototype.isSizePowerOfTwo=function(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0},Vc.prototype.destroy=function(){this.context.gl.deleteTexture(this.texture),this.texture=null};var Hc=function(t){var e=this;this._callback=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=function(){e._triggered=!1,e._callback()})};Hc.prototype.trigger=function(){var t=this;this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((function(){t._triggered=!1,t._callback()}),0))},Hc.prototype.remove=function(){delete this._channel,this._callback=function(){}};var qc=function(t,e,r){this.target=t,this.parent=e,this.mapId=r,this.callbacks={},this.tasks={},this.taskQueue=[],this.cancelCallbacks={},m([\"receive\",\"process\"],this),this.invoker=new Hc(this.process),this.target.addEventListener(\"message\",this.receive,!1),this.globalScope=k()?t:self};function Gc(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}qc.prototype.send=function(t,e,r,n,i){var a=this;void 0===i&&(i=!1);var o=Math.round(1e18*Math.random()).toString(36).substring(0,10);r&&(this.callbacks[o]=r);var s=S(this.globalScope)?void 0:[];return this.target.postMessage({id:o,type:t,hasCallback:!!r,targetMapId:n,mustQueue:i,sourceMapId:this.mapId,data:Hn(e,s)},s),{cancel:function(){r&&delete a.callbacks[o],a.target.postMessage({id:o,type:\"<cancel>\",targetMapId:n,sourceMapId:a.mapId})}}},qc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"<cancel>\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else k()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},qc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},qc.prototype.processTask=function(t,e){var r=this;if(\"<response>\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(qn(e.error)):n(null,qn(e.data)))}else{var i=!1,a=S(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"<response>\",sourceMapId:r.mapId,error:e?Hn(e):null,data:Hn(n,a)},a)}:function(t){i=!0},s=null,l=qn(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},qc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Yc.prototype.setNorthEast=function(t){return this._ne=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.setSouthWest=function(t){return this._sw=t instanceof Wc?new Wc(t.lng,t.lat):Wc.convert(t),this},Yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Wc)e=t,r=t;else{if(!(t instanceof Yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Yc.convert(a))}var o=t;return this.extend(Wc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Wc(e.lng,e.lat),this._ne=new Wc(r.lng,r.lat)),this},Yc.prototype.getCenter=function(){return new Wc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Yc.prototype.getSouthWest=function(){return this._sw},Yc.prototype.getNorthEast=function(){return this._ne},Yc.prototype.getNorthWest=function(){return new Wc(this.getWest(),this.getNorth())},Yc.prototype.getSouthEast=function(){return new Wc(this.getEast(),this.getSouth())},Yc.prototype.getWest=function(){return this._sw.lng},Yc.prototype.getSouth=function(){return this._sw.lat},Yc.prototype.getEast=function(){return this._ne.lng},Yc.prototype.getNorth=function(){return this._ne.lat},Yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Yc.prototype.contains=function(t){var e=Wc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Yc.convert=function(t){return!t||t instanceof Yc?t:new Yc(t)};var Wc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Wc.prototype.wrap=function(){return new Wc(c(this.lng,-180,180),this.lat)},Wc.prototype.toArray=function(){return[this.lng,this.lat]},Wc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Wc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return 6371008.8*Math.acos(Math.min(i,1))},Wc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Yc(new Wc(this.lng-r,this.lat-e),new Wc(this.lng+r,this.lat+e))},Wc.convert=function(t){if(t instanceof Wc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Wc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Wc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: <lng>, lat: <lat>}, an object {lon: <lng>, lat: <lat>}, or an array of [<lng>, <lat>]\")};var Xc=2*Math.PI*6371008.8;function Zc(t){return Xc*Math.cos(t*Math.PI/180)}function Jc(t){return(180+t)/360}function Kc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Qc(t,e){return t/Zc(e)}function $c(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var tu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};tu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Wc.convert(t);return new tu(Jc(r.lng),Kc(r.lat),Qc(e,r.lat))},tu.prototype.toLngLat=function(){return new Wc(360*this.x-180,$c(this.y))},tu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Zc($c(e));var t,e},tu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Xc*(t=$c(this.y),1/Math.cos(t*Math.PI/180));var t};var eu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=iu(0,t,t,e,r)};eu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},eu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<<a-1)?1:0)+(r&n?2:0);return i}(this.z,this.x,this.y);return t[(this.x+this.y)%t.length].replace(\"{prefix}\",(this.x%16).toString(16)+(this.y%16).toString(16)).replace(\"{z}\",String(this.z)).replace(\"{x}\",String(this.x)).replace(\"{y}\",String(\"tms\"===e?Math.pow(2,this.z)-this.y-1:this.y)).replace(\"{quadkey}\",l).replace(\"{bbox-epsg-3857}\",s)},eu.prototype.getTilePoint=function(t){var e=Math.pow(2,this.z);return new i(8192*(t.x*e-this.x),8192*(t.y*e-this.y))},eu.prototype.toString=function(){return this.z+\"/\"+this.x+\"/\"+this.y};var ru=function(t,e){this.wrap=t,this.canonical=e,this.key=iu(t,e.z,e.z,e.x,e.y)},nu=function(t,e,r,n,i){this.overscaledZ=t,this.wrap=e,this.canonical=new eu(r,+n,+i),this.key=iu(e,t,r,n,i)};function iu(t,e,r,n,i){(t*=2)<0&&(t=-1*t-1);var a=1<<r;return(a*a*t+a*i+n).toString(36)+r.toString(36)+e.toString(36)}nu.prototype.equals=function(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)},nu.prototype.scaledTo=function(t){var e=this.canonical.z-t;return t>this.canonical.z?new nu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new nu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},nu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?iu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):iu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},nu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ<this.overscaledZ&&t.canonical.x===this.canonical.x>>e&&t.canonical.y===this.canonical.y>>e},nu.prototype.children=function(t){if(this.overscaledZ>=t)return[new nu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new nu(e,this.wrap,e,r,n),new nu(e,this.wrap,e,r+1,n),new nu(e,this.wrap,e,r,n+1),new nu(e,this.wrap,e,r+1,n+1)]},nu.prototype.isLessThan=function(t){return this.wrap<t.wrap||!(this.wrap>t.wrap)&&(this.overscaledZ<t.overscaledZ||!(this.overscaledZ>t.overscaledZ)&&(this.canonical.x<t.canonical.x||!(this.canonical.x>t.canonical.x)&&this.canonical.y<t.canonical.y))},nu.prototype.wrapped=function(){return new nu(this.overscaledZ,0,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.unwrapTo=function(t){return new nu(this.overscaledZ,t,this.canonical.z,this.canonical.x,this.canonical.y)},nu.prototype.overscaleFactor=function(){return Math.pow(2,this.overscaledZ-this.canonical.z)},nu.prototype.toUnwrapped=function(){return new ru(this.wrap,this.canonical)},nu.prototype.toString=function(){return this.overscaledZ+\"/\"+this.canonical.x+\"/\"+this.canonical.y},nu.prototype.getTilePoint=function(t){return this.canonical.getTilePoint(new tu(t.x-this.wrap,t.y))},Nn(\"CanonicalTileID\",eu),Nn(\"OverscaledTileID\",nu,{omit:[\"posMatrix\"]});var au=function(t,e,r){if(this.uid=t,e.height!==e.width)throw new RangeError(\"DEM tiles must be square\");if(r&&\"mapbox\"!==r&&\"terrarium\"!==r)return _('\"'+r+'\" is not a valid encoding type. Valid types include \"mapbox\" and \"terrarium\".');this.stride=e.height;var n=this.dim=e.height-2;this.data=new Uint32Array(e.data.buffer),this.encoding=r||\"mapbox\";for(var i=0;i<n;i++)this.data[this._idx(-1,i)]=this.data[this._idx(0,i)],this.data[this._idx(n,i)]=this.data[this._idx(n-1,i)],this.data[this._idx(i,-1)]=this.data[this._idx(i,0)],this.data[this._idx(i,n)]=this.data[this._idx(i,n-1)];this.data[this._idx(-1,-1)]=this.data[this._idx(0,0)],this.data[this._idx(n,-1)]=this.data[this._idx(n-1,0)],this.data[this._idx(-1,n)]=this.data[this._idx(0,n-1)],this.data[this._idx(n,n)]=this.data[this._idx(n-1,n-1)]};au.prototype.get=function(t,e){var r=new Uint8Array(this.data.buffer),n=4*this._idx(t,e);return(\"terrarium\"===this.encoding?this._unpackTerrarium:this._unpackMapbox)(r[n],r[n+1],r[n+2])},au.prototype.getUnpackVector=function(){return\"terrarium\"===this.encoding?[256,1,1/256,32768]:[6553.6,25.6,.1,1e4]},au.prototype._idx=function(t,e){if(t<-1||t>=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},au.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},au.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},au.prototype.getPixels=function(){return new Eo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},au.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c<o;c++)for(var u=n;u<i;u++)this.data[this._idx(u,c)]=t.data[this._idx(u+s,c+l)]},Nn(\"DEMData\",au);var ou=function(t){this._stringToNumber={},this._numberToString=[];for(var e=0;e<t.length;e++){var r=t[e];this._stringToNumber[r]=e,this._numberToString[e]=r}};ou.prototype.encode=function(t){return this._stringToNumber[t]},ou.prototype.decode=function(t){return this._numberToString[t]};var su=function(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i},lu={geometry:{configurable:!0}};lu.geometry.get=function(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry},lu.geometry.set=function(t){this._geometry=t},su.prototype.toJSON=function(){var t={geometry:this.geometry};for(var e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t},Object.defineProperties(su.prototype,lu);var cu=function(){this.state={},this.stateChanges={},this.deletedStates={}};cu.prototype.updateState=function(t,e,r){var n=String(e);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][n]=this.stateChanges[t][n]||{},u(this.stateChanges[t][n],r),null===this.deletedStates[t])for(var i in this.deletedStates[t]={},this.state[t])i!==n&&(this.deletedStates[t][i]=null);else if(this.deletedStates[t]&&null===this.deletedStates[t][n])for(var a in this.deletedStates[t][n]={},this.state[t][n])r[a]||(this.deletedStates[t][n][a]=null);else for(var o in r){this.deletedStates[t]&&this.deletedStates[t][n]&&null===this.deletedStates[t][n][o]&&delete this.deletedStates[t][n][o]}},cu.prototype.removeFeatureState=function(t,e,r){if(!(null===this.deletedStates[t])){var n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e){if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null}else this.deletedStates[t]=null}},cu.prototype.getState=function(t,e){var r=String(e),n=this.state[t]||{},i=this.stateChanges[t]||{},a=u({},n[r],i[r]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){var o=this.deletedStates[t][e];if(null===o)return{};for(var s in o)delete a[s]}return a},cu.prototype.initializeTileState=function(t,e){t.setFeatureState(this.state,e)},cu.prototype.coalesceChanges=function(t,e){var r={};for(var n in this.stateChanges){this.state[n]=this.state[n]||{};var i={};for(var a in this.stateChanges[n])this.state[n][a]||(this.state[n][a]={}),u(this.state[n][a],this.stateChanges[n][a]),i[a]=this.state[n][a];r[n]=i}for(var o in this.deletedStates){this.state[o]=this.state[o]||{};var s={};if(null===this.deletedStates[o])for(var l in this.state[o])s[l]={},this.state[o][l]={};else for(var c in this.deletedStates[o]){if(null===this.deletedStates[o][c])this.state[o][c]={};else for(var f=0,h=Object.keys(this.deletedStates[o][c]);f<h.length;f+=1){var p=h[f];delete this.state[o][c][p]}s[c]=this.state[o][c]}r[o]=r[o]||{},u(r[o],s)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(r).length)for(var d in t){t[d].setFeatureState(r,e)}};var uu=function(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new zn(8192,16,0),this.grid3D=new zn(8192,16,0),this.featureIndexArray=new fa,this.promoteId=e};function fu(t,e,r,n,i){return v(t,(function(t,a){var o=e instanceof _i?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function hu(t){for(var e=1/0,r=1/0,n=-1/0,i=-1/0,a=0,o=t;a<o.length;a+=1){var s=o[a];e=Math.min(e,s.x),r=Math.min(r,s.y),n=Math.max(n,s.x),i=Math.max(i,s.y)}return{minX:e,minY:r,maxX:n,maxY:i}}function pu(t,e){return e-t}uu.prototype.insert=function(t,e,r,n,i,a){var o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);for(var s=a?this.grid3D:this.grid,l=0;l<e.length;l++){for(var c=e[l],u=[1/0,1/0,-1/0,-1/0],f=0;f<c.length;f++){var h=c[f];u[0]=Math.min(u[0],h.x),u[1]=Math.min(u[1],h.y),u[2]=Math.max(u[2],h.x),u[3]=Math.max(u[3],h.y)}u[0]<8192&&u[1]<8192&&u[2]>=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},uu.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Ls.VectorTile(new al(this.rawTileData)).layers,this.sourceLayerCoder=new ou(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},uu.prototype.query=function(t,e,r,n){var a=this;this.loadVTLayers();for(var o=t.params||{},s=8192/t.tileSize/t.scale,l=sn(o.filter),c=t.queryGeometry,u=t.queryPadding*s,f=hu(c),h=this.grid.query(f.minX-u,f.minY-u,f.maxX+u,f.maxY+u),p=hu(t.cameraQueryGeometry),d=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,a){return function(t,e,r,n,a){for(var o=0,s=t;o<s.length;o+=1){var l=s[o];if(e<=l.x&&r<=l.y&&n>=l.x&&a>=l.y)return!0}var c=[new i(e,r),new i(e,a),new i(n,a),new i(n,r)];if(t.length>2)for(var u=0,f=c;u<f.length;u+=1){if(io(t,f[u]))return!0}for(var h=0;h<t.length-1;h++){if(ao(t[h],t[h+1],c))return!0}return!1}(t.cameraQueryGeometry,e-u,r-u,n+u,a+u)})),m=0,g=d;m<g.length;m+=1){var v=g[m];h.push(v)}h.sort(pu);for(var y,x={},b=function(i){var u=h[i];if(u!==y){y=u;var f=a.featureIndexArray.get(u),p=null;a.loadMatchingFeature(x,f.bucketIndex,f.sourceLayerIndex,f.featureIndex,l,o.layers,o.availableImages,e,r,n,(function(e,r,n){return p||(p=Ya(e)),r.queryIntersectsFeature(c,e,n,p,a.z,t.transform,s,t.pixelPosMatrix)}))}},_=0;_<h.length;_++)b(_);return x},uu.prototype.loadMatchingFeature=function(t,e,r,n,i,a,o,s,l,c,u){var f=this.bucketLayerIDs[e];if(!a||function(t,e){for(var r=0;r<t.length;r++)if(e.indexOf(t[r])>=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new pi(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),m=0;m<f.length;m++){var g=f[m];if(!(a&&a.indexOf(g)<0)){var v=s[g];if(v){var y={};void 0!==d&&c&&(y=c.getState(v.sourceLayer||\"_geojsonTileLayer\",d));var x=l[g];x.paint=fu(x.paint,v.paint,p,y,o),x.layout=fu(x.layout,v.layout,p,y,o);var b=!u||u(p,v,y);if(b){var _=new su(p,this.z,this.x,this.y,d);_.layer=x;var w=t[g];void 0===w&&(w=t[g]=[]),w.push({featureIndex:n,feature:_,intersectionZ:b})}}}}}},uu.prototype.lookupSymbolFeatures=function(t,e,r,n,i,a,o,s){var l={};this.loadVTLayers();for(var c=sn(i),u=0,f=t;u<f.length;u+=1){var h=f[u];this.loadMatchingFeature(l,r,n,h,c,a,o,s,e)}return l},uu.prototype.hasLayer=function(t){for(var e=0,r=this.bucketLayerIDs;e<r.length;e+=1)for(var n=0,i=r[e];n<i.length;n+=1){if(t===i[n])return!0}return!1},uu.prototype.getId=function(t,e){var r=t.id;if(this.promoteId){var n=\"string\"==typeof this.promoteId?this.promoteId:this.promoteId[e];\"boolean\"==typeof(r=t.properties[n])&&(r=Number(r))}return r},Nn(\"FeatureIndex\",uu,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});var du=function(t,e){this.tileID=t,this.uid=h(),this.uses=0,this.tileSize=e,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.expiredRequestCount=0,this.state=\"loading\"};du.prototype.registerFadeDuration=function(t){var e=t+this.timeAdded;e<R.now()||this.fadeEndTime&&e<this.fadeEndTime||(this.fadeEndTime=e)},du.prototype.wasRequested=function(){return\"errored\"===this.state||\"loaded\"===this.state||\"reloading\"===this.state},du.prototype.loadVectorData=function(t,e,r){if(this.hasData()&&this.unloadVectorData(),this.state=\"loaded\",t){for(var n in t.featureIndex&&(this.latestFeatureIndex=t.featureIndex,t.rawTileData?(this.latestRawTileData=t.rawTileData,this.latestFeatureIndex.rawTileData=t.rawTileData):this.latestRawTileData&&(this.latestFeatureIndex.rawTileData=this.latestRawTileData)),this.collisionBoxArray=t.collisionBoxArray,this.buckets=function(t,e){var r={};if(!e)return r;for(var n=function(){var t=a[i],n=t.layerIds.map((function(t){return e.getLayer(t)})).filter(Boolean);if(0!==n.length){t.layers=n,t.stateDependentLayerIds&&(t.stateDependentLayers=t.stateDependentLayerIds.map((function(t){return n.filter((function(e){return e.id===t}))[0]})));for(var o=0,s=n;o<s.length;o+=1){var l=s[o];r[l.id]=t}}},i=0,a=t;i<a.length;i+=1)n();return r}(t.buckets,e.style),this.hasSymbolBuckets=!1,this.buckets){var i=this.buckets[n];if(i instanceof Mc){if(this.hasSymbolBuckets=!0,!r)break;i.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(var a in this.buckets){var o=this.buckets[a];if(o instanceof Mc&&o.hasRTLText){this.hasRTLText=!0,hi.isLoading()||hi.isLoaded()||\"deferred\"!==ui()||fi();break}}for(var s in this.queryPadding=0,this.buckets){var l=this.buckets[s];this.queryPadding=Math.max(this.queryPadding,e.style.getLayer(s).queryRadius(l))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new na},du.prototype.unloadVectorData=function(){for(var t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"},du.prototype.getBucket=function(t){return this.buckets[t.id]},du.prototype.upload=function(t){for(var e in this.buckets){var r=this.buckets[e];r.uploadPending()&&r.upload(t)}var n=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new Vc(t,this.imageAtlas.image,n.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new Vc(t,this.glyphAtlasImage,n.ALPHA),this.glyphAtlasImage=null)},du.prototype.prepare=function(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)},du.prototype.queryRenderedFeatures=function(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}},du.prototype.querySourceFeatures=function(t,e){var r=this.latestFeatureIndex;if(r&&r.rawTileData){var n=r.loadVTLayers(),i=e?e.sourceLayer:\"\",a=n._geojsonTileLayer||n[i];if(a)for(var o=sn(e&&e.filter),s=this.tileID.canonical,l=s.z,c=s.x,u=s.y,f={z:l,x:c,y:u},h=0;h<a.length;h++){var p=a.feature(h);if(o.filter(new pi(this.tileID.overscaledZ),p)){var d=r.getId(p,i),m=new su(p,l,c,u,d);m.tile=f,t.push(m)}}}},du.prototype.hasData=function(){return\"loaded\"===this.state||\"reloading\"===this.state||\"expired\"===this.state},du.prototype.patternsLoaded=function(){return this.imageAtlas&&!!Object.keys(this.imageAtlas.patternPositions).length},du.prototype.setExpiryData=function(t){var e=this.expirationTime;if(t.cacheControl){var r=A(t.cacheControl);r[\"max-age\"]&&(this.expirationTime=Date.now()+1e3*r[\"max-age\"])}else t.expires&&(this.expirationTime=new Date(t.expires).getTime());if(this.expirationTime){var n=Date.now(),i=!1;if(this.expirationTime>n)i=!1;else if(e)if(this.expirationTime<e)i=!0;else{var a=this.expirationTime-e;a?this.expirationTime=n+Math.max(a,3e4):i=!0}else i=!0;i?(this.expiredRequestCount++,this.state=\"expired\"):this.expiredRequestCount=0}},du.prototype.getExpiryTimeout=function(){if(this.expirationTime)return this.expiredRequestCount?1e3*(1<<Math.min(this.expiredRequestCount-1,31)):Math.min(this.expirationTime-(new Date).getTime(),Math.pow(2,31)-1)},du.prototype.setFeatureState=function(t,e){if(this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData&&0!==Object.keys(t).length){var r=this.latestFeatureIndex.loadVTLayers();for(var n in this.buckets)if(e.style.hasLayer(n)){var i=this.buckets[n],a=i.layers[0].sourceLayer||\"_geojsonTileLayer\",o=r[a],s=t[a];if(o&&s&&0!==Object.keys(s).length){i.update(s,o,this.imageAtlas&&this.imageAtlas.patternPositions||{});var l=e&&e.style&&e.style.getLayer(n);l&&(this.queryPadding=Math.max(this.queryPadding,l.queryRadius(i)))}}}},du.prototype.holdingForFade=function(){return void 0!==this.symbolFadeHoldUntil},du.prototype.symbolFadeFinished=function(){return!this.symbolFadeHoldUntil||this.symbolFadeHoldUntil<R.now()},du.prototype.clearFadeHold=function(){this.symbolFadeHoldUntil=void 0},du.prototype.setHoldDuration=function(t){this.symbolFadeHoldUntil=R.now()+t},du.prototype.setDependencies=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){r[i[n]]=!0}this.dependencies[t]=r},du.prototype.hasDependency=function(t,e){for(var r=0,n=t;r<n.length;r+=1){var i=n[r],a=this.dependencies[i];if(a)for(var o=0,s=e;o<s.length;o+=1){if(a[s[o]])return!0}}return!1};var 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r},t.makeRequest=yt,t.mapObject=v,t.mercatorXfromLng=Jc,t.mercatorYfromLat=Kc,t.mercatorZfromAltitude=Qc,t.mul=mo,t.multiply=po,t.mvt=Ls,t.normalize=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=qe,t.offscreenCanvasSupported=pt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new al(t).readFields(Tl,[])},t.pbf=al,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=8192/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new 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g=(c+1)%2;(0===c?r-i<=d:n-i<=m)&&(o.push(f),o.push(p-1),o.push(g)),(0===c?r+i>=d:n+i>=m)&&(o.push(p+1),o.push(u),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var j={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},U=function(t){this.options=X(Object.create(j),t),this.trees=new Array(this.options.maxZoom+1)};function V(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function H(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function q(t){return{type:\"Feature\",id:t.id,properties:G(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function G(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return 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r=0;r<e.length;r+=3)t.minX=Math.min(t.minX,e[r]),t.minY=Math.min(t.minY,e[r+1]),t.maxX=Math.max(t.maxX,e[r]),t.maxY=Math.max(t.maxY,e[r+1])}function tt(t,e,r,n){if(e.geometry){var i=e.geometry.coordinates,a=e.geometry.type,o=Math.pow(r.tolerance/((1<<r.maxZoom)*r.extent),2),s=[],l=e.id;if(r.promoteId?l=e.properties[r.promoteId]:r.generateId&&(l=n||0),\"Point\"===a)et(i,s);else if(\"MultiPoint\"===a)for(var c=0;c<i.length;c++)et(i[c],s);else if(\"LineString\"===a)rt(i,s,o,!1);else if(\"MultiLineString\"===a){if(r.lineMetrics){for(c=0;c<i.length;c++)s=[],rt(i[c],s,o,!1),t.push(Q(l,\"LineString\",s,e.properties));return}nt(i,s,o,!1)}else if(\"Polygon\"===a)nt(i,s,o,!0);else{if(\"MultiPolygon\"!==a){if(\"GeometryCollection\"===a){for(c=0;c<e.geometry.geometries.length;c++)tt(t,{id:l,geometry:e.geometry.geometries[c],properties:e.properties},r,n);return}throw new Error(\"Input data is not a valid GeoJSON object.\")}for(c=0;c<i.length;c++){var 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u=t[c],f=u.geometry,h=u.type,p=0===i?u.minX:u.minY,d=0===i?u.maxX:u.maxY;if(p>=r&&d<n)l.push(u);else if(!(d<r||p>=n)){var m=[];if(\"Point\"===h||\"MultiPoint\"===h)st(f,m,r,n,i);else if(\"LineString\"===h)lt(f,m,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ut(f,m,r,n,i,!1);else if(\"Polygon\"===h)ut(f,m,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g<f.length;g++){var v=[];ut(f[g],v,r,n,i,!0),v.length&&m.push(v)}if(m.length){if(s.lineMetrics&&\"LineString\"===h){for(g=0;g<m.length;g++)l.push(Q(u.id,h,m[g],u.tags));continue}\"LineString\"!==h&&\"MultiLineString\"!==h||(1===m.length?(h=\"LineString\",m=m[0]):h=\"MultiLineString\"),\"Point\"!==h&&\"MultiPoint\"!==h||(h=3===m.length?\"Point\":\"MultiPoint\"),l.push(Q(u.id,h,m,u.tags))}}}return l.length?l:null}function st(t,e,r,n,i){for(var a=0;a<t.length;a+=3){var o=t[a+i];o>=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function lt(t,e,r,n,i,a,o){for(var 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r=[],n=0;n<t.length;n++){var i,a=t[n],o=a.type;if(\"Point\"===o||\"MultiPoint\"===o||\"LineString\"===o)i=mt(a.geometry,e);else if(\"MultiLineString\"===o||\"Polygon\"===o){i=[];for(var s=0;s<a.geometry.length;s++)i.push(mt(a.geometry[s],e))}else if(\"MultiPolygon\"===o)for(i=[],s=0;s<a.geometry.length;s++){for(var l=[],c=0;c<a.geometry[s].length;c++)l.push(mt(a.geometry[s][c],e));i.push(l)}r.push(Q(a.id,o,i,a.tags))}return r}function mt(t,e){var r=[];r.size=t.size,void 0!==t.start&&(r.start=t.start,r.end=t.end);for(var n=0;n<t.length;n+=3)r.push(t[n]+e,t[n+1],t[n+2]);return r}function gt(t,e){if(t.transformed)return t;var r,n,i,a=1<<t.z,o=t.x,s=t.y;for(r=0;r<t.features.length;r++){var l=t.features[r],c=l.geometry,u=l.type;if(l.geometry=[],1===u)for(n=0;n<c.length;n+=2)l.geometry.push(vt(c[n],c[n+1],e,a,o,s));else for(n=0;n<c.length;n++){var f=[];for(i=0;i<c[n].length;i+=2)f.push(vt(c[n][i],c[n][i+1],e,a,o,s));l.geometry.push(f)}}return t.transformed=!0,t}function 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u={},e.tags)u[f]=e.tags[f];u.mapbox_clip_start=i.start/i.size,u.mapbox_clip_end=i.end/i.size}var h={geometry:o,type:\"Polygon\"===a||\"MultiPolygon\"===a?3:\"LineString\"===a||\"MultiLineString\"===a?2:1,tags:u};null!==e.id&&(h.id=e.id),t.features.push(h)}}function bt(t,e,r,n,i,a){var o=n*n;if(n>0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;l<e.length;l+=3)(0===n||e[l+2]>o)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n<i;a=n,n+=2)r+=(t[n]-t[a])*(t[n+1]+t[a+1]);if(r>0===e)for(n=0,i=t.length;n<i/2;n+=2){var o=t[n],s=t[n+1];t[n]=t[i-2-n],t[n+1]=t[i-1-n],t[i-2-n]=o,t[i-1-n]=s}}(s,a),t.push(s)}}function _t(t,e){var r=(e=this.options=function(t,e){for(var r in e)t[r]=e[r];return t}(Object.create(this.options),e)).debug;if(r&&console.time(\"preprocess data\"),e.maxZoom<0||e.maxZoom>24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n<t.features.length;n++)tt(r,t.features[n],e,n);else\"Feature\"===t.type?tt(r,t,e):tt(r,{geometry:t},e);return r}(t,e);this.tiles={},this.tileCoords=[],r&&(console.timeEnd(\"preprocess data\"),console.log(\"index: maxZoom: %d, maxPoints: %d\",e.indexMaxZoom,e.indexMaxPoints),console.time(\"generate tiles\"),this.stats={},this.total=0),(n=function(t,e){var r=e.buffer/e.extent,n=t,i=ot(t,1,-1-r,r,0,-1,2,e),a=ot(t,1,1-r,2+r,0,-1,2,e);return(i||a)&&(n=ot(t,1,-r,1+r,0,-1,2,e)||[],i&&(n=dt(i,1).concat(n)),a&&(n=n.concat(dt(a,-1)))),n}(n,e)).length&&this.splitTile(n,0,0,0),r&&(n.length&&console.log(\"features: %d, points: %d\",this.tiles[0].numFeatures,this.tiles[0].numPoints),console.timeEnd(\"generate tiles\"),console.log(\"tiles generated:\",this.total,JSON.stringify(this.stats)))}function wt(t,e,r){return 32*((1<<t)*r+e)+t}function Tt(t,e){var r=t.tileID.canonical;if(!this._geoJSONIndex)return e(null,null);var n=this._geoJSONIndex.getTile(r.z,r.x,r.y);if(!n)return e(null,null);var i=new g(n.features),a=_(i);0===a.byteOffset&&a.byteLength===a.buffer.byteLength||(a=new Uint8Array(a)),e(null,{vectorTile:i,rawData:a.buffer})}U.prototype.load=function(t){var e=this.options,r=e.log,n=e.minZoom,i=e.maxZoom,a=e.nodeSize;r&&console.time(\"total time\");var o=\"prepare \"+t.length+\" points\";r&&console.time(o),this.points=t;for(var s=[],l=0;l<t.length;l++)t[l].geometry&&s.push(H(t[l],l));this.trees[i+1]=new N(s,Z,J,a,Float32Array),r&&console.timeEnd(o);for(var c=i;c>=n;c--){var u=+Date.now();s=this._cluster(s,c),this.trees[c]=new N(s,Z,J,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",c,s.length,+Date.now()-u)}return r&&console.timeEnd(\"total time\"),this},U.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],c=[],u=0,f=l.range(Y(r),W(a),Y(i),W(n));u<f.length;u+=1){var h=f[u],p=l.points[h];c.push(p.numPoints?q(p):this.points[p.index])}return c},U.prototype.getChildren=function(t){var e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);var a=i.points[e];if(!a)throw new Error(n);for(var o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=[],l=0,c=i.within(a.x,a.y,o);l<c.length;l+=1){var u=c[l],f=i.points[u];f.parentId===t&&s.push(f.numPoints?q(f):this.points[f.index])}if(0===s.length)throw new Error(n);return s},U.prototype.getLeaves=function(t,e,r){e=e||10,r=r||0;var n=[];return this._appendLeaves(n,t,e,r,0),n},U.prototype.getTile=function(t,e,r){var n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),a=this.options,o=a.extent,s=a.radius/o,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.points,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.points,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.points,-1,r,i,u),u.features.length?u:null},U.prototype.getClusterExpansionZoom=function(t){for(var e=this._getOriginZoom(t)-1;e<=this.options.maxZoom;){var r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e},U.prototype._appendLeaves=function(t,e,r,n,i){for(var a=0,o=this.getChildren(e);a<o.length;a+=1){var s=o[a],l=s.properties;if(l&&l.cluster?i+l.point_count<=n?i+=l.point_count:i=this._appendLeaves(t,l.cluster_id,r,n,i):i<n?i++:t.push(s),t.length===r)break}return i},U.prototype._addTileFeatures=function(t,e,r,n,i,a){for(var o=0,s=t;o<s.length;o+=1){var l=e[s[o]],c=l.numPoints,u={type:1,geometry:[[Math.round(this.options.extent*(l.x*i-r)),Math.round(this.options.extent*(l.y*i-n))]],tags:c?G(l):this.points[l.index].properties},f=void 0;c?f=l.id:this.options.generateId?f=l.index:this.points[l.index].id&&(f=this.points[l.index].id),void 0!==f&&(u.id=f),a.features.push(u)}},U.prototype._limitZoom=function(t){return Math.max(this.options.minZoom,Math.min(t,this.options.maxZoom+1))},U.prototype._cluster=function(t,e){for(var r=[],n=this.options,i=n.radius,a=n.extent,o=n.reduce,s=i/(a*Math.pow(2,e)),l=0;l<t.length;l++){var c=t[l];if(!(c.zoom<=e)){c.zoom=e;for(var u=this.trees[e+1],f=u.within(c.x,c.y,s),h=c.numPoints||1,p=c.x*h,d=c.y*h,m=o&&h>1?this._map(c,!0):null,g=(l<<5)+(e+1)+this.points.length,v=0,y=f;v<y.length;v+=1){var x=y[v],b=u.points[x];if(!(b.zoom<=e)){b.zoom=e;var _=b.numPoints||1;p+=b.x*_,d+=b.y*_,h+=_,b.parentId=g,o&&(m||(m=this._map(c,!0)),o(m,this._map(b)))}}1===h?r.push(c):(c.parentId=g,r.push(V(p/h,d/h,g,h,m)))}}return r},U.prototype._getOriginId=function(t){return t-this.points.length>>5},U.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},U.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},_t.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},_t.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,c=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var u=1<<e,f=wt(e,r,n),h=this.tiles[f];if(!h&&(c>1&&console.time(\"creation\"),h=this.tiles[f]=yt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<<i-e;if(r!==Math.floor(a/d)||n!==Math.floor(o/d))continue}else if(e===l.indexMaxZoom||h.numPoints<=l.indexMaxPoints)continue;if(h.source=null,0!==t.length){c>1&&console.time(\"clipping\");var m,g,v,y,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;m=g=v=y=null,x=ot(t,u,r-_,r+T,0,h.minX,h.maxX,l),b=ot(t,u,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(m=ot(x,u,n-_,n+T,1,h.minY,h.maxY,l),g=ot(x,u,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(v=ot(b,u,n-_,n+T,1,h.minY,h.maxY,l),y=ot(b,u,n+w,n+k,1,h.minY,h.maxY,l),b=null),c>1&&console.timeEnd(\"clipping\"),s.push(m||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(v||[],e+1,2*r+1,2*n),s.push(y||[],e+1,2*r+1,2*n+1)}}},_t.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<<t,s=wt(t,e=(e%o+o)%o,r);if(this.tiles[s])return gt(this.tiles[s],i);a>1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,c=t,u=e,f=r;!l&&c>0;)c--,u=Math.floor(u/2),f=Math.floor(f/2),l=this.tiles[wt(c,u,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",c,u,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,c,u,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?gt(this.tiles[s],i):null):null};var kt=function(e){function r(t,r,n,i){e.call(this,t,r,n,Tt),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new U(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c<u.length;c+=1){var f=u[c],h=n[f],p=h[0],d=h[1],m=t.createExpression(d),g=t.createExpression(\"string\"==typeof p?[p,[\"accumulated\"],[\"get\",f]]:p);i[f]=m.value,a[f]=g.value}return r.map=function(t){s.properties=t;for(var e={},r=0,n=l;r<n.length;r+=1){var a=n[r];e[a]=i[a].evaluate(o,s)}return e},r.reduce=function(t,e){s.properties=e;for(var r=0,n=l;r<n.length;r+=1){var i=n[r];o.accumulated=t[i],t[i]=a[i].evaluate(o,s)}},r}(n)).load(o.features):function(t,e){return new _t(t,e)}(o,n.geojsonVtOptions)}catch(a){return r(a)}e.loaded={};var s={};if(i){var l=i.finish();l&&(s.resourceTiming={},s.resourceTiming[n.source]=JSON.parse(JSON.stringify(l)))}r(null,s)}))}},r.prototype.coalesce=function(){\"Coalescing\"===this._state?this._state=\"Idle\":\"NeedsLoadData\"===this._state&&(this._state=\"Coalescing\",this._loadData())},r.prototype.reloadTile=function(t,r){var n=this.loaded,i=t.uid;return n&&n[i]?e.prototype.reloadTile.call(this,t,r):this.loadTile(t,r)},r.prototype.loadGeoJSON=function(e,r){if(e.request)t.getJSON(e.request,r);else{if(\"string\"!=typeof e.data)return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON object.\"));try{return r(null,JSON.parse(e.data))}catch(t){return r(new Error(\"Input data given to '\"+e.source+\"' is not a valid GeoJSON 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t.Point(r.clientX-n.left-e.clientLeft,r.clientY-n.top-e.clientTop)},r.touchPos=function(e,r){for(var n=e.getBoundingClientRect(),i=[],a=0;a<r.length;a++)i.push(new t.Point(r[a].clientX-n.left-e.clientLeft,r[a].clientY-n.top-e.clientTop));return i},r.mouseButton=function(e){return void 0!==t.window.InstallTrigger&&2===e.button&&e.ctrlKey&&t.window.navigator.platform.toUpperCase().indexOf(\"MAC\")>=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e<r.length;e+=1){var n=r[e],i=n.ids,a=n.callback;this._notify(i,a)}this.requestors=[]}},r.prototype.getImage=function(t){return this.images[t]},r.prototype.addImage=function(t,e){this._validate(t,e)&&(this.images[t]=e)},r.prototype._validate=function(e,r){var n=!0;return this._validateStretch(r.stretchX,r.data&&r.data.width)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchX\" value'))),n=!1),this._validateStretch(r.stretchY,r.data&&r.data.height)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"stretchY\" value'))),n=!1),this._validateContent(r.content,r)||(this.fire(new t.ErrorEvent(new Error('Image \"'+e+'\" has invalid \"content\" value'))),n=!1),n},r.prototype._validateStretch=function(t,e){if(!t)return!0;for(var r=0,n=0,i=t;n<i.length;n+=1){var a=i[n];if(a[0]<r||a[1]<a[0]||e<a[1])return!1;r=a[1]}return!0},r.prototype._validateContent=function(t,e){return!t||4===t.length&&(!(t[0]<0||e.data.width<t[0])&&(!(t[1]<0||e.data.height<t[1])&&(!(t[2]<0||e.data.width<t[2])&&(!(t[3]<0||e.data.height<t[3])&&(!(t[2]<t[0])&&!(t[3]<t[1]))))))},r.prototype.updateImage=function(t,e){var r=this.images[t];e.version=r.version+1,this.images[t]=e,this.updatedImages[t]=!0},r.prototype.removeImage=function(t){var e=this.images[t];delete this.images[t],delete this.patterns[t],e.userImage&&e.userImage.onRemove&&e.userImage.onRemove()},r.prototype.listImages=function(){return Object.keys(this.images)},r.prototype.getImages=function(t,e){var r=!0;if(!this.isLoaded())for(var n=0,i=t;n<i.length;n+=1){var a=i[n];this.images[a]||(r=!1)}this.isLoaded()||r?this._notify(t,e):this.requestors.push({ids:t,callback:e})},r.prototype._notify=function(e,r){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i];this.images[o]||this.fire(new t.Event(\"styleimagemissing\",{id:o}));var s=this.images[o];s?n[o]={data:s.data.clone(),pixelRatio:s.pixelRatio,sdf:s.sdf,version:s.version,stretchX:s.stretchX,stretchY:s.stretchY,content:s.content,hasRenderCallback:Boolean(s.userImage&&s.userImage.render)}:t.warnOnce('Image \"'+o+'\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.')}r(null,n)},r.prototype.getPixelSize=function(){var t=this.atlasImage;return{width:t.width,height:t.height}},r.prototype.getPattern=function(e){var r=this.patterns[e],n=this.getImage(e);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{var i={w:n.data.width+2,h:n.data.height+2,x:0,y:0},a=new t.ImagePosition(i,n);this.patterns[e]={bin:i,position:a}}return this._updatePatternAtlas(),this.patterns[e].position},r.prototype.bind=function(e){var r=e.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new t.Texture(e,this.atlasImage,r.RGBA),this.atlasTexture.bind(r.LINEAR,r.CLAMP_TO_EDGE)},r.prototype._updatePatternAtlas=function(){var e=[];for(var r in this.patterns)e.push(this.patterns[r].bin);var n=t.potpack(e),i=n.w,a=n.h,o=this.atlasImage;for(var s in o.resize({width:i||1,height:a||1}),this.patterns){var l=this.patterns[s].bin,c=l.x+1,u=l.y+1,f=this.images[s].data,h=f.width,p=f.height;t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u},{width:h,height:p}),t.RGBAImage.copy(f,o,{x:0,y:p-1},{x:c,y:u-1},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c,y:u+p},{width:h,height:1}),t.RGBAImage.copy(f,o,{x:h-1,y:0},{x:c-1,y:u},{width:1,height:p}),t.RGBAImage.copy(f,o,{x:0,y:0},{x:c+h,y:u},{width:1,height:p})}this.dirty=!0},r.prototype.beginFrame=function(){this.callbackDispatchedThisFrame={}},r.prototype.dispatchRenderCallbacks=function(t){for(var e=0,r=t;e<r.length;e+=1){var n=r[e];if(!this.callbackDispatchedThisFrame[n]){this.callbackDispatchedThisFrame[n]=!0;var i=this.images[n];f(i)&&this.updateImage(n,i)}}},r}(t.Evented);var p=g,d=g,m=1e20;function g(t,e,r,n,i,a){this.fontSize=t||24,this.buffer=void 0===e?3:e,this.cutoff=n||.25,this.fontFamily=i||\"sans-serif\",this.fontWeight=a||\"normal\",this.radius=r||8;var o=this.size=this.fontSize+2*this.buffer;this.canvas=document.createElement(\"canvas\"),this.canvas.width=this.canvas.height=o,this.ctx=this.canvas.getContext(\"2d\"),this.ctx.font=this.fontWeight+\" \"+this.fontSize+\"px \"+this.fontFamily,this.ctx.textBaseline=\"middle\",this.ctx.fillStyle=\"black\",this.gridOuter=new Float64Array(o*o),this.gridInner=new Float64Array(o*o),this.f=new Float64Array(o),this.d=new Float64Array(o),this.z=new Float64Array(o+1),this.v=new Int16Array(o),this.middle=Math.round(o/2*(navigator.userAgent.indexOf(\"Gecko/\")>=0?1.2:1))}function v(t,e,r,n,i,a,o){for(var s=0;s<e;s++){for(var l=0;l<r;l++)n[l]=t[l*e+s];for(y(n,i,a,o,r),l=0;l<r;l++)t[l*e+s]=i[l]}for(l=0;l<r;l++){for(s=0;s<e;s++)n[s]=t[l*e+s];for(y(n,i,a,o,e),s=0;s<e;s++)t[l*e+s]=Math.sqrt(i[s])}}function y(t,e,r,n,i){r[0]=0,n[0]=-m,n[1]=+m;for(var a=1,o=0;a<i;a++){for(var s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);s<=n[o];)o--,s=(t[a]+a*a-(t[r[o]]+r[o]*r[o]))/(2*a-2*r[o]);r[++o]=a,n[o]=s,n[o+1]=+m}for(a=0,o=0;a<i;a++){for(;n[o+1]<a;)o++;e[a]=(a-r[o])*(a-r[o])+t[r[o]]}}g.prototype.draw=function(t){this.ctx.clearRect(0,0,this.size,this.size),this.ctx.fillText(t,this.buffer,this.middle);for(var e=this.ctx.getImageData(0,0,this.size,this.size),r=new Uint8ClampedArray(this.size*this.size),n=0;n<this.size*this.size;n++){var i=e.data[4*n+3]/255;this.gridOuter[n]=1===i?0:0===i?m:Math.pow(Math.max(0,.5-i),2),this.gridInner[n]=1===i?m:0===i?0:Math.pow(Math.max(0,i-.5),2)}for(v(this.gridOuter,this.size,this.size,this.f,this.d,this.v,this.z),v(this.gridInner,this.size,this.size,this.f,this.d,this.v,this.z),n=0;n<this.size*this.size;n++){var a=this.gridOuter[n]-this.gridInner[n];r[n]=Math.max(0,Math.min(255,Math.round(255-255*(a/this.radius+this.cutoff))))}return r},p.default=d;var x=function(t,e){this.requestManager=t,this.localIdeographFontFamily=e,this.entries={}};x.prototype.setURL=function(t){this.url=t},x.prototype.getGlyphs=function(e,r){var n=this,i=[];for(var a in e)for(var o=0,s=e[a];o<s.length;o+=1){var l=s[o];i.push({stack:a,id:l})}t.asyncAll(i,(function(t,e){var r=t.stack,i=t.id,a=n.entries[r];a||(a=n.entries[r]={glyphs:{},requests:{},ranges:{}});var o=a.glyphs[i];if(void 0===o){if(o=n._tinySDF(a,r,i))return a.glyphs[i]=o,void e(null,{stack:r,id:i,glyph:o});var s=Math.floor(i/256);if(256*s>65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i<o.length;i+=1){(0,o[i])(t,e)}delete a.requests[s]}))),l.push((function(t,n){t?e(t):n&&e(null,{stack:r,id:i,glyph:n[i]||null})}))}}else e(null,{stack:r,id:i,glyph:o})}),(function(t,e){if(t)r(t);else if(e){for(var n={},i=0,a=e;i<a.length;i+=1){var o=a[i],s=o.stack,l=o.id,c=o.glyph;(n[s]||(n[s]={}))[l]=c&&{id:c.id,bitmap:c.bitmap.clone(),metrics:c.metrics}}r(null,n)}}))},x.prototype._doesCharSupportLocalGlyph=function(e){return!!this.localIdeographFontFamily&&(t.isChar[\"CJK Unified Ideographs\"](e)||t.isChar[\"Hangul Syllables\"](e)||t.isChar.Hiragana(e)||t.isChar.Katakana(e))},x.prototype._tinySDF=function(e,r,n){var i=this.localIdeographFontFamily;if(i&&this._doesCharSupportLocalGlyph(n)){var a=e.tinySDF;if(!a){var o=\"400\";/bold/i.test(r)?o=\"900\":/medium/i.test(r)?o=\"500\":/light/i.test(r)&&(o=\"200\"),a=e.tinySDF=new x.TinySDF(24,3,8,.25,i,o)}return{id:n,bitmap:new t.AlphaImage({width:30,height:30},a.draw(String.fromCharCode(n))),metrics:{width:24,height:24,left:0,top:-8,advance:24}}}},x.loadGlyphRange=function(e,r,n,i,a){var o=256*r,s=o+255,l=i.transformRequest(i.normalizeGlyphsURL(n).replace(\"{fontstack}\",e).replace(\"{range}\",o+\"-\"+s),t.ResourceType.Glyphs);t.getArrayBuffer(l,(function(e,r){if(e)a(e);else if(r){for(var n={},i=0,o=t.parseGlyphPBF(r);i<o.length;i+=1){var s=o[i];n[s.id]=s}a(null,n)}}))},x.TinySDF=p;var b=function(){this.specification=t.styleSpec.light.position};b.prototype.possiblyEvaluate=function(e,r){return t.sphericalToCartesian(e.expression.evaluate(r))},b.prototype.interpolate=function(e,r,n){return{x:t.number(e.x,r.x,n),y:t.number(e.y,r.y,n),z:t.number(e.z,r.z,n)}};var _=new t.Properties({anchor:new t.DataConstantProperty(t.styleSpec.light.anchor),position:new b,color:new t.DataConstantProperty(t.styleSpec.light.color),intensity:new t.DataConstantProperty(t.styleSpec.light.intensity)}),w=function(e){function r(r){e.call(this),this._transitionable=new t.Transitionable(_),this.setLight(r),this._transitioning=this._transitionable.untransitioned()}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getLight=function(){return this._transitionable.serialize()},r.prototype.setLight=function(e,r){if(void 0===r&&(r={}),!this._validate(t.validateLight,e,r))for(var n in e){var i=e[n];t.endsWith(n,\"-transition\")?this._transitionable.setTransition(n.slice(0,-\"-transition\".length),i):this._transitionable.setValue(n,i)}},r.prototype.updateTransitions=function(t){this._transitioning=this._transitionable.transitioned(t,this._transitioning)},r.prototype.hasTransition=function(){return this._transitioning.hasTransition()},r.prototype.recalculate=function(t){this.properties=this._transitioning.possiblyEvaluate(t)},r.prototype._validate=function(e,r,n){return(!n||!1!==n.validate)&&t.emitValidationErrors(this,e.call(t.validateStyle,t.extend({value:r,style:{glyphs:!0,sprite:!0},styleSpec:t.styleSpec})))},r}(t.Evented),T=function(t,e){this.width=t,this.height=e,this.nextRow=0,this.data=new Uint8Array(this.width*this.height),this.dashEntry={}};T.prototype.getDash=function(t,e){var r=t.join(\",\")+String(e);return this.dashEntry[r]||(this.dashEntry[r]=this.addDash(t,e)),this.dashEntry[r]},T.prototype.getDashRanges=function(t,e,r){var n=[],i=t.length%2==1?-t[t.length-1]*r:0,a=t[0]*r,o=!0;n.push({left:i,right:a,isDash:o,zeroLength:0===t[0]});for(var s=t[0],l=1;l<t.length;l++){o=!o;var c=t[l];i=s*r,a=(s+=c)*r,n.push({left:i,right:a,isDash:o,zeroLength:0===c})}return n},T.prototype.addRoundDash=function(t,e,r){for(var n=e/2,i=-r;i<=r;i++)for(var a=this.nextRow+r+i,o=this.width*a,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(h*h+m*m)}else p=n-Math.sqrt(h*h+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c<this.width;c++){c/l.right>1&&(l=t[++s]);var u=Math.abs(c-l.left),f=Math.abs(c-l.right),h=Math.min(u,f),p=l.isDash?h:-h;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},T.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o<e.length;o++)a+=e[o];if(0!==a){var s=this.width/a,l=this.getDashRanges(e,this.width,s);r?this.addRoundDash(l,s,n):this.addRegularDash(l)}var c={y:(this.nextRow+n+.5)/this.height,height:2*n/this.height,width:a};return this.nextRow+=i,this.dirty=!0,c},T.prototype.bind=function(t){var e=t.gl;this.texture?(e.bindTexture(e.TEXTURE_2D,this.texture),this.dirty&&(this.dirty=!1,e.texSubImage2D(e.TEXTURE_2D,0,0,0,this.width,this.height,e.ALPHA,e.UNSIGNED_BYTE,this.data))):(this.texture=e.createTexture(),e.bindTexture(e.TEXTURE_2D,this.texture),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_S,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_WRAP_T,e.REPEAT),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,e.LINEAR),e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,e.LINEAR),e.texImage2D(e.TEXTURE_2D,0,e.ALPHA,this.width,this.height,0,e.ALPHA,e.UNSIGNED_BYTE,this.data))};var k=function e(r,n){this.workerPool=r,this.actors=[],this.currentActor=0,this.id=t.uniqueId();for(var i=this.workerPool.acquire(this.id),a=0;a<i.length;a++){var o=i[a],s=new e.Actor(o,n,this.id);s.name=\"Worker \"+a,this.actors.push(s)}};function A(e,r,n){var i=function(i,a){if(i)return n(i);if(a){var o=t.pick(t.extend(a,e),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"mapbox_logo\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);a.vector_layers&&(o.vectorLayers=a.vector_layers,o.vectorLayerIds=o.vectorLayers.map((function(t){return t.id}))),o.tiles=r.canonicalizeTileset(o,e.url),n(null,o)}};return e.url?t.getJSON(r.transformRequest(r.normalizeSourceURL(e.url),t.ResourceType.Source),i):t.browser.frame((function(){return i(null,e)}))}k.prototype.broadcast=function(e,r,n){n=n||function(){},t.asyncAll(this.actors,(function(t,n){t.send(e,r,n)}),n)},k.prototype.getActor=function(){return this.currentActor=(this.currentActor+1)%this.actors.length,this.actors[this.currentActor]},k.prototype.remove=function(){this.actors.forEach((function(t){t.remove()})),this.actors=[],this.workerPool.release(this.id)},k.Actor=t.Actor;var M=function(e,r,n){this.bounds=t.LngLatBounds.convert(this.validateBounds(e)),this.minzoom=r||0,this.maxzoom=n||24};M.prototype.validateBounds=function(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]},M.prototype.contains=function(e){var r=Math.pow(2,e.z),n=Math.floor(t.mercatorXfromLng(this.bounds.getWest())*r),i=Math.floor(t.mercatorYfromLat(this.bounds.getNorth())*r),a=Math.ceil(t.mercatorXfromLng(this.bounds.getEast())*r),o=Math.ceil(t.mercatorYfromLat(this.bounds.getSouth())*r);return e.x>=n&&e.x<a&&e.y>=i&&e.y<o};var S=function(e){function r(r,n,i,a){if(e.call(this),this.id=r,this.dispatcher=i,this.type=\"vector\",this.minzoom=0,this.maxzoom=22,this.scheme=\"xyz\",this.tileSize=512,this.reparseOverscaled=!0,this.isTileClipped=!0,this._loaded=!1,t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\",\"promoteId\"])),this._options=t.extend({type:\"vector\"},n),this._collectResourceTiming=n.collectResourceTiming,512!==this.tileSize)throw new Error(\"vector tile sources must have a tileSize of 512\");this.setEventedParent(a)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles,e.map._requestManager._customAccessToken),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken,e.map._requestManager._customAccessToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme)),i={request:this.map._requestManager.transformRequest(n,t.ResourceType.Tile),uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,tileSize:this.tileSize*e.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};function a(n,i){return delete e.request,e.aborted?r(null):n&&404!==n.status?r(n):(i&&i.resourceTiming&&(e.resourceTiming=i.resourceTiming),this.map._refreshExpiredTiles&&i&&e.setExpiryData(i),e.loadVectorData(i,this.map.painter),t.cacheEntryPossiblyAdded(this.dispatcher),r(null),void(e.reloadCallback&&(this.loadTile(e,e.reloadCallback),e.reloadCallback=null)))}i.request.collectResourceTiming=this._collectResourceTiming,e.actor&&\"expired\"!==e.state?\"loading\"===e.state?e.reloadCallback=r:e.request=e.actor.send(\"reloadTile\",i,a.bind(this)):(e.actor=this.dispatcher.getActor(),e.request=e.actor.send(\"loadTile\",i,a.bind(this)))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.actor&&t.actor.send(\"abortTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.unloadTile=function(t){t.unloadVectorData(),t.actor&&t.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id},void 0)},r.prototype.hasTransition=function(){return!1},r}(t.Evented),E=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.dispatcher=i,this.setEventedParent(a),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=t.extend({type:\"raster\"},n),t.extend(this,t.pick(n,[\"url\",\"scheme\",\"tileSize\"]))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=A(this._options,this.map._requestManager,(function(r,n){e._tileJSONRequest=null,e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(t.extend(e,n),n.bounds&&(e.tileBounds=new M(n.bounds,e.minzoom,e.maxzoom)),t.postTurnstileEvent(n.tiles),t.postMapLoadEvent(n.tiles,e.map._getMapId(),e.map._requestManager._skuToken),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),e.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.onRemove=function(){this._tileJSONRequest&&(this._tileJSONRequest.cancel(),this._tileJSONRequest=null)},r.prototype.serialize=function(){return t.extend({},this._options)},r.prototype.hasTile=function(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)},r.prototype.loadTile=function(e,r){var n=this,i=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);e.request=t.getImage(this.map._requestManager.transformRequest(i,t.ResourceType.Tile),(function(i,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(i)e.state=\"errored\",r(i);else if(a){n.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=n.map.painter.context,s=o.gl;e.texture=n.map.painter.getTileTexture(a.width),e.texture?e.texture.update(a,{useMipmap:!0}):(e.texture=new t.Texture(o,a,s.RGBA,{useMipmap:!0}),e.texture.bind(s.LINEAR,s.CLAMP_TO_EDGE,s.LINEAR_MIPMAP_NEAREST),o.extTextureFilterAnisotropic&&s.texParameterf(s.TEXTURE_2D,o.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,o.extTextureFilterAnisotropicMax)),e.state=\"loaded\",t.cacheEntryPossiblyAdded(n.dispatcher),r(null)}}))},r.prototype.abortTile=function(t,e){t.request&&(t.request.cancel(),delete t.request),e()},r.prototype.unloadTile=function(t,e){t.texture&&this.map.painter.saveTileTexture(t.texture),e()},r.prototype.hasTransition=function(){return!1},r}(t.Evented),L=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),this.type=\"raster-dem\",this.maxzoom=22,this._options=t.extend({type:\"raster-dem\"},n),this.encoding=n.encoding||\"mapbox\"}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.serialize=function(){return{type:\"raster-dem\",url:this.url,tileSize:this.tileSize,tiles:this.tiles,bounds:this.bounds,encoding:this.encoding}},r.prototype.loadTile=function(e,r){var n=this.map._requestManager.normalizeTileURL(e.tileID.canonical.url(this.tiles,this.scheme),this.tileSize);function i(t,n){t&&(e.state=\"errored\",r(t)),n&&(e.dem=n,e.needsHillshadePrepare=!0,e.state=\"loaded\",r(null))}e.request=t.getImage(this.map._requestManager.transformRequest(n,t.ResourceType.Tile),function(n,a){if(delete e.request,e.aborted)e.state=\"unloaded\",r(null);else if(n)e.state=\"errored\",r(n);else if(a){this.map._refreshExpiredTiles&&e.setExpiryData(a),delete a.cacheControl,delete a.expires;var o=t.window.ImageBitmap&&a instanceof t.window.ImageBitmap&&t.offscreenCanvasSupported()?a:t.browser.getImageData(a,1),s={uid:e.uid,coord:e.tileID,source:this.id,rawImageData:o,encoding:this.encoding};e.actor&&\"expired\"!==e.state||(e.actor=this.dispatcher.getActor(),e.actor.send(\"loadDEMTile\",s,i.bind(this)))}}.bind(this)),e.neighboringTiles=this._getNeighboringTiles(e.tileID)},r.prototype._getNeighboringTiles=function(e){var r=e.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?e.wrap-1:e.wrap,o=(r.x+1+n)%n,s=r.x+1===n?e.wrap+1:e.wrap,l={};return l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+1<n&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y+1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y+1).key]={backfilled:!1}),l},r.prototype.unloadTile=function(t){t.demTexture&&this.map.painter.saveTileTexture(t.demTexture),t.fbo&&(t.fbo.destroy(),delete t.fbo),t.dem&&delete t.dem,delete t.neighboringTiles,t.state=\"unloaded\",t.actor&&t.actor.send(\"removeDEMTile\",{uid:t.uid,source:this.id})},r}(E),C=function(e){function r(r,n,i,a){e.call(this),this.id=r,this.type=\"geojson\",this.minzoom=0,this.maxzoom=18,this.tileSize=512,this.isTileClipped=!0,this.reparseOverscaled=!0,this._removed=!1,this._loaded=!1,this.actor=i.getActor(),this.setEventedParent(a),this._data=n.data,this._options=t.extend({},n),this._collectResourceTiming=n.collectResourceTiming,this._resourceTiming=[],void 0!==n.maxzoom&&(this.maxzoom=n.maxzoom),n.type&&(this.type=n.type),n.attribution&&(this.attribution=n.attribution),this.promoteId=n.promoteId;var o=t.EXTENT/this.tileSize;this.workerOptions=t.extend({source:this.id,cluster:n.cluster||!1,geojsonVtOptions:{buffer:(void 0!==n.buffer?n.buffer:128)*o,tolerance:(void 0!==n.tolerance?n.tolerance:.375)*o,extent:t.EXTENT,maxZoom:this.maxzoom,lineMetrics:n.lineMetrics||!1,generateId:n.generateId||!1},superclusterOptions:{maxZoom:void 0!==n.clusterMaxZoom?Math.min(n.clusterMaxZoom,this.maxzoom-1):this.maxzoom-1,extent:t.EXTENT,radius:(n.clusterRadius||50)*o,log:!1,generateId:n.generateId||!1},clusterProperties:n.clusterProperties},n.workerOptions)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(r){if(r)e.fire(new t.ErrorEvent(r));else{var n={dataType:\"source\",sourceDataType:\"metadata\"};e._collectResourceTiming&&e._resourceTiming&&e._resourceTiming.length>0&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;o<s.length;o+=1){var l=s[o];r=Math.min(r,l.x),n=Math.min(n,l.y),i=Math.max(i,l.x),a=Math.max(a,l.y)}var c=i-r,u=a-n,f=Math.max(c,u),h=Math.max(0,Math.floor(-Math.log(f)/Math.LN2)),p=Math.pow(2,h);return new t.CanonicalTileID(h,Math.floor((r+i)/2*p),Math.floor((n+a)/2*p))}(n),this.minzoom=this.maxzoom=this.tileID.z;var i=n.map((function(t){return r.tileID.getTilePoint(t)._round()}));return this._boundsArray=new t.StructArrayLayout4i8,this._boundsArray.emplaceBack(i[0].x,i[0].y,0,0),this._boundsArray.emplaceBack(i[1].x,i[1].y,t.EXTENT,0),this._boundsArray.emplaceBack(i[3].x,i[3].y,0,t.EXTENT),this._boundsArray.emplaceBack(i[2].x,i[2].y,t.EXTENT,t.EXTENT),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this},r.prototype.prepare=function(){if(0!==Object.keys(this.tiles).length&&this.image){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture||(this.texture=new t.Texture(e,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.loadTile=function(t,e){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={},e(null)):(t.state=\"errored\",e(null))},r.prototype.serialize=function(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return!1},r}(t.Evented);var O=function(e){function r(t,r,n,i){e.call(this,t,r,n,i),this.roundZoom=!0,this.type=\"video\",this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){var e=this;this._loaded=!1;var r=this.options;this.urls=[];for(var n=0,i=r.urls;n<i.length;n+=1){var a=i[n];this.urls.push(this.map._requestManager.transformRequest(a,t.ResourceType.Source).url)}t.getVideo(this.urls,(function(r,n){e._loaded=!0,r?e.fire(new t.ErrorEvent(r)):n&&(e.video=n,e.video.loop=!0,e.video.addEventListener(\"playing\",(function(){e.map.triggerRepaint()})),e.map&&e.video.play(),e._finishLoading())}))},r.prototype.pause=function(){this.video&&this.video.pause()},r.prototype.play=function(){this.video&&this.video.play()},r.prototype.seek=function(e){if(this.video){var r=this.video.seekable;e<r.start(0)||e>r.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];t<e.length;t+=1){var 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e=this.gl;e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0),this.current=t,this.dirty=!1}},e}(_t),Tt=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.set=function(t){if(t!==this.current||this.dirty){this.context.bindFramebuffer.set(this.parent);var e=this.gl;e.framebufferRenderbuffer(e.FRAMEBUFFER,e.DEPTH_ATTACHMENT,e.RENDERBUFFER,t),this.current=t,this.dirty=!1}},e}(_t),kt=function(t,e,r,n){this.context=t,this.width=e,this.height=r;var i=t.gl,a=this.framebuffer=i.createFramebuffer();this.colorAttachment=new wt(t,a),n&&(this.depthAttachment=new Tt(t,a))};kt.prototype.destroy=function(){var t=this.context.gl,e=this.colorAttachment.get();if(e&&t.deleteTexture(e),this.depthAttachment){var r=this.depthAttachment.get();r&&t.deleteRenderbuffer(r)}t.deleteFramebuffer(this.framebuffer)};var At=function(t,e,r){this.func=t,this.mask=e,this.range=r};At.ReadOnly=!1,At.ReadWrite=!0,At.disabled=new At(519,At.ReadOnly,[0,1]);var Mt=function(t,e,r,n,i,a){this.test=t,this.ref=e,this.mask=r,this.fail=n,this.depthFail=i,this.pass=a};Mt.disabled=new Mt({func:519,mask:0},0,0,7680,7680,7680);var St=function(t,e,r){this.blendFunction=t,this.blendColor=e,this.mask=r};St.disabled=new St(St.Replace=[1,0],t.Color.transparent,[!1,!1,!1,!1]),St.unblended=new St(St.Replace,t.Color.transparent,[!0,!0,!0,!0]),St.alphaBlended=new St([1,771],t.Color.transparent,[!0,!0,!0,!0]);var Et=function(t,e,r){this.enable=t,this.mode=e,this.frontFace=r};Et.disabled=new Et(!1,1029,2305),Et.backCCW=new Et(!0,1029,2305);var Lt=function(t){this.gl=t,this.extVertexArrayObject=this.gl.getExtension(\"OES_vertex_array_object\"),this.clearColor=new q(this),this.clearDepth=new G(this),this.clearStencil=new Y(this),this.colorMask=new W(this),this.depthMask=new X(this),this.stencilMask=new Z(this),this.stencilFunc=new J(this),this.stencilOp=new K(this),this.stencilTest=new Q(this),this.depthRange=new $(this),this.depthTest=new tt(this),this.depthFunc=new et(this),this.blend=new rt(this),this.blendFunc=new nt(this),this.blendColor=new it(this),this.blendEquation=new at(this),this.cullFace=new ot(this),this.cullFaceSide=new st(this),this.frontFace=new lt(this),this.program=new ct(this),this.activeTexture=new ut(this),this.viewport=new ft(this),this.bindFramebuffer=new ht(this),this.bindRenderbuffer=new pt(this),this.bindTexture=new dt(this),this.bindVertexBuffer=new mt(this),this.bindElementBuffer=new gt(this),this.bindVertexArrayOES=this.extVertexArrayObject&&new vt(this),this.pixelStoreUnpack=new yt(this),this.pixelStoreUnpackPremultiplyAlpha=new xt(this),this.pixelStoreUnpackFlipY=new bt(this),this.extTextureFilterAnisotropic=t.getExtension(\"EXT_texture_filter_anisotropic\")||t.getExtension(\"MOZ_EXT_texture_filter_anisotropic\")||t.getExtension(\"WEBKIT_EXT_texture_filter_anisotropic\"),this.extTextureFilterAnisotropic&&(this.extTextureFilterAnisotropicMax=t.getParameter(this.extTextureFilterAnisotropic.MAX_TEXTURE_MAX_ANISOTROPY_EXT)),this.extTextureHalfFloat=t.getExtension(\"OES_texture_half_float\"),this.extTextureHalfFloat&&(t.getExtension(\"OES_texture_half_float_linear\"),this.extRenderToTextureHalfFloat=t.getExtension(\"EXT_color_buffer_half_float\")),this.extTimerQuery=t.getExtension(\"EXT_disjoint_timer_query\")};Lt.prototype.setDefault=function(){this.unbindVAO(),this.clearColor.setDefault(),this.clearDepth.setDefault(),this.clearStencil.setDefault(),this.colorMask.setDefault(),this.depthMask.setDefault(),this.stencilMask.setDefault(),this.stencilFunc.setDefault(),this.stencilOp.setDefault(),this.stencilTest.setDefault(),this.depthRange.setDefault(),this.depthTest.setDefault(),this.depthFunc.setDefault(),this.blend.setDefault(),this.blendFunc.setDefault(),this.blendColor.setDefault(),this.blendEquation.setDefault(),this.cullFace.setDefault(),this.cullFaceSide.setDefault(),this.frontFace.setDefault(),this.program.setDefault(),this.activeTexture.setDefault(),this.bindFramebuffer.setDefault(),this.pixelStoreUnpack.setDefault(),this.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.pixelStoreUnpackFlipY.setDefault()},Lt.prototype.setDirty=function(){this.clearColor.dirty=!0,this.clearDepth.dirty=!0,this.clearStencil.dirty=!0,this.colorMask.dirty=!0,this.depthMask.dirty=!0,this.stencilMask.dirty=!0,this.stencilFunc.dirty=!0,this.stencilOp.dirty=!0,this.stencilTest.dirty=!0,this.depthRange.dirty=!0,this.depthTest.dirty=!0,this.depthFunc.dirty=!0,this.blend.dirty=!0,this.blendFunc.dirty=!0,this.blendColor.dirty=!0,this.blendEquation.dirty=!0,this.cullFace.dirty=!0,this.cullFaceSide.dirty=!0,this.frontFace.dirty=!0,this.program.dirty=!0,this.activeTexture.dirty=!0,this.viewport.dirty=!0,this.bindFramebuffer.dirty=!0,this.bindRenderbuffer.dirty=!0,this.bindTexture.dirty=!0,this.bindVertexBuffer.dirty=!0,this.bindElementBuffer.dirty=!0,this.extVertexArrayObject&&(this.bindVertexArrayOES.dirty=!0),this.pixelStoreUnpack.dirty=!0,this.pixelStoreUnpackPremultiplyAlpha.dirty=!0,this.pixelStoreUnpackFlipY.dirty=!0},Lt.prototype.createIndexBuffer=function(t,e){return new j(this,t,e)},Lt.prototype.createVertexBuffer=function(t,e,r){return new V(this,t,e,r)},Lt.prototype.createRenderbuffer=function(t,e,r){var n=this.gl,i=n.createRenderbuffer();return this.bindRenderbuffer.set(i),n.renderbufferStorage(n.RENDERBUFFER,t,e,r),this.bindRenderbuffer.set(null),i},Lt.prototype.createFramebuffer=function(t,e,r){return new kt(this,t,e,r)},Lt.prototype.clear=function(t){var e=t.color,r=t.depth,n=this.gl,i=0;e&&(i|=n.COLOR_BUFFER_BIT,this.clearColor.set(e),this.colorMask.set([!0,!0,!0,!0])),void 0!==r&&(i|=n.DEPTH_BUFFER_BIT,this.depthRange.set([0,1]),this.clearDepth.set(r),this.depthMask.set(!0)),n.clear(i)},Lt.prototype.setCullFace=function(t){!1===t.enable?this.cullFace.set(!1):(this.cullFace.set(!0),this.cullFaceSide.set(t.mode),this.frontFace.set(t.frontFace))},Lt.prototype.setDepthMode=function(t){t.func!==this.gl.ALWAYS||t.mask?(this.depthTest.set(!0),this.depthFunc.set(t.func),this.depthMask.set(t.mask),this.depthRange.set(t.range)):this.depthTest.set(!1)},Lt.prototype.setStencilMode=function(t){t.test.func!==this.gl.ALWAYS||t.mask?(this.stencilTest.set(!0),this.stencilMask.set(t.mask),this.stencilOp.set([t.fail,t.depthFail,t.pass]),this.stencilFunc.set({func:t.test.func,ref:t.ref,mask:t.test.mask})):this.stencilTest.set(!1)},Lt.prototype.setColorMode=function(e){t.deepEqual(e.blendFunction,St.Replace)?this.blend.set(!1):(this.blend.set(!0),this.blendFunc.set(e.blendFunction),this.blendColor.set(e.blendColor)),this.colorMask.set(e.mask)},Lt.prototype.unbindVAO=function(){this.extVertexArrayObject&&this.bindVertexArrayOES.set(null)};var Ct=function(e){function r(r,n,i){var a=this;e.call(this),this.id=r,this.dispatcher=i,this.on(\"data\",(function(t){\"source\"===t.dataType&&\"metadata\"===t.sourceDataType&&(a._sourceLoaded=!0),a._sourceLoaded&&!a._paused&&\"source\"===t.dataType&&\"content\"===t.sourceDataType&&(a.reload(),a.transform&&a.update(a.transform))})),this.on(\"error\",(function(){a._sourceErrored=!0})),this._source=function(e,r,n,i){var a=new D[r.type](e,r,n,i);if(a.id!==e)throw new Error(\"Expected Source id to be \"+e+\" instead of \"+a.id);return t.bindAll([\"load\",\"abort\",\"unload\",\"serialize\",\"prepare\"],a),a}(r,n,i,this),this._tiles={},this._cache=new N(0,this._unloadTile.bind(this)),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new t.SourceFeatureState}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.onAdd=function(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._source&&this._source.onAdd&&this._source.onAdd(t)},r.prototype.onRemove=function(t){this._source&&this._source.onRemove&&this._source.onRemove(t)},r.prototype.loaded=function(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;for(var t in this._tiles){var e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0},r.prototype.getSource=function(){return this._source},r.prototype.pause=function(){this._paused=!0},r.prototype.resume=function(){if(this._paused){var t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform)}},r.prototype._loadTile=function(t,e){return this._source.loadTile(t,e)},r.prototype._unloadTile=function(t){if(this._source.unloadTile)return this._source.unloadTile(t,(function(){}))},r.prototype._abortTile=function(t){if(this._source.abortTile)return this._source.abortTile(t,(function(){}))},r.prototype.serialize=function(){return this._source.serialize()},r.prototype.prepare=function(t){for(var e in this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null),this._tiles){var r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}},r.prototype.getIds=function(){return t.values(this._tiles).map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.getRenderableIds=function(e){var r=this,n=[];for(var i in this._tiles)this._isIdRenderable(i,e)&&n.push(this._tiles[i]);return e?n.sort((function(e,n){var i=e.tileID,a=n.tileID,o=new t.Point(i.canonical.x,i.canonical.y)._rotate(r.transform.angle),s=new t.Point(a.canonical.x,a.canonical.y)._rotate(r.transform.angle);return i.overscaledZ-a.overscaledZ||s.y-o.y||s.x-o.x})).map((function(t){return t.tileID.key})):n.map((function(t){return t.tileID})).sort(Pt).map((function(t){return t.key}))},r.prototype.hasRenderableParent=function(t){var e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)},r.prototype._isIdRenderable=function(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())},r.prototype.reload=function(){if(this._paused)this._shouldReloadOnResume=!0;else for(var t in this._cache.reset(),this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")},r.prototype._reloadTile=function(t,e){var r=this._tiles[t];r&&(\"loading\"!==r.state&&(r.state=e),this._loadTile(r,this._tileLoaded.bind(this,r,t,e)))},r.prototype._tileLoaded=function(e,r,n,i){if(i)return e.state=\"errored\",void(404!==i.status?this._source.fire(new t.ErrorEvent(i,{tile:e})):this.update(this.transform));e.timeAdded=t.browser.now(),\"expired\"===n&&(e.refreshedUponExpiration=!0),this._setTileReloadTimer(r,e),\"raster-dem\"===this.getSource().type&&e.dem&&this._backfillDEM(e),this._state.initializeTileState(e,this.map?this.map.painter:null),this._source.fire(new t.Event(\"data\",{dataType:\"source\",tile:e,coord:e.tileID}))},r.prototype._backfillDEM=function(t){for(var e=this.getRenderableIds(),r=0;r<e.length;r++){var n=e[r];if(t.neighboringTiles&&t.neighboringTiles[n]){var i=this.getTileByID(n);a(t,i),a(i,t)}}function a(t,e){t.needsHillshadePrepare=!0;var r=e.tileID.canonical.x-t.tileID.canonical.x,n=e.tileID.canonical.y-t.tileID.canonical.y,i=Math.pow(2,t.tileID.canonical.z),a=e.tileID.key;0===r&&0===n||Math.abs(n)>1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(It(this._source.type)){for(var c={},u={},f=0,h=Object.keys(l);f<h.length;f+=1){var p=h[f],d=l[p],m=this._tiles[p];if(m&&!(m.fadeEndTime&&m.fadeEndTime<=t.browser.now())){var g=this.findLoadedParent(d,o);g&&(this._addTile(g.tileID),c[g.tileID.key]=g.tileID),u[p]=d}}for(var v in this._retainLoadedChildren(u,a,s,l),c)l[v]||(this._coveredTiles[v]=!0,l[v]=c[v])}for(var y in l)this._tiles[y].clearFadeHold();for(var x=0,b=t.keysDifference(this._tiles,l);x<b.length;x+=1){var _=b[x],w=this._tiles[_];w.hasSymbolBuckets&&!w.holdingForFade()?w.setHoldDuration(this.map._fadeDuration):w.hasSymbolBuckets&&!w.symbolFadeFinished()||this._removeTile(_)}this._updateLoadedParentTileCache()}},r.prototype.releaseSymbolFadeTiles=function(){for(var t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)},r.prototype._updateRetainedTiles=function(t,e){for(var n={},i={},a=Math.max(e-r.maxOverzooming,this._source.minzoom),o=Math.max(e+r.maxUnderzooming,this._source.minzoom),s={},l=0,c=t;l<c.length;l+=1){var u=c[l],f=this._addTile(u);n[u.key]=u,f.hasData()||e<this._source.maxzoom&&(s[u.key]=u)}this._retainLoadedChildren(s,e,o,n);for(var h=0,p=t;h<p.length;h+=1){var d=p[h],m=this._tiles[d.key];if(!m.hasData()){if(e+1>this._source.maxzoom){var g=d.children(this._source.maxzoom)[0],v=this.getTile(g);if(v&&v.hasData()){n[g.key]=g;continue}}else{var y=d.children(this._source.maxzoom);if(n[y[0].key]&&n[y[1].key]&&n[y[2].key]&&n[y[3].key])continue}for(var x=m.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(m=this.getTile(_))&&x&&(m=this._addTile(_)),m&&(n[_.key]=_,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a<o.length;a+=1){var s=o[a];this._loadedParentTiles[s]=r}}},r.prototype._addTile=function(e){var r=this._tiles[e.key];if(r)return r;(r=this._cache.getAndRemove(e))&&(this._setTileReloadTimer(e.key,r),r.tileID=e,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[e.key]&&(clearTimeout(this._cacheTimers[e.key]),delete this._cacheTimers[e.key],this._setTileReloadTimer(e.key,r)));var n=Boolean(r);return n||(r=new t.Tile(e,this._source.tileSize*e.overscaleFactor()),this._loadTile(r,this._tileLoaded.bind(this,r,e.key,r.state))),r?(r.uses++,this._tiles[e.key]=r,n||this._source.fire(new t.Event(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r):null},r.prototype._setTileReloadTimer=function(t,e){var r=this;t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);var n=e.getExpiryTimeout();n&&(this._timers[t]=setTimeout((function(){r._reloadTile(t,\"expired\"),delete r._timers[t]}),n))},r.prototype._removeTile=function(t){var e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,m=0,g=c;m<g.length;m+=1){var v=g[m];f=Math.min(f,v.x),h=Math.min(h,v.y),p=Math.max(p,v.x),d=Math.max(d,v.y)}for(var y=function(e){var n=i._tiles[u[e]];if(!n.holdingForFade()){var s=n.tileID,m=Math.pow(2,o.zoom-n.tileID.overscaledZ),g=r*n.queryPadding*t.EXTENT/n.tileSize/m,v=[s.getTilePoint(new t.MercatorCoordinate(f,h)),s.getTilePoint(new t.MercatorCoordinate(p,d))];if(v[0].x-g<t.EXTENT&&v[0].y-g<t.EXTENT&&v[1].x+g>=0&&v[1].y+g>=0){var y=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:y,cameraQueryGeometry:x,scale:m})}}},x=0;x<u.length;x++)y(x);return a},r.prototype.getVisibleCoordinates=function(t){for(var e=this,r=this.getRenderableIds(t).map((function(t){return e._tiles[t].tileID})),n=0,i=r;n<i.length;n+=1){var a=i[n];a.posMatrix=this.transform.calculatePosMatrix(a.toUnwrapped())}return r},r.prototype.hasTransition=function(){if(this._source.hasTransition())return!0;if(It(this._source.type))for(var e in this._tiles){var r=this._tiles[e];if(void 0!==r.fadeEndTime&&r.fadeEndTime>=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles){this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\")}this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function Pt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function It(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Ot(){return new t.window.Worker(Zi.workerUrl)}Ct.maxOverzooming=10,Ct.maxUnderzooming=3;var zt=\"mapboxgl_preloaded_worker_pool\",Dt=function(){this.active={}};Dt.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length<Dt.workerCount;)this.workers.push(new Ot);return this.active[t]=!0,this.workers.slice()},Dt.prototype.release=function(t){delete this.active[t],0===this.numActive()&&(this.workers.forEach((function(t){t.terminate()})),this.workers=null)},Dt.prototype.isPreloaded=function(){return!!this.active[zt]},Dt.prototype.numActive=function(){return Object.keys(this.active).length};var Rt,Ft=Math.floor(t.browser.hardwareConcurrency/2);function Bt(){return Rt||(Rt=new Dt),Rt}function Nt(e,r){var n={};for(var i in e)\"ref\"!==i&&(n[i]=e[i]);return t.refProperties.forEach((function(t){t in r&&(n[t]=r[t])})),n}function jt(t){t=t.slice();for(var e=Object.create(null),r=0;r<t.length;r++)e[t[r].id]=t[r];for(var n=0;n<t.length;n++)\"ref\"in t[n]&&(t[n]=Nt(t[n],e[t[n].ref]));return t}Dt.workerCount=Math.max(Math.min(Ft,6),1);var Ut={setStyle:\"setStyle\",addLayer:\"addLayer\",removeLayer:\"removeLayer\",setPaintProperty:\"setPaintProperty\",setLayoutProperty:\"setLayoutProperty\",setFilter:\"setFilter\",addSource:\"addSource\",removeSource:\"removeSource\",setGeoJSONSourceData:\"setGeoJSONSourceData\",setLayerZoomRange:\"setLayerZoomRange\",setLayerProperty:\"setLayerProperty\",setCenter:\"setCenter\",setZoom:\"setZoom\",setBearing:\"setBearing\",setPitch:\"setPitch\",setSprite:\"setSprite\",setGlyphs:\"setGlyphs\",setTransition:\"setTransition\",setLight:\"setLight\"};function Vt(t,e,r){r.push({command:Ut.addSource,args:[t,e[t]]})}function Ht(t,e,r){e.push({command:Ut.removeSource,args:[t]}),r[t]=!0}function qt(t,e,r,n){Ht(t,r,n),Vt(t,e,r)}function Gt(e,r,n){var i;for(i in e[n])if(e[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;for(i in r[n])if(r[n].hasOwnProperty(i)&&\"data\"!==i&&!t.deepEqual(e[n][i],r[n][i]))return!1;return!0}function Yt(e,r,n,i,a,o){var s;for(s in r=r||{},e=e||{})e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}));for(s in r)r.hasOwnProperty(s)&&!e.hasOwnProperty(s)&&(t.deepEqual(e[s],r[s])||n.push({command:o,args:[i,s,r[s],a]}))}function Wt(t){return t.id}function Xt(t,e){return t[e.id]=e,t}function Zt(e,r){if(!e)return[{command:Ut.setStyle,args:[r]}];var n=[];try{if(!t.deepEqual(e.version,r.version))return[{command:Ut.setStyle,args:[r]}];t.deepEqual(e.center,r.center)||n.push({command:Ut.setCenter,args:[r.center]}),t.deepEqual(e.zoom,r.zoom)||n.push({command:Ut.setZoom,args:[r.zoom]}),t.deepEqual(e.bearing,r.bearing)||n.push({command:Ut.setBearing,args:[r.bearing]}),t.deepEqual(e.pitch,r.pitch)||n.push({command:Ut.setPitch,args:[r.pitch]}),t.deepEqual(e.sprite,r.sprite)||n.push({command:Ut.setSprite,args:[r.sprite]}),t.deepEqual(e.glyphs,r.glyphs)||n.push({command:Ut.setGlyphs,args:[r.glyphs]}),t.deepEqual(e.transition,r.transition)||n.push({command:Ut.setTransition,args:[r.transition]}),t.deepEqual(e.light,r.light)||n.push({command:Ut.setLight,args:[r.light]});var i={},a=[];!function(e,r,n,i){var a;for(a in r=r||{},e=e||{})e.hasOwnProperty(a)&&(r.hasOwnProperty(a)||Ht(a,n,i));for(a in r)r.hasOwnProperty(a)&&(e.hasOwnProperty(a)?t.deepEqual(e[a],r[a])||(\"geojson\"===e[a].type&&\"geojson\"===r[a].type&&Gt(e,r,a)?n.push({command:Ut.setGeoJSONSourceData,args:[a,r[a].data]}):qt(a,r,n,i)):Vt(a,r,n))}(e.sources,r.sources,a,i);var o=[];e.layers&&e.layers.forEach((function(t){i[t.source]?n.push({command:Ut.removeLayer,args:[t.id]}):o.push(t)})),n=n.concat(a),function(e,r,n){r=r||[];var i,a,o,s,l,c,u,f=(e=e||[]).map(Wt),h=r.map(Wt),p=e.reduce(Xt,{}),d=r.reduce(Xt,{}),m=f.slice(),g=Object.create(null);for(i=0,a=0;i<f.length;i++)o=f[i],d.hasOwnProperty(o)?a++:(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.indexOf(o,a),1));for(i=0,a=0;i<h.length;i++)o=h[h.length-1-i],m[m.length-1-i]!==o&&(p.hasOwnProperty(o)?(n.push({command:Ut.removeLayer,args:[o]}),m.splice(m.lastIndexOf(o,m.length-a),1)):a++,c=m[m.length-i],n.push({command:Ut.addLayer,args:[d[o],c]}),m.splice(m.length-i,0,o),g[o]=!0);for(i=0;i<h.length;i++)if(s=p[o=h[i]],l=d[o],!g[o]&&!t.deepEqual(s,l))if(t.deepEqual(s.source,l.source)&&t.deepEqual(s[\"source-layer\"],l[\"source-layer\"])&&t.deepEqual(s.type,l.type)){for(u in Yt(s.layout,l.layout,n,o,null,Ut.setLayoutProperty),Yt(s.paint,l.paint,n,o,null,Ut.setPaintProperty),t.deepEqual(s.filter,l.filter)||n.push({command:Ut.setFilter,args:[o,l.filter]}),t.deepEqual(s.minzoom,l.minzoom)&&t.deepEqual(s.maxzoom,l.maxzoom)||n.push({command:Ut.setLayerZoomRange,args:[o,l.minzoom,l.maxzoom]}),s)s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}));for(u in l)l.hasOwnProperty(u)&&!s.hasOwnProperty(u)&&\"layout\"!==u&&\"paint\"!==u&&\"filter\"!==u&&\"metadata\"!==u&&\"minzoom\"!==u&&\"maxzoom\"!==u&&(0===u.indexOf(\"paint.\")?Yt(s[u],l[u],n,o,u.slice(6),Ut.setPaintProperty):t.deepEqual(s[u],l[u])||n.push({command:Ut.setLayerProperty,args:[o,u,l[u]]}))}else n.push({command:Ut.removeLayer,args:[o]}),c=m[m.lastIndexOf(o)+1],n.push({command:Ut.addLayer,args:[l,c]})}(o,r.layers,n)}catch(t){console.warn(\"Unable to compute style diff:\",t),n=[{command:Ut.setStyle,args:[r]}]}return n}var Jt=function(t,e){this.reset(t,e)};Jt.prototype.reset=function(t,e){this.points=t||[],this._distances=[0];for(var r=1;r<this.points.length;r++)this._distances[r]=this._distances[r-1]+this.points[r].dist(this.points[r-1]);this.length=this._distances[this._distances.length-1],this.padding=Math.min(e||0,.5*this.length),this.paddedLength=this.length-2*this.padding},Jt.prototype.lerp=function(e){if(1===this.points.length)return this.points[0];e=t.clamp(e,0,1);for(var r=1,n=this._distances[r],i=e*this.paddedLength+this.padding;n<i&&r<this._distances.length;)n=this._distances[++r];var a=r-1,o=this._distances[a],s=n-o,l=s>0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Kt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a<this.xCellCount*this.yCellCount;a++)n.push([]),i.push([]);this.circleKeys=[],this.boxKeys=[],this.bboxes=[],this.circles=[],this.width=t,this.height=e,this.xScale=this.xCellCount/t,this.yScale=this.yCellCount/e,this.boxUid=0,this.circleUid=0};function Qt(e,r,n,i,a){var o=t.create();return r?(t.scale(o,o,[1/a,1/a,1]),n||t.rotateZ(o,o,i.angle)):t.multiply(o,i.labelPlaneMatrix,e),o}function $t(e,r,n,i,a){if(r){var o=t.clone(e);return t.scale(o,o,[a,a,1]),n||t.rotateZ(o,o,-i.angle),o}return i.glCoordMatrix}function te(e,r){var n=[e.x,e.y,0,1];fe(n,n,r);var i=n[3];return{point:new t.Point(n[0]/i,n[1]/i),signedDistanceFromCamera:i}}function ee(t,e){return.5+t/e*.5}function re(t,e){var r=t[0]/t[3],n=t[1]/t[3];return r>=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ne(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,v=0;v<d.length;v++){var y=d.get(v);if(y.hidden||y.writingMode===t.WritingMode.vertical&&!g)ue(y.numGlyphs,h);else{g=!1;var x=[y.anchorX,y.anchorY,0,1];if(t.transformMat4(x,x,r),re(x,f)){var b=x[3],_=ee(n.transform.cameraToCenterDistance,b),w=t.evaluateSizeForFeature(c,u,y),T=s?w/_:w*_,k=new t.Point(y.anchorX,y.anchorY),A=te(k,a).point,M={},S=oe(y,T,!1,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m);g=S.useVertical,(S.notEnoughRoom||g||S.needsFlipping&&oe(y,T,!0,l,r,a,o,e.glyphOffsetArray,p,h,A,k,M,m).notEnoughRoom)&&ue(y.numGlyphs,h)}else ue(y.numGlyphs,h)}}i?e.text.dynamicLayoutVertexBuffer.updateData(h):e.icon.dynamicLayoutVertexBuffer.updateData(h)}function ie(t,e,r,n,i,a,o,s,l,c,u){var f=s.glyphStartIndex+s.numGlyphs,h=s.lineStartIndex,p=s.lineStartIndex+s.lineLength,d=e.getoffsetX(s.glyphStartIndex),m=e.getoffsetX(f-1),g=le(t*d,r,n,i,a,o,s.segment,h,p,l,c,u);if(!g)return null;var v=le(t*m,r,n,i,a,o,s.segment,h,p,l,c,u);return v?{first:g,last:v}:null}function ae(e,r,n,i){if(e===t.WritingMode.horizontal&&Math.abs(n.y-r.y)>Math.abs(n.x-r.x)*i)return{useVertical:!0};return(e===t.WritingMode.vertical?r.y<n.y:r.x>n.x)?{needsFlipping:!0}:null}function oe(e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=r/24,v=e.lineOffsetX*g,y=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=ie(g,l,v,y,n,f,h,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=te(w.first.point,s).point,k=te(w.last.point,s).point;if(i&&!n){var A=ae(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M<x-1;M++)m.push(le(g*l.getoffsetX(M),v,y,n,f,h,e.segment,b,_,c,o,p));m.push(w.last)}else{if(i&&!n){var S=te(h,a).point,E=e.lineStartIndex+e.segment+1,L=new t.Point(c.getx(E),c.gety(E)),C=te(L,a),P=C.signedDistanceFromCamera>0?C.point:se(h,L,S,1,a),I=ae(e.writingMode,S,P,d);if(I)return I}var O=le(g*l.getoffsetX(e.glyphStartIndex),v,y,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!O)return{notEnoughRoom:!0};m=[O]}for(var z=0,D=m;z<D.length;z+=1){var R=D[z];t.addDynamicAttributes(u,R.point,R.angle)}return{}}function se(t,e,r,n,i){var a=te(t.add(t.sub(e)._unit()),i).point,o=r.sub(a);return r.add(o._mult(n/o.mag()))}function le(e,r,n,i,a,o,s,l,c,u,f,h){var p=i?e-r:e+r,d=p>0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,v=a,y=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)<l||g>=c)return null;if(y=v,w.push(v),void 0===(v=h[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=te(T,f);if(k.signedDistanceFromCamera>0)v=h[g]=k.point;else{var A=g-d;v=se(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,y,_-x+1,f)}}x+=b,b=y.dist(v)}var M=(_-x)/b,S=v.sub(y),E=S.mult(M)._add(y);E._add(S._unit()._perp()._mult(n*d));var L=m+Math.atan2(v.y-y.y,v.x-y.x);return w.push(E),{point:E,angle:L,path:w}}Kt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Kt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Kt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Kt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Kt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Kt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s<this.boxKeys.length;s++)o.push({key:this.boxKeys[s],x1:this.bboxes[4*s],y1:this.bboxes[4*s+1],x2:this.bboxes[4*s+2],y2:this.bboxes[4*s+3]});for(var l=0;l<this.circleKeys.length;l++){var c=this.circles[3*l],u=this.circles[3*l+1],f=this.circles[3*l+2];o.push({key:this.circleKeys[l],x1:c-f,y1:u-f,x2:c+f,y2:u+f})}return a?o.filter(a):o}var h={hitTest:i,seenUids:{box:{},circle:{}}};return this._forEachCell(t,e,r,n,this._queryCell,o,h,a),i?o.length>0:o},Kt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Kt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Kt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Kt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Kt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,f=0,h=c;f<h.length;f+=1){var p=h[f];if(!l.box[p]){l.box[p]=!0;var d=4*p;if(t<=u[d+2]&&e<=u[d+3]&&r>=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,v=0,y=m;v<y.length;v+=1){var x=y[v];if(!l.circle[x]){l.circle[x]=!0;var b=3*x;if(this._circleAndRectCollide(g[b],g[b+1],g[b+2],t,e,r,n)&&(!s||s(this.circleKeys[x]))){if(o.hitTest)return a.push(!0),!0;var _=g[b],w=g[b+1],T=g[b+2];a.push({key:this.circleKeys[x],x1:_-T,y1:w-T,x2:_+T,y2:w+T})}}}},Kt.prototype._queryCellCircle=function(t,e,r,n,i,a,o,s){var l=o.circle,c=o.seenUids,u=this.boxCells[i];if(null!==u)for(var f=this.bboxes,h=0,p=u;h<p.length;h+=1){var d=p[h];if(!c.box[d]){c.box[d]=!0;var m=4*d;if(this._circleAndRectCollide(l.x,l.y,l.radius,f[m+0],f[m+1],f[m+2],f[m+3])&&(!s||s(this.boxKeys[d])))return a.push(!0),!0}}var g=this.circleCells[i];if(null!==g)for(var v=this.circles,y=0,x=g;y<x.length;y+=1){var b=x[y];if(!c.circle[b]){c.circle[b]=!0;var _=3*b;if(this._circlesCollide(v[_],v[_+1],v[_+2],l.x,l.y,l.radius)&&(!s||s(this.circleKeys[b])))return a.push(!0),!0}}},Kt.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),f=this._convertToYCellCoord(n),h=l;h<=u;h++)for(var p=c;p<=f;p++){var d=this.xCellCount*p+h;if(i.call(this,t,e,r,n,d,a,o,s))return}},Kt.prototype._convertToXCellCoord=function(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))},Kt.prototype._convertToYCellCoord=function(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))},Kt.prototype._circlesCollide=function(t,e,r,n,i,a){var o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s},Kt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var f=l-s,h=u-c;return f*f+h*h<=r*r};var ce=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function ue(t,e){for(var r=0;r<t;r++){var n=e.length;e.resize(n+4),e.float32.set(ce,3*n)}}function fe(t,e,r){var n=e[0],i=e[1];return t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15],t}var he=function(t,e,r){void 0===e&&(e=new Kt(t.width+200,t.height+200,25)),void 0===r&&(r=new Kt(t.width+200,t.height+200,25)),this.transform=t,this.grid=e,this.ignoredGrid=r,this.pitchfactor=Math.cos(t._pitch)*t.cameraToCenterDistance,this.screenRightBoundary=t.width+100,this.screenBottomBoundary=t.height+100,this.gridRightBoundary=t.width+200,this.gridBottomBoundary=t.height+200};function pe(e,r,n){return r*(t.EXTENT/(e.tileSize*Math.pow(2,n-e.tileID.overscaledZ)))}he.prototype.placeCollisionBox=function(t,e,r,n,i){var a=this.projectAndGetPerspectiveRatio(n,t.anchorPointX,t.anchorPointY),o=r*a.perspectiveRatio,s=t.x1*o+a.point.x,l=t.y1*o+a.point.y,c=t.x2*o+a.point.x,u=t.y2*o+a.point.y;return!this.isInsideGrid(s,l,c,u)||!e&&this.grid.hitTest(s,l,c,u,i)?{box:[],offscreen:!1}:{box:[s,l,c,u],offscreen:this.isOffscreen(s,l,c,u)}},he.prototype.placeCollisionCircles=function(e,r,n,i,a,o,s,l,c,u,f,h,p){var d=[],m=new t.Point(r.anchorX,r.anchorY),g=te(m,o),v=ee(this.transform.cameraToCenterDistance,g.signedDistanceFromCamera),y=(u?a/v:a*v)/t.ONE_EM,x=te(m,s).point,b=ie(y,i,r.lineOffsetX*y,r.lineOffsetY*y,!1,x,m,r,n,s,{}),_=!1,w=!1,T=!0;if(b){for(var k=.5*h*v+p,A=new t.Point(-100,-100),M=new t.Point(this.screenRightBoundary,this.screenBottomBoundary),S=new Jt,E=b.first,L=b.last,C=[],P=E.path.length-1;P>=1;P--)C.push(E.path[P]);for(var I=1;I<L.path.length;I++)C.push(L.path[I]);var O=2.5*k;if(l){var z=C.map((function(t){return te(t,l)}));C=z.some((function(t){return t.signedDistanceFromCamera<=0}))?[]:z.map((function(t){return t.point}))}var D=[];if(C.length>0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B<C.length;B++)R.x=Math.min(R.x,C[B].x),R.y=Math.min(R.y,C[B].y),F.x=Math.max(F.x,C[B].x),F.y=Math.max(F.y,C[B].y);D=R.x>=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.x<A.x||R.x>M.x||F.y<A.y||R.y>M.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N<j.length;N+=1){var U=j[N];S.reset(U,.25*k);var V=0;V=S.length<=.5*k?1:Math.ceil(S.paddedLength/O)+1;for(var H=0;H<V;H++){var q=H/Math.max(V-1,1),G=S.lerp(q),Y=G.x+100,W=G.y+100;d.push(Y,W,k,0);var X=Y-k,Z=W-k,J=Y+k,K=W+k;if(T=T&&this.isOffscreen(X,Z,J,K),w=w||this.isInsideGrid(X,Z,J,K),!e&&this.grid.hitTestCircle(Y,W,k,f)&&(_=!0,!c))return{circles:[],offscreen:!1,collisionDetected:_}}}}return{circles:!c&&_||!w?[]:d,offscreen:T,collisionDetected:_}},he.prototype.queryRenderedSymbols=function(e){if(0===e.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};for(var r=[],n=1/0,i=1/0,a=-1/0,o=-1/0,s=0,l=e;s<l.length;s+=1){var c=l[s],u=new t.Point(c.x+100,c.y+100);n=Math.min(n,u.x),i=Math.min(i,u.y),a=Math.max(a,u.x),o=Math.max(o,u.y),r.push(u)}for(var f={},h={},p=0,d=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o));p<d.length;p+=1){var m=d[p],g=m.key;if(void 0===f[g.bucketInstanceId]&&(f[g.bucketInstanceId]={}),!f[g.bucketInstanceId][g.featureIndex]){var v=[new t.Point(m.x1,m.y1),new t.Point(m.x2,m.y1),new t.Point(m.x2,m.y2),new t.Point(m.x1,m.y2)];t.polygonIntersectsPolygon(r,v)&&(f[g.bucketInstanceId][g.featureIndex]=!0,void 0===h[g.bucketInstanceId]&&(h[g.bucketInstanceId]=[]),h[g.bucketInstanceId].push(g.featureIndex))}}return h},he.prototype.insertCollisionBox=function(t,e,r,n,i){var a={bucketInstanceId:r,featureIndex:n,collisionGroupID:i};(e?this.ignoredGrid:this.grid).insert(a,t[0],t[1],t[2],t[3])},he.prototype.insertCollisionCircles=function(t,e,r,n,i){for(var a=e?this.ignoredGrid:this.grid,o={bucketInstanceId:r,featureIndex:n,collisionGroupID:i},s=0;s<t.length;s+=4)a.insertCircle(o,t[s],t[s+1],t[s+2])},he.prototype.projectAndGetPerspectiveRatio=function(e,r,n){var i=[r,n,0,1];return fe(i,i,e),{point:new t.Point((i[0]/i[3]+1)/2*this.transform.width+100,(-i[1]/i[3]+1)/2*this.transform.height+100),perspectiveRatio:.5+this.transform.cameraToCenterDistance/i[3]*.5}},he.prototype.isOffscreen=function(t,e,r,n){return r<100||t>=this.screenRightBoundary||n<100||e>this.screenBottomBoundary},he.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t<this.gridRightBoundary&&n>=0&&e<this.gridBottomBoundary},he.prototype.getViewportMatrix=function(){var e=t.identity([]);return t.translate(e,e,[-100,-100,0]),e};var de=function(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r};de.prototype.isHidden=function(){return 0===this.opacity&&!this.placed};var me=function(t,e,r,n,i){this.text=new de(t?t.text:null,e,r,i),this.icon=new de(t?t.icon:null,e,n,i)};me.prototype.isHidden=function(){return this.text.isHidden()&&this.icon.isHidden()};var ge=function(t,e,r){this.text=t,this.icon=e,this.skipFade=r},ve=function(){this.invProjMatrix=t.create(),this.viewportMatrix=t.create(),this.circles=[]},ye=function(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i},xe=function(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}};function be(e,r,n,i,a){var o=t.getAnchorAlignment(e),s=-(o.horizontalAlign-.5)*r,l=-(o.verticalAlign-.5)*n,c=t.evaluateVariableOffset(e,i);return new t.Point(s+c[0]*a,l+c[1]*a)}function _e(e,r,n,i,a,o){var s=e.x1,l=e.x2,c=e.y1,u=e.y2,f=e.anchorPointX,h=e.anchorPointY,p=new t.Point(r,n);return i&&p._rotate(a?o:-o),{x1:s+p.x,y1:c+p.y,x2:l+p.x,y2:u+p.y,anchorPointX:f,anchorPointY:h}}xe.prototype.get=function(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){var e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:function(t){return t.collisionGroupID===e}}}return this.collisionGroups[t]};var we=function(t,e,r,n){this.transform=t.clone(),this.collisionIndex=new he(this.transform),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=e,this.retainedQueryData={},this.collisionGroups=new xe(r),this.collisionCircleArrays={},this.prevPlacement=n,n&&(n.prevPlacement=void 0),this.placedOrientations={}};function Te(t,e,r,n,i){t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0),t.emplaceBack(e?1:0,r?1:0,n||0,i||0)}we.prototype.getBucketParts=function(e,r,n,i){var a=n.getBucket(r),o=n.latestFeatureIndex;if(a&&o&&r.id===a.layerIds[0]){var s=n.collisionBoxArray,l=a.layers[0].layout,c=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),u=n.tileSize/t.EXTENT,f=this.transform.calculatePosMatrix(n.tileID.toUnwrapped()),h=\"map\"===l.get(\"text-pitch-alignment\"),p=\"map\"===l.get(\"text-rotation-alignment\"),d=pe(n,1,this.transform.zoom),m=Qt(f,h,p,this.transform,d),g=null;if(h){var v=$t(f,h,p,this.transform,d);g=t.multiply([],this.transform.labelPlaneMatrix,v)}this.retainedQueryData[a.bucketInstanceId]=new ye(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);var y={bucket:a,layout:l,posMatrix:f,textLabelPlaneMatrix:m,labelToScreenMatrix:g,scale:c,textPixelRatio:u,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:t.evaluateSizeForZoom(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(var x=0,b=a.sortKeyRanges;x<b.length;x+=1){var _=b[x],w=_.sortKey,T=_.symbolInstanceStart,k=_.symbolInstanceEnd;e.push({sortKey:w,symbolInstanceStart:T,symbolInstanceEnd:k,parameters:y})}else e.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:y})}},we.prototype.attemptAnchorPlacement=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d){var m,g=[f.textOffset0,f.textOffset1],v=be(t,r,n,g,i),y=this.collisionIndex.placeCollisionBox(_e(e,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate);if(d&&0===this.collisionIndex.placeCollisionBox(_e(d,v.x,v.y,a,o,this.transform.angle),u,s,l,c.predicate).box.length)return;if(y.box.length>0)return this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(m=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:v,placedGlyphBoxes:y}},we.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),v=s.get(\"icon-optional\"),y=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=y&&(x||!o.hasIconData()||v),A=x&&(y||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new ge(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,I=null,O=0,z=0,D=0;a.textFeatureIndex?O=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(O=e.featureIndex),a.verticalTextFeatureIndex&&(z=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i<s.length;i+=1){if(s[i]===t.WritingMode.vertical?(L=n(),C=L):L=r(),L&&L.box&&L.box.length)break}else L=r()};if(s.get(\"text-variable-anchor\")){var N=s.get(\"text-variable-anchor\");if(i.prevPlacement&&i.prevPlacement.variableOffsets[e.crossTileID]){var j=i.prevPlacement.variableOffsets[e.crossTileID];N.indexOf(j.anchor)>0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,h={box:[],offscreen:!1},p=y?2*N.length:N.length,d=0;d<p;++d){var g=N[d%N.length],v=d>=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,b,_,f,l,m,v,e,o,n,u);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,y,f,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Y=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),X=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(y,G,o.lineVertexArray,o.glyphOffsetArray,Y,l,c,u,n,_,m.predicate,X,W),T=y||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var Z=function(t){var e=w&&E?_e(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,m.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(I=Z(a.verticalIconBox)).box.length>0:(I=Z(a.iconBox)).box.length>0,S=S&&I.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=v||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&z?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID)),M&&I&&i.collisionIndex.insertCollisionBox(I.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID),n)){var Q=o.bucketInstanceId,$=i.collisionCircleArrays[Q];void 0===$&&($=i.collisionCircleArrays[Q]=new ve);for(var tt=0;tt<P.circles.length;tt+=4)$.circles.push(P.circles[tt+0]),$.circles.push(P.circles[tt+1]),$.circles.push(P.circles[tt+2]),$.circles.push(P.collisionDetected?1:0)}i.placements[e.crossTileID]=new ge(T||k,M||A,S||o.justReloaded),r[e.crossTileID]=!0}};if(T)for(var S=o.getSortedSymbolIndexes(this.transform.angle),E=S.length-1;E>=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C<e.symbolInstanceEnd;C++)M(o.symbolInstances.get(C),o.collisionArrays[C]);if(n&&o.bucketInstanceId in this.collisionCircleArrays){var P=this.collisionCircleArrays[o.bucketInstanceId];t.invert(P.invProjMatrix,l),P.viewportMatrix=this.collisionIndex.getViewportMatrix()}o.justReloaded=!1},we.prototype.markUsedJustification=function(e,r,n,i){var a,o={left:n.leftJustifiedTextSymbolIndex,center:n.centerJustifiedTextSymbolIndex,right:n.rightJustifiedTextSymbolIndex};a=i===t.WritingMode.vertical?n.verticalPlacedTextSymbolIndex:o[t.getAnchorJustification(r)];for(var s=0,l=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex,n.verticalPlacedTextSymbolIndex];s<l.length;s+=1){var c=l[s];c>=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},we.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o<s.length;o+=1){var l=s[o];e.text.placedSymbolArray.get(l).placedOrientation=i}n.verticalPlacedTextSymbolIndex&&(e.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)},we.prototype.commit=function(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;var e=this.prevPlacement,r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;var n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(var s in this.placements){var l=this.placements[s],c=i[s];c?(this.opacities[s]=new me(c,n,l.text,l.icon),r=r||l.text!==c.text.placed||l.icon!==c.icon.placed):(this.opacities[s]=new me(null,n,l.text,l.icon,l.skipFade),r=r||l.text||l.icon)}for(var u in i){var f=i[u];if(!this.opacities[u]){var h=new me(f,n,!1,!1);h.isHidden()||(this.opacities[u]=h,r=r||f.text.placed||f.icon.placed)}}for(var p in a)this.variableOffsets[p]||!this.opacities[p]||this.opacities[p].isHidden()||(this.variableOffsets[p]=a[p]);for(var d in o)this.placedOrientations[d]||!this.opacities[d]||this.opacities[d].isHidden()||(this.placedOrientations[d]=o[d]);r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)},we.prototype.updateLayerOpacities=function(t,e){for(var r={},n=0,i=e;n<i.length;n+=1){var a=i[n],o=a.getBucket(t);o&&a.latestFeatureIndex&&t.id===o.layerIds[0]&&this.updateBucketOpacities(o,r,a.collisionBoxArray)}},we.prototype.updateBucketOpacities=function(e,r,n){var i=this;e.hasTextData()&&e.text.opacityVertexArray.clear(),e.hasIconData()&&e.icon.opacityVertexArray.clear(),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexArray.clear(),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexArray.clear();var a=e.layers[0].layout,o=new me(null,0,!1,!1,!0),s=a.get(\"text-allow-overlap\"),l=a.get(\"icon-allow-overlap\"),c=a.get(\"text-variable-anchor\"),u=\"map\"===a.get(\"text-rotation-alignment\"),f=\"map\"===a.get(\"text-pitch-alignment\"),h=\"none\"!==a.get(\"icon-text-fit\"),p=new me(null,0,s&&(l||!e.hasIconData()||a.get(\"icon-optional\")),l&&(s||!e.hasTextData()||a.get(\"text-optional\")),!0);!e.collisionArrays&&n&&(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData())&&e.deserializeCollisionBoxes(n);for(var d=function(t,e,r){for(var n=0;n<e/4;n++)t.opacityVertexArray.emplaceBack(r)},m=function(n){var a=e.symbolInstances.get(n),s=a.numHorizontalGlyphVertices,l=a.numVerticalGlyphVertices,m=a.crossTileID,g=r[m],v=i.opacities[m];g?v=o:v||(v=p,i.opacities[m]=v),r[m]=!0;var y=s>0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(y){var T=Pe(v.text),k=_?Ie:T;d(e.text,s,k);var A=w?Ie:T;d(e.text,l,A);var M=v.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=Pe(v.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:Ie;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=v.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var I=C?Ie:L;d(e.icon,a.numVerticalIconVertices,I),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=v.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var O=e.collisionArrays[n];if(O){var z=new t.Point(0,0);if(O.textBox||O.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(z=be(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&z._rotate(f?i.transform.angle:-i.transform.angle)):D=!1}O.textBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||_,z.x,z.y),O.verticalTextBox&&Te(e.textCollisionBox.collisionVertexArray,v.text.placed,!D||w,z.x,z.y)}var F=Boolean(!w&&O.verticalIconBox);O.iconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,F,h?z.x:0,h?z.y:0),O.verticalIconBox&&Te(e.iconCollisionBox.collisionVertexArray,v.icon.placed,!F,h?z.x:0,h?z.y:0)}}},g=0;g<e.symbolInstances.length;g++)m(g);if(e.sortFeatures(this.transform.angle),this.retainedQueryData[e.bucketInstanceId]&&(this.retainedQueryData[e.bucketInstanceId].featureSortOrder=e.featureSortOrder),e.hasTextData()&&e.text.opacityVertexBuffer&&e.text.opacityVertexBuffer.updateData(e.text.opacityVertexArray),e.hasIconData()&&e.icon.opacityVertexBuffer&&e.icon.opacityVertexBuffer.updateData(e.icon.opacityVertexArray),e.hasIconCollisionBoxData()&&e.iconCollisionBox.collisionVertexBuffer&&e.iconCollisionBox.collisionVertexBuffer.updateData(e.iconCollisionBox.collisionVertexArray),e.hasTextCollisionBoxData()&&e.textCollisionBox.collisionVertexBuffer&&e.textCollisionBox.collisionVertexBuffer.updateData(e.textCollisionBox.collisionVertexArray),e.bucketInstanceId in this.collisionCircleArrays){var v=this.collisionCircleArrays[e.bucketInstanceId];e.placementInvProjMatrix=v.invProjMatrix,e.placementViewportMatrix=v.viewportMatrix,e.collisionCircleArray=v.circles,delete this.collisionCircleArrays[e.bucketInstanceId]}},we.prototype.symbolFadeChange=function(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment},we.prototype.zoomAdjustment=function(t){return Math.max(0,(this.transform.zoom-t)/1.5)},we.prototype.hasTransitions=function(t){return this.stale||t-this.lastPlacementChangeTime<this.fadeDuration},we.prototype.stillRecent=function(t,e){var r=this.zoomAtLastRecencyCheck===e?1-this.zoomAdjustment(e):1;return this.zoomAtLastRecencyCheck=e,this.commitTime+this.fadeDuration*r>t},we.prototype.setStale=function(){this.stale=!0};var ke=Math.pow(2,25),Ae=Math.pow(2,24),Me=Math.pow(2,17),Se=Math.pow(2,16),Ee=Math.pow(2,9),Le=Math.pow(2,8),Ce=Math.pow(2,1);function Pe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*ke+e*Ae+r*Me+e*Se+r*Ee+e*Le+r*Ce+e}var Ie=0,Oe=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Oe.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex<t.length;){var o=t[this._currentTileIndex];if(e.getBucketParts(a,n,o,this._sortAcrossTiles),this._currentTileIndex++,i())return!0}for(this._sortAcrossTiles&&(this._sortAcrossTiles=!1,a.sort((function(t,e){return t.sortKey-e.sortKey})));this._currentPartIndex<a.length;){var s=a[this._currentPartIndex];if(e.placeLayerBucketPart(s,this._seenCrossTileIDs,r),this._currentPartIndex++,i())return!0}return!1};var ze=function(t,e,r,n,i,a,o){this.placement=new we(t,i,a,o),this._currentPlacementIndex=e.length-1,this._forceFullPlacement=r,this._showCollisionBoxes=n,this._done=!1};ze.prototype.isDone=function(){return this._done},ze.prototype.continuePlacement=function(e,r,n){for(var i=this,a=t.browser.now(),o=function(){var e=t.browser.now()-a;return!i._forceFullPlacement&&e>2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Oe(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},ze.prototype.commit=function(t){return this.placement.commit(t),this.placement};var De=512/t.EXTENT/2,Re=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;n<e.length;n++){var i=e.get(n),a=i.key;this.indexedSymbolInstances[a]||(this.indexedSymbolInstances[a]=[]),this.indexedSymbolInstances[a].push({crossTileID:i.crossTileID,coord:this.getScaledCoordinates(i,t)})}};Re.prototype.getScaledCoordinates=function(e,r){var n=r.canonical.z-this.tileID.canonical.z,i=De/Math.pow(2,n);return{x:Math.floor((r.canonical.x*t.EXTENT+e.anchorX)*i),y:Math.floor((r.canonical.y*t.EXTENT+e.anchorY)*i)}},Re.prototype.findMatches=function(t,e,r){for(var n=this.tileID.canonical.z<e.canonical.z?1:Math.pow(2,this.tileID.canonical.z-e.canonical.z),i=0;i<t.length;i++){var a=t.get(i);if(!a.crossTileID){var o=this.indexedSymbolInstances[a.key];if(o)for(var s=this.getScaledCoordinates(a,e),l=0,c=o;l<c.length;l+=1){var u=c[l];if(Math.abs(u.coord.x-s.x)<=n&&Math.abs(u.coord.y-s.y)<=n&&!r[u.crossTileID]){r[u.crossTileID]=!0,a.crossTileID=u.crossTileID;break}}}}};var Fe=function(){this.maxCrossTileID=0};Fe.prototype.generate=function(){return++this.maxCrossTileID};var Be=function(){this.indexes={},this.usedCrossTileIDs={},this.lng=0};Be.prototype.handleWrapJump=function(t){var e=Math.round((t-this.lng)/360);if(0!==e)for(var r in this.indexes){var n=this.indexes[r],i={};for(var a in n){var o=n[a];o.tileID=o.tileID.unwrapTo(o.tileID.wrap+e),i[o.tileID.key]=o}this.indexes[r]=i}this.lng=t},Be.prototype.addBucket=function(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(var n=0;n<e.symbolInstances.length;n++){e.symbolInstances.get(n).crossTileID=0}this.usedCrossTileIDs[t.overscaledZ]||(this.usedCrossTileIDs[t.overscaledZ]={});var i=this.usedCrossTileIDs[t.overscaledZ];for(var a in this.indexes){var o=this.indexes[a];if(Number(a)>t.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u<e.symbolInstances.length;u++){var f=e.symbolInstances.get(u);f.crossTileID||(f.crossTileID=r.generate(),i[f.crossTileID]=!0)}return void 0===this.indexes[t.overscaledZ]&&(this.indexes[t.overscaledZ]={}),this.indexes[t.overscaledZ][t.key]=new Re(t,e.symbolInstances,e.bucketInstanceId),!0},Be.prototype.removeBucketCrossTileIDs=function(t,e){for(var r in e.indexedSymbolInstances)for(var n=0,i=e.indexedSymbolInstances[r];n<i.length;n+=1){var a=i[n];delete this.usedCrossTileIDs[t][a.crossTileID]}},Be.prototype.removeStaleBuckets=function(t){var e=!1;for(var r in this.indexes){var n=this.indexes[r];for(var i in n)t[n[i].bucketInstanceId]||(this.removeBucketCrossTileIDs(r,n[i]),delete n[i],e=!0)}return e};var Ne=function(){this.layerIndexes={},this.crossTileIDs=new Fe,this.maxBucketInstanceId=0,this.bucketsInCurrentPlacement={}};Ne.prototype.addLayer=function(t,e,r){var n=this.layerIndexes[t.id];void 0===n&&(n=this.layerIndexes[t.id]=new Be);var i=!1,a={};n.handleWrapJump(r);for(var o=0,s=e;o<s.length;o+=1){var l=s[o],c=l.getBucket(t);c&&t.id===c.layerIds[0]&&(c.bucketInstanceId||(c.bucketInstanceId=++this.maxBucketInstanceId),n.addBucket(l.tileID,c,this.crossTileIDs)&&(i=!0),a[c.bucketInstanceId]=!0)}return n.removeStaleBuckets(a)&&(i=!0),i},Ne.prototype.pruneUnusedLayers=function(t){var e={};for(var r in t.forEach((function(t){e[t]=!0})),this.layerIndexes)e[r]||delete this.layerIndexes[r]};var je=function(e,r){return t.emitValidationErrors(e,r&&r.filter((function(t){return\"source.canvas\"!==t.identifier})))},Ue=t.pick(Ut,[\"addLayer\",\"removeLayer\",\"setPaintProperty\",\"setLayoutProperty\",\"setFilter\",\"addSource\",\"removeSource\",\"setLayerZoomRange\",\"setLight\",\"setTransition\",\"setGeoJSONSourceData\"]),Ve=t.pick(Ut,[\"setCenter\",\"setZoom\",\"setBearing\",\"setPitch\"]),He=function(){var e={},r=t.styleSpec.$version;for(var n in t.styleSpec.$root){var i=t.styleSpec.$root[n];if(i.required){var a=null;null!=(a=\"version\"===n?r:\"array\"===i.type?[]:{})&&(e[n]=a)}}return e}(),qe=function(e){function r(n,i){var a=this;void 0===i&&(i={}),e.call(this),this.map=n,this.dispatcher=new k(Bt(),this),this.imageManager=new h,this.imageManager.setEventedParent(this),this.glyphManager=new x(n._requestManager,i.localIdeographFontFamily),this.lineAtlas=new T(256,512),this.crossTileSymbolIndex=new Ne,this._layers={},this._serializedLayers={},this._order=[],this.sourceCaches={},this.zoomHistory=new t.ZoomHistory,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"setReferrer\",t.getReferrer());var o=this;this._rtlTextPluginCallback=r.registerForPluginStateChange((function(e){var r={pluginStatus:e.pluginStatus,pluginURL:e.pluginURL};o.dispatcher.broadcast(\"syncRTLPluginState\",r,(function(e,r){if((t.triggerPluginCompletionEvent(e),r)&&r.every((function(t){return t})))for(var n in o.sourceCaches)o.sourceCaches[n].reload()}))})),this.on(\"data\",(function(t){if(\"source\"===t.dataType&&\"metadata\"===t.sourceDataType){var e=a.sourceCaches[t.sourceId];if(e){var r=e.getSource();if(r&&r.vectorLayerIds)for(var n in a._layers){var i=a._layers[n];i.source===r.id&&a._validateLayer(i)}}}}))}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadURL=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"}));var i=\"boolean\"==typeof r.validate?r.validate:!t.isMapboxURL(e);e=this.map._requestManager.normalizeStyleURL(e,r.accessToken);var a=this.map._requestManager.transformRequest(e,t.ResourceType.Style);this._request=t.getJSON(a,(function(e,r){n._request=null,e?n.fire(new t.ErrorEvent(e)):r&&n._load(r,i)}))},r.prototype.loadJSON=function(e,r){var n=this;void 0===r&&(r={}),this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._request=t.browser.frame((function(){n._request=null,n._load(e,!1!==r.validate)}))},r.prototype.loadEmpty=function(){this.fire(new t.Event(\"dataloading\",{dataType:\"style\"})),this._load(He,!1)},r.prototype._load=function(e,r){if(!r||!je(this,t.validateStyle(e))){for(var n in this._loaded=!0,this.stylesheet=e,e.sources)this.addSource(n,e.sources[n],{validate:!1});e.sprite?this._loadSprite(e.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(e.glyphs);var i=jt(this.stylesheet.layers);this._order=i.map((function(t){return t.id})),this._layers={},this._serializedLayers={};for(var a=0,o=i;a<o.length;a+=1){var s=o[a];(s=t.createStyleLayer(s)).setEventedParent(this,{layer:{id:s.id}}),this._layers[s.id]=s,this._serializedLayers[s.id]=s.serialize()}this.dispatcher.broadcast(\"setLayers\",this._serializeLayers(this._order)),this.light=new w(this.stylesheet.light),this.fire(new t.Event(\"data\",{dataType:\"style\"})),this.fire(new t.Event(\"style.load\"))}},r.prototype._loadSprite=function(e){var r=this;this._spriteRequest=function(e,r,n){var i,a,o,s=t.browser.devicePixelRatio>1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,v=l.content,y=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,y,{x:f,y:h},{x:0,y:0},{width:c,height:u}),r[s]={data:y,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:v}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=this._layers[i];\"custom\"!==a.type&&e.push(a.serialize())}return e},r.prototype.hasTransitions=function(){if(this.light&&this.light.hasTransition())return!0;for(var t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(var e in this._layers)if(this._layers[e].hasTransition())return!0;return!1},r.prototype._checkLoaded=function(){if(!this._loaded)throw new Error(\"Style is not done loading\")},r.prototype.update=function(e){if(this._loaded){var r=this._changed;if(this._changed){var n=Object.keys(this._updatedLayers),i=Object.keys(this._removedLayers);for(var a in(n.length||i.length)&&this._updateWorkerLayers(n,i),this._updatedSources){var o=this._updatedSources[a];\"reload\"===o?this._reloadSource(a):\"clear\"===o&&this._clearSource(a)}for(var s in this._updateTilesForChangedImages(),this._updatedPaintProps)this._layers[s].updateTransitions(e);this.light.updateTransitions(e),this._resetUpdates()}for(var l in this.sourceCaches)this.sourceCaches[l].used=!1;for(var c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];h.recalculate(e,this._availableImages),!h.isHidden(e.zoom)&&h.source&&(this.sourceCaches[h.source].used=!0)}this.light.recalculate(e),this.z=e.zoom,r&&this.fire(new t.Event(\"data\",{dataType:\"style\"}))}},r.prototype._updateTilesForChangedImages=function(){var t=Object.keys(this._changedImages);if(t.length){for(var e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}},r.prototype._updateWorkerLayers=function(t,e){this.dispatcher.broadcast(\"updateLayers\",{layers:this._serializeLayers(t),removedIds:e})},r.prototype._resetUpdates=function(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={}},r.prototype.setState=function(e){var r=this;if(this._checkLoaded(),je(this,t.validateStyle(e)))return!1;(e=t.clone$1(e)).layers=jt(e.layers);var n=Zt(this.serialize(),e).filter((function(t){return!(t.command in Ve)}));if(0===n.length)return!1;var i=n.filter((function(t){return!(t.command in Ue)}));if(i.length>0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0)||!this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n)){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ct(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(je(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s<l.length;s+=1){var c=l[s][o];if(c)for(var u=0,f=c;u<f.length;u+=1){var h=f[u];i.push(h)}}}}i.sort((function(t,e){return e.intersectionZ-t.intersectionZ}));for(var p=[],d=this._order.length-1;d>=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var v=i[g].feature;if(n[v.layer.id]<d)break;p.push(v),i.pop()}else for(var y=0,x=t;y<x.length;y+=1){var b=x[y][m];if(b)for(var _=0,w=b;_<w.length;_+=1){var T=w[_];p.push(T.feature)}}}return p},r.prototype.queryRenderedFeatures=function(e,r,n){r&&r.filter&&this._validate(t.validateStyle.filter,\"queryRenderedFeatures.filter\",r.filter,null,r);var i={};if(r&&r.layers){if(!Array.isArray(r.layers))return this.fire(new t.ErrorEvent(new Error(\"parameters.layers must be an Array.\"))),[];for(var a=0,o=r.layers;a<o.length;a+=1){var s=o[a],l=this._layers[s];if(!l)return this.fire(new t.ErrorEvent(new Error(\"The layer '\"+s+\"' does not exist in the map's style and cannot be queried for features.\"))),[];i[l.source]=!0}}var c=[];for(var u in r.availableImages=this._availableImages,this.sourceCaches)r.layers&&!i[u]||c.push(F(this.sourceCaches[u],this._layers,this._serializedLayers,e,r,n));return this.placement&&c.push(function(t,e,r,n,i,a,o){for(var s={},l=a.queryRenderedSymbols(n),c=[],u=0,f=Object.keys(l).map(Number);u<f.length;u+=1){var h=f[u];c.push(o[h])}c.sort(B);for(var p=function(){var r=m[d],n=r.featureIndex.lookupSymbolFeatures(l[r.bucketInstanceId],e,r.bucketIndex,r.sourceLayerIndex,i.filter,i.layers,i.availableImages,t);for(var a in n){var o=s[a]=s[a]||[],c=n[a];c.sort((function(t,e){var n=r.featureSortOrder;if(n){var i=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-i}return e.featureIndex-t.featureIndex}));for(var u=0,f=c;u<f.length;u+=1){var h=f[u];o.push(h)}}},d=0,m=c;d<m.length;d+=1)p();var g=function(e){s[e].forEach((function(n){var i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}))};for(var v in s)g(v);return s}(this._layers,this._serializedLayers,this.sourceCaches,e,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(c)},r.prototype.querySourceFeatures=function(e,r){r&&r.filter&&this._validate(t.validateStyle.filter,\"querySourceFeatures.filter\",r.filter,null,r);var n=this.sourceCaches[e];return n?function(t,e){for(var r=t.getRenderableIds().map((function(e){return t.getTileByID(e)})),n=[],i={},a=0;a<r.length;a++){var o=r[a],s=o.tileID.canonical.key;i[s]||(i[s]=!0,o.querySourceFeatures(n,e))}return n}(n,r):[]},r.prototype.addSourceType=function(t,e,n){return r.getSourceType(t)?n(new Error('A source type called \"'+t+'\" already exists.')):(r.setSourceType(t,e),e.workerSourceURL?void this.dispatcher.broadcast(\"loadWorkerSource\",{name:t,url:e.workerSourceURL},n):n(null,null))},r.prototype.getLight=function(){return this.light.getLight()},r.prototype.setLight=function(e,r){void 0===r&&(r={}),this._checkLoaded();var n=this.light.getLight(),i=!1;for(var a in e)if(!t.deepEqual(e[a],n[a])){i=!0;break}if(i){var o={now:t.browser.now(),transition:t.extend({duration:300,delay:0},this.stylesheet.transition)};this.light.setLight(e,r),this.light.updateTransitions(o)}},r.prototype._validate=function(e,r,n,i,a){return void 0===a&&(a={}),(!a||!1!==a.validate)&&je(this,e.call(t.validateStyle,t.extend({key:r,style:this.serialize(),value:n,styleSpec:t.styleSpec},i)))},r.prototype._remove=function(){for(var e in this._request&&(this._request.cancel(),this._request=null),this._spriteRequest&&(this._spriteRequest.cancel(),this._spriteRequest=null),t.evented.off(\"pluginStateChange\",this._rtlTextPluginCallback),this._layers){this._layers[e].setEventedParent(null)}for(var r in this.sourceCaches)this.sourceCaches[r].clearTiles(),this.sourceCaches[r].setEventedParent(null);this.imageManager.setEventedParent(null),this.setEventedParent(null),this.dispatcher.remove()},r.prototype._clearSource=function(t){this.sourceCaches[t].clearTiles()},r.prototype._reloadSource=function(t){this.sourceCaches[t].resume(),this.sourceCaches[t].reload()},r.prototype._updateSources=function(t){for(var e in this.sourceCaches)this.sourceCaches[e].update(t)},r.prototype._generateCollisionBoxes=function(){for(var t in this.sourceCaches)this._reloadSource(t)},r.prototype._updatePlacement=function(e,r,n,i,a){void 0===a&&(a=!1);for(var o=!1,s=!1,l={},c=0,u=this._order;c<u.length;c+=1){var f=u[c],h=this._layers[f];if(\"symbol\"===h.type){if(!l[h.source]){var p=this.sourceCaches[h.source];l[h.source]=p.getRenderableIds(!0).map((function(t){return p.getTileByID(t)})).sort((function(t,e){return e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)}))}var d=this.crossTileSymbolIndex.addLayer(h,l[h.source],e.center.lng);o=o||d}}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((a=a||this._layerOrderChanged||0===n)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(t.browser.now(),e.zoom))&&(this.pauseablePlacement=new ze(e,this._order,a,r,n,i,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(t.browser.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(var m=0,g=this._order;m<g.length;m+=1){var v=g[m],y=this._layers[v];\"symbol\"===y.type&&this.placement.updateLayerOpacities(y,l[y.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(t.browser.now())},r.prototype._releaseSymbolFadeTiles=function(){for(var t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()},r.prototype.getImages=function(t,e,r){this.imageManager.getImages(e.icons,r),this._updateTilesForChangedImages();var n=this.sourceCaches[e.source];n&&n.setDependencies(e.tileID.key,e.type,e.icons)},r.prototype.getGlyphs=function(t,e,r){this.glyphManager.getGlyphs(e.stacks,r)},r.prototype.getResource=function(e,r,n){return t.makeRequest(r,n)},r}(t.Evented);qe.getSourceType=function(t){return D[t]},qe.setSourceType=function(t,e){D[t]=e},qe.registerForPluginStateChange=t.registerForPluginStateChange;var Ge=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2}]),Ye=yr(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\"),We=yr(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Xe=yr(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),Ze=yr(\"varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,0,1);} else {gl_Position=u_matrix*vec4(circle_center,0,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),Je=yr(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),Ke=yr(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),Qe=yr(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),$e=yr(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),tr=yr(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd  =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz  /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),er=yr(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),rr=yr(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),nr=yr(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ir=yr(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),ar=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),or=yr(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),sr=yr(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),lr=yr(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),cr=yr(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),ur=yr(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),fr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),hr=yr(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),pr=yr(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),dr=yr(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=yr(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),gr=yr(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),vr=yr(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function yr(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n    \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n    \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var xr=Object.freeze({__proto__:null,prelude:Ye,background:We,backgroundPattern:Xe,circle:Ze,clippingMask:Je,heatmap:Ke,heatmapTexture:Qe,collisionBox:$e,collisionCircle:tr,debug:er,fill:rr,fillOutline:nr,fillOutlinePattern:ir,fillPattern:ar,fillExtrusion:or,fillExtrusionPattern:sr,hillshadePrepare:lr,hillshade:cr,line:ur,lineGradient:fr,linePattern:hr,lineSDF:pr,raster:dr,symbolIcon:mr,symbolSDF:gr,symbolTextAndIcon:vr}),br=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};br.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,c=0;!l&&c<n.length;c++)this.boundPaintVertexBuffers[c]!==n[c]&&(l=!0);var u=!this.vao||this.boundProgram!==e||this.boundLayoutVertexBuffer!==r||l||this.boundIndexBuffer!==i||this.boundVertexOffset!==a||this.boundDynamicVertexBuffer!==o||this.boundDynamicVertexBuffer2!==s;!t.extVertexArrayObject||u?this.freshBind(e,r,n,i,a,o,s):(t.bindVertexArrayOES.set(this.vao),o&&o.bind(),i&&i.dynamicDraw&&i.bind(),s&&s.bind())},br.prototype.freshBind=function(t,e,r,n,i,a,o){var s,l=t.numAttributes,c=this.context,u=c.gl;if(c.extVertexArrayObject)this.vao&&this.destroy(),this.vao=c.extVertexArrayObject.createVertexArrayOES(),c.bindVertexArrayOES.set(this.vao),s=0,this.boundProgram=t,this.boundLayoutVertexBuffer=e,this.boundPaintVertexBuffers=r,this.boundIndexBuffer=n,this.boundVertexOffset=i,this.boundDynamicVertexBuffer=a,this.boundDynamicVertexBuffer2=o;else{s=c.currentNumAttributes||0;for(var f=l;f<s;f++)u.disableVertexAttribArray(f)}e.enableAttributes(u,t);for(var h=0,p=r;h<p.length;h+=1){p[h].enableAttributes(u,t)}a&&a.enableAttributes(u,t),o&&o.enableAttributes(u,t),e.bind(),e.setVertexAttribPointers(u,t,i);for(var d=0,m=r;d<m.length;d+=1){var g=m[d];g.bind(),g.setVertexAttribPointers(u,t,i)}a&&(a.bind(),a.setVertexAttribPointers(u,t,i)),n&&n.bind(),o&&(o.bind(),o.setVertexAttribPointers(u,t,i)),c.currentNumAttributes=l},br.prototype.destroy=function(){this.vao&&(this.context.extVertexArrayObject.deleteVertexArrayOES(this.vao),this.vao=null)};var _r=function(t,e,r,n,i){var a=t.gl;this.program=a.createProgram();var o=r?r.defines():[];i&&o.push(\"#define OVERDRAW_INSPECTOR;\");var s=o.concat(Ye.fragmentSource,e.fragmentSource).join(\"\\n\"),l=o.concat(Ye.vertexSource,e.vertexSource).join(\"\\n\"),c=a.createShader(a.FRAGMENT_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(c,s),a.compileShader(c),a.attachShader(this.program,c);var u=a.createShader(a.VERTEX_SHADER);if(a.isContextLost())this.failedToCreate=!0;else{a.shaderSource(u,l),a.compileShader(u),a.attachShader(this.program,u);for(var f=r?r.layoutAttributes:[],h=0;h<f.length;h++)a.bindAttribLocation(this.program,h,f[h].name);a.linkProgram(this.program),a.deleteShader(u),a.deleteShader(c),this.numAttributes=a.getProgramParameter(this.program,a.ACTIVE_ATTRIBUTES),this.attributes={};for(var p={},d=0;d<this.numAttributes;d++){var m=a.getActiveAttrib(this.program,d);m&&(this.attributes[m.name]=a.getAttribLocation(this.program,m.name))}for(var g=a.getProgramParameter(this.program,a.ACTIVE_UNIFORMS),v=0;v<g;v++){var y=a.getActiveUniform(this.program,v);y&&(p[y.name]=a.getUniformLocation(this.program,y.name))}this.fixedUniforms=n(t,p),this.binderUniforms=r?r.getUniforms(t,p):[]}}};function wr(t,e,r){var n=1/pe(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}_r.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m){var g,v=t.gl;if(!this.failedToCreate){for(var y in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[y].set(o[y]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[v.LINES]=2,g[v.TRIANGLES]=3,g[v.LINE_STRIP]=1,g)[e],b=0,_=u.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new br)).bind(t,this,l,p?p.getPaintVertexBuffers():[],c,w.vertexOffset,d,m),v.drawElements(e,w.primitiveLength*x,v.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Tr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var c=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},kr=function(e,r,n,i,a,o,s){return t.extend(Tr(e,r,n,i),wr(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Ar=function(t){return{u_matrix:t}},Mr=function(e,r,n,i){return t.extend(Ar(e),wr(n,r,i))},Sr=function(t,e){return{u_matrix:t,u_world:e}},Er=function(e,r,n,i,a){return t.extend(Mr(e,r,n,i),{u_world:a})},Lr=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=pe(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Cr=function(t,e,r){var n=pe(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Pr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},Ir=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Or=function(t){return{u_matrix:t}},zr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:pe(e,1,r),u_intensity:n}};function Dr(e,r){var n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new t.MercatorCoordinate(0,i/n).toLngLat().lat,new t.MercatorCoordinate(0,(i+1)/n).toLngLat().lat]}var Rr=function(e,r,n){var i=e.transform;return{u_matrix:Ur(e,r,n),u_ratio:1/pe(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Fr=function(e,r,n){return t.extend(Rr(e,r,n),{u_image:0})},Br=function(e,r,n,i){var a=e.transform,o=jr(r,a);return{u_matrix:Ur(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/pe(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Nr=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=jr(r,o),c=\"round\"===n.layout.get(\"line-cap\"),u=s.getDash(i.from,c),f=s.getDash(i.to,c),h=u.width*a.fromScale,p=f.width*a.toScale;return t.extend(Rr(e,r,n),{u_patternscale_a:[l/h,-u.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:u.y,u_tex_y_b:f.y,u_mix:a.t})};function jr(t,e){return 1/pe(t,1,e.tileZoom)}function Ur(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var Vr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Hr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Hr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var qr,Gr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Yr=function(e,r,n,i,a,o,s,l,c,u,f){var h=a.transform;return t.extend(Gr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Wr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Yr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},Xr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Zr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,f=u*(n.tileID.canonical.x+n.tileID.wrap*c),h=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/pe(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},Jr={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Kr(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d<i.length;d++){var m=i[d],g=r.getTile(m),v=g.getBucket(n);if(v){var y=m.posMatrix;0===a[0]&&0===a[1]||(y=e.translatePosMatrix(m.posMatrix,g,a,o));var x=s?v.textCollisionBox:v.iconCollisionBox,b=v.collisionCircleArray;if(b.length>0){var _=t.create(),w=y;t.mul(_,v.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,v.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&u.draw(l,c.LINES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,Cr(y,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M<S.length;M+=1)for(var E=S[M],L=0;L<E.circleArray.length/4;L++){var C=4*L,P=E.circleArray[C+0],I=E.circleArray[C+1],O=E.circleArray[C+2],z=E.circleArray[C+3];k.emplace(A++,P,I,O,z,0),k.emplace(A++,P,I,O,z,1),k.emplace(A++,P,I,O,z,2),k.emplace(A++,P,I,O,z,3)}(!qr||qr.length<2*h)&&(qr=function(e){var r=2*e,n=new t.StructArrayLayout3ui6;n.resize(r),n._trim();for(var i=0;i<r;i++){var a=6*i;n.uint16[a+0]=4*i+0,n.uint16[a+1]=4*i+1,n.uint16[a+2]=4*i+2,n.uint16[a+3]=4*i+2,n.uint16[a+4]=4*i+3,n.uint16[a+5]=4*i+0}return n}(h));for(var D=l.createIndexBuffer(qr,!0),R=l.createVertexBuffer(k,t.collisionCircleLayout.members,!0),F=0,B=f;F<B.length;F+=1){var N=B[F],j=Pr(N.transform,N.invTransform,e.transform);T.draw(l,c.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,j,n.id,R,D,t.SegmentVector.simpleSegment(0,2*N.circleOffset,N.circleArray.length,N.circleArray.length/2),null,e.transform.zoom,null,null,null)}R.destroy(),D.destroy()}}var Qr=t.identity(new Float32Array(16));function $r(e,r,n,i,a,o){var s=t.getAnchorAlignment(e),l=-(s.horizontalAlign-.5)*r,c=-(s.verticalAlign-.5)*n,u=t.evaluateVariableOffset(e,i);return new t.Point((l/a+u[0])*o,(c/a+u[1])*o)}function tn(e,r,n,i,a,o,s,l,c,u,f){var h=e.text.placedSymbolArray,p=e.text.dynamicLayoutVertexArray,d=e.icon.dynamicLayoutVertexArray,m={};p.clear();for(var g=0;g<h.length;g++){var v=h.get(g),y=e.allowVerticalPlacement&&!v.placedOrientation,x=v.hidden||!v.crossTileID||y?null:i[v.crossTileID];if(x){var b=new t.Point(v.anchorX,v.anchorY),_=te(b,n?l:s),w=ee(o.cameraToCenterDistance,_.signedDistanceFromCamera),T=a.evaluateSizeForFeature(e.textSizeData,u,v)*w/t.ONE_EM;n&&(T*=e.tilePixelRatio/c);for(var k=x.width,A=x.height,M=$r(x.anchor,k,A,x.textOffset,x.textBoxScale,T),S=n?te(b.add(M),s).point:_.point.add(r?M.rotate(-o.angle):M),E=e.allowVerticalPlacement&&v.placedOrientation===t.WritingMode.vertical?Math.PI/2:0,L=0;L<v.numGlyphs;L++)t.addDynamicAttributes(p,S,E);f&&v.associatedIconIndex>=0&&(m[v.associatedIconIndex]={shiftedAnchor:S,angle:E})}else ue(v.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P<C.length;P++){var I=C.get(P);if(I.hidden)ue(I.numGlyphs,d);else{var O=m[P];if(O)for(var z=0;z<I.numGlyphs;z++)t.addDynamicAttributes(d,O.shiftedAnchor,O.angle);else ue(I.numGlyphs,d)}}e.icon.dynamicLayoutVertexBuffer.updateData(d)}e.text.dynamicLayoutVertexBuffer.updateData(p)}function en(t,e,r){return r.iconsInText&&e?\"symbolTextAndIcon\":t?\"symbolSDF\":\"symbolIcon\"}function rn(e,r,n,i,a,o,s,l,c,u,f,h){for(var p=e.context,d=p.gl,m=e.transform,g=\"map\"===l,v=\"map\"===c,y=g&&\"point\"!==n.layout.get(\"symbol-placement\"),x=g&&!v&&!y,b=void 0!==n.layout.get(\"symbol-sort-key\").constantOr(1),_=e.depthModeForSublayer(0,At.ReadOnly),w=n.layout.get(\"text-variable-anchor\"),T=[],k=0,A=i;k<A.length;k+=1){var M=A[k],S=r.getTile(M),E=S.getBucket(n);if(E){var L=a?E.text:E.icon;if(L&&L.segments.get().length){var C=L.programConfigurations.get(n.id),P=a||E.sdfIcons,I=a?E.textSizeData:E.iconSizeData,O=v||0!==m.pitch,z=e.useProgram(en(P,a,E),C),D=t.evaluateSizeForZoom(I,m.zoom),R=void 0,F=[0,0],B=void 0,N=void 0,j=null,U=void 0;if(a){if(B=S.glyphAtlasTexture,N=d.LINEAR,R=S.glyphAtlasTexture.size,E.iconsInText){F=S.imageAtlasTexture.size,j=S.imageAtlasTexture;var V=\"composite\"===I.kind||\"camera\"===I.kind;U=O||e.options.rotating||e.options.zooming||V?d.LINEAR:d.NEAREST}}else{var H=1!==n.layout.get(\"icon-size\").constantOr(0)||E.iconsNeedLinear;B=S.imageAtlasTexture,N=P||e.options.rotating||e.options.zooming||H||O?d.LINEAR:d.NEAREST,R=S.imageAtlasTexture.size}var q=pe(S,1,e.transform.zoom),G=Qt(M.posMatrix,v,g,e.transform,q),Y=$t(M.posMatrix,v,g,e.transform,q),W=w&&E.hasTextData(),X=\"none\"!==n.layout.get(\"icon-text-fit\")&&W&&E.hasIconData();y&&ne(E,M.posMatrix,e,a,G,Y,v,u);var Z=e.translatePosMatrix(M.posMatrix,S,o,s),J=y||a&&w||X?Qr:G,K=e.translatePosMatrix(Y,S,o,s,!0),Q=P&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1),$={program:z,buffers:L,uniformValues:P?E.iconsInText?Wr(I.kind,D,x,v,e,Z,J,K,R,F):Yr(I.kind,D,x,v,e,Z,J,K,a,R,!0):Gr(I.kind,D,x,v,e,Z,J,K,a,R),atlasTexture:B,atlasTextureIcon:j,atlasInterpolation:N,atlasInterpolationIcon:U,isSDF:P,hasHalo:Q};if(b)for(var tt=0,et=L.segments.get();tt<et.length;tt+=1){var rt=et[tt];T.push({segments:new t.SegmentVector([rt]),sortKey:rt.sortKey,state:$})}else T.push({segments:L.segments,sortKey:0,state:$})}}}b&&T.sort((function(t,e){return t.sortKey-e.sortKey}));for(var nt=0,it=T;nt<it.length;nt+=1){var at=it[nt],ot=at.state;if(p.activeTexture.set(d.TEXTURE0),ot.atlasTexture.bind(ot.atlasInterpolation,d.CLAMP_TO_EDGE),ot.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),ot.atlasTextureIcon&&ot.atlasTextureIcon.bind(ot.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),ot.isSDF){var st=ot.uniformValues;ot.hasHalo&&(st.u_is_halo=1,nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,st)),st.u_is_halo=0}nn(ot.buffers,at.segments,n,e,ot.program,_,f,h,ot.uniformValues)}}function nn(t,e,r,n,i,a,o,s,l){var c=n.context,u=c.gl;i.draw(c,u.TRIANGLES,a,o,s,Et.disabled,l,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function an(t,e,r,n,i,a,o){var s,l,c,u,f,h=t.context.gl,p=r.paint.get(\"fill-pattern\"),d=p&&p.constantOr(1),m=r.getCrossfadeParameters();o?(l=d&&!r.getPaintProperty(\"fill-outline-color\")?\"fillOutlinePattern\":\"fillOutline\",s=h.LINES):(l=d?\"fillPattern\":\"fill\",s=h.TRIANGLES);for(var g=0,v=n;g<v.length;g+=1){var y=v[g],x=e.getTile(y);if(!d||x.patternsLoaded()){var b=x.getBucket(r);if(b){var _=b.programConfigurations.get(r.id),w=t.useProgram(l,_);d&&(t.context.activeTexture.set(h.TEXTURE0),x.imageAtlasTexture.bind(h.LINEAR,h.CLAMP_TO_EDGE),_.updatePaintBuffers(m));var T=p.constantOr(null);if(T&&x.imageAtlas){var k=x.imageAtlas,A=k.patternPositions[T.to.toString()],M=k.patternPositions[T.from.toString()];A&&M&&_.setConstantPatternPositions(A,M)}var S=t.translatePosMatrix(y.posMatrix,x,r.paint.get(\"fill-translate\"),r.paint.get(\"fill-translate-anchor\"));if(o){u=b.indexBuffer2,f=b.segments2;var E=[h.drawingBufferWidth,h.drawingBufferHeight];c=\"fillOutlinePattern\"===l&&d?Er(S,t,m,x,E):Sr(S,E)}else u=b.indexBuffer,f=b.segments,c=d?Mr(S,t,m,x):Ar(S);w.draw(t.context,s,i,t.stencilModeForClipping(y),a,Et.disabled,c,r.id,b.layoutVertexBuffer,u,f,r.paint,t.transform.zoom,_)}}}}function on(t,e,r,n,i,a,o){for(var s=t.context,l=s.gl,c=r.paint.get(\"fill-extrusion-pattern\"),u=c.constantOr(1),f=r.getCrossfadeParameters(),h=r.paint.get(\"fill-extrusion-opacity\"),p=0,d=n;p<d.length;p+=1){var m=d[p],g=e.getTile(m),v=g.getBucket(r);if(v){var y=v.programConfigurations.get(r.id),x=t.useProgram(u?\"fillExtrusionPattern\":\"fillExtrusion\",y);u&&(t.context.activeTexture.set(l.TEXTURE0),g.imageAtlasTexture.bind(l.LINEAR,l.CLAMP_TO_EDGE),y.updatePaintBuffers(f));var b=c.constantOr(null);if(b&&g.imageAtlas){var _=g.imageAtlas,w=_.patternPositions[b.to.toString()],T=_.patternPositions[b.from.toString()];w&&T&&y.setConstantPatternPositions(w,T)}var k=t.translatePosMatrix(m.posMatrix,g,r.paint.get(\"fill-extrusion-translate\"),r.paint.get(\"fill-extrusion-translate-anchor\")),A=r.paint.get(\"fill-extrusion-vertical-gradient\"),M=u?kr(k,t,A,h,m,f,g):Tr(k,t,A,h);x.draw(s,s.gl.TRIANGLES,i,a,o,Et.backCCW,M,r.id,v.layoutVertexBuffer,v.indexBuffer,v.segments,r.paint,t.transform.zoom,y)}}}function sn(t,e,r,n,i,a){var o=t.context,s=o.gl,l=e.fbo;if(l){var c=t.useProgram(\"hillshade\");o.activeTexture.set(s.TEXTURE0),s.bindTexture(s.TEXTURE_2D,l.colorAttachment.get());var u=function(t,e,r){var n=r.paint.get(\"hillshade-shadow-color\"),i=r.paint.get(\"hillshade-highlight-color\"),a=r.paint.get(\"hillshade-accent-color\"),o=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(o-=t.transform.angle);var s=!t.options.moving;return{u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),s),u_image:0,u_latrange:Dr(t,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),o],u_shadow:n,u_highlight:i,u_accent:a}}(t,e,r);c.draw(o,s.TRIANGLES,n,i,a,Et.disabled,u,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}function ln(e,r,n,i,a,o,s){var l=e.context,c=l.gl,u=r.dem;if(u&&u.data){var f=u.dim,h=u.stride,p=u.getPixels();if(l.activeTexture.set(c.TEXTURE1),l.pixelStoreUnpackPremultiplyAlpha.set(!1),r.demTexture=r.demTexture||e.getTileTexture(h),r.demTexture){var d=r.demTexture;d.update(p,{premultiply:!1}),d.bind(c.NEAREST,c.CLAMP_TO_EDGE)}else r.demTexture=new t.Texture(l,p,c.RGBA,{premultiply:!1}),r.demTexture.bind(c.NEAREST,c.CLAMP_TO_EDGE);l.activeTexture.set(c.TEXTURE0);var m=r.fbo;if(!m){var g=new t.Texture(l,{width:f,height:f,data:null},c.RGBA);g.bind(c.LINEAR,c.CLAMP_TO_EDGE),(m=r.fbo=l.createFramebuffer(f,f,!0)).colorAttachment.set(g.texture)}l.bindFramebuffer.set(m.framebuffer),l.viewport.set([0,0,f,f]),e.useProgram(\"hillshadePrepare\").draw(l,c.TRIANGLES,a,o,s,Et.disabled,function(e,r,n){var i=r.stride,a=t.create();return t.ortho(a,0,t.EXTENT,-t.EXTENT,0,0,1),t.translate(a,a,[0,-t.EXTENT,0]),{u_matrix:a,u_image:1,u_dimension:[i,i],u_zoom:e.overscaledZ,u_maxzoom:n,u_unpack:r.getUnpackVector()}}(r.tileID,u,i),n.id,e.rasterBoundsBuffer,e.quadTriangleIndexBuffer,e.rasterBoundsSegments),r.needsHillshadePrepare=!1}}function cn(e,r,n,i,a){var o=i.paint.get(\"raster-fade-duration\");if(o>0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),f=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var un=new t.Color(1,0,0,1),fn=new t.Color(0,1,0,1),hn=new t.Color(0,0,1,1),pn=new t.Color(1,0,1,1),dn=new t.Color(0,1,1,1);function mn(t){var e=t.transform.padding;gn(t,t.transform.height-(e.top||0),3,un),gn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,hn),vn(t,t.transform.width-(e.right||0),3,pn);var r=t.transform.centerPoint;!function(t,e,r,n){yn(t,e-1,r-10,2,20,n),yn(t,e-10,r-1,20,2,n)}(t,r.x,t.transform.height-r.y,dn)}function gn(t,e,r,n){yn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){yn(t,e-r/2,0,r,t.transform.height,n)}function yn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function xn(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=At.disabled,c=Mt.disabled,u=e.colorModeForRenderPass();i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Et.disabled,Ir(o,t.Color.red),\"$debug\",e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,h=f&&f.byteLength||0,p=Math.floor(h/1024),d=r.getTile(n).tileSize,m=512/Math.min(d,512)*(n.overscaledZ/e.transform.zoom)*.5,g=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(g+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,g+\" \"+p+\"kb\"),s.draw(i,a.TRIANGLES,l,c,St.alphaBlended,Et.disabled,Ir(o,t.Color.transparent,m),\"$debug\",e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var bn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Mt.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,f=0,h=e;f<h.length;f+=1){var p=h[f],d=i.getTile(p),m=d.getBucket(n);if(m&&m.text&&m.text.segments.get().length){var g=m.textSizeData,v=t.evaluateSizeForZoom(g,l.zoom),y=pe(d,1,r.transform.zoom),x=Qt(p.posMatrix,u,c,r.transform,y),b=\"none\"!==n.layout.get(\"icon-text-fit\")&&m.hasIconData();if(v){var _=Math.pow(2,l.zoom-d.tileID.overscaledZ);tn(m,c,u,s,t.symbolSize,l,x,p.posMatrix,_,v,b)}}}}(i,e,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&rn(e,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&rn(e,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Kr(e,r,n,i,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),!0),Kr(e,r,n,i,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),!1))}},circle:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=void 0!==n.layout.get(\"circle-sort-key\").constantOr(1);if(0!==a.constantOr(1)||0!==o.constantOr(1)&&0!==s.constantOr(1)){for(var c=e.context,u=c.gl,f=e.depthModeForSublayer(0,At.ReadOnly),h=Mt.disabled,p=e.colorModeForRenderPass(),d=[],m=0;m<i.length;m++){var g=i[m],v=r.getTile(g),y=v.getBucket(n);if(y){var x=y.programConfigurations.get(n.id),b={programConfiguration:x,program:e.useProgram(\"circle\",x),layoutVertexBuffer:y.layoutVertexBuffer,indexBuffer:y.indexBuffer,uniformValues:Lr(e,g,v,n)};if(l)for(var _=0,w=y.segments.get();_<w.length;_+=1){var T=w[_];d.push({segments:new t.SegmentVector([T]),sortKey:T.sortKey,state:b})}else d.push({segments:y.segments,sortKey:0,state:b})}}l&&d.sort((function(t,e){return t.sortKey-e.sortKey}));for(var k=0,A=d;k<A.length;k+=1){var M=A[k],S=M.state,E=S.programConfiguration,L=S.program,C=S.layoutVertexBuffer,P=S.indexBuffer,I=S.uniformValues,O=M.segments;L.draw(c,u.TRIANGLES,f,h,p,Et.disabled,I,n.id,C,P,O,n.paint,e.transform.zoom,E)}}}},heatmap:function(e,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===e.renderPass){var a=e.context,o=a.gl,s=Mt.disabled,l=new St([o.ONE,o.ONE],t.Color.transparent,[!0,!0,!0,!0]);!function(t,e,r){var n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);var i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{var a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1),function(t,e,r,n){var i=t.gl,a=t.extRenderToTextureHalfFloat?t.extTextureHalfFloat.HALF_FLOAT_OES:i.UNSIGNED_BYTE;i.texImage2D(i.TEXTURE_2D,0,i.RGBA,e.width/4,e.height/4,0,i.RGBA,a,null),n.colorAttachment.set(r)}(t,e,a,i)}}(a,e,n),a.clear({color:t.Color.transparent});for(var c=0;c<i.length;c++){var u=i[c];if(!r.hasRenderableParent(u)){var f=r.getTile(u),h=f.getBucket(n);if(h){var p=h.programConfigurations.get(n.id),d=e.useProgram(\"heatmap\",p),m=e.transform.zoom;d.draw(a,o.TRIANGLES,At.disabled,s,l,Et.disabled,zr(u.posMatrix,f,m,n.paint.get(\"heatmap-intensity\")),n.id,h.layoutVertexBuffer,h.indexBuffer,h.segments,n.paint,e.transform.zoom,p)}}}a.viewport.set([0,0,e.width,e.height])}else\"translucent\"===e.renderPass&&(e.context.setColorMode(e.colorModeForRenderPass()),function(e,r){var n=e.context,i=n.gl,a=r.heatmapFbo;if(!a)return;n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,a.colorAttachment.get()),n.activeTexture.set(i.TEXTURE1);var o=r.colorRampTexture;o||(o=r.colorRampTexture=new t.Texture(n,r.colorRamp,i.RGBA));o.bind(i.LINEAR,i.CLAMP_TO_EDGE),e.useProgram(\"heatmapTexture\").draw(n,i.TRIANGLES,At.disabled,Mt.disabled,e.colorModeForRenderPass(),Et.disabled,function(e,r,n,i){var a=t.create();t.ortho(a,0,e.width,e.height,0,0,1);var o=e.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}}(e,r,0,1),r.id,e.viewportBuffer,e.quadTriangleIndexBuffer,e.viewportSegments,r.paint,e.transform.zoom)}(e,n))},line:function(e,r,n,i){if(\"translucent\"===e.renderPass){var a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0!==a.constantOr(1)&&0!==o.constantOr(1)){var s=e.depthModeForSublayer(0,At.ReadOnly),l=e.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),f=u.constantOr(1),h=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=f?\"linePattern\":c?\"lineSDF\":h?\"lineGradient\":\"line\",m=e.context,g=m.gl,v=!0;if(h){m.activeTexture.set(g.TEXTURE0);var y=n.gradientTexture;if(!n.gradient)return;y||(y=n.gradientTexture=new t.Texture(m,n.gradient,g.RGBA)),y.bind(g.LINEAR,g.CLAMP_TO_EDGE)}for(var x=0,b=i;x<b.length;x+=1){var _=b[x],w=r.getTile(_);if(!f||w.patternsLoaded()){var T=w.getBucket(n);if(T){var k=T.programConfigurations.get(n.id),A=e.context.program.get(),M=e.useProgram(d,k),S=v||M.program!==A,E=u.constantOr(null);if(E&&w.imageAtlas){var L=w.imageAtlas,C=L.patternPositions[E.to.toString()],P=L.patternPositions[E.from.toString()];C&&P&&k.setConstantPatternPositions(C,P)}var I=f?Br(e,w,n,p):c?Nr(e,w,n,c,p):h?Fr(e,w,n):Rr(e,w,n);f?(m.activeTexture.set(g.TEXTURE0),w.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),k.updatePaintBuffers(p)):c&&(S||e.lineAtlas.dirty)&&(m.activeTexture.set(g.TEXTURE0),e.lineAtlas.bind(m)),M.draw(m,g.TRIANGLES,s,e.stencilModeForClipping(_),l,Et.disabled,I,n.id,T.layoutVertexBuffer,T.indexBuffer,T.segments,n.paint,e.transform.zoom,k),v=!1}}}}}},fill:function(e,r,n,i){var a=n.paint.get(\"fill-color\"),o=n.paint.get(\"fill-opacity\");if(0!==o.constantOr(1)){var s=e.colorModeForRenderPass(),l=n.paint.get(\"fill-pattern\"),c=e.opaquePassEnabledForLayer()&&!l.constantOr(1)&&1===a.constantOr(t.Color.transparent).a&&1===o.constantOr(0)?\"opaque\":\"translucent\";if(e.renderPass===c){var u=e.depthModeForSublayer(1,\"opaque\"===e.renderPass?At.ReadWrite:At.ReadOnly);an(e,r,n,i,u,s,!1)}if(\"translucent\"===e.renderPass&&n.paint.get(\"fill-antialias\")){var f=e.depthModeForSublayer(n.getPaintProperty(\"fill-outline-color\")?2:0,At.ReadOnly);an(e,r,n,i,f,s,!0)}}},\"fill-extrusion\":function(t,e,r,n){var i=r.paint.get(\"fill-extrusion-opacity\");if(0!==i&&\"translucent\"===t.renderPass){var a=new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D);if(1!==i||r.paint.get(\"fill-extrusion-pattern\").constantOr(1))on(t,e,r,n,a,Mt.disabled,St.disabled),on(t,e,r,n,a,t.stencilModeFor3D(),t.colorModeForRenderPass());else{var o=t.colorModeForRenderPass();on(t,e,r,n,a,Mt.disabled,o)}}},hillshade:function(t,e,r,n){if(\"offscreen\"===t.renderPass||\"translucent\"===t.renderPass){for(var i=t.context,a=e.getSource().maxzoom,o=t.depthModeForSublayer(0,At.ReadOnly),s=t.colorModeForRenderPass(),l=\"translucent\"===t.renderPass?t.stencilConfigForOverlap(n):[{},n],c=l[0],u=0,f=l[1];u<f.length;u+=1){var h=f[u],p=e.getTile(h);p.needsHillshadePrepare&&\"offscreen\"===t.renderPass?ln(t,p,r,a,o,Mt.disabled,s):\"translucent\"===t.renderPass&&sn(t,p,r,o,c[h.overscaledZ],s)}i.viewport.set([0,0,t.width,t.height])}},raster:function(t,e,r,n){if(\"translucent\"===t.renderPass&&0!==r.paint.get(\"raster-opacity\")&&n.length)for(var i=t.context,a=i.gl,o=e.getSource(),s=t.useProgram(\"raster\"),l=t.colorModeForRenderPass(),c=o instanceof I?[{},n]:t.stencilConfigForOverlap(n),u=c[0],f=c[1],h=f[f.length-1].overscaledZ,p=!t.options.moving,d=0,m=f;d<m.length;d+=1){var g=m[d],v=t.depthModeForSublayer(g.overscaledZ-h,1===r.paint.get(\"raster-opacity\")?At.ReadWrite:At.ReadOnly,a.LESS),y=e.getTile(g),x=t.transform.calculatePosMatrix(g.toUnwrapped(),p);y.registerFadeDuration(r.paint.get(\"raster-fade-duration\"));var b=e.findLoadedParent(g,0),_=cn(y,b,e,r,t.transform),w=void 0,T=void 0,k=\"nearest\"===r.paint.get(\"raster-resampling\")?a.NEAREST:a.LINEAR;i.activeTexture.set(a.TEXTURE0),y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),i.activeTexture.set(a.TEXTURE1),b?(b.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST),w=Math.pow(2,b.tileID.overscaledZ-y.tileID.overscaledZ),T=[y.tileID.canonical.x*w%1,y.tileID.canonical.y*w%1]):y.texture.bind(k,a.CLAMP_TO_EDGE,a.LINEAR_MIPMAP_NEAREST);var A=Vr(x,T||[0,0],w||1,_,r);o instanceof I?s.draw(i,a.TRIANGLES,v,Mt.disabled,l,Et.disabled,A,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,v,u[g.overscaledZ],l,Et.disabled,A,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}},background:function(t,e,r){var n=r.paint.get(\"background-color\"),i=r.paint.get(\"background-opacity\");if(0!==i){var a=t.context,o=a.gl,s=t.transform,l=s.tileSize,c=r.paint.get(\"background-pattern\");if(!t.isPatternMissing(c)){var u=!c&&1===n.a&&1===i&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass===u){var f=Mt.disabled,h=t.depthModeForSublayer(0,\"opaque\"===u?At.ReadWrite:At.ReadOnly),p=t.colorModeForRenderPass(),d=t.useProgram(c?\"backgroundPattern\":\"background\"),m=s.coveringTiles({tileSize:l});c&&(a.activeTexture.set(o.TEXTURE0),t.imageManager.bind(t.context));for(var g=r.getCrossfadeParameters(),v=0,y=m;v<y.length;v+=1){var x=y[v],b=t.transform.calculatePosMatrix(x.toUnwrapped()),_=c?Zr(b,i,t,c,{tileID:x,tileSize:l},g):Xr(b,i,n);d.draw(a,o.TRIANGLES,h,f,p,Et.disabled,_,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}}}},debug:function(t,e,r){for(var n=0;n<r.length;n++)xn(t,e,r[n])},custom:function(t,e,r){var n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){var a=i.prerender;a&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),a.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Mt.disabled);var o=\"3d\"===i.renderingMode?new At(t.context.gl.LEQUAL,At.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,At.ReadOnly);n.setDepthMode(o),i.render(n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}},_n=function(t,e){this.context=new Lt(t),this.transform=e,this._tileTextures={},this.setup(),this.numSublayers=Ct.maxUnderzooming+Ct.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new Ne,this.gpuTimers={}};_n.prototype.resize=function(e,r){if(this.width=e*t.browser.devicePixelRatio,this.height=r*t.browser.devicePixelRatio,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(var n=0,i=this.style._order;n<i.length;n+=1){var a=i[n];this.style._layers[a].resize()}},_n.prototype.setup=function(){var e=this.context,r=new t.StructArrayLayout2i4;r.emplaceBack(0,0),r.emplaceBack(t.EXTENT,0),r.emplaceBack(0,t.EXTENT),r.emplaceBack(t.EXTENT,t.EXTENT),this.tileExtentBuffer=e.createVertexBuffer(r,Ge.members),this.tileExtentSegments=t.SegmentVector.simpleSegment(0,0,4,2);var n=new t.StructArrayLayout2i4;n.emplaceBack(0,0),n.emplaceBack(t.EXTENT,0),n.emplaceBack(0,t.EXTENT),n.emplaceBack(t.EXTENT,t.EXTENT),this.debugBuffer=e.createVertexBuffer(n,Ge.members),this.debugSegments=t.SegmentVector.simpleSegment(0,0,4,5);var i=new t.StructArrayLayout4i8;i.emplaceBack(0,0,0,0),i.emplaceBack(t.EXTENT,0,t.EXTENT,0),i.emplaceBack(0,t.EXTENT,0,t.EXTENT),i.emplaceBack(t.EXTENT,t.EXTENT,t.EXTENT,t.EXTENT),this.rasterBoundsBuffer=e.createVertexBuffer(i,P.members),this.rasterBoundsSegments=t.SegmentVector.simpleSegment(0,0,4,2);var a=new t.StructArrayLayout2i4;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=e.createVertexBuffer(a,Ge.members),this.viewportSegments=t.SegmentVector.simpleSegment(0,0,4,2);var o=new t.StructArrayLayout1ui2;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=e.createIndexBuffer(o);var s=new t.StructArrayLayout3ui6;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=e.createIndexBuffer(s),this.emptyTexture=new t.Texture(e,{width:1,height:1,data:new Uint8Array([0,0,0,0])},e.gl.RGBA);var l=this.context.gl;this.stencilClearMode=new Mt({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)},_n.prototype.clearStencil=function(){var e=this.context,r=e.gl;this.nextStencilID=1,this.currentStencilSource=void 0;var n=t.create();t.ortho(n,0,this.width,this.height,0,0,1),t.scale(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(e,r.TRIANGLES,At.disabled,this.stencilClearMode,St.disabled,Et.disabled,Or(n),\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)},_n.prototype._renderTileClippingMasks=function(t,e){if(this.currentStencilSource!==t.source&&t.isTileClipped()&&e&&e.length){this.currentStencilSource=t.source;var r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(St.disabled),r.setDepthMode(At.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a<o.length;a+=1){var s=o[a],l=this._tileClippingMaskIDs[s.key]=this.nextStencilID++;i.draw(r,n.TRIANGLES,At.disabled,new Mt({func:n.ALWAYS,mask:0},l,255,n.KEEP,n.KEEP,n.REPLACE),St.disabled,Et.disabled,Or(s.posMatrix),\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}},_n.prototype.stencilModeFor3D=function(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Mt({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Mt({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},_n.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s<a;s++)o[s+i]=new Mt({func:r.GEQUAL,mask:255},s+this.nextStencilID,255,r.KEEP,r.KEEP,r.REPLACE);return this.nextStencilID+=a,[o,n]}return[(e={},e[i]=Mt.disabled,e),n]},_n.prototype.colorModeForRenderPass=function(){var e=this.context.gl;if(this._showOverdrawInspector){return new St([e.CONSTANT_COLOR,e.ONE],new t.Color(1/8,1/8,1/8,0),[!0,!0,!0,!0])}return\"opaque\"===this.renderPass?St.unblended:St.alphaBlended},_n.prototype.depthModeForSublayer=function(t,e,r){if(!this.opaquePassEnabledForLayer())return At.disabled;var n=1-((1+this.currentLayer)*this.numSublayers+t)*this.depthEpsilon;return new At(r||this.context.gl.LEQUAL,e,[n,n])},_n.prototype.opaquePassEnabledForLayer=function(){return this.currentLayer<this.opaquePassCutoff},_n.prototype.render=function(e,r){var n=this;this.style=e,this.options=r,this.lineAtlas=e.lineAtlas,this.imageManager=e.imageManager,this.glyphManager=e.glyphManager,this.symbolFadeChange=e.placement.symbolFadeChange(t.browser.now()),this.imageManager.beginFrame();var i=this.style._order,a=this.style.sourceCaches;for(var o in a){var s=a[o];s.used&&s.prepare(this.context)}var l,c,u={},f={},h={};for(var p in a){var d=a[p];u[p]=d.getVisibleCoordinates(),f[p]=u[p].slice().reverse(),h[p]=d.getVisibleCoordinates(!0).reverse()}this.opaquePassCutoff=1/0;for(var m=0;m<i.length;m++){var g=i[m];if(this.style._layers[g].is3D()){this.opaquePassCutoff=m;break}}this.renderPass=\"offscreen\";for(var v=0,y=i;v<y.length;v+=1){var x=y[v],b=this.style._layers[x];if(b.hasOffscreenPass()&&!b.isHidden(this.transform.zoom)){var _=f[b.source];(\"custom\"===b.type||_.length)&&this.renderLayer(this,a[b.source],b,_)}}for(this.context.bindFramebuffer.set(null),this.context.clear({color:r.showOverdrawInspector?t.Color.black:t.Color.transparent,depth:1}),this.clearStencil(),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(e._order.length+2)*this.numSublayers*this.depthEpsilon],this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer<i.length;this.currentLayer++){var A=this.style._layers[i[this.currentLayer]],M=a[A.source],S=(\"symbol\"===A.type?h:f)[A.source];this._renderTileClippingMasks(A,u[A.source]),this.renderLayer(this,M,A,S)}this.options.showTileBoundaries&&(t.values(this.style._layers).forEach((function(t){t.source&&!t.isHidden(n.transform.zoom)&&(t.source!==(c&&c.id)&&(c=n.style.sourceCaches[t.source]),(!l||l.getSource().maxzoom<c.getSource().maxzoom)&&(l=c))})),l&&bn.debug(this,l,l.getVisibleCoordinates()));this.options.showPadding&&mn(this),this.context.setDefault()},_n.prototype.renderLayer=function(t,e,r,n){r.isHidden(this.transform.zoom)||(\"background\"===r.type||\"custom\"===r.type||n.length)&&(this.id=r.id,this.gpuTimingStart(r),bn[r.type](t,e,r,n,this.style.placement.variableOffsets),this.gpuTimingEnd())},_n.prototype.gpuTimingStart=function(t){if(this.options.gpuTiming){var e=this.context.extTimerQuery,r=this.gpuTimers[t.id];r||(r=this.gpuTimers[t.id]={calls:0,cpuTime:0,query:e.createQueryEXT()}),r.calls++,e.beginQueryEXT(e.TIME_ELAPSED_EXT,r.query)}},_n.prototype.gpuTimingEnd=function(){if(this.options.gpuTiming){var t=this.context.extTimerQuery;t.endQueryEXT(t.TIME_ELAPSED_EXT)}},_n.prototype.collectGpuTimers=function(){var t=this.gpuTimers;return this.gpuTimers={},t},_n.prototype.queryGpuTimers=function(t){var e={};for(var r in t){var n=t[r],i=this.context.extTimerQuery,a=i.getQueryObjectEXT(n.query,i.QUERY_RESULT_EXT)/1e6;i.deleteQueryEXT(n.query),e[r]=a}return e},_n.prototype.translatePosMatrix=function(e,r,n,i,a){if(!n[0]&&!n[1])return e;var o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){var s=Math.sin(o),l=Math.cos(o);n=[n[0]*l-n[1]*s,n[0]*s+n[1]*l]}var c=[a?n[0]:pe(r,n[0],this.transform.zoom),a?n[1]:pe(r,n[1],this.transform.zoom),0],u=new Float32Array(16);return t.translate(u,e,c),u},_n.prototype.saveTileTexture=function(t){var e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]},_n.prototype.getTileTexture=function(t){var e=this._tileTextures[t];return e&&e.length>0?e.pop():null},_n.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},_n.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new _r(this.context,xr[t],e,Jr[t],this._showOverdrawInspector)),this.cache[r]},_n.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},_n.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},_n.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},_n.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var wn=function(t,e){this.points=t,this.planes=e};wn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new wn(a,o)};var Tn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Tn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a<r.length;a++)n[a]=r[a]?this.min[a]:this.center[a],i[a]=r[a]?this.center[a]:this.max[a];return i[2]=this.max[2],new Tn(n,i)},Tn.prototype.distanceX=function(t){return Math.max(Math.min(this.max[0],t[0]),this.min[0])-t[0]},Tn.prototype.distanceY=function(t){return Math.max(Math.min(this.max[1],t[1]),this.min[1])-t[1]},Tn.prototype.intersects=function(e){for(var r=[[this.min[0],this.min[1],0,1],[this.max[0],this.min[1],0,1],[this.max[0],this.max[1],0,1],[this.min[0],this.max[1],0,1]],n=!0,i=0;i<e.planes.length;i++){for(var a=e.planes[i],o=0,s=0;s<r.length;s++)o+=t.dot$1(a,r[s])>=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,f=0;f<e.points.length;f++){var h=e.points[f][l]-this.min[l];c=Math.min(c,h),u=Math.max(u,h)}if(u<0||c>this.max[l]-this.min[l])return 0}return 1};var kn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};kn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},kn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},kn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},kn.prototype.clone=function(){return new kn(this.top,this.bottom,this.left,this.right)},kn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var An=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new kn,this._posMatrixCache={},this._alignedPosMatrixCache={}},Mn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};An.prototype.clone=function(){var t=new An(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Mn.minZoom.get=function(){return this._minZoom},Mn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Mn.maxZoom.get=function(){return this._maxZoom},Mn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Mn.minPitch.get=function(){return this._minPitch},Mn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Mn.maxPitch.get=function(){return this._maxPitch},Mn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Mn.renderWorldCopies.get=function(){return this._renderWorldCopies},Mn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Mn.worldSize.get=function(){return this.tileSize*this.scale},Mn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Mn.size.get=function(){return new t.Point(this.width,this.height)},Mn.bearing.get=function(){return-this.angle/Math.PI*180},Mn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Mn.pitch.get=function(){return this._pitch/Math.PI*180},Mn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Mn.fov.get=function(){return this._fov/Math.PI*180},Mn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Mn.zoom.get=function(){return this._zoom},Mn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Mn.center.get=function(){return this._center},Mn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Mn.padding.get=function(){return this._edgeInsets.toJSON()},Mn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Mn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},An.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},An.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},An.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},An.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},An.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&r<e.minzoom)return[];void 0!==e.maxzoom&&r>e.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=wn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Tn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,v=m.y,y=m.fullyVisible;if(!y){var x=m.aabb.intersects(s);if(0===x)continue;y=2===x}var b=m.aabb.distanceX(o),_=m.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<<h-m.zoom)-2;if(m.zoom===h||w>T&&m.zoom>=l)f.push({tileID:new t.OverscaledTileID(m.zoom===h?p:m.zoom,m.wrap,m.zoom,g,v),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-v])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(v<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:y})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},An.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Mn.unmodified.get=function(){return this._unmodified},An.prototype.zoomScale=function(t){return Math.pow(2,t)},An.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},An.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},An.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Mn.point.get=function(){return this.project(this.center)},An.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},An.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},An.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},An.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},An.prototype.coordinateLocation=function(t){return t.toLngLat()},An.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,f=n[2]/a,h=u===f?0:(0-u)/(f-u);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,c,h)/this.worldSize)},An.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},An.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},An.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},An.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},An.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},An.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},An.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-a<c.y?c.y/(o-a):0}if(this.lngRange){var h=this.lngRange;s=t.mercatorXfromLng(h[0])*this.worldSize,r=(l=t.mercatorXfromLng(h[1])*this.worldSize)-s<c.x?c.x/(l-s):0}var p=this.point,d=Math.max(r||0,e||0);if(d)return this.center=this.unproject(new t.Point(r?(l+s)/2:p.x,e?(o+a)/2:p.y)),this.zoom+=this.scaleZoom(d),this._unmodified=u,void(this._constraining=!1);if(this.latRange){var m=p.y,g=c.y/2;m-g<a&&(i=a+g),m+g>o&&(i=o-g)}if(this.lngRange){var v=p.x,y=c.x/2;v-y<s&&(n=s+y),v+y>l&&(n=l-y)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},An.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,u,c),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*h+m*p,v=l-Math.round(l)+d*p+m*h,y=new Float64Array(f);if(t.translate(y,y,[g>.5?g-1:g,v>.5?v-1:v,0]),this.alignedProjMatrix=y,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},An.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},An.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},An.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s<l.length;s+=1){var c=l[s];n=Math.min(n,c.x),i=Math.min(i,c.y),a=Math.max(a,c.x),o=Math.max(o,c.y)}return[new t.Point(n,i),new t.Point(a,i),new t.Point(a,o),new t.Point(n,o),new t.Point(n,i)]},Object.defineProperties(An.prototype,Mn);var Sn=function(e){var r,n,i,a,o;this._hashName=e&&encodeURIComponent(e),t.bindAll([\"_getCurrentHash\",\"_onHashChange\",\"_updateHash\"],this),this._updateHash=(r=this._updateHashUnthrottled.bind(this),n=300,i=!1,a=null,o=function(){a=null,i&&(r(),a=setTimeout(o,n),i=!1)},function(){return i=!0,a||o(),a})};Sn.prototype.addTo=function(e){return this._map=e,t.window.addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this},Sn.prototype.remove=function(){return t.window.removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),delete this._map,this},Sn.prototype.getHashString=function(e){var r=this._map.getCenter(),n=Math.round(100*this._map.getZoom())/100,i=Math.ceil((n*Math.LN2+Math.log(512/360/.5))/Math.LN10),a=Math.pow(10,i),o=Math.round(r.lng*a)/a,s=Math.round(r.lat*a)/a,l=this._map.getBearing(),c=this._map.getPitch(),u=\"\";if(u+=e?\"/\"+o+\"/\"+s+\"/\"+n:n+\"/\"+s+\"/\"+o,(l||c)&&(u+=\"/\"+Math.round(10*l)/10),c&&(u+=\"/\"+Math.round(c)),this._hashName){var f=this._hashName,h=!1,p=t.window.location.hash.slice(1).split(\"&\").map((function(t){var e=t.split(\"=\")[0];return e===f?(h=!0,e+\"=\"+u):t})).filter((function(t){return t}));return h||p.push(f+\"=\"+u),\"#\"+p.join(\"&\")}return\"#\"+u},Sn.prototype._getCurrentHash=function(){var e,r=this,n=t.window.location.hash.replace(\"#\",\"\");return this._hashName?(n.split(\"&\").map((function(t){return t.split(\"=\")})).forEach((function(t){t[0]===r._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")):n.split(\"/\")},Sn.prototype._onHashChange=function(){var t=this._getCurrentHash();if(t.length>=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Sn.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var En={linearity:.3,easing:t.bezier(0,0,.3,1)},Ln=t.extend({deceleration:2500,maxSpeed:1400},En),Cn=t.extend({deceleration:20,maxSpeed:1400},En),Pn=t.extend({deceleration:1e3,maxSpeed:360},En),In=t.extend({deceleration:1e3,maxSpeed:90},En),On=function(t){this._map=t,this.clear()};function zn(t,e){(!t.duration||t.duration<e.duration)&&(t.duration=e.duration,t.easing=e.easing)}function Dn(e,r,n){var i=n.maxSpeed,a=n.linearity,o=n.deceleration,s=t.clamp(e*a/(r/1e3),-i,i),l=Math.abs(s)/(o*a);return{easing:n.easing,duration:1e3*l,amount:s*(l/2)}}On.prototype.clear=function(){this._inertiaBuffer=[]},On.prototype.record=function(e){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:t.browser.now(),settings:e})},On.prototype._drainInertiaBuffer=function(){for(var e=this._inertiaBuffer,r=t.browser.now();e.length>0&&r-e[0].time>160;)e.shift()},On.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n<i.length;n+=1){var a=i[n].settings;r.zoom+=a.zoomDelta||0,r.bearing+=a.bearingDelta||0,r.pitch+=a.pitchDelta||0,a.panDelta&&r.pan._add(a.panDelta),a.around&&(r.around=a.around),a.pinchAround&&(r.pinchAround=a.pinchAround)}var o=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,s={};if(r.pan.mag()){var l=Dn(r.pan.mag(),o,t.extend({},Ln,e||{}));s.offset=r.pan.mult(l.amount/r.pan.mag()),s.center=this._map.transform.center,zn(s,l)}if(r.zoom){var c=Dn(r.zoom,o,Cn);s.zoom=this._map.transform.zoom+c.amount,zn(s,c)}if(r.bearing){var u=Dn(r.bearing,o,Pn);s.bearing=this._map.transform.bearing+t.clamp(u.amount,-179,179),zn(s,u)}if(r.pitch){var f=Dn(r.pitch,o,In);s.pitch=this._map.transform.pitch+f.amount,zn(s,f)}if(s.zoom||s.bearing){var h=void 0===r.pinchAround?r.around:r.pinchAround;s.around=h?this._map.unproject(h):this._map.getCenter()}return this.clear(),t.extend(s,{noMoveStart:!0})}};var Rn=function(e){function n(n,i,a,o){void 0===o&&(o={});var s=r.mousePos(i.getCanvasContainer(),a),l=i.unproject(s);e.call(this,n,t.extend({point:s,lngLat:l,originalEvent:a},o)),this._defaultPrevented=!1,this.target=i}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Fn=function(e){function n(n,i,a){var o=\"touchend\"===n?a.changedTouches:a.touches,s=r.touchPos(i.getCanvasContainer(),o),l=s.map((function(t){return i.unproject(t)})),c=s.reduce((function(t,e,r,n){return t.add(e.div(n.length))}),new t.Point(0,0)),u=i.unproject(c);e.call(this,n,{points:s,point:c,lngLats:l,lngLat:u,originalEvent:a}),this._defaultPrevented=!1}e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n;var i={defaultPrevented:{configurable:!0}};return n.prototype.preventDefault=function(){this._defaultPrevented=!0},i.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(n.prototype,i),n}(t.Event),Bn=function(t){function e(e,r,n){t.call(this,e,{originalEvent:n}),this._defaultPrevented=!1}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={defaultPrevented:{configurable:!0}};return e.prototype.preventDefault=function(){this._defaultPrevented=!0},r.defaultPrevented.get=function(){return this._defaultPrevented},Object.defineProperties(e.prototype,r),e}(t.Event),Nn=function(t,e){this._map=t,this._clickTolerance=e.clickTolerance};Nn.prototype.reset=function(){delete this._mousedownPos},Nn.prototype.wheel=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Nn.prototype.mousedown=function(t,e){return this._mousedownPos=e,this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseup=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.click=function(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.dblclick=function(t){return this._firePreventable(new Rn(t.type,this._map,t))},Nn.prototype.mouseover=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.mouseout=function(t){this._map.fire(new Rn(t.type,this._map,t))},Nn.prototype.touchstart=function(t){return this._firePreventable(new Fn(t.type,this._map,t))},Nn.prototype.touchmove=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchend=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype.touchcancel=function(t){this._map.fire(new Fn(t.type,this._map,t))},Nn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Nn.prototype.isEnabled=function(){return!0},Nn.prototype.isActive=function(){return!1},Nn.prototype.enable=function(){},Nn.prototype.disable=function(){};var jn=function(t){this._map=t};jn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},jn.prototype.mousemove=function(t){this._map.fire(new Rn(t.type,this._map,t))},jn.prototype.mousedown=function(){this._delayContextMenu=!0},jn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Rn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},jn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Rn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},jn.prototype.isEnabled=function(){return!0},jn.prototype.isActive=function(){return!1},jn.prototype.enable=function(){},jn.prototype.disable=function(){};var Un=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Vn(t,e){for(var r={},n=0;n<t.length;n++)r[t[n].identifier]=e[n];return r}Un.prototype.isEnabled=function(){return!!this._enabled},Un.prototype.isActive=function(){return!!this._active},Un.prototype.enable=function(){this.isEnabled()||(this._enabled=!0)},Un.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},Un.prototype.mousedown=function(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(r.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)},Un.prototype.mousemoveWindow=function(t,e){if(this._active){var n=e;if(!(this._lastPos.equals(n)||!this._box&&n.dist(this._startPos)<this._clickTolerance)){var i=this._startPos;this._lastPos=n,this._box||(this._box=r.create(\"div\",\"mapboxgl-boxzoom\",this._container),this._container.classList.add(\"mapboxgl-crosshair\"),this._fireEvent(\"boxzoomstart\",t));var a=Math.min(i.x,n.x),o=Math.max(i.x,n.x),s=Math.min(i.y,n.y),l=Math.max(i.y,n.y);r.setTransform(this._box,\"translate(\"+a+\"px,\"+s+\"px)\"),this._box.style.width=o-a+\"px\",this._box.style.height=l-s+\"px\"}}},Un.prototype.mouseupWindow=function(e,n){var i=this;if(this._active&&0===e.button){var a=this._startPos,o=n;if(this.reset(),r.suppressClick(),a.x!==o.x||a.y!==o.y)return this._map.fire(new t.Event(\"boxzoomend\",{originalEvent:e})),{cameraAnimation:function(t){return t.fitScreenCoordinates(a,o,i._map.getBearing(),{linear:!0})}};this._fireEvent(\"boxzoomcancel\",e)}},Un.prototype.keydown=function(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))},Un.prototype.reset=function(){this._active=!1,this._container.classList.remove(\"mapboxgl-crosshair\"),this._box&&(r.remove(this._box),this._box=null),r.enableDrag(),delete this._startPos,delete this._lastPos},Un.prototype._fireEvent=function(e,r){return this._map.fire(new t.Event(e,{originalEvent:r}))};var Hn=function(t){this.reset(),this.numTouches=t.numTouches};Hn.prototype.reset=function(){delete this.centroid,delete this.startTime,delete this.touches,this.aborted=!1},Hn.prototype.touchstart=function(e,r,n){(this.centroid||n.length>this.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n<i.length;n+=1){var a=i[n];r._add(a)}return r.div(e.length)}(r),this.touches=Vn(n,r)))},Hn.prototype.touchmove=function(t,e,r){if(!this.aborted&&this.centroid){var n=Vn(r,e);for(var i in this.touches){var a=this.touches[i],o=n[i];(!o||o.dist(a)>30)&&(this.aborted=!0)}}},Hn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var qn=function(t){this.singleTap=new Hn(t),this.numTaps=t.numTaps,this.reset()};qn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},qn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},qn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},qn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Gn=function(){this._zoomIn=new qn({numTouches:1,numTaps:2}),this._zoomOut=new qn({numTouches:2,numTaps:1}),this.reset()};Gn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Gn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Gn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Gn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Gn.prototype.touchcancel=function(){this.reset()},Gn.prototype.enable=function(){this._enabled=!0},Gn.prototype.disable=function(){this._enabled=!1,this.reset()},Gn.prototype.isEnabled=function(){return this._enabled},Gn.prototype.isActive=function(){return this._active};var Yn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Yn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Yn.prototype._correctButton=function(t,e){return!1},Yn.prototype._move=function(t,e){return{}},Yn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Yn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)<this._clickTolerance)))return this._moved=!0,this._lastPoint=e,this._move(r,e)},Yn.prototype.mouseupWindow=function(t){r.mouseButton(t)===this._eventButton&&(this._moved&&r.suppressClick(),this.reset())},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.mousedown=function(e,r){t.prototype.mousedown.call(this,e,r),this._lastPoint&&(this._active=!0)},e.prototype._correctButton=function(t,e){return 0===e&&!t.ctrlKey},e.prototype._move=function(t,e){return{around:e,panDelta:e.sub(t)}},e}(Yn),Xn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=.8*(e.x-t.x);if(r)return this._active=!0,{bearingDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Zn=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._correctButton=function(t,e){return 0===e&&t.ctrlKey||2===e},e.prototype._move=function(t,e){var r=-.5*(e.y-t.y);if(r)return this._active=!0,{pitchDelta:r}},e.prototype.contextmenu=function(t){t.preventDefault()},e}(Yn),Jn=function(t){this._minTouches=1,this._clickTolerance=t.clickTolerance||1,this.reset()};Jn.prototype.reset=function(){this._active=!1,this._touches={},this._sum=new t.Point(0,0)},Jn.prototype.touchstart=function(t,e,r){return this._calculateTransform(t,e,r)},Jn.prototype.touchmove=function(t,e,r){if(this._active)return t.preventDefault(),this._calculateTransform(t,e,r)},Jn.prototype.touchend=function(t,e,r){this._calculateTransform(t,e,r),this._active&&r.length<this._minTouches&&this.reset()},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype._calculateTransform=function(e,r,n){n.length>0&&(this._active=!0);var i=Vn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(s<this._minTouches)&&o.mag()){var f=o.div(s);if(this._sum._add(f),!(this._sum.mag()<this._clickTolerance))return{around:a.div(s),panDelta:f}}},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn=function(){this.reset()};function Qn(t,e,r){for(var n=0;n<t.length;n++)if(t[n].identifier===r)return e[n]}Kn.prototype.reset=function(){this._active=!1,delete this._firstTwoTouches},Kn.prototype._start=function(t){},Kn.prototype._move=function(t,e,r){return{}},Kn.prototype.touchstart=function(t,e,r){this._firstTwoTouches||r.length<2||(this._firstTwoTouches=[r[0].identifier,r[1].identifier],this._start([e[0],e[1]]))},Kn.prototype.touchmove=function(t,e,r){if(this._firstTwoTouches){t.preventDefault();var n=this._firstTwoTouches,i=n[0],a=n[1],o=Qn(r,e,i),s=Qn(r,e,a);if(o&&s){var l=this._aroundCenter?null:o.add(s).div(2);return this._move([o,s],l,t)}}},Kn.prototype.touchend=function(t,e,n){if(this._firstTwoTouches){var i=this._firstTwoTouches,a=i[0],o=i[1],s=Qn(n,e,a),l=Qn(n,e,o);s&&l||(this._active&&r.suppressClick(),this.reset())}},Kn.prototype.touchcancel=function(){this.reset()},Kn.prototype.enable=function(t){this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around},Kn.prototype.disable=function(){this._enabled=!1,this.reset()},Kn.prototype.isEnabled=function(){return this._enabled},Kn.prototype.isActive=function(){return this._active};function $n(t,e){return Math.log(t/e)/Math.LN2}var ti=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._distance,delete this._startDistance},e.prototype._start=function(t){this._startDistance=this._distance=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._distance;if(this._distance=t[0].dist(t[1]),this._active||!(Math.abs($n(this._distance,this._startDistance))<.1))return this._active=!0,{zoomDelta:$n(this._distance,r),pinchAround:e}},e}(Kn);function ei(t,e){return 180*t.angleWith(e)/Math.PI}var ri=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),delete this._minDiameter,delete this._startVector,delete this._vector},e.prototype._start=function(t){this._startVector=this._vector=t[0].sub(t[1]),this._minDiameter=t[0].dist(t[1])},e.prototype._move=function(t,e){var r=this._vector;if(this._vector=t[0].sub(t[1]),this._active||!this._isBelowThreshold(this._vector))return this._active=!0,{bearingDelta:ei(this._vector,r),pinchAround:e}},e.prototype._isBelowThreshold=function(t){this._minDiameter=Math.min(this._minDiameter,t.mag());var e=25/(Math.PI*this._minDiameter)*360,r=ei(t,this._startVector);return Math.abs(r)<e},e}(Kn);function ni(t){return Math.abs(t.y)>Math.abs(t.x)}var ii=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ni(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid){this._lastPoints=t,this._active=!0;return{pitchDelta:-.5*((n.y+i.y)/2)}}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ni(t)&&ni(e)&&a}},e}(Kn),ai={panStep:100,bearingStep:15,pitchStep:10},oi=function(){var t=ai;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function si(t){return t*(2-t)}oi.prototype.reset=function(){this._active=!1},oi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:si,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},oi.prototype.enable=function(){this._enabled=!0},oi.prototype.disable=function(){this._enabled=!1,this.reset()},oi.prototype.isEnabled=function(){return this._enabled},oi.prototype.isActive=function(){return this._active};var li=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=1/450,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};li.prototype.setZoomRate=function(t){this._defaultZoomRate=t},li.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},li.prototype.isEnabled=function(){return!!this._enabled},li.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},li.prototype.isZooming=function(){return!!this._zooming},li.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},li.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},li.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%4.000244140625==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},li.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},li.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},li.prototype.renderFrame=function(){return this._onScrollFrame()},li.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>4.000244140625?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=c(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},li.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},li.prototype.reset=function(){this._active=!1};var ci=function(t,e){this._clickZoom=t,this._tapZoom=e};ci.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},ci.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},ci.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},ci.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var ui=function(){this.reset()};ui.prototype.reset=function(){this._active=!1},ui.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},ui.prototype.enable=function(){this._enabled=!0},ui.prototype.disable=function(){this._enabled=!1,this.reset()},ui.prototype.isEnabled=function(){return this._enabled},ui.prototype.isActive=function(){return this._active};var fi=function(){this._tap=new qn({numTouches:1,numTaps:1}),this.reset()};fi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},fi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},fi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},fi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},fi.prototype.touchcancel=function(){this.reset()},fi.prototype.enable=function(){this._enabled=!0},fi.prototype.disable=function(){this._enabled=!1,this.reset()},fi.prototype.isEnabled=function(){return this._enabled},fi.prototype.isActive=function(){return this._active};var hi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};hi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},hi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},hi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},hi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var pi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};pi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},pi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},pi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},pi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var di=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};di.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},di.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},di.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},di.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},di.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},di.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},gi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function vi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var yi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new On(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;a<o.length;a+=1){var s=o[a],l=s[0],c=s[1],u=s[2];r.addEventListener(l,c,l===t.window.document?this.handleWindowEvent:this.handleEvent,u)}};yi.prototype.destroy=function(){for(var e=0,n=this._listeners;e<n.length;e+=1){var i=n[e],a=i[0],o=i[1],s=i[2];r.removeEventListener(a,o,a===t.window.document?this.handleWindowEvent:this.handleEvent,s)}},yi.prototype._addDefaultHandlers=function(t){var e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Nn(e,t));var n=e.boxZoom=new Un(e,t);this._add(\"boxZoom\",n);var i=new Gn,a=new ui;e.doubleClickZoom=new ci(a,i),this._add(\"tapZoom\",i),this._add(\"clickZoom\",a);var o=new fi;this._add(\"tapDragZoom\",o);var s=e.touchPitch=new ii;this._add(\"touchPitch\",s);var l=new Xn(t),c=new Zn(t);e.dragRotate=new pi(t,l,c),this._add(\"mouseRotate\",l,[\"mousePitch\"]),this._add(\"mousePitch\",c,[\"mouseRotate\"]);var u=new Wn(t),f=new Jn(t);e.dragPan=new hi(r,u,f),this._add(\"mousePan\",u),this._add(\"touchPan\",f,[\"touchZoom\",\"touchRotate\"]);var h=new ri,p=new ti;e.touchZoomRotate=new di(r,p,h,o),this._add(\"touchRotate\",h,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",p,[\"touchPan\",\"touchRotate\"]);var d=e.scrollZoom=new li(e,this);this._add(\"scrollZoom\",d,[\"mousePan\"]);var m=e.keyboard=new oi;this._add(\"keyboard\",m),this._add(\"blockableMapEvent\",new jn(e));for(var g=0,v=[\"boxZoom\",\"doubleClickZoom\",\"tapDragZoom\",\"touchPitch\",\"dragRotate\",\"dragPan\",\"touchZoomRotate\",\"scrollZoom\",\"keyboard\"];g<v.length;g+=1){var y=v[g];t.interactive&&t[y]&&e[y].enable(t[y])}},yi.prototype._add=function(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e},yi.prototype.stop=function(){if(!this._updatingCamera){for(var t=0,e=this._handlers;t<e.length;t+=1){e[t].handler.reset()}this._inertia.clear(),this._fireEvents({},{}),this._changes=[]}},yi.prototype.isActive=function(){for(var t=0,e=this._handlers;t<e.length;t+=1){if(e[t].handler.isActive())return!0}return!1},yi.prototype.isZooming=function(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()},yi.prototype.isRotating=function(){return!!this._eventsInProgress.rotate},yi.prototype.isMoving=function(){return Boolean(mi(this._eventsInProgress))||this.isZooming()},yi.prototype._blockedByActive=function(t,e,r){for(var n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1},yi.prototype.handleWindowEvent=function(t){this.handleEvent(t,t.type+\"Window\")},yi.prototype._getMapTouches=function(t){for(var e=[],r=0,n=t;r<n.length;r+=1){var i=n[r],a=i.target;this._el.contains(a)&&e.push(i)}return e},yi.prototype.handleEvent=function(t,e){if(\"blur\"!==t.type){this._updatingCamera=!0;for(var n=\"renderFrame\"===t.type?void 0:t,i={needsRenderFrame:!1},a={},o={},s=t.touches?this._getMapTouches(t.touches):void 0,l=s?r.touchPos(this._el,s):r.mousePos(this._el,t),c=0,u=this._handlers;c<u.length;c+=1){var f=u[c],h=f.handlerName,p=f.handler,d=f.allowed;if(p.isEnabled()){var m=void 0;this._blockedByActive(o,d,h)?p.reset():p[e||t.type]&&(m=p[e||t.type](t,l,s),this.mergeHandlerResult(i,a,m,h,n),m&&m.needsRenderFrame&&this._triggerRenderFrame()),(m||p.isActive())&&(o[h]=p)}}var g={};for(var v in this._previousActiveHandlers)o[v]||(g[v]=n);this._previousActiveHandlers=o,(Object.keys(g).length||vi(i))&&(this._changes.push([i,a,g]),this._triggerRenderFrame()),(Object.keys(o).length||vi(i))&&this._map._stop(!0),this._updatingCamera=!1;var y=i.cameraAnimation;y&&(this._inertia.clear(),this._fireEvents({},{}),this._changes=[],y(this._map))}else this.stop()},yi.prototype.mergeHandlerResult=function(e,r,n,i,a){if(n){t.extend(e,n);var o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}},yi.prototype._applyChanges=function(){for(var e={},r={},n={},i=0,a=this._changes;i<a.length;i+=1){var o=a[i],s=o[0],l=o[1],c=o[2];s.panDelta&&(e.panDelta=(e.panDelta||new t.Point(0,0))._add(s.panDelta)),s.zoomDelta&&(e.zoomDelta=(e.zoomDelta||0)+s.zoomDelta),s.bearingDelta&&(e.bearingDelta=(e.bearingDelta||0)+s.bearingDelta),s.pitchDelta&&(e.pitchDelta=(e.pitchDelta||0)+s.pitchDelta),void 0!==s.around&&(e.around=s.around),void 0!==s.pinchAround&&(e.pinchAround=s.pinchAround),s.noInertia&&(e.noInertia=s.noInertia),t.extend(r,l),t.extend(n,c)}this._updateMapTransform(e,r,n),this._changes=[]},yi.prototype._updateMapTransform=function(t,e,r){var n=this._map,i=n.transform;if(!vi(t))return this._fireEvents(e,r);var a=t.panDelta,o=t.zoomDelta,s=t.bearingDelta,l=t.pitchDelta,c=t.around,u=t.pinchAround;void 0!==u&&(c=u),n._stop(!0),c=c||n.transform.centerPoint;var f=i.pointLocation(a?c.sub(a):c);s&&(i.bearing+=s),l&&(i.pitch+=l),o&&(i.zoom+=o),i.setLocationAtPoint(f,c),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r)},yi.prototype._fireEvents=function(e,r){var n=this,i=mi(this._eventsInProgress),a=mi(e),o={};for(var s in e){var l=e[s].originalEvent;this._eventsInProgress[s]||(o[s+\"start\"]=l),this._eventsInProgress[s]=e[s]}for(var c in!i&&a&&this._fireEvent(\"movestart\",a.originalEvent),o)this._fireEvent(c,o[c]);for(var u in e.rotate&&(this._bearingChanged=!0),a&&this._fireEvent(\"move\",a.originalEvent),e){var f=e[u].originalEvent;this._fireEvent(u,f)}var h,p={};for(var d in this._eventsInProgress){var m=this._eventsInProgress[d],g=m.handlerName,v=m.originalEvent;this._handlersById[g].isActive()||(delete this._eventsInProgress[d],h=r[g]||v,p[d+\"end\"]=h)}for(var y in p)this._fireEvent(y,p[y]);var x=mi(this._eventsInProgress);if((i||a)&&!x){this._updatingCamera=!0;var b=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),_=function(t){return 0!==t&&-n._bearingSnap<t&&t<n._bearingSnap};b?(_(b.bearing||this._map.getBearing())&&(b.bearing=0),this._map.easeTo(b,{originalEvent:h})):(this._map.fire(new t.Event(\"moveend\",{originalEvent:h})),_(this._map.getBearing())&&this._map.resetNorth()),this._bearingChanged=!1,this._updatingCamera=!1}},yi.prototype._fireEvent=function(e,r){this._map.fire(new t.Event(e,r?{originalEvent:r}:{}))},yi.prototype._triggerRenderFrame=function(){var t=this;void 0===this._frameId&&(this._frameId=this._map._requestRenderFrame((function(e){delete t._frameId,t.handleEvent(new gi(\"renderFrame\",{timeStamp:e})),t._applyChanges()})))};var xi=function(e){function r(r,n){e.call(this),this._moving=!1,this._zooming=!1,this.transform=r,this._bearingSnap=n.bearingSnap,t.bindAll([\"_renderFrameCallback\"],this)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.getCenter=function(){return new t.LngLat(this.transform.center.lng,this.transform.center.lat)},r.prototype.setCenter=function(t,e){return this.jumpTo({center:t},e)},r.prototype.panBy=function(e,r,n){return e=t.Point.convert(e).mult(-1),this.panTo(this.transform.center,t.extend({offset:e},r),n)},r.prototype.panTo=function(e,r,n){return this.easeTo(t.extend({center:e},r),n)},r.prototype.getZoom=function(){return this.transform.zoom},r.prototype.setZoom=function(t,e){return this.jumpTo({zoom:t},e),this},r.prototype.zoomTo=function(e,r,n){return this.easeTo(t.extend({zoom:e},r),n)},r.prototype.zoomIn=function(t,e){return this.zoomTo(this.getZoom()+1,t,e),this},r.prototype.zoomOut=function(t,e){return this.zoomTo(this.getZoom()-1,t,e),this},r.prototype.getBearing=function(){return this.transform.bearing},r.prototype.setBearing=function(t,e){return this.jumpTo({bearing:t},e),this},r.prototype.getPadding=function(){return this.transform.padding},r.prototype.setPadding=function(t,e){return this.jumpTo({padding:t},e),this},r.prototype.rotateTo=function(e,r,n){return this.easeTo(t.extend({bearing:e},r),n)},r.prototype.resetNorth=function(e,r){return this.rotateTo(0,t.extend({duration:1e3},e),r),this},r.prototype.resetNorthPitch=function(e,r){return this.easeTo(t.extend({bearing:0,pitch:0,duration:1e3},e),r),this},r.prototype.snapToNorth=function(t,e){return Math.abs(this.getBearing())<this._bearingSnap?this.resetNorth(t,e):this},r.prototype.getPitch=function(){return this.transform.pitch},r.prototype.setPitch=function(t,e){return this.jumpTo({pitch:t},e),this},r.prototype.cameraForBounds=function(e,r){return e=t.LngLatBounds.convert(e),this._cameraForBoxAndBearing(e.getNorthWest(),e.getSouthEast(),0,r)},r.prototype._cameraForBoxAndBearing=function(e,r,n,i){var a={top:0,bottom:0,right:0,left:0};if(\"number\"==typeof(i=t.extend({padding:a,offset:[0,0],maxZoom:this.transform.maxZoom},i)).padding){var o=i.padding;i.padding={top:o,bottom:o,right:o,left:o}}i.padding=t.extend(a,i.padding);var s=this.transform,l=s.padding,c=s.project(t.LngLat.convert(e)),u=s.project(t.LngLat.convert(r)),f=c.rotate(-n*Math.PI/180),h=u.rotate(-n*Math.PI/180),p=new t.Point(Math.max(f.x,h.x),Math.max(f.y,h.y)),d=new t.Point(Math.min(f.x,h.x),Math.min(f.y,h.y)),m=p.sub(d),g=(s.width-(l.left+l.right+i.padding.left+i.padding.right))/m.x,v=(s.height-(l.top+l.bottom+i.padding.top+i.padding.bottom))/m.y;if(!(v<0||g<0)){var y=Math.min(s.scaleZoom(s.scale*Math.min(g,v)),i.maxZoom),x=t.Point.convert(i.offset),b=(i.padding.left-i.padding.right)/2,_=(i.padding.top-i.padding.bottom)/2,w=new t.Point(x.x+b,x.y+_).mult(s.scale/s.zoomScale(y));return{center:s.unproject(c.add(u).div(2).sub(w)),zoom:y,bearing:n}}t.warnOnce(\"Map cannot fit within canvas with the given bounds, padding, and/or offset.\")},r.prototype.fitBounds=function(t,e,r){return this._fitInternal(this.cameraForBounds(t,e),e,r)},r.prototype.fitScreenCoordinates=function(e,r,n,i,a){return this._fitInternal(this._cameraForBoxAndBearing(this.transform.pointLocation(t.Point.convert(e)),this.transform.pointLocation(t.Point.convert(r)),n,i),i,a)},r.prototype._fitInternal=function(e,r,n){return e?(delete(r=t.extend(e,r)).padding,r.linear?this.easeTo(r,n):this.flyTo(r,n)):this},r.prototype.jumpTo=function(e,r){this.stop();var n=this.transform,i=!1,a=!1,o=!1;return\"zoom\"in e&&n.zoom!==+e.zoom&&(i=!0,n.zoom=+e.zoom),void 0!==e.center&&(n.center=t.LngLat.convert(e.center)),\"bearing\"in e&&n.bearing!==+e.bearing&&(a=!0,n.bearing=+e.bearing),\"pitch\"in e&&n.pitch!==+e.pitch&&(o=!0,n.pitch=+e.pitch),null==e.padding||n.isPaddingEqual(e.padding)||(n.padding=e.padding),this.fire(new t.Event(\"movestart\",r)).fire(new t.Event(\"move\",r)),i&&this.fire(new t.Event(\"zoomstart\",r)).fire(new t.Event(\"zoom\",r)).fire(new t.Event(\"zoomend\",r)),a&&this.fire(new t.Event(\"rotatestart\",r)).fire(new t.Event(\"rotate\",r)).fire(new t.Event(\"rotateend\",r)),o&&this.fire(new t.Event(\"pitchstart\",r)).fire(new t.Event(\"pitch\",r)).fire(new t.Event(\"pitchend\",r)),this.fire(new t.Event(\"moveend\",r))},r.prototype.easeTo=function(e,r){var n=this;this._stop(!1,e.easeId),(!1===(e=t.extend({offset:[0,0],duration:500,easing:t.ease},e)).animate||!e.essential&&t.browser.prefersReducedMotion)&&(e.duration=0);var i=this.transform,a=this.getZoom(),o=this.getBearing(),s=this.getPitch(),l=this.getPadding(),c=\"zoom\"in e?+e.zoom:a,u=\"bearing\"in e?this._normalizeBearing(e.bearing,o):o,f=\"pitch\"in e?+e.pitch:s,h=\"padding\"in e?e.padding:i.padding,p=t.Point.convert(e.offset),d=i.centerPoint.add(p),m=i.pointLocation(d),g=t.LngLat.convert(e.center||m);this._normalizeCenter(g);var v,y,x=i.project(m),b=i.project(g).sub(x),_=i.zoomScale(c-a);e.around&&(v=t.LngLat.convert(e.around),y=i.locationPoint(v));var w={moving:this._moving,zooming:this._zooming,rotating:this._rotating,pitching:this._pitching};return this._zooming=this._zooming||c!==a,this._rotating=this._rotating||o!==u,this._pitching=this._pitching||f!==s,this._padding=!i.isPaddingEqual(h),this._easeId=e.easeId,this._prepareEase(r,e.noMoveStart,w),clearTimeout(this._easeEndTimeoutID),this._ease((function(e){if(n._zooming&&(i.zoom=t.number(a,c,e)),n._rotating&&(i.bearing=t.number(o,u,e)),n._pitching&&(i.pitch=t.number(s,f,e)),n._padding&&(i.interpolatePadding(l,h,e),d=i.centerPoint.add(p)),v)i.setLocationAtPoint(v,y);else{var m=i.zoomScale(i.zoom-a),g=c>a?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),v=a.pointLocation(g),y=t.LngLat.convert(e.center||v);this._normalizeCenter(y);var x=a.project(v),b=a.project(y).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),I=function(t){return C(P)/C(P+_*t)},O=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},z=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(z)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=T<w?-1:1;z=Math.abs(Math.log(T/w))/_,O=function(){return 0},I=function(t){return Math.exp(D*_*t)}}if(\"duration\"in e)e.duration=+e.duration;else{var R=\"screenSpeed\"in e?+e.screenSpeed/_:+e.speed;e.duration=1e3*z/R}return e.maxDuration&&e.duration>e.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*z,d=1/I(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var v=1===e?y:a.unproject(x.add(b.mult(O(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?v.wrap():v,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)<n&&(e-=360),Math.abs(e+360-r)<n&&(e+=360),e},r.prototype._normalizeCenter=function(t){var e=this.transform;if(e.renderWorldCopies&&!e.lngRange){var r=t.lng-e.center.lng;t.lng+=r>180?-360:r<-180?360:0}},r}(t.Evented),bi=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};bi.prototype.getDefaultPosition=function(){return\"bottom-right\"},bi.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},bi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},bi.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n<r.length-1?\"&\":\"\")),t}),\"?\");e.href=t.config.FEEDBACK_URL+\"/\"+n+(this._map._hash?this._map._hash.getHashString(!0):\"\"),e.rel=\"noopener nofollow\"}},bi.prototype._updateData=function(t){!t||\"metadata\"!==t.sourceDataType&&\"style\"!==t.dataType||(this._updateAttributions(),this._updateEditLink())},bi.prototype._updateAttributions=function(){if(this._map.style){var t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((function(t){return\"string\"!=typeof t?\"\":t}))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){var e=this._map.style.stylesheet;this.styleOwner=e.owner,this.styleId=e.id}var r=this._map.style.sourceCaches;for(var n in r){var i=r[n];if(i.used){var a=i.getSource();a.attribution&&t.indexOf(a.attribution)<0&&t.push(a.attribution)}}t.sort((function(t,e){return t.length-e.length}));var o=(t=t.filter((function(e,r){for(var n=r+1;n<t.length;n++)if(t[n].indexOf(e)>=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},bi.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var _i=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};_i.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},_i.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},_i.prototype.getDefaultPosition=function(){return\"bottom-left\"},_i.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},_i.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t){if(t[e].getSource().mapbox_logo)return!0}return!1}},_i.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var wi=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};wi.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},wi.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;r<n.length;r+=1){var i=n[r];if(i.id===t)return void(i.cancelled=!0)}},wi.prototype.run=function(t){void 0===t&&(t=0);var e=this._currentlyRunning=this._queue;this._queue=[];for(var r=0,n=e;r<n.length;r+=1){var i=n[r];if(!i.cancelled&&(i.callback(t),this._cleared))break}this._cleared=!1,this._currentlyRunning=!1},wi.prototype.clear=function(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]};var Ti={\"FullscreenControl.Enter\":\"Enter fullscreen\",\"FullscreenControl.Exit\":\"Exit fullscreen\",\"GeolocateControl.FindMyLocation\":\"Find my location\",\"GeolocateControl.LocationNotAvailable\":\"Location not available\",\"LogoControl.Title\":\"Mapbox logo\",\"NavigationControl.ResetBearing\":\"Reset bearing to north\",\"NavigationControl.ZoomIn\":\"Zoom in\",\"NavigationControl.ZoomOut\":\"Zoom out\",\"ScaleControl.Feet\":\"ft\",\"ScaleControl.Meters\":\"m\",\"ScaleControl.Kilometers\":\"km\",\"ScaleControl.Miles\":\"mi\",\"ScaleControl.NauticalMiles\":\"nm\"},ki=t.window.HTMLImageElement,Ai=t.window.HTMLElement,Mi=t.window.ImageBitmap,Si={center:[0,0],zoom:0,bearing:0,pitch:0,minZoom:-2,maxZoom:22,minPitch:0,maxPitch:60,interactive:!0,scrollZoom:!0,boxZoom:!0,dragRotate:!0,dragPan:!0,keyboard:!0,doubleClickZoom:!0,touchZoomRotate:!0,touchPitch:!0,bearingSnap:7,clickTolerance:3,pitchWithRotate:!0,hash:!1,attributionControl:!0,failIfMajorPerformanceCaveat:!1,preserveDrawingBuffer:!1,trackResize:!0,renderWorldCopies:!0,refreshExpiredTiles:!0,maxTileCacheSize:null,localIdeographFontFamily:\"sans-serif\",transformRequest:null,accessToken:null,fadeDuration:300,crossSourceCollisions:!0},Ei=function(n){function i(e){var r=this;if(null!=(e=t.extend({},Si,e)).minZoom&&null!=e.maxZoom&&e.minZoom>e.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>60)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new An(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new wi,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ti,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ai))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new yi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Sn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new bi({customAttribution:e.customAttribution})),this.addControl(new _i,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()<t&&this.setZoom(t),this;throw new Error(\"minZoom must be between -2 and the current maxZoom, inclusive\")},i.prototype.getMinZoom=function(){return this.transform.minZoom},i.prototype.setMaxZoom=function(t){if((t=null==t?22:t)>=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()<t&&this.setPitch(t),this;throw new Error(\"minPitch must be between 0 and the current maxPitch, inclusive\")},i.prototype.getMinPitch=function(){return this.transform.minPitch},i.prototype.setMaxPitch=function(t){if((t=null==t?60:t)>60)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Rn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Rn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Rn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o<a.length;o++){var s=a[o];if(s.layer===e&&s.listener===r){for(var l in s.delegates)i.off(l,s.delegates[l]);return a.splice(o,1),i}}}(this._delegatedListeners),this},i.prototype.queryRenderedFeatures=function(e,r){if(!this.style)return[];var n;if(void 0!==r||void 0===e||e instanceof t.Point||Array.isArray(e)||(r=e,e=void 0),r=r||{},(e=e||[[0,0],[this.transform.width,this.transform.height]])instanceof t.Point||\"number\"==typeof e[0])n=[t.Point.convert(e)];else{var i=t.Point.convert(e[0]),a=t.Point.convert(e[1]);n=[i,new t.Point(a.x,i.y),a,new t.Point(i.x,a.y),i]}return this.style.queryRenderedFeatures(n,r,this.transform)},i.prototype.querySourceFeatures=function(t,e){return this.style.querySourceFeatures(t,e)},i.prototype.setStyle=function(e,r){return!1!==(r=t.extend({},{localIdeographFontFamily:this._localIdeographFontFamily},r)).diff&&r.localIdeographFontFamily===this._localIdeographFontFamily&&this.style&&e?(this._diffStyle(e,r),this):(this._localIdeographFontFamily=r.localIdeographFontFamily,this._updateStyle(e,r))},i.prototype._getUIString=function(t){var e=this._locale[t];if(null==e)throw new Error(\"Missing UI string '\"+t+\"'\");return e},i.prototype._updateStyle=function(t,e){return this.style&&(this.style.setEventedParent(null),this.style._remove()),t?(this.style=new qe(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t):this.style.loadJSON(t),this):(delete this.style,this)},i.prototype._lazyInitEmptyStyle=function(){this.style||(this.style=new qe(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())},i.prototype._diffStyle=function(e,r){var n=this;if(\"string\"==typeof e){var i=this._requestManager.normalizeStyleURL(e),a=this._requestManager.transformRequest(i,t.ResourceType.Style);t.getJSON(a,(function(e,i){e?n.fire(new t.ErrorEvent(e)):i&&n._updateDiff(i,r)}))}else\"object\"==typeof e&&this._updateDiff(e,r)},i.prototype._updateDiff=function(e,r){try{this.style.setState(e)&&this._update(!0)}catch(n){t.warnOnce(\"Unable to perform style diff: \"+(n.message||n.error||n)+\".  Rebuilding the style from scratch.\"),this._updateStyle(e,r)}},i.prototype.getStyle=function(){if(this.style)return this.style.serialize()},i.prototype.isStyleLoaded=function(){return this.style?this.style.loaded():t.warnOnce(\"There is no style added to the map.\")},i.prototype.addSource=function(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)},i.prototype.isSourceLoaded=function(e){var r=this.style&&this.style.sourceCaches[e];if(void 0!==r)return r.loaded();this.fire(new t.ErrorEvent(new Error(\"There is no source with ID '\"+e+\"'\")))},i.prototype.areTilesLoaded=function(){var t=this.style&&this.style.sourceCaches;for(var e in t){var r=t[e]._tiles;for(var n in r){var i=r[n];if(\"loaded\"!==i.state&&\"errored\"!==i.state)return!1}}return!0},i.prototype.addSourceType=function(t,e,r){return this._lazyInitEmptyStyle(),this.style.addSourceType(t,e,r)},i.prototype.removeSource=function(t){return this.style.removeSource(t),this._update(!0)},i.prototype.getSource=function(t){return this.style.getSource(t)},i.prototype.addImage=function(e,r,n){void 0===n&&(n={});var i=n.pixelRatio;void 0===i&&(i=1);var a=n.sdf;void 0===a&&(a=!1);var o=n.stretchX,s=n.stretchY,l=n.content;this._lazyInitEmptyStyle();if(r instanceof ki||Mi&&r instanceof Mi){var c=t.browser.getImageData(r),u=c.width,f=c.height,h=c.data;this.style.addImage(e,{data:new t.RGBAImage({width:u,height:f},h),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0})}else{if(void 0===r.width||void 0===r.height)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));var p=r.width,d=r.height,m=r.data,g=r;this.style.addImage(e,{data:new t.RGBAImage({width:p,height:d},new Uint8Array(m)),pixelRatio:i,stretchX:o,stretchY:s,content:l,sdf:a,version:0,userImage:g}),g.onAdd&&g.onAdd(this,e)}},i.prototype.updateImage=function(e,r){var n=this.style.getImage(e);if(!n)return this.fire(new t.ErrorEvent(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));var i=r instanceof ki||Mi&&r instanceof Mi?t.browser.getImageData(r):r,a=i.width,o=i.height,s=i.data;if(void 0===a||void 0===o)return this.fire(new t.ErrorEvent(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(a!==n.data.width||o!==n.data.height)return this.fire(new t.ErrorEvent(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));var l=!(r instanceof ki||Mi&&r instanceof Mi);n.data.replace(s,l),this.style.updateImage(e,n)},i.prototype.hasImage=function(e){return e?!!this.style.getImage(e):(this.fire(new t.ErrorEvent(new Error(\"Missing required image id\"))),!1)},i.prototype.removeImage=function(t){this.style.removeImage(t)},i.prototype.loadImage=function(e,r){t.getImage(this._requestManager.transformRequest(e,t.ResourceType.Image),r)},i.prototype.listImages=function(){return this.style.listImages()},i.prototype.addLayer=function(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)},i.prototype.moveLayer=function(t,e){return this.style.moveLayer(t,e),this._update(!0)},i.prototype.removeLayer=function(t){return this.style.removeLayer(t),this._update(!0)},i.prototype.getLayer=function(t){return this.style.getLayer(t)},i.prototype.setLayerZoomRange=function(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)},i.prototype.setFilter=function(t,e,r){return void 0===r&&(r={}),this.style.setFilter(t,e,r),this._update(!0)},i.prototype.getFilter=function(t){return this.style.getFilter(t)},i.prototype.setPaintProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setPaintProperty(t,e,r,n),this._update(!0)},i.prototype.getPaintProperty=function(t,e){return this.style.getPaintProperty(t,e)},i.prototype.setLayoutProperty=function(t,e,r,n){return void 0===n&&(n={}),this.style.setLayoutProperty(t,e,r,n),this._update(!0)},i.prototype.getLayoutProperty=function(t,e){return this.style.getLayoutProperty(t,e)},i.prototype.setLight=function(t,e){return void 0===e&&(e={}),this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)},i.prototype.getLight=function(){return this.style.getLight()},i.prototype.setFeatureState=function(t,e){return this.style.setFeatureState(t,e),this._update()},i.prototype.removeFeatureState=function(t,e){return this.style.removeFeatureState(t,e),this._update()},i.prototype.getFeatureState=function(t){return this.style.getFeatureState(t)},i.prototype.getContainer=function(){return this._container},i.prototype.getCanvasContainer=function(){return this._canvasContainer},i.prototype.getCanvas=function(){return this._canvas},i.prototype._containerDimensions=function(){var t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]},i.prototype._detectMissingCSS=function(){\"rgb(250, 128, 114)\"!==t.window.getComputedStyle(this._missingCSSCanary).getPropertyValue(\"background-color\")&&t.warnOnce(\"This page appears to be missing CSS declarations for Mapbox GL JS, which may cause the map to display incorrectly. Please ensure your page includes mapbox-gl.css, as described in https://www.mapbox.com/mapbox-gl-js/api/.\")},i.prototype._setupContainer=function(){var t=this._container;t.classList.add(\"mapboxgl-map\"),(this._missingCSSCanary=r.create(\"div\",\"mapboxgl-canary\",t)).style.visibility=\"hidden\",this._detectMissingCSS();var e=this._canvasContainer=r.create(\"div\",\"mapboxgl-canvas-container\",t);this._interactive&&e.classList.add(\"mapboxgl-interactive\"),this._canvas=r.create(\"canvas\",\"mapboxgl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",\"0\"),this._canvas.setAttribute(\"aria-label\",\"Map\");var n=this._containerDimensions();this._resizeCanvas(n[0],n[1]);var i=this._controlContainer=r.create(\"div\",\"mapboxgl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((function(t){a[t]=r.create(\"div\",\"mapboxgl-ctrl-\"+t,i)}))},i.prototype._resizeCanvas=function(e,r){var n=t.browser.devicePixelRatio||1;this._canvas.width=n*e,this._canvas.height=n*r,this._canvas.style.width=e+\"px\",this._canvas.style.height=r+\"px\"},i.prototype._setupPainter=function(){var r=t.extend({},e.webGLContextAttributes,{failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1}),n=this._canvas.getContext(\"webgl\",r)||this._canvas.getContext(\"experimental-webgl\",r);n?(this.painter=new _n(n,this.transform),t.webpSupported.testSupport(n)):this.fire(new t.ErrorEvent(new Error(\"Failed to initialize WebGL\")))},i.prototype._contextLost=function(e){e.preventDefault(),this._frame&&(this._frame.cancel(),this._frame=null),this.fire(new t.Event(\"webglcontextlost\",{originalEvent:e}))},i.prototype._contextRestored=function(e){this._setupPainter(),this.resize(),this._update(),this.fire(new t.Event(\"webglcontextrestored\",{originalEvent:e}))},i.prototype.loaded=function(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()},i.prototype._update=function(t){return this.style?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this},i.prototype._requestRenderFrame=function(t){return this._update(),this._renderTaskQueue.add(t)},i.prototype._cancelRenderFrame=function(t){this._renderTaskQueue.remove(t)},i.prototype._render=function(e){var r,n=this,i=0,a=this.painter.context.extTimerQuery;if(this.listens(\"gpu-timing-frame\")&&(r=a.createQueryEXT(),a.beginQueryEXT(a.TIME_ELAPSED_EXT,r),i=t.browser.now()),this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(e),!this._removed){var o=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;var s=this.transform.zoom,l=t.browser.now();this.style.zoomHistory.update(s,l);var c=new t.EvaluationParameters(s,{now:l,fadeDuration:this._fadeDuration,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),u=c.crossFadingFactor();1===u&&u===this._crossFadingFactor||(o=!0,this._crossFadingFactor=u),this.style.update(c)}if(this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,this._fadeDuration,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:this._fadeDuration,showPadding:this.showPadding,gpuTiming:!!this.listens(\"gpu-timing-layer\")}),this.fire(new t.Event(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,this.fire(new t.Event(\"load\"))),this.style&&(this.style.hasTransitions()||o)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles(),this.listens(\"gpu-timing-frame\")){var f=t.browser.now()-i;a.endQueryEXT(a.TIME_ELAPSED_EXT,r),setTimeout((function(){var e=a.getQueryObjectEXT(r,a.QUERY_RESULT_EXT)/1e6;a.deleteQueryEXT(r),n.fire(new t.Event(\"gpu-timing-frame\",{cpuTime:f,gpuTime:e}))}),50)}if(this.listens(\"gpu-timing-layer\")){var h=this.painter.collectGpuTimers();setTimeout((function(){var e=n.painter.queryGpuTimers(h);n.fire(new t.Event(\"gpu-timing-layer\",{layerTimes:e}))}),50)}return this._sourcesDirty||this._styleDirty||this._placementDirty||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&(this._fullyLoaded||(this._fullyLoaded=!0),this.fire(new t.Event(\"idle\"))),this}},i.prototype.remove=function(){this._hash&&this._hash.remove();for(var e=0,r=this._controls;e<r.length;e+=1){r[e].onRemove(this)}this._controls=[],this._frame&&(this._frame.cancel(),this._frame=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),void 0!==t.window&&(t.window.removeEventListener(\"resize\",this._onWindowResize,!1),t.window.removeEventListener(\"online\",this._onWindowOnline,!1));var n=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");n&&n.loseContext(),Li(this._canvasContainer),Li(this._controlContainer),Li(this._missingCSSCanary),this._container.classList.remove(\"mapboxgl-map\"),this._removed=!0,this.fire(new t.Event(\"remove\"))},i.prototype.triggerRepaint=function(){var e=this;this.style&&!this._frame&&(this._frame=t.browser.frame((function(t){e._frame=null,e._render(t)})))},i.prototype._onWindowOnline=function(){this._update()},i.prototype._onWindowResize=function(t){this._trackResize&&this.resize({originalEvent:t})._update()},a.showTileBoundaries.get=function(){return!!this._showTileBoundaries},a.showTileBoundaries.set=function(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())},a.showPadding.get=function(){return!!this._showPadding},a.showPadding.set=function(t){this._showPadding!==t&&(this._showPadding=t,this._update())},a.showCollisionBoxes.get=function(){return!!this._showCollisionBoxes},a.showCollisionBoxes.set=function(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())},a.showOverdrawInspector.get=function(){return!!this._showOverdrawInspector},a.showOverdrawInspector.set=function(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())},a.repaint.get=function(){return!!this._repaint},a.repaint.set=function(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())},a.vertices.get=function(){return!!this._vertices},a.vertices.set=function(t){this._vertices=t,this._update()},i.prototype._setCacheLimits=function(e,r){t.setCacheLimits(e,r)},a.version.get=function(){return t.version},Object.defineProperties(i.prototype,a),i}(xi);function Li(t){t.parentNode&&t.parentNode.removeChild(t)}var Ci={showCompass:!0,showZoom:!0,visualizePitch:!1},Pi=function(e){var n=this;this.options=t.extend({},Ci,e),this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this.options.showZoom&&(t.bindAll([\"_setButtonTitle\",\"_updateZoomButtons\"],this),this._zoomInButton=this._createButton(\"mapboxgl-ctrl-zoom-in\",(function(t){return n._map.zoomIn({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomInButton).setAttribute(\"aria-hidden\",!0),this._zoomOutButton=this._createButton(\"mapboxgl-ctrl-zoom-out\",(function(t){return n._map.zoomOut({},{originalEvent:t})})),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._zoomOutButton).setAttribute(\"aria-hidden\",!0)),this.options.showCompass&&(t.bindAll([\"_rotateCompassArrow\"],this),this._compass=this._createButton(\"mapboxgl-ctrl-compass\",(function(t){n.options.visualizePitch?n._map.resetNorthPitch({},{originalEvent:t}):n._map.resetNorth({},{originalEvent:t})})),this._compassIcon=r.create(\"span\",\"mapboxgl-ctrl-icon\",this._compass),this._compassIcon.setAttribute(\"aria-hidden\",!0))};Pi.prototype._updateZoomButtons=function(){var t=this._map.getZoom();this._zoomInButton.disabled=t===this._map.getMaxZoom(),this._zoomOutButton.disabled=t===this._map.getMinZoom()},Pi.prototype._rotateCompassArrow=function(){var t=this.options.visualizePitch?\"scale(\"+1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)+\") rotateX(\"+this._map.transform.pitch+\"deg) rotateZ(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\":\"rotate(\"+this._map.transform.angle*(180/Math.PI)+\"deg)\";this._compassIcon.style.transform=t},Pi.prototype.onAdd=function(t){return this._map=t,this.options.showZoom&&(this._setButtonTitle(this._zoomInButton,\"ZoomIn\"),this._setButtonTitle(this._zoomOutButton,\"ZoomOut\"),this._map.on(\"zoom\",this._updateZoomButtons),this._updateZoomButtons()),this.options.showCompass&&(this._setButtonTitle(this._compass,\"ResetBearing\"),this.options.visualizePitch&&this._map.on(\"pitch\",this._rotateCompassArrow),this._map.on(\"rotate\",this._rotateCompassArrow),this._rotateCompassArrow(),this._handler=new Ii(this._map,this._compass,this.options.visualizePitch)),this._container},Pi.prototype.onRemove=function(){r.remove(this._container),this.options.showZoom&&this._map.off(\"zoom\",this._updateZoomButtons),this.options.showCompass&&(this.options.visualizePitch&&this._map.off(\"pitch\",this._rotateCompassArrow),this._map.off(\"rotate\",this._rotateCompassArrow),this._handler.off(),delete this._handler),delete this._map},Pi.prototype._createButton=function(t,e){var n=r.create(\"button\",t,this._container);return n.type=\"button\",n.addEventListener(\"click\",e),n},Pi.prototype._setButtonTitle=function(t,e){var r=this._map._getUIString(\"NavigationControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)};var Ii=function(e,n,i){void 0===i&&(i=!1),this._clickTolerance=10,this.element=n,this.mouseRotate=new Xn({clickTolerance:e.dragRotate._mouseRotate._clickTolerance}),this.map=e,i&&(this.mousePitch=new Zn({clickTolerance:e.dragRotate._mousePitch._clickTolerance})),t.bindAll([\"mousedown\",\"mousemove\",\"mouseup\",\"touchstart\",\"touchmove\",\"touchend\",\"reset\"],this),r.addEventListener(n,\"mousedown\",this.mousedown),r.addEventListener(n,\"touchstart\",this.touchstart,{passive:!1}),r.addEventListener(n,\"touchmove\",this.touchmove),r.addEventListener(n,\"touchend\",this.touchend),r.addEventListener(n,\"touchcancel\",this.reset)};function Oi(e,r,n){if(e=new t.LngLat(e.lng,e.lat),r){var i=new t.LngLat(e.lng-360,e.lat),a=new t.LngLat(e.lng+360,e.lat),o=n.locationPoint(e).distSqr(r);n.locationPoint(i).distSqr(r)<o?e=i:n.locationPoint(a).distSqr(r)<o&&(e=a)}for(;Math.abs(e.lng-n.center.lng)>180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ii.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ii.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ii.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ii.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ii.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ii.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ii.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ii.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ii.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)<this._clickTolerance&&this.element.click(),this.reset()},Ii.prototype.reset=function(){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()};var zi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Di(t,e,r){var n=t.classList;for(var i in zi)n.remove(\"mapboxgl-\"+r+\"-anchor-\"+i);n.add(\"mapboxgl-\"+r+\"-anchor-\"+e)}var Ri,Fi=function(e){function n(n,i){var a=this;if(e.call(this),(n instanceof t.window.HTMLElement||i)&&(n=t.extend({element:n},i)),t.bindAll([\"_update\",\"_onMove\",\"_onUp\",\"_addDragHandler\",\"_onMapClick\",\"_onKeyPress\"],this),this._anchor=n&&n.anchor||\"center\",this._color=n&&n.color||\"#3FB1CE\",this._draggable=n&&n.draggable||!1,this._state=\"inactive\",this._rotation=n&&n.rotation||0,this._rotationAlignment=n&&n.rotationAlignment||\"auto\",this._pitchAlignment=n&&n.pitchAlignment&&\"auto\"!==n.pitchAlignment?n.pitchAlignment:this._rotationAlignment,n&&n.element)this._element=n.element,this._offset=t.Point.convert(n&&n.offset||[0,0]);else{this._defaultMarker=!0,this._element=r.create(\"div\"),this._element.setAttribute(\"aria-label\",\"Map marker\");var o=r.createNS(\"http://www.w3.org/2000/svg\",\"svg\");o.setAttributeNS(null,\"display\",\"block\"),o.setAttributeNS(null,\"height\",\"41px\"),o.setAttributeNS(null,\"width\",\"27px\"),o.setAttributeNS(null,\"viewBox\",\"0 0 27 41\");var s=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"stroke\",\"none\"),s.setAttributeNS(null,\"stroke-width\",\"1\"),s.setAttributeNS(null,\"fill\",\"none\"),s.setAttributeNS(null,\"fill-rule\",\"evenodd\");var l=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"fill-rule\",\"nonzero\");var c=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");c.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),c.setAttributeNS(null,\"fill\",\"#000000\");for(var u=0,f=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];u<f.length;u+=1){var h=f[u],p=r.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");p.setAttributeNS(null,\"opacity\",\"0.04\"),p.setAttributeNS(null,\"cx\",\"10.5\"),p.setAttributeNS(null,\"cy\",\"5.80029008\"),p.setAttributeNS(null,\"rx\",h.rx),p.setAttributeNS(null,\"ry\",h.ry),c.appendChild(p)}var d=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"fill\",this._color);var m=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");m.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),d.appendChild(m);var g=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"fill\",\"#000000\");var v=r.createNS(\"http://www.w3.org/2000/svg\",\"path\");v.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),g.appendChild(v);var y=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");y.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),y.setAttributeNS(null,\"fill\",\"#FFFFFF\");var x=r.createNS(\"http://www.w3.org/2000/svg\",\"g\");x.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");var b=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");b.setAttributeNS(null,\"fill\",\"#000000\"),b.setAttributeNS(null,\"opacity\",\"0.25\"),b.setAttributeNS(null,\"cx\",\"5.5\"),b.setAttributeNS(null,\"cy\",\"5.5\"),b.setAttributeNS(null,\"r\",\"5.4999962\");var _=r.createNS(\"http://www.w3.org/2000/svg\",\"circle\");_.setAttributeNS(null,\"fill\",\"#FFFFFF\"),_.setAttributeNS(null,\"cx\",\"5.5\"),_.setAttributeNS(null,\"cy\",\"5.5\"),_.setAttributeNS(null,\"r\",\"5.4999962\"),x.appendChild(b),x.appendChild(_),l.appendChild(c),l.appendChild(d),l.appendChild(g),l.appendChild(y),l.appendChild(x),o.appendChild(l),this._element.appendChild(o),this._offset=t.Point.convert(n&&n.offset||[0,-14])}this._element.classList.add(\"mapboxgl-marker\"),this._element.addEventListener(\"dragstart\",(function(t){t.preventDefault()})),this._element.addEventListener(\"mousedown\",(function(t){t.preventDefault()})),this._element.addEventListener(\"focus\",(function(){var t=a._map.getContainer();t.scrollTop=0,t.scrollLeft=0})),Di(this._element,this._anchor,\"marker\"),this._popup=null}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(t){return this.remove(),this._map=t,t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this},n.prototype.remove=function(){return this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),r.remove(this._element),this._popup&&this._popup.remove(),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this},n.prototype.getElement=function(){return this._element},n.prototype.setPopup=function(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){var e=Math.sqrt(Math.pow(13.5,2)/2);t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-38.1],\"bottom-left\":[e,-1*(24.6+e)],\"bottom-right\":[-e,-1*(24.6+e)],left:[13.5,-24.6],right:[-13.5,-24.6]}:this._offset}this._popup=t,this._lngLat&&this._popup.setLngLat(this._lngLat),this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this},n.prototype._onKeyPress=function(t){var e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},n.prototype._onMapClick=function(t){var e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},n.prototype.getPopup=function(){return this._popup},n.prototype.togglePopup=function(){var t=this._popup;return t?(t.isOpen()?t.remove():t.addTo(this._map),this):this},n.prototype._update=function(t){if(this._map){this._map.transform.renderWorldCopies&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),this._pos=this._map.project(this._lngLat)._add(this._offset);var e=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?e=\"rotateZ(\"+this._rotation+\"deg)\":\"map\"===this._rotationAlignment&&(e=\"rotateZ(\"+(this._rotation-this._map.getBearing())+\"deg)\");var n=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?n=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(n=\"rotateX(\"+this._map.getPitch()+\"deg)\"),t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),r.setTransform(this._element,zi[this._anchor]+\" translate(\"+this._pos.x+\"px, \"+this._pos.y+\"px) \"+n+\" \"+e)}},n.prototype.getOffset=function(){return this._offset},n.prototype.setOffset=function(e){return this._offset=t.Point.convert(e),this._update(),this},n.prototype._onMove=function(e){this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\"))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Bi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Ni=0,ji=!1,Ui=function(e){function n(r){e.call(this),this.options=t.extend({},Bi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){var n;return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Ri?n(Ri):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Ri=\"denied\"!==t.state,n(Ri)})):(Ri=!!t.window.navigator.geolocation,n(Ri)),this._container},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ni=0,ji=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitude<e.getWest()||r.longitude>e.getEast()||r.latitude<e.getSouth()||r.latitude>e.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&ji)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Fi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Fi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ni--,ji=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Ni>1?(e={maximumAge:6e5,timeout:0},ji=!0):(e=this.options.positionOptions,ji=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Vi={maxWidth:100,unit:\"metric\"},Hi=function(e){this.options=t.extend({},Vi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;if(l>5280)Gi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\"));else Gi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else if(r&&\"nautical\"===r.unit){Gi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\"))}else s>=1e3?Gi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Gi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Gi(t,e,r,n){var i,a,o,s=(i=r,a=Math.pow(10,(\"\"+Math.floor(i)).length-1),o=(o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o),a*o),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\"&nbsp;\"+n}Hi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Hi.prototype._onMove=function(){qi(this._map,this._container,this.options)},Hi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Hi.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Hi.prototype.setUnit=function(t){this.options.unit=t,qi(this._map,this._container,this.options)};var Yi=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};Yi.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Yi.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Yi.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Yi.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Yi.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Yi.prototype._isFullscreen=function(){return this._fullscreen},Yi.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Yi.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Wi={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},Xi=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Wi),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"&#215;\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(e){var n=this,i=this._lngLat||this._trackPointer;if(this._map&&i&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return n._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Oi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||e)){var a=this._pos=this._trackPointer&&e?e:this._map.project(this._lngLat),o=this.options.anchor,s=function e(r){if(r){if(\"number\"==typeof r){var n=Math.round(Math.sqrt(.5*Math.pow(r,2)));return{center:new t.Point(0,0),top:new t.Point(0,r),\"top-left\":new t.Point(n,n),\"top-right\":new t.Point(-n,n),bottom:new t.Point(0,-r),\"bottom-left\":new t.Point(n,-n),\"bottom-right\":new t.Point(-n,-n),left:new t.Point(r,0),right:new t.Point(-r,0)}}if(r instanceof t.Point||Array.isArray(r)){var i=t.Point.convert(r);return{center:i,top:i,\"top-left\":i,\"top-right\":i,bottom:i,\"bottom-left\":i,\"bottom-right\":i,left:i,right:i}}return{center:t.Point.convert(r.center||[0,0]),top:t.Point.convert(r.top||[0,0]),\"top-left\":t.Point.convert(r[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(r[\"top-right\"]||[0,0]),bottom:t.Point.convert(r.bottom||[0,0]),\"bottom-left\":t.Point.convert(r[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(r[\"bottom-right\"]||[0,0]),left:t.Point.convert(r.left||[0,0]),right:t.Point.convert(r.right||[0,0])}}return e(new t.Point(0,0))}(this.options.offset);if(!o){var l,c=this._container.offsetWidth,u=this._container.offsetHeight;l=a.y+s.bottom.y<u?[\"top\"]:a.y>this._map.transform.height-u?[\"bottom\"]:[],a.x<c/2?l.push(\"left\"):a.x>this._map.transform.width-c/2&&l.push(\"right\"),o=0===l.length?\"bottom\":l.join(\"-\")}var f=a.add(s[o]).round();r.setTransform(this._container,zi[o]+\" translate(\"+f.x+\"px,\"+f.y+\"px)\"),Di(this._container,o,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);var Zi={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Ei,NavigationControl:Pi,GeolocateControl:Ui,AttributionControl:bi,ScaleControl:Hi,FullscreenControl:Yi,Popup:Xi,Marker:Fi,Style:qe,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){Bt().acquire(zt)},clearPrewarmedResources:function(){var t=Rt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(zt),Rt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Dt.workerCount},set workerCount(t){Dt.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return Zi})),r}))},{}],240:[function(t,e,r){\"use strict\";e.exports=Math.log2||function(t){return Math.log(t)*Math.LOG2E}},{}],241:[function(t,e,r){\"use strict\";e.exports=function(t,e){e||(e=t,t=window);var r=0,i=0,a=0,o={shift:!1,alt:!1,control:!1,meta:!1},s=!1;function l(t){var e=!1;return\"altKey\"in t&&(e=e||t.altKey!==o.alt,o.alt=!!t.altKey),\"shiftKey\"in t&&(e=e||t.shiftKey!==o.shift,o.shift=!!t.shiftKey),\"ctrlKey\"in t&&(e=e||t.ctrlKey!==o.control,o.control=!!t.ctrlKey),\"metaKey\"in t&&(e=e||t.metaKey!==o.meta,o.meta=!!t.metaKey),e}function c(t,s){var c=n.x(s),u=n.y(s);\"buttons\"in s&&(t=0|s.buttons),(t!==r||c!==i||u!==a||l(s))&&(r=0|t,i=c||0,a=u||0,e&&e(r,i,a,o))}function u(t){c(0,t)}function f(){(r||i||a||o.shift||o.alt||o.meta||o.control)&&(i=a=0,r=0,o.shift=o.alt=o.control=o.meta=!1,e&&e(0,0,0,o))}function h(t){l(t)&&e&&e(r,i,a,o)}function p(t){0===n.buttons(t)?c(0,t):c(r,t)}function d(t){c(r|n.buttons(t),t)}function m(t){c(r&~n.buttons(t),t)}function g(){s||(s=!0,t.addEventListener(\"mousemove\",p),t.addEventListener(\"mousedown\",d),t.addEventListener(\"mouseup\",m),t.addEventListener(\"mouseleave\",u),t.addEventListener(\"mouseenter\",u),t.addEventListener(\"mouseout\",u),t.addEventListener(\"mouseover\",u),t.addEventListener(\"blur\",f),t.addEventListener(\"keyup\",h),t.addEventListener(\"keydown\",h),t.addEventListener(\"keypress\",h),t!==window&&(window.addEventListener(\"blur\",f),window.addEventListener(\"keyup\",h),window.addEventListener(\"keydown\",h),window.addEventListener(\"keypress\",h)))}g();var v={element:t};return Object.defineProperties(v,{enabled:{get:function(){return s},set:function(e){e?g():function(){if(!s)return;s=!1,t.removeEventListener(\"mousemove\",p),t.removeEventListener(\"mousedown\",d),t.removeEventListener(\"mouseup\",m),t.removeEventListener(\"mouseleave\",u),t.removeEventListener(\"mouseenter\",u),t.removeEventListener(\"mouseout\",u),t.removeEventListener(\"mouseover\",u),t.removeEventListener(\"blur\",f),t.removeEventListener(\"keyup\",h),t.removeEventListener(\"keydown\",h),t.removeEventListener(\"keypress\",h),t!==window&&(window.removeEventListener(\"blur\",f),window.removeEventListener(\"keyup\",h),window.removeEventListener(\"keydown\",h),window.removeEventListener(\"keypress\",h))}()},enumerable:!0},buttons:{get:function(){return r},enumerable:!0},x:{get:function(){return i},enumerable:!0},y:{get:function(){return a},enumerable:!0},mods:{get:function(){return o},enumerable:!0}}),v};var n=t(\"mouse-event\")},{\"mouse-event\":243}],242:[function(t,e,r){var n={left:0,top:0};e.exports=function(t,e,r){e=e||t.currentTarget||t.srcElement,Array.isArray(r)||(r=[0,0]);var i=t.clientX||0,a=t.clientY||0,o=(s=e,s===window||s===document||s===document.body?n:s.getBoundingClientRect());var s;return r[0]=i-o.left,r[1]=a-o.top,r}},{}],243:[function(t,e,r){\"use strict\";function n(t){return t.target||t.srcElement||window}r.buttons=function(t){if(\"object\"==typeof t){if(\"buttons\"in t)return t.buttons;if(\"which\"in t){if(2===(e=t.which))return 4;if(3===e)return 2;if(e>0)return 1<<e-1}else if(\"button\"in t){var e;if(1===(e=t.button))return 4;if(2===e)return 2;if(e>=0)return 1<<e}}return 0},r.element=n,r.x=function(t){if(\"object\"==typeof t){if(\"offsetX\"in t)return t.offsetX;var e=n(t).getBoundingClientRect();return t.clientX-e.left}return 0},r.y=function(t){if(\"object\"==typeof t){if(\"offsetY\"in t)return t.offsetY;var e=n(t).getBoundingClientRect();return t.clientY-e.top}return 0}},{}],244:[function(t,e,r){\"use strict\";var n=t(\"to-px\");e.exports=function(t,e,r){\"function\"==typeof t&&(r=!!e,e=t,t=window);var i=n(\"ex\",t),a=function(t){r&&t.preventDefault();var n=t.deltaX||0,a=t.deltaY||0,o=t.deltaZ||0,s=1;switch(t.deltaMode){case 1:s=i;break;case 2:s=window.innerHeight}if(a*=s,o*=s,(n*=s)||a||o)return e(n,a,o,t)};return t.addEventListener(\"wheel\",a),a}},{\"to-px\":314}],245:[function(t,e,r){(function(t,r){(function(){\n",
+       "/*! Native Promise Only\n",
+       "    v0.8.1 (c) Kyle Simpson\n",
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+       "*/\n",
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+       "/*\n",
+       "object-assign\n",
+       "(c) Sindre Sorhus\n",
+       "@license MIT\n",
+       "*/\n",
+       "\"use strict\";var n=Object.getOwnPropertySymbols,i=Object.prototype.hasOwnProperty,a=Object.prototype.propertyIsEnumerable;function o(t){if(null==t)throw new TypeError(\"Object.assign cannot be called with null or undefined\");return Object(t)}e.exports=function(){try{if(!Object.assign)return!1;var t=new String(\"abc\");if(t[5]=\"de\",\"5\"===Object.getOwnPropertyNames(t)[0])return!1;for(var e={},r=0;r<10;r++)e[\"_\"+String.fromCharCode(r)]=r;if(\"0123456789\"!==Object.getOwnPropertyNames(e).map((function(t){return e[t]})).join(\"\"))return!1;var n={};return\"abcdefghijklmnopqrst\".split(\"\").forEach((function(t){n[t]=t})),\"abcdefghijklmnopqrst\"===Object.keys(Object.assign({},n)).join(\"\")}catch(t){return!1}}()?Object.assign:function(t,e){for(var r,s,l=o(t),c=1;c<arguments.length;c++){for(var u in r=Object(arguments[c]))i.call(r,u)&&(l[u]=r[u]);if(n){s=n(r);for(var f=0;f<s.length;f++)a.call(r,s[f])&&(l[s[f]]=r[s[f]])}}return l}},{}],248:[function(t,e,r){\"use strict\";function n(t,e){if(\"string\"!=typeof t)return[t];var r=[t];\"string\"==typeof e||Array.isArray(e)?e={brackets:e}:e||(e={});var n=e.brackets?Array.isArray(e.brackets)?e.brackets:[e.brackets]:[\"{}\",\"[]\",\"()\"],i=e.escape||\"___\",a=!!e.flat;n.forEach((function(t){var e=new RegExp([\"\\\\\",t[0],\"[^\\\\\",t[0],\"\\\\\",t[1],\"]*\\\\\",t[1]].join(\"\")),n=[];function a(e,a,o){var s=r.push(e.slice(t[0].length,-t[1].length))-1;return n.push(s),i+s+i}r.forEach((function(t,n){for(var i,o=0;t!=i;)if(i=t,t=t.replace(e,a),o++>1e4)throw Error(\"References have circular dependency. 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+       "/*\n",
+       " * @copyright 2016 Sean Connelly (@voidqk), http://syntheti.cc\n",
+       " * @license MIT\n",
+       " * @preserve Project Home: https://github.com/voidqk/polybooljs\n",
+       " */\n",
+       "var n,i=t(\"./lib/build-log\"),a=t(\"./lib/epsilon\"),o=t(\"./lib/intersecter\"),s=t(\"./lib/segment-chainer\"),l=t(\"./lib/segment-selector\"),c=t(\"./lib/geojson\"),u=!1,f=a();function h(t,e,r){var i=n.segments(t),a=n.segments(e),o=r(n.combine(i,a));return n.polygon(o)}n={buildLog:function(t){return!0===t?u=i():!1===t&&(u=!1),!1!==u&&u.list},epsilon:function(t){return f.epsilon(t)},segments:function(t){var e=o(!0,f,u);return 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r=0,n=t[t.length-1][0],i=t[t.length-1][1],a=[],o=0;o<t.length;o++){var s=t[o][0],l=t[o][1];a.push([s,l]),r+=l*n-s*i,n=s,i=l}return r<0!==e&&a.reverse(),a.push([a[0][0],a[0][1]]),a}var u=[];function f(t){var e=[c(t.region,!1)];u.push(e);for(var r=0;r<t.children.length;r++)e.push(h(t.children[r]))}function h(t){for(var e=0;e<t.children.length;e++)f(t.children[e]);return c(t.region,!0)}for(s=0;s<a.children.length;s++)f(a.children[s]);return u.length<=0?{type:\"Polygon\",coordinates:[]}:1==u.length?{type:\"Polygon\",coordinates:u[0]}:{type:\"MultiPolygon\",coordinates:u}}};e.exports=n},{}],258:[function(t,e,r){var n=t(\"./linked-list\");e.exports=function(t,e,r){function i(t,e,n){return{id:r?r.segmentId():-1,start:t,end:e,myFill:{above:n.myFill.above,below:n.myFill.below},otherFill:null}}var a=n.create();function o(t,r){a.insertBefore(t,(function(n){return function(t,r,n,i,a,o){var s=e.pointsCompare(r,a);return 0!==s?s:e.pointsSame(n,o)?0:t!==i?t?1:-1:e.pointAboveOrOnLine(n,i?a:o,i?o:a)?1:-1}(t.isStart,t.pt,r,n.isStart,n.pt,n.other.pt)<0}))}function s(t,e){var r=function(t,e){var r=n.node({isStart:!0,pt:t.start,seg:t,primary:e,other:null,status:null});return o(r,t.end),r}(t,e);return function(t,e,r){var i=n.node({isStart:!1,pt:e.end,seg:e,primary:r,other:t,status:null});t.other=i,o(i,t.pt)}(r,t,e),r}function l(t,e){var n=i(e,t.seg.end,t.seg);return function(t,e){r&&r.segmentChop(t.seg,e),t.other.remove(),t.seg.end=e,t.other.pt=e,o(t.other,t.pt)}(t,e),s(n,t.primary)}function c(i,o){var s=n.create();function c(t){return s.findTransition((function(r){var n,i,a,o,s,l;return(n=t,i=r.ev,a=n.seg.start,o=n.seg.end,s=i.seg.start,l=i.seg.end,e.pointsCollinear(a,s,l)?e.pointsCollinear(o,s,l)||e.pointAboveOrOnLine(o,s,l)?1:-1:e.pointAboveOrOnLine(a,s,l)?1:-1)>0}))}function u(t,n){var i=t.seg,a=n.seg,o=i.start,s=i.end,c=a.start,u=a.end;r&&r.checkIntersection(i,a);var f=e.linesIntersect(o,s,c,u);if(!1===f){if(!e.pointsCollinear(o,s,c))return!1;if(e.pointsSame(o,u)||e.pointsSame(s,c))return!1;var h=e.pointsSame(o,c),p=e.pointsSame(s,u);if(h&&p)return n;var d=!h&&e.pointBetween(o,c,u),m=!p&&e.pointBetween(s,c,u);if(h)return m?l(n,s):l(t,u),n;d&&(p||(m?l(n,s):l(t,u)),l(n,o))}else 0===f.alongA&&(-1===f.alongB?l(t,c):0===f.alongB?l(t,f.pt):1===f.alongB&&l(t,u)),0===f.alongB&&(-1===f.alongA?l(n,o):0===f.alongA?l(n,f.pt):1===f.alongA&&l(n,s));return!1}for(var f=[];!a.isEmpty();){var h=a.getHead();if(r&&r.vert(h.pt[0]),h.isStart){r&&r.segmentNew(h.seg,h.primary);var p=c(h),d=p.before?p.before.ev:null,m=p.after?p.after.ev:null;function g(){if(d){var t=u(h,d);if(t)return t}return!!m&&u(h,m)}r&&r.tempStatus(h.seg,!!d&&d.seg,!!m&&m.seg);var v,y=g();if(y){var x;if(t)(x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below)&&(y.seg.myFill.above=!y.seg.myFill.above);else y.seg.otherFill=h.seg.myFill;r&&r.segmentUpdate(y.seg),h.other.remove(),h.remove()}if(a.getHead()!==h){r&&r.rewind(h.seg);continue}if(t)x=null===h.seg.myFill.below||h.seg.myFill.above!==h.seg.myFill.below,h.seg.myFill.below=m?m.seg.myFill.above:i,h.seg.myFill.above=x?!h.seg.myFill.below:h.seg.myFill.below;else if(null===h.seg.otherFill)v=m?h.primary===m.primary?m.seg.otherFill.above:m.seg.myFill.above:h.primary?o:i,h.seg.otherFill={above:v,below:v};r&&r.status(h.seg,!!d&&d.seg,!!m&&m.seg),h.other.status=p.insert(n.node({ev:h}))}else{var b=h.status;if(null===b)throw new Error(\"PolyBool: Zero-length segment detected; your epsilon is probably too small or too large\");if(s.exists(b.prev)&&s.exists(b.next)&&u(b.prev.ev,b.next.ev),r&&r.statusRemove(b.ev.seg),b.remove(),!h.primary){var _=h.seg.myFill;h.seg.myFill=h.seg.otherFill,h.seg.otherFill=_}f.push(h.seg)}a.getHead().remove()}return r&&r.done(),f}return t?{addRegion:function(t){for(var n,i,a,o=t[t.length-1],l=0;l<t.length;l++){n=o,o=t[l];var c=e.pointsCompare(n,o);0!==c&&s((i=c<0?n:o,a=c<0?o:n,{id:r?r.segmentId():-1,start:i,end:a,myFill:{above:null,below:null},otherFill:null}),!0)}},calculate:function(t){return c(t,!1)}}:{calculate:function(t,e,r,n){return t.forEach((function(t){s(i(t.start,t.end,t),!0)})),r.forEach((function(t){s(i(t.start,t.end,t),!1)})),c(e,n)}}}},{\"./linked-list\":259}],259:[function(t,e,r){e.exports={create:function(){var t={root:{root:!0,next:null},exists:function(e){return null!==e&&e!==t.root},isEmpty:function(){return null===t.root.next},getHead:function(){return t.root.next},insertBefore:function(e,r){for(var n=t.root,i=t.root.next;null!==i;){if(r(i))return e.prev=i.prev,e.next=i,i.prev.next=e,void(i.prev=e);n=i,i=i.next}n.next=e,e.prev=n,e.next=null},findTransition:function(e){for(var r=t.root,n=t.root.next;null!==n&&!e(n);)r=n,n=n.next;return{before:r===t.root?null:r,after:n,insert:function(t){return t.prev=r,t.next=n,r.next=t,null!==n&&(n.prev=t),t}}}};return t},node:function(t){return t.prev=null,t.next=null,t.remove=function(){t.prev.next=t.next,t.next&&(t.next.prev=t.prev),t.prev=null,t.next=null},t}}},{}],260:[function(t,e,r){e.exports=function(t,e,r){var n=[],i=[];return t.forEach((function(t){var a=t.start,o=t.end;if(e.pointsSame(a,o))console.warn(\"PolyBool: Warning: Zero-length segment detected; your epsilon is probably too small or too large\");else{r&&r.chainStart(t);for(var s={index:0,matches_head:!1,matches_pt1:!1},l={index:0,matches_head:!1,matches_pt1:!1},c=s,u=0;u<n.length;u++){var f=(g=n[u])[0],h=(g[1],g[g.length-1]);g[g.length-2];if(e.pointsSame(f,a)){if(k(u,!0,!0))break}else if(e.pointsSame(f,o)){if(k(u,!0,!1))break}else if(e.pointsSame(h,a)){if(k(u,!1,!0))break}else if(e.pointsSame(h,o)&&k(u,!1,!1))break}if(c===s)return n.push([a,o]),void(r&&r.chainNew(a,o));if(c===l){r&&r.chainMatch(s.index);var p=s.index,d=s.matches_pt1?o:a,m=s.matches_head,g=n[p],v=m?g[0]:g[g.length-1],y=m?g[1]:g[g.length-2],x=m?g[g.length-1]:g[0],b=m?g[g.length-2]:g[1];return e.pointsCollinear(y,v,d)&&(m?(r&&r.chainRemoveHead(s.index,d),g.shift()):(r&&r.chainRemoveTail(s.index,d),g.pop()),v=y),e.pointsSame(x,d)?(n.splice(p,1),e.pointsCollinear(b,x,v)&&(m?(r&&r.chainRemoveTail(s.index,v),g.pop()):(r&&r.chainRemoveHead(s.index,v),g.shift())),r&&r.chainClose(s.index),void i.push(g)):void(m?(r&&r.chainAddHead(s.index,d),g.unshift(d)):(r&&r.chainAddTail(s.index,d),g.push(d)))}var _=s.index,w=l.index;r&&r.chainConnect(_,w);var T=n[_].length<n[w].length;s.matches_head?l.matches_head?T?(A(_),M(_,w)):(A(w),M(w,_)):M(w,_):l.matches_head?M(_,w):T?(A(_),M(w,_)):(A(w),M(_,w))}function k(t,e,r){return c.index=t,c.matches_head=e,c.matches_pt1=r,c===s?(c=l,!1):(c=null,!0)}function A(t){r&&r.chainReverse(t),n[t].reverse()}function M(t,i){var a=n[t],o=n[i],s=a[a.length-1],l=a[a.length-2],c=o[0],u=o[1];e.pointsCollinear(l,s,c)&&(r&&r.chainRemoveTail(t,s),a.pop(),s=l),e.pointsCollinear(s,c,u)&&(r&&r.chainRemoveHead(i,c),o.shift()),r&&r.chainJoin(t,i),n[t]=a.concat(o),n.splice(i,1)}})),i}},{}],261:[function(t,e,r){function n(t,e,r){var n=[];return t.forEach((function(t){var i=(t.myFill.above?8:0)+(t.myFill.below?4:0)+(t.otherFill&&t.otherFill.above?2:0)+(t.otherFill&&t.otherFill.below?1:0);0!==e[i]&&n.push({id:r?r.segmentId():-1,start:t.start,end:t.end,myFill:{above:1===e[i],below:2===e[i]},otherFill:null})})),r&&r.selected(n),n}var i={union:function(t,e){return n(t,[0,2,1,0,2,2,0,0,1,0,1,0,0,0,0,0],e)},intersect:function(t,e){return n(t,[0,0,0,0,0,2,0,2,0,0,1,1,0,2,1,0],e)},difference:function(t,e){return n(t,[0,0,0,0,2,0,2,0,1,1,0,0,0,1,2,0],e)},differenceRev:function(t,e){return n(t,[0,2,1,0,0,0,1,1,0,2,0,2,0,0,0,0],e)},xor:function(t,e){return n(t,[0,2,1,0,2,0,0,1,1,0,0,2,0,1,2,0],e)}};e.exports=i},{}],262:[function(t,e,r){\"use strict\";var n=t(\"stream\").Transform,i=t(\"stream-parser\");function a(){n.call(this,{readableObjectMode:!0})}function o(t,e,r){Error.call(this),Error.captureStackTrace(this,this.constructor),this.name=this.constructor.name,this.message=t,e&&(this.code=e),r&&(this.statusCode=r)}a.prototype=Object.create(n.prototype),a.prototype.constructor=a,i(a.prototype),r.ParserStream=a,r.sliceEq=function(t,e,r){for(var n=e,i=0;i<r.length;)if(t[n++]!==r[i++])return!1;return!0},r.str2arr=function(t,e){var r=[],n=0;if(e&&\"hex\"===e)for(;n<t.length;)r.push(parseInt(t.slice(n,n+2),16)),n+=2;else for(;n<t.length;n++)r.push(255&t.charCodeAt(n));return r},r.readUInt16LE=function(t,e){return t[e]|t[e+1]<<8},r.readUInt16BE=function(t,e){return t[e+1]|t[e]<<8},r.readUInt32LE=function(t,e){return t[e]|t[e+1]<<8|t[e+2]<<16|16777216*t[e+3]},r.readUInt32BE=function(t,e){return t[e+3]|t[e+2]<<8|t[e+1]<<16|16777216*t[e]},o.prototype=Object.create(Error.prototype),o.prototype.constructor=o,r.ProbeError=o},{stream:285,\"stream-parser\":301}],263:[function(t,e,r){\"use strict\";function n(t,e){var r=new Error(t);return r.code=e,r}function i(t){try{return decodeURIComponent(escape(t))}catch(e){return t}}function a(t,e,r){this.input=t.subarray(e,r),this.start=e;var i=String.fromCharCode.apply(null,this.input.subarray(0,4));if(\"II*\\0\"!==i&&\"MM\\0*\"!==i)throw n(\"invalid TIFF signature\",\"EBADDATA\");this.big_endian=\"M\"===i[0]}a.prototype.each=function(t){this.aborted=!1;var e=this.read_uint32(4);for(this.ifds_to_read=[{id:0,offset:e}];this.ifds_to_read.length>0&&!this.aborted;){var r=this.ifds_to_read.shift();r.offset&&this.scan_ifd(r.id,r.offset,t)}},a.prototype.read_uint16=function(t){var e=this.input;if(t+2>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?256*e[t]+e[t+1]:e[t]+256*e[t+1]},a.prototype.read_uint32=function(t){var e=this.input;if(t+4>e.length)throw n(\"unexpected EOF\",\"EBADDATA\");return this.big_endian?16777216*e[t]+65536*e[t+1]+256*e[t+2]+e[t+3]:e[t]+256*e[t+1]+65536*e[t+2]+16777216*e[t+3]},a.prototype.is_subifd_link=function(t,e){return 0===t&&34665===e||0===t&&34853===e||34665===t&&40965===e},a.prototype.exif_format_length=function(t){switch(t){case 1:case 2:case 6:case 7:return 1;case 3:case 8:return 2;case 4:case 9:case 11:return 4;case 5:case 10:case 12:return 8;default:return 0}},a.prototype.exif_format_read=function(t,e){var r;switch(t){case 1:case 2:return r=this.input[e];case 6:return(r=this.input[e])|33554430*(128&r);case 3:return r=this.read_uint16(e);case 8:return(r=this.read_uint16(e))|131070*(32768&r);case 4:return r=this.read_uint32(e);case 9:return 0|(r=this.read_uint32(e));case 5:case 10:case 11:case 12:case 7:default:return null}},a.prototype.scan_ifd=function(t,e,r){var a=this.read_uint16(e);e+=2;for(var o=0;o<a;o++){var s=this.read_uint16(e),l=this.read_uint16(e+2),c=this.read_uint32(e+4),u=this.exif_format_length(l),f=c*u,h=f<=4?e+8:this.read_uint32(e+8),p=!1;if(h+f>this.input.length)throw n(\"unexpected EOF\",\"EBADDATA\");for(var d=[],m=h,g=0;g<c;g++,m+=u){var v=this.exif_format_read(l,m);if(null===v){d=null;break}d.push(v)}if(Array.isArray(d)&&2===l&&(d=i(String.fromCharCode.apply(null,d)))&&\"\\0\"===d[d.length-1]&&(d=d.slice(0,-1)),this.is_subifd_link(t,s)&&Array.isArray(d)&&Number.isInteger(d[0])&&d[0]>0&&(this.ifds_to_read.push({id:s,offset:d[0]}),p=!0),!1===r({is_big_endian:this.big_endian,ifd:t,tag:s,format:l,count:c,entry_offset:e+this.start,data_length:f,data_offset:h+this.start,value:d,is_subifd_link:p}))return void(this.aborted=!0);e+=12}0===t&&this.ifds_to_read.push({id:1,offset:this.read_uint32(e)})},e.exports.ExifParser=a,e.exports.get_orientation=function(t){var e=0;try{return new a(t,0,t.length).each((function(t){if(0===t.ifd&&274===t.tag&&Array.isArray(t.value))return e=t.value[0],!1})),e}catch(t){return-1}}},{}],264:[function(t,e,r){\"use strict\";var n=t(\"./common\").readUInt16BE,i=t(\"./common\").readUInt32BE;function a(t,e){if(t.length<4+e)return null;var r=i(t,e);return t.length<r+e||r<8?null:{boxtype:String.fromCharCode.apply(null,t.slice(e+4,e+8)),data:t.slice(e+8,e+r),end:e+r}}function o(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;switch(n.boxtype){case\"ispe\":e.sizes.push({width:i(n.data,4),height:i(n.data,8)});break;case\"irot\":e.transforms.push({type:\"irot\",value:3&n.data[0]});break;case\"imir\":e.transforms.push({type:\"imir\",value:1&n.data[0]})}r=n.end}}function s(t,e,r){for(var n=0,i=0;i<r;i++)n=256*n+(t[e+i]||0);return n}function l(t,e){for(var r=t[4]>>4&15,i=15&t[4],a=t[5]>>4&15,o=n(t,6),l=8,c=0;c<o;c++){var u=n(t,l),f=n(t,l+=2),h=s(t,l+=2,a),p=n(t,l+=a);if(l+=2,0===f&&1===p){var d=s(t,l,r),m=s(t,l+r,i);e.item_loc[u]={length:m,offset:d+h}}l+=p*(r+i)}}function c(t,e){for(var r=n(t,4),i=6,o=0;o<r;o++){var s=a(t,i);if(!s)break;if(\"infe\"===s.boxtype){for(var l=n(s.data,4),c=\"\",u=8;u<s.data.length&&s.data[u];u++)c+=String.fromCharCode(s.data[u]);e.item_inf[c]=l}i=s.end}}function u(t,e){for(var r=0;;){var n=a(t,r);if(!n)break;\"ipco\"===n.boxtype&&o(n.data,e),r=n.end}}e.exports.unbox=a,e.exports.readSizeFromMeta=function(t){var e={sizes:[],transforms:[],item_inf:{},item_loc:{}};if(function(t,e){for(var r=4;;){var n=a(t,r);if(!n)break;\"iprp\"===n.boxtype&&u(n.data,e),\"iloc\"===n.boxtype&&l(n.data,e),\"iinf\"===n.boxtype&&c(n.data,e),r=n.end}}(t,e),e.sizes.length){var r,n,i,o=(r=e.sizes,n=r.reduce((function(t,e){return t.width>e.width||t.width===e.width&&t.height>e.height?t:e})),i=r.reduce((function(t,e){return t.height>e.height||t.height===e.height&&t.width>e.width?t:e})),n.width>i.height||n.width===i.height&&n.height>i.width?n:i),s=1;e.transforms.forEach((function(t){var e={1:6,2:5,3:8,4:7,5:4,6:3,7:2,8:1},r={1:4,2:3,3:2,4:1,5:6,6:5,7:8,8:7};if(\"imir\"===t.type&&(s=0===t.value?r[s]:e[s=e[s=r[s]]]),\"irot\"===t.type)for(var n=0;n<t.value;n++)s=e[s]}));var f=null;return e.item_inf.Exif&&(f=e.item_loc[e.item_inf.Exif]),{width:o.width,height:o.height,orientation:e.transforms.length?s:null,variants:e.sizes,exif_location:f}}},e.exports.getMimeType=function(t){var e=String.fromCharCode.apply(null,t.slice(0,4)),r={};r[e]=!0;for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt16LE,o=n(\"GIF87a\"),s=n(\"GIF89a\");e.exports=function(t){if(!(t.length<10)&&(i(t,0,o)||i(t,0,s)))return{width:a(t,6),height:a(t,8),type:\"gif\",mime:\"image/gif\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],268:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16LE;e.exports=function(t){var e=n(t,0),r=n(t,2),i=n(t,4);if(0===e&&1===r&&i){for(var a=[],o={width:0,height:0},s=0;s<i;s++){var l=t[6+16*s]||256,c=t[6+16*s+1]||256,u={width:l,height:c};a.push(u),(l>o.width||c>o.height)&&(o=u)}return{width:o.width,height:o.height,variants:a,type:\"ico\",mime:\"image/x-icon\",wUnits:\"px\",hUnits:\"px\"}}}},{\"../common\":262}],269:[function(t,e,r){\"use strict\";var n=t(\"../common\").readUInt16BE,i=t(\"../common\").str2arr,a=t(\"../common\").sliceEq,o=t(\"../exif_utils\"),s=i(\"Exif\\0\\0\");e.exports=function(t){if(!(t.length<2)&&255===t[0]&&216===t[1]&&255===t[2])for(var 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n=t(\"../common\").str2arr,i=t(\"../common\").sliceEq,a=t(\"../common\").readUInt32BE,o=n(\"8BPS\\0\\x01\");e.exports=function(t){if(!(t.length<22)&&i(t,0,o))return{width:a(t,18),height:a(t,14),type:\"psd\",mime:\"image/vnd.adobe.photoshop\",wUnits:\"px\",hUnits:\"px\"}}},{\"../common\":262}],272:[function(t,e,r){\"use strict\";function n(t){return\"number\"==typeof t&&isFinite(t)&&t>0}var i=/<[-_.:a-zA-Z0-9][^>]*>/,a=/^<([-_.:a-zA-Z0-9]+:)?svg\\s/,o=/[^-]\\bwidth=\"([^%]+?)\"|[^-]\\bwidth='([^%]+?)'/,s=/\\bheight=\"([^%]+?)\"|\\bheight='([^%]+?)'/,l=/\\bview[bB]ox=\"(.+?)\"|\\bview[bB]ox='(.+?)'/,c=/in$|mm$|cm$|pt$|pc$|px$|em$|ex$/;function u(t){return c.test(t)?t.match(c)[0]:\"px\"}e.exports=function(t){if(function(t){var e,r=0,n=t.length;for(239===t[0]&&187===t[1]&&191===t[2]&&(r=3);r<n&&(32===(e=t[r])||9===e||13===e||10===e);)r++;return r!==n&&60===t[r]}(t)){for(var e=\"\",r=0;r<t.length;r++)e+=String.fromCharCode(t[r]);var c=(e.match(i)||[\"\"])[0];if(a.test(c)){var 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h=[[1,0,0,1,0,0],[1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,-1,0,0],[-1,0,0,1,0,0],[1,0,0,1,0,0],[1,0,-1,0,0,1],[1,0,-1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,-1],[1,0,1,0,0,1],[1,0,-1,0,0,1],[-1,0,-1,0,0,1],[-1,0,-1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,-1],[-1,0,1,0,0,1],[-1,0,-1,0,0,1],[0,1,1,0,0,0],[0,1,-1,0,0,0],[0,-1,-1,0,0,0],[0,-1,-1,0,0,0],[0,1,1,0,0,0],[0,-1,1,0,0,0],[0,1,0,-1,1,0],[0,1,0,-1,-1,0],[0,1,0,1,-1,0],[0,1,0,1,1,0],[0,1,0,-1,1,0],[0,1,0,1,-1,0],[0,-1,0,-1,1,0],[0,-1,0,-1,-1,0],[0,-1,0,1,-1,0],[0,-1,0,1,1,0],[0,-1,0,-1,1,0],[0,-1,0,1,-1,0]]},{\"array-bounds\":71,\"color-normalize\":89,\"flatten-vertex-data\":191,\"object-assign\":247,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],280:[function(t,e,r){\"use strict\";var n=t(\"color-normalize\"),i=t(\"array-bounds\"),a=t(\"object-assign\"),o=t(\"glslify\"),s=t(\"pick-by-alias\"),l=t(\"flatten-vertex-data\"),c=t(\"earcut\"),u=t(\"array-normalize\"),f=t(\"to-float32\"),h=f.float32,p=f.fract32,d=t(\"es6-weak-map\"),m=t(\"parse-rect\"),g=t(\"array-find-index\");function v(t,e){if(!(this instanceof v))return new v(t,e);if(\"function\"==typeof t?(e||(e={}),e.regl=t):e=t,e.length&&(e.positions=e),!(t=e.regl).hasExtension(\"ANGLE_instanced_arrays\"))throw Error(\"regl-error2d: `ANGLE_instanced_arrays` extension should be enabled\");this.gl=t._gl,this.regl=t,this.passes=[],this.shaders=v.shaders.has(t)?v.shaders.get(t):v.shaders.set(t,v.createShaders(t)).get(t),this.update(e)}e.exports=v,v.dashMult=2,v.maxPatternLength=256,v.precisionThreshold=3e6,v.maxPoints=1e4,v.maxLines=2048,v.shaders=new d,v.createShaders=function(t){var e,r=t.buffer({usage:\"static\",type:\"float\",data:[0,1,0,0,1,1,1,0]}),n={primitive:\"triangle strip\",instances:t.prop(\"count\"),count:4,offset:0,uniforms:{miterMode:function(t,e){return\"round\"===e.join?2:1},miterLimit:t.prop(\"miterLimit\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),thickness:t.prop(\"thickness\"),dashTexture:t.prop(\"dashTexture\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),dashLength:t.prop(\"dashLength\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]},depth:t.prop(\"depth\")},blend:{enable:!0,color:[0,0,0,0],equation:{rgb:\"add\",alpha:\"add\"},func:{srcRGB:\"src alpha\",dstRGB:\"one minus src alpha\",srcAlpha:\"one minus dst alpha\",dstAlpha:\"one\"}},depth:{enable:function(t,e){return!e.overlay}},stencil:{enable:!1},scissor:{enable:!0,box:t.prop(\"viewport\")},viewport:t.prop(\"viewport\")},i=t(a({vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, aCoordFract, bCoordFract;\\nattribute vec4 color;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvec2 project(vec2 position, vec2 positionFract, vec2 scale, vec2 scaleFract, vec2 translate, vec2 translateFract) {\\n\\t// the order is important\\n\\treturn position * scale + translate\\n       + positionFract * scale + translateFract\\n       + position * scaleFract\\n       + positionFract * scaleFract;\\n}\\n\\nvoid main() {\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineOffset = lineTop * 2. - 1.;\\n\\n\\tvec2 diff = (bCoord + bCoordFract - aCoord - aCoordFract);\\n\\ttangent = normalize(diff * scale * viewport.zw);\\n\\tvec2 normal = vec2(-tangent.y, tangent.x);\\n\\n\\tvec2 position = project(aCoord, aCoordFract, scale, scaleFract, translate, translateFract) * lineStart\\n\\t\\t+ project(bCoord, bCoordFract, scale, scaleFract, translate, translateFract) * lineEnd\\n\\n\\t\\t+ thickness * normal * .5 * lineOffset / viewport.zw;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\n\\nvoid main() {\\n\\tfloat alpha = 1.;\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\"]),attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},aCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8,divisor:1},bCoordFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:16,divisor:1},color:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1}}},n));try{e=t(a({cull:{enable:!0,face:\"back\"},vert:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec2 aCoord, bCoord, nextCoord, prevCoord;\\nattribute vec4 aColor, bColor;\\nattribute float lineEnd, lineTop;\\n\\nuniform vec2 scale, translate;\\nuniform float thickness, pixelRatio, id, depth;\\nuniform vec4 viewport;\\nuniform float miterLimit, miterMode;\\n\\nvarying vec4 fragColor;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 tangent;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nconst float REVERSE_THRESHOLD = -.875;\\nconst float MIN_DIFF = 1e-6;\\n\\n// TODO: possible optimizations: avoid overcalculating all for vertices and calc just one instead\\n// TODO: precalculate dot products, normalize things beforehead etc.\\n// TODO: refactor to rectangular algorithm\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nbool isNaN( float val ){\\n  return ( val < 0.0 || 0.0 < val || val == 0.0 ) ? false : true;\\n}\\n\\nvoid main() {\\n\\tvec2 aCoord = aCoord, bCoord = bCoord, prevCoord = prevCoord, nextCoord = nextCoord;\\n\\n  vec2 adjustedScale;\\n  adjustedScale.x = (abs(scale.x) < MIN_DIFF) ? MIN_DIFF : scale.x;\\n  adjustedScale.y = (abs(scale.y) < MIN_DIFF) ? MIN_DIFF : scale.y;\\n\\n  vec2 scaleRatio = adjustedScale * viewport.zw;\\n\\tvec2 normalWidth = thickness / scaleRatio;\\n\\n\\tfloat lineStart = 1. - lineEnd;\\n\\tfloat lineBot = 1. - lineTop;\\n\\n\\tfragColor = (lineStart * aColor + lineEnd * bColor) / 255.;\\n\\n\\tif (isNaN(aCoord.x) || isNaN(aCoord.y) || isNaN(bCoord.x) || isNaN(bCoord.y)) return;\\n\\n\\tif (aCoord == prevCoord) prevCoord = aCoord + normalize(bCoord - aCoord);\\n\\tif (bCoord == nextCoord) nextCoord = bCoord - normalize(bCoord - aCoord);\\n\\n\\tvec2 prevDiff = aCoord - prevCoord;\\n\\tvec2 currDiff = bCoord - aCoord;\\n\\tvec2 nextDiff = nextCoord - bCoord;\\n\\n\\tvec2 prevTangent = normalize(prevDiff * scaleRatio);\\n\\tvec2 currTangent = normalize(currDiff * scaleRatio);\\n\\tvec2 nextTangent = normalize(nextDiff * scaleRatio);\\n\\n\\tvec2 prevNormal = vec2(-prevTangent.y, prevTangent.x);\\n\\tvec2 currNormal = vec2(-currTangent.y, currTangent.x);\\n\\tvec2 nextNormal = vec2(-nextTangent.y, nextTangent.x);\\n\\n\\tvec2 startJoinDirection = normalize(prevTangent - currTangent);\\n\\tvec2 endJoinDirection = normalize(currTangent - nextTangent);\\n\\n\\t// collapsed/unidirectional segment cases\\n\\t// FIXME: there should be more elegant solution\\n\\tvec2 prevTanDiff = abs(prevTangent - currTangent);\\n\\tvec2 nextTanDiff = abs(nextTangent - currTangent);\\n\\tif (max(prevTanDiff.x, prevTanDiff.y) < MIN_DIFF) {\\n\\t\\tstartJoinDirection = currNormal;\\n\\t}\\n\\tif (max(nextTanDiff.x, nextTanDiff.y) < MIN_DIFF) {\\n\\t\\tendJoinDirection = currNormal;\\n\\t}\\n\\tif (aCoord == bCoord) {\\n\\t\\tendJoinDirection = startJoinDirection;\\n\\t\\tcurrNormal = prevNormal;\\n\\t\\tcurrTangent = prevTangent;\\n\\t}\\n\\n\\ttangent = currTangent;\\n\\n\\t//calculate join shifts relative to normals\\n\\tfloat startJoinShift = dot(currNormal, startJoinDirection);\\n\\tfloat endJoinShift = dot(currNormal, endJoinDirection);\\n\\n\\tfloat startMiterRatio = abs(1. / startJoinShift);\\n\\tfloat endMiterRatio = abs(1. / endJoinShift);\\n\\n\\tvec2 startJoin = startJoinDirection * startMiterRatio;\\n\\tvec2 endJoin = endJoinDirection * endMiterRatio;\\n\\n\\tvec2 startTopJoin, startBotJoin, endTopJoin, endBotJoin;\\n\\tstartTopJoin = sign(startJoinShift) * startJoin * .5;\\n\\tstartBotJoin = -startTopJoin;\\n\\n\\tendTopJoin = sign(endJoinShift) * endJoin * .5;\\n\\tendBotJoin = -endTopJoin;\\n\\n\\tvec2 aTopCoord = aCoord + normalWidth * startTopJoin;\\n\\tvec2 bTopCoord = bCoord + normalWidth * endTopJoin;\\n\\tvec2 aBotCoord = aCoord + normalWidth * startBotJoin;\\n\\tvec2 bBotCoord = bCoord + normalWidth * endBotJoin;\\n\\n\\t//miter anti-clipping\\n\\tfloat baClipping = distToLine(bCoord, aCoord, aBotCoord) / dot(normalize(normalWidth * endBotJoin), normalize(normalWidth.yx * vec2(-startBotJoin.y, startBotJoin.x)));\\n\\tfloat abClipping = distToLine(aCoord, bCoord, bTopCoord) / dot(normalize(normalWidth * startBotJoin), normalize(normalWidth.yx * vec2(-endBotJoin.y, endBotJoin.x)));\\n\\n\\t//prevent close to reverse direction switch\\n\\tbool prevReverse = dot(currTangent, prevTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, prevNormal)) * min(length(prevDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\tbool nextReverse = dot(currTangent, nextTangent) <= REVERSE_THRESHOLD && abs(dot(currTangent, nextNormal)) * min(length(nextDiff), length(currDiff)) <  length(normalWidth * currNormal);\\n\\n\\tif (prevReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * startJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / startMiterRatio, 1.);\\n\\t\\taBotCoord = aCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\taTopCoord = aCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!nextReverse && baClipping > 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position  * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x)  / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\"]),frag:o([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>v.precisionThreshold||e.scale[1]*e.viewport.height>v.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=v.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},v.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,o=this.gl;if(t.forEach((function(t,f){var d=e.passes[f];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=s(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin 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Uint8Array})},t=a({},v.defaults,t)),null!=t.thickness&&(d.thickness=parseFloat(t.thickness)),null!=t.opacity&&(d.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(d.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(d.overlay=!!t.overlay,f<v.maxLines&&(d.depth=2*(v.maxLines-1-f%v.maxLines)/v.maxLines-1)),null!=t.join&&(d.join=t.join),null!=t.hole&&(d.hole=t.hole),null!=t.fill&&(d.fill=t.fill?n(t.fill,\"uint8\"):null),null!=t.viewport&&(d.viewport=m(t.viewport)),d.viewport||(d.viewport=m([o.drawingBufferWidth,o.drawingBufferHeight])),null!=t.close&&(d.close=t.close),null===t.positions&&(t.positions=[]),t.positions){var y,x;if(t.positions.x&&t.positions.y){var b=t.positions.x,_=t.positions.y;x=d.count=Math.max(b.length,_.length),y=new Float64Array(2*x);for(var w=0;w<x;w++)y[2*w]=b[w],y[2*w+1]=_[w]}else y=l(t.positions,\"float64\"),x=d.count=Math.floor(y.length/2);var T=d.bounds=i(y,2);if(d.fill){for(var k=[],A={},M=0,S=0,E=0,L=d.count;S<L;S++){var C=y[2*S],P=y[2*S+1];isNaN(C)||isNaN(P)||null==C||null==P?(C=y[2*M],P=y[2*M+1],A[S]=M):M=S,k[E++]=C,k[E++]=P}if(t.splitNull){d.count-1 in A||(A[d.count]=d.count-1);var I=Object.keys(A).map(Number).sort((function(t,e){return t-e})),O=[],z=0,D=null!=d.hole?d.hole[0]:null;if(null!=D){var R=g(I,(function(t){return t>=D}));(I=I.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*z,2*I[t]).concat(D?k.slice(2*D):[]),r=(d.hole||[]).map((function(e){return e-D+(I[t]-z)})),n=c(e,r);n=n.map((function(e){return e+z+(e+z<I[t]?0:D-I[t])})),O.push.apply(O,n),z=I[t]+1},B=0;B<I.length;B++)F(B);for(var N=0,j=O.length;N<j;N++)null!=A[O[N]]&&(O[N]=A[O[N]]);d.triangles=O}else{for(var U=c(k,d.hole||[]),V=0,H=U.length;V<H;V++)null!=A[U[V]]&&(U[V]=A[U[V]]);d.triangles=U}}var q=new Float64Array(y);u(q,2,T);var G=new 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at=0;at<t.dashes.length;++at){for(var ot=0,st=t.dashes[at]*v.dashMult*.5;ot<st;++ot)$[rt++]=nt;nt^=255}}d.dashLength=tt,d.dashTexture({channels:1,data:$,width:$.length,height:1,mag:\"linear\",min:\"linear\"},0,0)}if(t.color){var lt=d.count,ct=t.color;ct||(ct=\"transparent\");var ut=new Uint8Array(4*lt+4);if(Array.isArray(ct)&&\"number\"!=typeof ct[0]){for(var ft=0;ft<lt;ft++){var ht=n(ct[ft],\"uint8\");ut.set(ht,4*ft)}ut.set(n(ct[0],\"uint8\"),4*lt)}else for(var pt=n(ct,\"uint8\"),dt=0;dt<lt+1;dt++)ut.set(pt,4*dt);d.colorBuffer({usage:\"dynamic\",type:\"uint8\",data:ut})}}else e.passes[f]=null})),t.length<this.passes.length){for(var f=t.length;f<this.passes.length;f++){var d=this.passes[f];d&&(d.colorBuffer.destroy(),d.positionBuffer.destroy(),d.dashTexture.destroy())}this.passes.length=t.length}for(var y=[],x=0;x<this.passes.length;x++)null!==this.passes[x]&&y.push(this.passes[x]);return this.passes=y,this}},v.prototype.destroy=function(){return 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o={uniforms:{constPointSize:!!e.constPointSize,opacity:t.prop(\"opacity\"),paletteSize:function(t,e){return[r.tooManyColors?0:255,n.height]},pixelRatio:t.context(\"pixelRatio\"),scale:t.prop(\"scale\"),scaleFract:t.prop(\"scaleFract\"),translate:t.prop(\"translate\"),translateFract:t.prop(\"translateFract\"),markerTexture:t.prop(\"markerTexture\"),paletteTexture:n},attributes:{x:function(t,e){return e.xAttr||{buffer:e.positionBuffer,stride:8,offset:0}},y:function(t,e){return e.yAttr||{buffer:e.positionBuffer,stride:8,offset:4}},xFract:function(t,e){return e.xAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:0}},yFract:function(t,e){return e.yAttr?{constant:[0,0]}:{buffer:e.positionFractBuffer,stride:8,offset:4}},size:function(t,e){return e.size.length?{buffer:e.sizeBuffer,stride:2,offset:0}:{constant:[Math.round(255*e.size/r.maxSize)]}},borderSize:function(t,e){return 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mix(color, fragColor, colorAmt);\\n    color.a *= opacity;\\n\\n    gl_FragColor = color;\\n  }\\n\\n}\\n\"]),s.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 scale, scaleFract, translate, translateFract, paletteSize;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\nconst float borderLevel = .5;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragPointSize, fragBorderRadius, fragWidth, fragBorderColorLevel, fragColorLevel;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = 2. * size * pointSizeScale;\\n  fragPointSize = size * pixelRatio;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragColor = color;\\n  fragBorderColor = borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n\\n  fragBorderColorLevel = clamp(borderLevel - borderLevel * borderSize / size, 0., 1.);\\n  fragColorLevel = clamp(borderLevel + (1. - borderLevel) * borderSize / size, 0., 1.);\\n}\"]),this.drawMarker=t(s);var l=f({},o);l.frag=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nuniform float opacity;\\n\\nfloat smoothStep(float edge0, float edge1, float x) {\\n\\tfloat t;\\n\\tt = clamp((x - edge0) / (edge1 - edge0), 0.0, 1.0);\\n\\treturn t * t * (3.0 - 2.0 * t);\\n}\\n\\nvoid main() {\\n\\tfloat radius, alpha = 1.0, delta = fragWidth;\\n\\n\\tradius = length(2.0 * gl_PointCoord.xy - 1.0);\\n\\n\\tif (radius > 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, borderSize;\\nattribute vec4 colorId, borderColorId;\\nattribute float isActive;\\n\\nuniform bool constPointSize;\\nuniform float pixelRatio;\\nuniform vec2 paletteSize, scale, scaleFract, translate, translateFract;\\nuniform sampler2D paletteTexture;\\n\\nconst float maxSize = 100.;\\n\\nvarying vec4 fragColor, fragBorderColor;\\nvarying float fragBorderRadius, fragWidth;\\n\\nfloat pointSizeScale = (constPointSize) ? 2. : pixelRatio;\\n\\nbool isDirect = (paletteSize.x < 1.);\\n\\nvec4 getColor(vec4 id) {\\n  return isDirect ? id / 255. : texture2D(paletteTexture,\\n    vec2(\\n      (id.x + .5) / paletteSize.x,\\n      (id.y + .5) / paletteSize.y\\n    )\\n  );\\n}\\n\\nvoid main() {\\n  // ignore inactive points\\n  if (isActive == 0.) return;\\n\\n  vec2 position = vec2(x, y);\\n  vec2 positionFract = vec2(xFract, yFract);\\n\\n  vec4 color = getColor(colorId);\\n  vec4 borderColor = getColor(borderColorId);\\n\\n  float size = size * maxSize / 255.;\\n  float borderSize = borderSize * maxSize / 255.;\\n\\n  gl_PointSize = (size + borderSize) * pointSizeScale;\\n\\n  vec2 pos = (position + translate) * scale\\n      + (positionFract + translateFract) * scale\\n      + (position + translate) * scaleFract\\n      + (positionFract + translateFract) * scaleFract;\\n\\n  gl_Position = vec4(pos * 2. - 1., 0., 1.);\\n\\n  fragBorderRadius = 1. - 2. * borderSize / (size + borderSize);\\n  fragColor = color;\\n  fragBorderColor = borderColor.a == 0. || borderSize == 0. ? vec4(color.rgb, 0.) : borderColor;\\n  fragWidth = 1. / gl_PointSize;\\n}\\n\"]),g&&(l.frag=l.frag.replace(\"smoothstep\",\"smoothStep\"),s.frag=s.frag.replace(\"smoothstep\",\"smoothStep\")),this.drawCircle=t(l)}b.defaults={color:\"black\",borderColor:\"transparent\",borderSize:0,size:12,opacity:1,marker:void 0,viewport:null,range:null,pixelSize:null,count:0,offset:0,bounds:null,positions:[],snap:1e4},b.prototype.render=function(){return arguments.length&&this.update.apply(this,arguments),this.draw(),this},b.prototype.draw=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];var i=this.groups;if(1===r.length&&Array.isArray(r[0])&&(null===r[0][0]||Array.isArray(r[0][0]))&&(r=r[0]),this.regl._refresh(),r.length)for(var a=0;a<r.length;a++)this.drawItem(a,r[a]);else i.forEach((function(e,r){t.drawItem(r)}));return this},b.prototype.drawItem=function(t,e){var r=this.groups,n=r[t];if(\"number\"==typeof e&&(t=e,n=r[e],e=null),n&&n.count&&n.opacity){n.activation[0]&&this.drawCircle(this.getMarkerDrawOptions(0,n,e));for(var a=[],o=1;o<n.activation.length;o++)n.activation[o]&&(!0===n.activation[o]||n.activation[o].data.length)&&a.push.apply(a,i(this.getMarkerDrawOptions(o,n,e)));a.length&&this.drawMarker(a)}},b.prototype.getMarkerDrawOptions=function(t,e,r){var i=e.range,a=e.tree,o=e.viewport,s=e.activation,l=e.selectionBuffer,c=e.count;this.regl;if(!a)return r?[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],count:r.length,elements:r,offset:0})]:[f({},e,{markerTexture:this.markerTextures[t],activation:s[t],offset:0})];var u=[],h=a.range(i,{lod:!0,px:[(i[2]-i[0])/o.width,(i[3]-i[1])/o.height]});if(r){for(var p=s[t].data,d=new Uint8Array(c),m=0;m<r.length;m++){var g=r[m];d[g]=p?p[g]:1}l.subdata(d)}for(var v=h.length;v--;){var y=n(h[v],2),x=y[0],b=y[1];u.push(f({},e,{markerTexture:this.markerTextures[t],activation:r?l:s[t],offset:x,count:b-x}))}return u},b.prototype.update=function(){for(var t=this,e=arguments.length,r=new Array(e),n=0;n<e;n++)r[n]=arguments[n];if(r.length){1===r.length&&Array.isArray(r[0])&&(r=r[0]);var i=this.groups,a=this.gl,o=this.regl,s=this.maxSize,c=this.maxColors,h=this.palette;this.groups=i=r.map((function(e,r){var n=i[r];if(void 0===e)return n;null===e?e={positions:null}:\"function\"==typeof e?e={ondraw:e}:\"number\"==typeof e[0]&&(e={positions:e}),null===(e=p(e,{positions:\"positions data points\",snap:\"snap cluster lod tree\",size:\"sizes size radius\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",color:\"colors color fill fill-color fillColor\",borderColor:\"borderColors borderColor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range dataBox databox\",viewport:\"viewport viewPort viewBox viewbox\",opacity:\"opacity alpha transparency\",bounds:\"bound bounds boundaries limits\",tooManyColors:\"tooManyColors palette paletteMode optimizePalette enablePalette\"})).positions&&(e.positions=[]),null!=e.tooManyColors&&(t.tooManyColors=e.tooManyColors),n||(i[r]=n={id:r,scale:null,translate:null,scaleFract:null,translateFract:null,activation:[],selectionBuffer:o.buffer({data:new Uint8Array(0),usage:\"stream\",type:\"uint8\"}),sizeBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),colorBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"uint8\"}),positionBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"}),positionFractBuffer:o.buffer({data:new Uint8Array(0),usage:\"dynamic\",type:\"float\"})},e=f({},b.defaults,e)),e.positions&&!(\"marker\"in e)&&(e.marker=n.marker,delete n.marker),e.marker&&!(\"positions\"in e)&&(e.positions=n.positions,delete n.positions);var g=0,x=0;if(d(n,e,[{snap:!0,size:function(t,e){return null==t&&(t=b.defaults.size),g+=t&&t.length?1:0,t},borderSize:function(t,e){return null==t&&(t=b.defaults.borderSize),g+=t&&t.length?1:0,t},opacity:parseFloat,color:function(e,r){return null==e&&(e=b.defaults.color),e=t.updateColor(e),x++,e},borderColor:function(e,r){return null==e&&(e=b.defaults.borderColor),e=t.updateColor(e),x++,e},bounds:function(t,e,r){return\"range\"in r||(r.range=null),t},positions:function(t,e,r){var n=e.snap,i=e.positionBuffer,a=e.positionFractBuffer,s=e.selectionBuffer;if(t.x||t.y)return t.x.length?e.xAttr={buffer:o.buffer(t.x),offset:0,stride:4,count:t.x.length}:e.xAttr={buffer:t.x.buffer,offset:4*t.x.offset||0,stride:4*(t.x.stride||1),count:t.x.count},t.y.length?e.yAttr={buffer:o.buffer(t.y),offset:0,stride:4,count:t.y.length}:e.yAttr={buffer:t.y.buffer,offset:4*t.y.offset||0,stride:4*(t.y.stride||1),count:t.y.count},e.count=Math.max(e.xAttr.count,e.yAttr.count),t;t=m(t,\"float64\");var c=e.count=Math.floor(t.length/2),f=e.bounds=c?l(t,2):null;if(r.range||e.range||(delete e.range,r.range=f),r.marker||e.marker||(delete e.marker,r.marker=null),n&&(!0===n||c>n)?e.tree=u(t,{bounds:f}):n&&n.length&&(e.tree=n),e.tree){var h={primitive:\"points\",usage:\"static\",data:e.tree,type:\"uint32\"};e.elements?e.elements(h):e.elements=o.elements(h)}var p=v.float32(t);return i({data:p,usage:\"dynamic\"}),a({data:v.fract32(t,p),usage:\"dynamic\"}),s({data:new Uint8Array(c),type:\"uint8\",usage:\"stream\"}),t}},{marker:function(e,r,n){var i=r.activation;if(i.forEach((function(t){return t&&t.destroy&&t.destroy()})),i.length=0,e&&\"number\"!=typeof e[0]){for(var a=[],s=0,l=Math.min(e.length,r.count);s<l;s++){var c=t.addMarker(e[s]);a[c]||(a[c]=new Uint8Array(r.count)),a[c][s]=1}for(var u=0;u<a.length;u++)if(a[u]){var f={data:a[u],type:\"uint8\",usage:\"static\"};i[u]?i[u](f):i[u]=o.buffer(f),i[u].data=a[u]}}else{i[t.addMarker(e)]=!0}return e},range:function(t,e,r){var n=e.bounds;if(n)return t||(t=n),e.scale=[1/(t[2]-t[0]),1/(t[3]-t[1])],e.translate=[-t[0],-t[1]],e.scaleFract=v.fract(e.scale),e.translateFract=v.fract(e.translate),t},viewport:function(t){return y(t||[a.drawingBufferWidth,a.drawingBufferHeight])}}]),g){var _=n,w=_.count,T=_.size,k=_.borderSize,A=_.sizeBuffer,M=new Uint8Array(2*w);if(T.length||k.length)for(var S=0;S<w;S++)M[2*S]=Math.round(255*(null==T[S]?T:T[S])/s),M[2*S+1]=Math.round(255*(null==k[S]?k:k[S])/s);A({data:M,usage:\"dynamic\"})}if(x){var E,L=n,C=L.count,P=L.color,I=L.borderColor,O=L.colorBuffer;if(t.tooManyColors){if(P.length||I.length){E=new Uint8Array(8*C);for(var z=0;z<C;z++){var D=P[z];E[8*z]=h[4*D],E[8*z+1]=h[4*D+1],E[8*z+2]=h[4*D+2],E[8*z+3]=h[4*D+3];var R=I[z];E[8*z+4]=h[4*R],E[8*z+5]=h[4*R+1],E[8*z+6]=h[4*R+2],E[8*z+7]=h[4*R+3]}}}else if(P.length||I.length){E=new Uint8Array(4*C+2);for(var F=0;F<C;F++)null!=P[F]&&(E[4*F]=P[F]%c,E[4*F+1]=Math.floor(P[F]/c)),null!=I[F]&&(E[4*F+2]=I[F]%c,E[4*F+3]=Math.floor(I[F]/c))}O({data:E||new Uint8Array(0),type:\"uint8\",usage:\"dynamic\"})}return n}))}},b.prototype.addMarker=function(t){var e,r=this.markerTextures,n=this.regl,i=this.markerCache,a=null==t?0:i.indexOf(t);if(a>=0)return a;if(t instanceof Uint8Array||t instanceof Uint8ClampedArray)e=t;else{e=new Uint8Array(t.length);for(var o=0,s=t.length;o<s;o++)e[o]=255*t[o]}var l=Math.floor(Math.sqrt(e.length));return a=r.length,i.push(t),r.push(n.texture({channels:1,data:e,radius:l,mag:\"linear\",min:\"linear\"})),a},b.prototype.updateColor=function(t){var e=this.paletteIds,r=this.palette,n=this.maxColors;Array.isArray(t)||(t=[t]);var i=[];if(\"number\"==typeof t[0]){var a=[];if(Array.isArray(t))for(var o=0;o<t.length;o+=4)a.push(t.slice(o,o+4));else for(var l=0;l<t.length;l+=4)a.push(t.subarray(l,l+4));t=a}for(var u=0;u<t.length;u++){var f=t[u];f=s(f,\"uint8\");var h=c(f,!1);if(null==e[h]){var p=r.length;e[h]=Math.floor(p/4),r[p]=f[0],r[p+1]=f[1],r[p+2]=f[2],r[p+3]=f[3]}i[u]=e[h]}return!this.tooManyColors&&r.length>4*n&&(this.tooManyColors=!0),this.updatePalette(r),1===i.length?i[0]:i},b.prototype.updatePalette=function(t){if(!this.tooManyColors){var e=this.maxColors,r=this.paletteTexture,n=Math.ceil(.25*t.length/e);if(n>1)for(var i=.25*(t=t.slice()).length%e;i<n*e;i++)t.push(0,0,0,0);r.height<n&&r.resize(e,n),r.subimage({width:Math.min(.25*t.length,e),height:n,data:t},0,0)}},b.prototype.destroy=function(){return this.groups.forEach((function(t){t.sizeBuffer.destroy(),t.positionBuffer.destroy(),t.positionFractBuffer.destroy(),t.colorBuffer.destroy(),t.activation.forEach((function(t){return t&&t.destroy&&t.destroy()})),t.selectionBuffer.destroy(),t.elements&&t.elements.destroy()})),this.groups.length=0,this.paletteTexture.destroy(),this.markerTextures.forEach((function(t){return t&&t.destroy&&t.destroy()})),this};var _=t(\"object-assign\");e.exports=function(t,e){var r=new x(t,e),n=r.render.bind(r);return _(n,{render:n,update:r.update.bind(r),draw:r.draw.bind(r),destroy:r.destroy.bind(r),regl:r.regl,gl:r.gl,canvas:r.gl.canvas,groups:r.groups,markers:r.markerCache,palette:r.palette}),n}},{\"@plotly/point-cluster\":59,\"array-bounds\":71,\"color-id\":87,\"color-normalize\":89,\"flatten-vertex-data\":191,glslify:227,\"is-iexplorer\":233,\"object-assign\":247,\"parse-rect\":249,\"pick-by-alias\":253,\"to-float32\":313,\"update-diff\":329}],282:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"pick-by-alias\"),a=t(\"array-bounds\"),o=t(\"raf\"),s=t(\"array-range\"),l=t(\"parse-rect\"),c=t(\"flatten-vertex-data\");function u(t,e){if(!(this instanceof u))return new u(t,e);this.traces=[],this.passes={},this.regl=t,this.scatter=n(t),this.canvas=this.scatter.canvas}function f(t,e,r){return(null!=t.id?t.id:t)<<16|(255&e)<<8|255&r}function h(t,e,r){var n,i,a,o,s=t[e],l=t[r];return s.length>2?(s[0],s[2],n=s[1],i=s[3]):s.length?(n=s[0],i=s[1]):(s.x,n=s.y,s.x+s.width,i=s.y+s.height),l.length>2?(a=l[0],o=l[2],l[1],l[3]):l.length?(a=l[0],o=l[1]):(a=l.x,l.y,o=l.x+l.width,l.y+l.height),[a,n,o,i]}function p(t){if(\"number\"==typeof t)return[t,t,t,t];if(2===t.length)return[t[0],t[1],t[0],t[1]];var e=l(t);return[e.x,e.y,e.x+e.width,e.y+e.height]}e.exports=u,u.prototype.render=function(){for(var t,e=this,r=[],n=arguments.length;n--;)r[n]=arguments[n];return r.length&&(t=this).update.apply(t,r),this.regl.attributes.preserveDrawingBuffer?this.draw():(this.dirty?null==this.planned&&(this.planned=o((function(){e.draw(),e.dirty=!0,e.planned=null}))):(this.draw(),this.dirty=!0,o((function(){e.dirty=!1}))),this)},u.prototype.update=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=0;n<e.length;n++)this.updateItem(n,e[n]);this.traces=this.traces.filter(Boolean);for(var i=[],a=0,o=0;o<this.traces.length;o++){for(var s=this.traces[o],l=this.traces[o].passes,c=0;c<l.length;c++)i.push(this.passes[l[c]]);s.passOffset=a,a+=s.passes.length}return(t=this.scatter).update.apply(t,i),this}},u.prototype.updateItem=function(t,e){var r=this.regl;if(null===e)return this.traces[t]=null,this;if(!e)return this;var n,o=i(e,{data:\"data items columns rows values dimensions samples x\",snap:\"snap cluster\",size:\"sizes size radius\",color:\"colors color fill fill-color fillColor\",opacity:\"opacity alpha transparency opaque\",borderSize:\"borderSizes borderSize border-size bordersize borderWidth borderWidths border-width borderwidth stroke-width strokeWidth strokewidth outline\",borderColor:\"borderColors borderColor bordercolor stroke stroke-color strokeColor\",marker:\"markers marker shape\",range:\"range ranges databox dataBox\",viewport:\"viewport viewBox viewbox\",domain:\"domain domains area areas\",padding:\"pad padding paddings pads margin margins\",transpose:\"transpose transposed\",diagonal:\"diagonal diag showDiagonal\",upper:\"upper up top upperhalf upperHalf showupperhalf showUpper showUpperHalf\",lower:\"lower low bottom lowerhalf lowerHalf showlowerhalf showLowerHalf showLower\"}),s=this.traces[t]||(this.traces[t]={id:t,buffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),color:\"black\",marker:null,size:12,borderColor:\"transparent\",borderSize:1,viewport:l([r._gl.drawingBufferWidth,r._gl.drawingBufferHeight]),padding:[0,0,0,0],opacity:1,diagonal:!0,upper:!0,lower:!0});if(null!=o.color&&(s.color=o.color),null!=o.size&&(s.size=o.size),null!=o.marker&&(s.marker=o.marker),null!=o.borderColor&&(s.borderColor=o.borderColor),null!=o.borderSize&&(s.borderSize=o.borderSize),null!=o.opacity&&(s.opacity=o.opacity),o.viewport&&(s.viewport=l(o.viewport)),null!=o.diagonal&&(s.diagonal=o.diagonal),null!=o.upper&&(s.upper=o.upper),null!=o.lower&&(s.lower=o.lower),o.data){s.buffer(c(o.data)),s.columns=o.data.length,s.count=o.data[0].length,s.bounds=[];for(var u=0;u<s.columns;u++)s.bounds[u]=a(o.data[u],1)}o.range&&(s.range=o.range,n=s.range&&\"number\"!=typeof s.range[0]),o.domain&&(s.domain=o.domain);var d=!1;null!=o.padding&&(Array.isArray(o.padding)&&o.padding.length===s.columns&&\"number\"==typeof o.padding[o.padding.length-1]?(s.padding=o.padding.map(p),d=!0):s.padding=p(o.padding));var m=s.columns,g=s.count,v=s.viewport.width,y=s.viewport.height,x=s.viewport.x,b=s.viewport.y,_=v/m,w=y/m;s.passes=[];for(var T=0;T<m;T++)for(var k=0;k<m;k++)if((s.diagonal||k!==T)&&(s.upper||!(T>k))&&(s.lower||!(T<k))){var A=f(s.id,T,k),M=this.passes[A]||(this.passes[A]={});if(o.data&&(o.transpose?M.positions={x:{buffer:s.buffer,offset:k,count:g,stride:m},y:{buffer:s.buffer,offset:T,count:g,stride:m}}:M.positions={x:{buffer:s.buffer,offset:k*g,count:g},y:{buffer:s.buffer,offset:T*g,count:g}},M.bounds=h(s.bounds,T,k)),o.domain||o.viewport||o.data){var S=d?h(s.padding,T,k):s.padding;if(s.domain){var E=h(s.domain,T,k),L=E[0],C=E[1],P=E[2],I=E[3];M.viewport=[x+L*v+S[0],b+C*y+S[1],x+P*v-S[2],b+I*y-S[3]]}else M.viewport=[x+k*_+_*S[0],b+T*w+w*S[1],x+(k+1)*_-_*S[2],b+(T+1)*w-w*S[3]]}o.color&&(M.color=s.color),o.size&&(M.size=s.size),o.marker&&(M.marker=s.marker),o.borderSize&&(M.borderSize=s.borderSize),o.borderColor&&(M.borderColor=s.borderColor),o.opacity&&(M.opacity=s.opacity),o.range&&(M.range=n?h(s.range,T,k):s.range||M.bounds),s.passes.push(A)}return this},u.prototype.draw=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];if(e.length){for(var n=[],i=0;i<e.length;i++)if(\"number\"==typeof e[i]){var a=this.traces[e[i]],o=a.passes,l=a.passOffset;n.push.apply(n,s(l,l+o.length))}else if(e[i].length){var c=e[i],u=this.traces[i],f=u.passes,h=u.passOffset;f=f.map((function(t,e){n[h+e]=c}))}(t=this.scatter).draw.apply(t,n)}else this.scatter.draw();return this},u.prototype.destroy=function(){return 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),13},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),400},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/8)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]},daysInWeek:function(){return 8},dayOfWeek:function(t,e,r){return(this._validate(t,e,r,n.local.invalidDate).day()+1)%8},weekDay:function(t,e,r){var n=this.dayOfWeek(t,e,r);return n>=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},{\"../main\":346,\"object-assign\":247}],335:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s<e;s++)o+=this.daysInMonth(t,s)}else for(s=7;s<e;s++)o+=this.daysInMonth(t,s);return o},_delay1:function(t){var e=Math.floor((235*t-234)/19),r=12084+13753*e,n=29*e+Math.floor(r/25920);return o(3*(n+1),7)<3&&n++,n},_delay2:function(t){var e=this._delay1(t-1),r=this._delay1(t);return this._delay1(t+1)-r==356?2:r-e==382?1:0},fromJD:function(t){t=Math.floor(t)+.5;for(var e=Math.floor(98496*(t-this.jdEpoch)/35975351)-1;t>=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=t<this.toJD(e,1,1)?7:1;t>this.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return 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n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},{\"../main\":346,\"object-assign\":247}],339:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r<t.length;r++){var n=parseInt(t[r],10);if(Math.abs(n)>19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o((t-=this.jdEpoch)+8+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s((t-=this.jdEpoch)+20,20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},{\"../main\":346,\"object-assign\":247}],340:[function(t,e,r){var n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new 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a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return 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9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r<t+2;r++)void 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e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 682*((e.year()-(e.year()>0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var 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n=t(\"../main\"),i=t(\"object-assign\");function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thal\\u0101th\\u0101\\u2019\",\"Yawm al-Arba\\u2018\\u0101\\u2019\",\"Yawm al-Kham\\u012bs\",\"Yawm al-Jum\\u2018a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;a<o.length;a++){if(o[a]>r)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;n<o.length&&!(o[n]>e);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return 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n=t(\"object-assign\");function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return this._validate(t,this.minMonth,this.minDay,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),this._correctAdd(t,this._add(t,e,r),e,r)},_add:function(t,e,r){if(this._validateLevel++,\"d\"===r||\"w\"===r){var n=t.toJD()+e*(\"w\"===r?this.daysInWeek():1),i=t.calendar().fromJD(n);return this._validateLevel--,[i.year(),i.month(),i.day()]}try{var a=t.year()+(\"y\"===r?e:0),o=t.monthOfYear()+(\"m\"===r?e:0);i=t.day();\"y\"===r?(t.month()!==this.fromMonthOfYear(a,o)&&(o=this.newDate(a,t.month(),this.minDay).monthOfYear()),o=Math.min(o,this.monthsInYear(a)),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o)))):\"m\"===r&&(!function(t){for(;o<t.minMonth;)a--,o+=t.monthsInYear(a);for(var e=t.monthsInYear(a);o>e-1+t.minMonth;)a++,o-=e,e=t.monthsInYear(a)}(this),i=Math.min(i,this.daysInMonth(a,this.fromMonthOfYear(a,o))));var s=[a,this.fromMonthOfYear(a,o),i];return this._validateLevel--,s}catch(t){throw 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r,s,l=o(t,e),c=l.on,u=l.off.concat(l.explicitOff),f={},h=t._fullLayout.annotations;if(!c.length&&!u.length)return;for(r=0;r<c.length;r++)(s=a(t.layout,\"annotations\",h[c[r]])).modifyItem(\"visible\",!0),n.extendFlat(f,s.getUpdateObj());for(r=0;r<u.length;r++)(s=a(t.layout,\"annotations\",h[u[r]])).modifyItem(\"visible\",!1),n.extendFlat(f,s.getUpdateObj());return i.call(\"update\",t,{},f)}}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../../registry\":638}],352:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../color\");e.exports=function(t,e,r,a){a(\"opacity\");var o=a(\"bgcolor\"),s=a(\"bordercolor\"),l=i.opacity(s);a(\"borderpad\");var c=a(\"borderwidth\"),u=a(\"showarrow\");if(a(\"text\",u?\" \":r._dfltTitle.annotation),a(\"textangle\"),n.coerceFont(a,\"font\",r.font),a(\"width\"),a(\"align\"),a(\"height\")&&a(\"valign\"),u){var f,h,p=a(\"arrowside\");-1!==p.indexOf(\"end\")&&(f=a(\"arrowhead\"),h=a(\"arrowsize\")),-1!==p.indexOf(\"start\")&&(a(\"startarrowhead\",f),a(\"startarrowsize\",h)),a(\"arrowcolor\",l?e.bordercolor:i.defaultLine),a(\"arrowwidth\",2*(l&&c||1)),a(\"standoff\"),a(\"startstandoff\")}var d=a(\"hovertext\"),m=r.hoverlabel||{};if(d){var g=a(\"hoverlabel.bgcolor\",m.bgcolor||(i.opacity(o)?i.rgb(o):i.defaultLine)),v=a(\"hoverlabel.bordercolor\",m.bordercolor||i.contrast(g));n.coerceFont(a,\"hoverlabel.font\",{family:m.font.family,size:m.font.size,color:m.font.color||v})}a(\"captureevents\",!!d)}},{\"../../lib\":503,\"../color\":366}],353:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib/to_log_range\");e.exports=function(t,e,r,a){e=e||{};var o=\"log\"===r&&\"linear\"===e.type,s=\"linear\"===r&&\"log\"===e.type;if(o||s)for(var l,c,u=t._fullLayout.annotations,f=e._id.charAt(0),h=0;h<u.length;h++)l=u[h],c=\"annotations[\"+h+\"].\",l[f+\"ref\"]===e._id&&p(f),l[\"a\"+f+\"ref\"]===e._id&&p(\"a\"+f);function p(t){var r=l[t],s=null;s=o?i(r,e.range):Math.pow(10,r),n(s)||(s=null),a(c+t,s)}}},{\"../../lib/to_log_range\":531,\"fast-isnumeric\":190}],354:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../plots/cartesian/axes\"),a=t(\"../../plots/array_container_defaults\"),o=t(\"./common_defaults\"),s=t(\"./attributes\");function l(t,e,r){function a(r,i){return n.coerce(t,e,s,r,i)}var l=a(\"visible\"),c=a(\"clicktoshow\");if(l||c){o(t,e,r,a);for(var u=e.showarrow,f=[\"x\",\"y\"],h=[-10,-30],p={_fullLayout:r},d=0;d<2;d++){var m=f[d],g=i.coerceRef(t,e,p,m,\"\",\"paper\");if(\"paper\"!==g)i.getFromId(p,g)._annIndices.push(e._index);if(i.coercePosition(e,p,a,g,m,.5),u){var v=\"a\"+m,y=i.coerceRef(t,e,p,v,\"pixel\",[\"pixel\",\"paper\"]);\"pixel\"!==y&&y!==g&&(y=e[v]=\"pixel\");var 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r=t._fullLayout.annotations[e]||{},n=l.getFromId(t,r.xref),i=l.getFromId(t,r.yref);n&&n.setScale(),i&&i.setScale(),x(t,r,e,!1,n,i)}function y(t,e,r,n,i){var a=i[r],o=i[r+\"ref\"],s=-1!==r.indexOf(\"y\"),c=\"domain\"===l.getRefType(o),u=s?n.h:n.w;return t?c?a+(s?-e:e)/t._length:t.p2r(t.r2p(a)+e):a+(s?-e:e)/u}function x(t,e,r,a,v,x){var b,_,w=t._fullLayout,T=t._fullLayout._size,k=t._context.edits;a?(b=\"annotation-\"+a,_=a+\".annotations\"):(b=\"annotation\",_=\"annotations\");var A=m(t.layout,_,e),M=A.modifyBase,S=A.modifyItem,E=A.getUpdateObj;w._infolayer.selectAll(\".\"+b+'[data-index=\"'+r+'\"]').remove();var L=\"clip\"+w._uid+\"_ann\"+r;if(e._input&&!1!==e.visible){var C={x:{},y:{}},P=+e.textangle||0,I=w._infolayer.append(\"g\").classed(b,!0).attr(\"data-index\",String(r)).style(\"opacity\",e.opacity),O=I.append(\"g\").classed(\"annotation-text-g\",!0),z=k[e.showarrow?\"annotationTail\":\"annotationPosition\"],D=e.captureevents||k.annotationText||z,R=O.append(\"g\").style(\"pointer-events\",D?\"all\":null).call(p,\"pointer\").on(\"click\",(function(){t._dragging=!1,t.emit(\"plotly_clickannotation\",Y(n.event))}));e.hovertext&&R.on(\"mouseover\",(function(){var r=e.hoverlabel,n=r.font,i=this.getBoundingClientRect(),a=t.getBoundingClientRect();f.loneHover({x0:i.left-a.left,x1:i.right-a.left,y:(i.top+i.bottom)/2-a.top,text:e.hovertext,color:r.bgcolor,borderColor:r.bordercolor,fontFamily:n.family,fontSize:n.size,fontColor:n.color},{container:w._hoverlayer.node(),outerContainer:w._paper.node(),gd:t})})).on(\"mouseout\",(function(){f.loneUnhover(w._hoverlayer.node())}));var F=e.borderwidth,B=e.borderpad,N=F+B,j=R.append(\"rect\").attr(\"class\",\"bg\").style(\"stroke-width\",F+\"px\").call(c.stroke,e.bordercolor).call(c.fill,e.bgcolor),U=e.width||e.height,V=w._topclips.selectAll(\"#\"+L).data(U?[0]:[]);V.enter().append(\"clipPath\").classed(\"annclip\",!0).attr(\"id\",L).append(\"rect\"),V.exit().remove();var H=e.font,q=w._meta?o.templateString(e.text,w._meta):e.text,G=R.append(\"text\").classed(\"annotation-text\",!0).text(q);k.annotationText?G.call(h.makeEditable,{delegate:R,gd:t}).call(W).on(\"edit\",(function(r){e.text=r,this.call(W),S(\"text\",r),v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0),i.call(\"_guiRelayout\",t,E())})):G.call(W)}else n.selectAll(\"#\"+L).remove();function Y(t){var n={index:r,annotation:e._input,fullAnnotation:e,event:t};return a&&(n.subplotId=a),n}function W(r){return r.call(u.font,H).attr({\"text-anchor\":{left:\"start\",right:\"end\"}[e.align]||\"middle\"}),h.convertToTspans(r,t,X),r}function X(){var r=G.selectAll(\"a\");1===r.size()&&r.text()===G.text()&&R.insert(\"a\",\":first-child\").attr({\"xlink:xlink:href\":r.attr(\"xlink:href\"),\"xlink:xlink:show\":r.attr(\"xlink:show\")}).style({cursor:\"pointer\"}).node().appendChild(j.node());var n=R.select(\".annotation-text-math-group\"),f=!n.empty(),m=u.bBox((f?n:G).node()),b=m.width,_=m.height,A=e.width||b,D=e.height||_,B=Math.round(A+2*N),H=Math.round(D+2*N);function q(t,e){return\"auto\"===e&&(e=t<1/3?\"left\":t>2/3?\"right\":\"center\"),{center:0,middle:0,left:.5,bottom:-.5,right:-.5,top:.5}[e]}for(var W=!1,X=[\"x\",\"y\"],Z=0;Z<X.length;Z++){var J,K,Q,$,tt,et=X[Z],rt=e[et+\"ref\"]||et,nt=e[\"a\"+et+\"ref\"],it={x:v,y:x}[et],at=(P+(\"x\"===et?0:-90))*Math.PI/180,ot=B*Math.cos(at),st=H*Math.sin(at),lt=Math.abs(ot)+Math.abs(st),ct=e[et+\"anchor\"],ut=e[et+\"shift\"]*(\"x\"===et?1:-1),ft=C[et],ht=l.getRefType(rt);if(it&&\"domain\"!==ht){var pt=it.r2fraction(e[et]);(pt<0||pt>1)&&(nt===rt?((pt=it.r2fraction(e[\"a\"+et]))<0||pt>1)&&(W=!0):W=!0),J=it._offset+it.r2p(e[et]),$=.5}else{var dt=\"domain\"===ht;\"x\"===et?(Q=e[et],J=dt?it._offset+it._length*Q:J=T.l+T.w*Q):(Q=1-e[et],J=dt?it._offset+it._length*Q:J=T.t+T.h*Q),$=e.showarrow?.5:Q}if(e.showarrow){ft.head=J;var mt=e[\"a\"+et];if(tt=ot*q(.5,e.xanchor)-st*q(.5,e.yanchor),nt===rt){var gt=l.getRefType(nt);\"domain\"===gt?(\"y\"===et&&(mt=1-mt),ft.tail=it._offset+it._length*mt):\"paper\"===gt?\"y\"===et?(mt=1-mt,ft.tail=T.t+T.h*mt):ft.tail=T.l+T.w*mt:ft.tail=it._offset+it.r2p(mt),K=tt}else ft.tail=J+mt,K=tt+mt;ft.text=ft.tail+tt;var vt=w[\"x\"===et?\"width\":\"height\"];if(\"paper\"===rt&&(ft.head=o.constrain(ft.head,1,vt-1)),\"pixel\"===nt){var yt=-Math.max(ft.tail-3,ft.text),xt=Math.min(ft.tail+3,ft.text)-vt;yt>0?(ft.tail+=yt,ft.text+=yt):xt>0&&(ft.tail-=xt,ft.text-=xt)}ft.tail+=ut,ft.head+=ut}else K=tt=lt*q($,ct),ft.text=J+tt;ft.text+=ut,tt+=ut,K+=ut,e[\"_\"+et+\"padplus\"]=lt/2+K,e[\"_\"+et+\"padminus\"]=lt/2-K,e[\"_\"+et+\"size\"]=lt,e[\"_\"+et+\"shift\"]=tt}if(W)R.remove();else{var bt=0,_t=0;if(\"left\"!==e.align&&(bt=(A-b)*(\"center\"===e.align?.5:1)),\"top\"!==e.valign&&(_t=(D-_)*(\"middle\"===e.valign?.5:1)),f)n.select(\"svg\").attr({x:N+bt-1,y:N+_t}).call(u.setClipUrl,U?L:null,t);else{var wt=N+_t-m.top,Tt=N+bt-m.left;G.call(h.positionText,Tt,wt).call(u.setClipUrl,U?L:null,t)}V.select(\"rect\").call(u.setRect,N,N,A,D),j.call(u.setRect,F/2,F/2,B-F,H-F),R.call(u.setTranslate,Math.round(C.x.text-B/2),Math.round(C.y.text-H/2)),O.attr({transform:\"rotate(\"+P+\",\"+C.x.text+\",\"+C.y.text+\")\"});var kt,At=function(r,n){I.selectAll(\".annotation-arrow-g\").remove();var l=C.x.head,f=C.y.head,h=C.x.tail+r,p=C.y.tail+n,m=C.x.text+r,b=C.y.text+n,_=o.rotationXYMatrix(P,m,b),w=o.apply2DTransform(_),A=o.apply2DTransform2(_),L=+j.attr(\"width\"),z=+j.attr(\"height\"),D=m-.5*L,F=D+L,B=b-.5*z,N=B+z,U=[[D,B,D,N],[D,N,F,N],[F,N,F,B],[F,B,D,B]].map(A);if(!U.reduce((function(t,e){return t^!!o.segmentsIntersect(l,f,l+1e6,f+1e6,e[0],e[1],e[2],e[3])}),!1)){U.forEach((function(t){var e=o.segmentsIntersect(h,p,l,f,t[0],t[1],t[2],t[3]);e&&(h=e.x,p=e.y)}));var V=e.arrowwidth,H=e.arrowcolor,q=e.arrowside,G=I.append(\"g\").style({opacity:c.opacity(H)}).classed(\"annotation-arrow-g\",!0),Y=G.append(\"path\").attr(\"d\",\"M\"+h+\",\"+p+\"L\"+l+\",\"+f).style(\"stroke-width\",V+\"px\").call(c.stroke,c.rgb(H));if(g(Y,q,e),k.annotationPosition&&Y.node().parentNode&&!a){var W=l,X=f;if(e.standoff){var Z=Math.sqrt(Math.pow(l-h,2)+Math.pow(f-p,2));W+=e.standoff*(h-l)/Z,X+=e.standoff*(p-f)/Z}var J,K,Q=G.append(\"path\").classed(\"annotation-arrow\",!0).classed(\"anndrag\",!0).classed(\"cursor-move\",!0).attr({d:\"M3,3H-3V-3H3ZM0,0L\"+(h-W)+\",\"+(p-X),transform:s(W,X)}).style(\"stroke-width\",V+6+\"px\").call(c.stroke,\"rgba(0,0,0,0)\").call(c.fill,\"rgba(0,0,0,0)\");d.init({element:Q.node(),gd:t,prepFn:function(){var t=u.getTranslate(R);J=t.x,K=t.y,v&&v.autorange&&M(v._name+\".autorange\",!0),x&&x.autorange&&M(x._name+\".autorange\",!0)},moveFn:function(t,r){var n=w(J,K),i=n[0]+t,a=n[1]+r;R.call(u.setTranslate,i,a),S(\"x\",y(v,t,\"x\",T,e)),S(\"y\",y(x,r,\"y\",T,e)),e.axref===e.xref&&S(\"ax\",y(v,t,\"ax\",T,e)),e.ayref===e.yref&&S(\"ay\",y(x,r,\"ay\",T,e)),G.attr(\"transform\",s(t,r)),O.attr({transform:\"rotate(\"+P+\",\"+i+\",\"+a+\")\"})},doneFn:function(){i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}};if(e.showarrow&&At(0,0),z)d.init({element:R.node(),gd:t,prepFn:function(){kt=O.attr(\"transform\")},moveFn:function(t,r){var n=\"pointer\";if(e.showarrow)e.axref===e.xref?S(\"ax\",y(v,t,\"ax\",T,e)):S(\"ax\",e.ax+t),e.ayref===e.yref?S(\"ay\",y(x,r,\"ay\",T.w,e)):S(\"ay\",e.ay+r),At(t,r);else{if(a)return;var i,o;if(v)i=y(v,t,\"x\",T,e);else{var l=e._xsize/T.w,c=e.x+(e._xshift-e.xshift)/T.w-l/2;i=d.align(c+t/T.w,l,0,1,e.xanchor)}if(x)o=y(x,r,\"y\",T,e);else{var u=e._ysize/T.h,f=e.y-(e._yshift+e.yshift)/T.h-u/2;o=d.align(f-r/T.h,u,0,1,e.yanchor)}S(\"x\",i),S(\"y\",o),v&&x||(n=d.getCursor(v?.5:i,x?.5:o,e.xanchor,e.yanchor))}O.attr({transform:s(t,r)+kt}),p(R,n)},clickFn:function(r,n){e.captureevents&&t.emit(\"plotly_clickannotation\",Y(n))},doneFn:function(){p(R),i.call(\"_guiRelayout\",t,E());var e=document.querySelector(\".js-notes-box-panel\");e&&e.redraw(e.selectedObj)}})}}}e.exports={draw:function(t){var e=t._fullLayout;e._infolayer.selectAll(\".annotation\").remove();for(var r=0;r<e.annotations.length;r++)e.annotations[r].visible&&v(t,r);return a.previousPromises(t)},drawOne:v,drawRaw:x}},{\"../../lib\":503,\"../../lib/setcursor\":524,\"../../lib/svg_text_utils\":529,\"../../plot_api/plot_template\":543,\"../../plots/cartesian/axes\":554,\"../../plots/plots\":619,\"../../registry\":638,\"../color\":366,\"../dragelement\":385,\"../drawing\":388,\"../fx\":406,\"./draw_arrow_head\":356,\"@plotly/d3\":58}],356:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../color\"),a=t(\"./arrow_paths\"),o=t(\"../../lib\"),s=o.strScale,l=o.strRotate,c=o.strTranslate;e.exports=function(t,e,r){var o,u,f,h,p=t.node(),d=a[r.arrowhead||0],m=a[r.startarrowhead||0],g=(r.arrowwidth||1)*(r.arrowsize||1),v=(r.arrowwidth||1)*(r.startarrowsize||1),y=e.indexOf(\"start\")>=0,x=e.indexOf(\"end\")>=0,b=d.backoff*g+r.standoff,_=m.backoff*v+r.startstandoff;if(\"line\"===p.nodeName){o={x:+t.attr(\"x1\"),y:+t.attr(\"y1\")},u={x:+t.attr(\"x2\"),y:+t.attr(\"y2\")};var w=o.x-u.x,T=o.y-u.y;if(h=(f=Math.atan2(T,w))+Math.PI,b&&_&&b+_>Math.sqrt(w*w+T*T))return void z();if(b){if(b*b>w*w+T*T)return void z();var k=b*Math.cos(f),A=b*Math.sin(f);u.x+=k,u.y+=A,t.attr({x2:u.x,y2:u.y})}if(_){if(_*_>w*w+T*T)return void z();var M=_*Math.cos(f),S=_*Math.sin(f);o.x-=M,o.y-=S,t.attr({x1:o.x,y1:o.y})}}else if(\"path\"===p.nodeName){var E=p.getTotalLength(),L=\"\";if(E<b+_)return void z();var C=p.getPointAtLength(0),P=p.getPointAtLength(.1);f=Math.atan2(C.y-P.y,C.x-P.x),o=p.getPointAtLength(Math.min(_,E)),L=\"0px,\"+_+\"px,\";var 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n=t(\"./draw\"),i=t(\"./click\");e.exports={moduleType:\"component\",name:\"annotations\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),includeBasePlot:t(\"../../plots/cartesian/include_components\")(\"annotations\"),calcAutorange:t(\"./calc_autorange\"),draw:n.draw,drawOne:n.drawOne,drawRaw:n.drawRaw,hasClickToShow:i.hasClickToShow,onClick:i.onClick,convertCoords:t(\"./convert_coords\")}},{\"../../plots/cartesian/include_components\":567,\"./attributes\":349,\"./calc_autorange\":350,\"./click\":351,\"./convert_coords\":353,\"./defaults\":354,\"./draw\":355}],358:[function(t,e,r){\"use strict\";var 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n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../plots/cartesian/axes\"),l=t(\"../dragelement\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../lib/extend\").extendFlat,h=t(\"../../lib/setcursor\"),p=t(\"../drawing\"),d=t(\"../color\"),m=t(\"../titles\"),g=t(\"../../lib/svg_text_utils\"),v=t(\"../colorscale/helpers\").flipScale,y=t(\"../../plots/cartesian/axis_defaults\"),x=t(\"../../plots/cartesian/position_defaults\"),b=t(\"../../plots/cartesian/layout_attributes\"),_=t(\"../../constants/alignment\"),w=_.LINE_SPACING,T=_.FROM_TL,k=_.FROM_BR,A=t(\"./constants\").cn;e.exports={draw:function(t){var e=t._fullLayout._infolayer.selectAll(\"g.\"+A.colorbar).data(function(t){var e,r,n,i,a=t._fullLayout,o=t.calcdata,s=[];function l(t){return f(t,{_fillcolor:null,_line:{color:null,width:null,dash:null},_levels:{start:null,end:null,size:null},_filllevels:null,_fillgradient:null,_zrange:null})}function c(){\"function\"==typeof 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n=t._fullLayout,i=\"v\"===e.orientation,a={type:\"linear\",range:r,tickmode:e.tickmode,nticks:e.nticks,tick0:e.tick0,dtick:e.dtick,tickvals:e.tickvals,ticktext:e.ticktext,ticks:e.ticks,ticklen:e.ticklen,tickwidth:e.tickwidth,tickcolor:e.tickcolor,showticklabels:e.showticklabels,ticklabelposition:e.ticklabelposition,ticklabeloverflow:e.ticklabeloverflow,tickfont:e.tickfont,tickangle:e.tickangle,tickformat:e.tickformat,exponentformat:e.exponentformat,minexponent:e.minexponent,separatethousands:e.separatethousands,showexponent:e.showexponent,showtickprefix:e.showtickprefix,tickprefix:e.tickprefix,showticksuffix:e.showticksuffix,ticksuffix:e.ticksuffix,title:e.title,showline:!0,anchor:\"free\",side:i?\"right\":\"bottom\",position:1},o=i?\"y\":\"x\",s={type:\"linear\",_id:o+e._id},l={letter:o,font:n.font,noHover:!0,noTickson:!0,noTicklabelmode:!0,calendar:n.calendar};function u(t,e){return c.coerce(a,s,b,t,e)}return y(a,s,u,l,n),x(a,s,u,l),s}(r,e,U);it.position=Z+(o?O+P/R.w:z+I/R.h);var at=-1!==[\"top\",\"bottom\"].indexOf(j);o&&at&&(it.title.side=j,it.titlex=O+P/R.w,it.titley=rt+(\"top\"===N.side?K-I/R.h:I/R.h));o||at||(it.title.side=j,it.titley=z+I/R.h,it.titlex=rt+P/R.w);if(B.color&&\"auto\"===e.tickmode){it.tickmode=\"linear\",it.tick0=q.start;var ot=q.size,st=c.constrain(J/50,4,15)+1,lt=(U[1]-U[0])/((e.nticks||st)*ot);if(lt>1){var ct=Math.pow(10,Math.floor(Math.log(lt)/Math.LN10));ot*=ct*c.roundUp(lt/ct,[2,5,10]),(Math.abs(q.start)/q.size+1e-6)%1<2e-6&&(it.tick0=0)}it.dtick=ot}it.domain=o?[rt+I/R.h,rt+K-I/R.h]:[rt+P/R.w,rt+K-P/R.w],it.setScale(),t.attr(\"transform\",u(Math.round(R.l),Math.round(R.t)));var ut,ft=t.select(\".\"+A.cbtitleunshift).attr(\"transform\",u(-Math.round(R.l),-Math.round(R.t))),ht=it.ticklabelposition,pt=it.title.font.size,dt=t.select(\".\"+A.cbaxis),mt=0,gt=0;function vt(n,i){var 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x=U.map(it.c2p).map(Math.round).sort((function(t,e){return t-e}));y.each((function(t,a){var s=[0===a?U[0]:(Y[a]+Y[a-1])/2,a===Y.length-1?U[1]:(Y[a]+Y[a+1])/2].map(it.c2p).map(Math.round);o&&(s[1]=c.constrain(s[1]+(s[1]>s[0])?1:-1,x[0],x[1]));var l=n.select(this).attr(o?\"x\":\"y\",Q).attr(o?\"y\":\"x\",n.min(s)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(n.max(s)-n.min(s),2));if(e._fillgradient)p.gradient(l,r,e._id,o?\"vertical\":\"horizontalreversed\",e._fillgradient,\"fill\");else{var u=H(t).replace(\"e-\",\"\");l.attr(\"fill\",i(u).toHexString())}}));var b=t.select(\".\"+A.cblines).selectAll(\"path.\"+A.cbline).data(B.color&&B.width?W:[]);b.enter().append(\"path\").classed(A.cbline,!0),b.exit().remove(),b.each((function(t){var e=Q,r=Math.round(it.c2p(t))+B.width/2%1;n.select(this).attr(\"d\",\"M\"+(o?e+\",\"+r:r+\",\"+e)+(o?\"h\":\"v\")+X).call(p.lineGroupStyle,B.width,V(t),B.dash)})),dt.selectAll(\"g.\"+it._id+\"tick,path\").remove();var _=Q+X+(M||0)/2-(\"outside\"===e.ticks?1:0),T=s.calcTicks(it),k=s.getTickSigns(it)[2];return s.drawTicks(r,it,{vals:\"inside\"===it.ticks?s.clipEnds(it,T):T,layer:dt,path:s.makeTickPath(it,_,k),transFn:s.makeTransTickFn(it)}),s.drawLabels(r,it,{vals:T,layer:dt,transFn:s.makeTransTickLabelFn(it),labelFns:s.makeLabelFns(it,_)})},function(){if(o&&!at||!o&&at){var t,i,a=it.position||0,s=it._offset+it._length/2;if(\"right\"===j)i=s,t=R.l+R.w*a+10+pt*(it.showticklabels?1:.5);else if(t=s,\"bottom\"===j&&(i=R.t+R.h*a+10+(-1===ht.indexOf(\"inside\")?it.tickfont.size:0)+(\"intside\"!==it.ticks&&e.ticklen||0)),\"top\"===j){var l=N.text.split(\"<br>\").length;i=R.t+R.h*a+10-X-w*pt*l}vt((o?\"h\":\"v\")+it._id+\"title\",{avoid:{selection:n.select(r).selectAll(\"g.\"+it._id+\"tick\"),side:j,offsetTop:o?0:R.t,offsetLeft:o?R.l:0,maxShift:o?D.width:D.height},attributes:{x:t,y:i,\"text-anchor\":\"middle\"},transform:{rotate:o?-90:0,offset:0}})}},a.previousPromises,function(){var n,s=X+M/2;-1===ht.indexOf(\"inside\")&&(n=p.bBox(dt.node()),s+=o?n.width:n.height),ut=ft.select(\"text\");var c=0,f=o&&\"top\"===j,m=!o&&\"right\"===j,g=0;if(ut.node()&&!ut.classed(A.jsPlaceholder)){var y,x=ft.select(\".h\"+it._id+\"title-math-group\").node();x&&(o&&at||!o&&!at)?(c=(n=p.bBox(x)).width,y=n.height):(c=(n=p.bBox(ft.node())).right-R.l-(o?Q:nt),y=n.bottom-R.t-(o?nt:Q),o||\"top\"!==j||(s+=n.height,g=n.height)),m&&(ut.attr(\"transform\",u(c/2+pt/2,0)),c*=2),s=Math.max(s,o?c:y)}var b=2*(o?P:I)+s+S+M/2,w=0;!o&&N.text&&\"bottom\"===C&&z<=0&&(b+=w=b/2,g+=w),D._hColorbarMoveTitle=w,D._hColorbarMoveCBTitle=g;var F=S+M;t.select(\".\"+A.cbbg).attr(\"x\",(o?Q:nt)-F/2-(o?P:0)).attr(\"y\",(o?nt:Q)-(o?J:I+g-w)).attr(o?\"width\":\"height\",Math.max(b-w,2)).attr(o?\"height\":\"width\",Math.max(J+F,2)).call(d.fill,E).call(d.stroke,e.bordercolor).style(\"stroke-width\",S);var B=m?Math.max(c-10,0):0;if(t.selectAll(\".\"+A.cboutline).attr(\"x\",(o?Q:nt+P)+B).attr(\"y\",(o?nt+I-J:Q)+(f?mt:0)).attr(o?\"width\":\"height\",Math.max(X,2)).attr(o?\"height\":\"width\",Math.max(J-(o?2*I+mt:2*P+B),2)).call(d.stroke,e.outlinecolor).style({fill:\"none\",\"stroke-width\":M}),t.attr(\"transform\",u(R.l-(o?$*b:0),R.t-(o?0:(1-tt)*b-g))),!o&&(S||i(E).getAlpha()&&!i.equals(D.paper_bgcolor,E))){var U=dt.selectAll(\"text\"),V=U[0].length,H=t.select(\".\"+A.cbbg).node(),q=p.bBox(H),G=p.getTranslate(t);U.each((function(t,e){var r=V-1;if(0===e||e===r){var n,i=p.bBox(this),a=p.getTranslate(this);if(e===r){var o=i.right+a.x;(n=q.right+G.x+nt-S-2+O-o)>0&&(n=0)}else if(0===e){var s=i.left+a.x;(n=q.left+G.x+nt+S+2-s)<0&&(n=0)}n&&(V<3?this.setAttribute(\"transform\",\"translate(\"+n+\",0) \"+this.getAttribute(\"transform\")):this.setAttribute(\"visibility\",\"hidden\"))}}))}var 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o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ct=1/0;function ut(t,r){for(G=0;G<rt.length;G++)if((Y=rt[G])&&Y[0]&&Y[0].trace&&!0===(W=Y[0].trace).visible&&0!==W._length&&-1===[\"carpet\",\"contourcarpet\"].indexOf(W._module.name)){if(\"splom\"===W.type?X=l[Z=0]:(X=v.getSubplot(W),Z=l.indexOf(X)),J=P,v.isUnifiedHover(J)&&(J=J.charAt(0)),$={cd:Y,trace:W,xa:_[Z],ya:w[Z],maxHoverDistance:O,maxSpikeDistance:B,index:!1,distance:Math.min(ct,O),spikeDistance:1/0,xSpike:void 0,ySpike:void 0,color:p.defaultLine,name:W.name,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},u[X]&&($.subplot=u[X]._subplot),u._splomScenes&&u._splomScenes[W.uid]&&($.scene=u._splomScenes[W.uid]),tt=et.length,\"array\"===J){var n=e[G];\"pointNumber\"in n?($.index=n.pointNumber,J=\"closest\"):(J=\"\",\"xval\"in n&&(K=n.xval,J=\"x\"),\"yval\"in n&&(Q=n.yval,J=J?\"closest\":\"y\"))}else void 0!==t&&void 0!==r?(K=t,Q=r):(K=V[Z],Q=H[Z]);if(0!==O)if(W._module&&W._module.hoverPoints){var a=W._module.hoverPoints($,K,Q,J,{finiteRange:!0,hoverLayer:u._hoverlayer});if(a)for(var s,c=0;c<a.length;c++)s=a[c],i(s.x0)&&i(s.y0)&&et.push(D(s,P))}else o.log(\"Unrecognized trace type in hover:\",W);if(\"closest\"===P&&et.length>tt&&(et.splice(0,tt),ct=et[0].distance),y&&0!==B&&0===et.length){$.distance=B,$.index=!1;var f=W._module.hoverPoints($,K,Q,\"closest\",{hoverLayer:u._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=B}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var m=d[0];i(m.x0)&&i(m.y0)&&(h=ht(m),(!nt.vLinePoint||nt.vLinePoint.spikeDistance>h.spikeDistance)&&(nt.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=ht(x),(!nt.hLinePoint||nt.hLinePoint.spikeDistance>h.spikeDistance)&&(nt.hLinePoint=h))}}}}}function ft(t,e,r){for(var n,i=null,a=1/0,o=0;o<t.length;o++)n=t[o].spikeDistance,r&&0===o&&(n=-1/0),n<=a&&n<=e&&(i=t[o],a=n);return i}function ht(t){return t?{xa:t.xa,ya:t.ya,x:void 0!==t.xSpike?t.xSpike:(t.x0+t.x1)/2,y:void 0!==t.ySpike?t.ySpike:(t.y0+t.y1)/2,distance:t.distance,spikeDistance:t.spikeDistance,curveNumber:t.trace.index,color:t.color,pointNumber:t.index}:null}ut();var pt={fullLayout:u,container:u._hoverlayer,event:e},dt=t._spikepoints,mt={vLinePoint:nt.vLinePoint,hLinePoint:nt.hLinePoint};t._spikepoints=mt;var gt=function(){et.sort((function(t,e){return t.distance-e.distance})),et=function(t,e){for(var r=e.charAt(0),n=[],i=[],a=[],o=0;o<t.length;o++){var s=t[o];g.traceIs(s.trace,\"bar-like\")||g.traceIs(s.trace,\"box-violin\")?a.push(s):s.trace[r+\"period\"]?i.push(s):n.push(s)}return n.concat(i).concat(a)}(et,P)};gt();var vt=P.charAt(0),yt=(\"x\"===vt||\"y\"===vt)&&et[0]&&L[et[0].trace.type];if(y&&0!==B&&0!==et.length){var xt=ft(et.filter((function(t){return t.ya.showspikes})),B,yt);nt.hLinePoint=ht(xt);var bt=ft(et.filter((function(t){return t.xa.showspikes})),B,yt);nt.vLinePoint=ht(bt)}if(0===et.length){var _t=d.unhoverRaw(t,e);return!y||null===nt.hLinePoint&&null===nt.vLinePoint||F(dt)&&R(t,nt,pt),_t}y&&F(dt)&&R(t,nt,pt);if(v.isXYhover(J)&&0!==et[0].length&&\"splom\"!==et[0].trace.type){var wt=et[0],Tt=(et=E[wt.trace.type]?et.filter((function(t){return t.trace.index===wt.trace.index})):[wt]).length,kt=N(\"x\",wt,u),At=N(\"y\",wt,u);ut(kt,At);var Mt,St=[],Et={},Lt=0,Ct=function(t){var e=E[t.trace.type]?C(t):t.trace.index;if(Et[e]){var r=Et[e]-1,n=St[r];r>0&&Math.abs(t.distance)<Math.abs(n.distance)&&(St[r]=t)}else Lt++,Et[e]=Lt,St.push(t)};for(Mt=0;Mt<Tt;Mt++)Ct(et[Mt]);for(Mt=et.length-1;Mt>Tt-1;Mt--)Ct(et[Mt]);et=St,gt()}var Pt=t._hoverdata,It=[],Ot=j(t),zt=U(t);for(q=0;q<et.length;q++){var Dt=et[q],Rt=v.makeEventData(Dt,Dt.trace,Dt.cd);if(!1!==Dt.hovertemplate){var Ft=!1;Dt.cd[Dt.index]&&Dt.cd[Dt.index].ht&&(Ft=Dt.cd[Dt.index].ht),Dt.hovertemplate=Ft||Dt.trace.hovertemplate||!1}if(Dt.xa&&Dt.ya){var Bt=Dt.x0+Dt.xa._offset,Nt=Dt.x1+Dt.xa._offset,jt=Dt.y0+Dt.ya._offset,Ut=Dt.y1+Dt.ya._offset,Vt=Math.min(Bt,Nt),Ht=Math.max(Bt,Nt),qt=Math.min(jt,Ut),Gt=Math.max(jt,Ut);Rt.bbox={x0:Vt+zt,x1:Ht+zt,y0:qt+Ot,y1:Gt+Ot}}Dt.eventData=[Rt],It.push(Rt)}t._hoverdata=It;var Yt=\"y\"===P&&(rt.length>1||et.length>1)||\"closest\"===P&&it&&et.length>1,Wt=p.combine(u.plot_bgcolor||p.background,u.paper_bgcolor),Xt=I(et,{gd:t,hovermode:P,rotateLabels:Yt,bgColor:Wt,container:u._hoverlayer,outerContainer:u._paper.node(),commonLabelOpts:u.hoverlabel,hoverdistance:u.hoverdistance});v.isUnifiedHover(P)||(!function(t,e,r){var n,i,a,o,s,l,c,u=0,f=1,h=t.size(),p=new Array(h),d=0;function m(t){var e=t[0],r=t[t.length-1];if(i=e.pmin-e.pos-e.dp+e.size,a=r.pos+r.dp+r.size-e.pmax,i>.01){for(s=t.length-1;s>=0;s--)t[s].dp+=i;n=!1}if(!(a<.01)){if(i<-.01){for(s=t.length-1;s>=0;s--)t[s].dp-=a;n=!1}if(n){var c=0;for(o=0;o<t.length;o++)(l=t[o]).pos+l.dp+l.size>e.pmax&&c++;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos>e.pmax-1&&(l.del=!0,c--);for(o=0;o<t.length&&!(c<=0);o++)if((l=t[o]).pos<e.pmin+1)for(l.del=!0,c--,a=2*l.size,s=t.length-1;s>=0;s--)t[s].dp-=a;for(o=t.length-1;o>=0&&!(c<=0);o--)(l=t[o]).pos+l.dp+l.size>e.pmax&&(l.del=!0,c--)}}}t.each((function(t){var n=t[e],i=\"x\"===n._id.charAt(0),a=n.range;0===d&&a&&a[0]>a[1]!==i&&(f=-1),p[d++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(i?T:1)/2,pmin:0,pmax:i?r.width:r.height}]})),p.sort((function(t,e){return t[0].posref-e[0].posref||f*(e[0].traceIndex-t[0].traceIndex)}));for(;!n&&u<=h;){for(u++,n=!0,o=0;o<p.length-1;){var g=p[o],v=p[o+1],y=g[g.length-1],x=v[0];if((i=y.pos+y.dp+y.size-x.pos-x.dp+x.size)>.01&&y.pmin===x.pmin&&y.pmax===x.pmax){for(s=v.length-1;s>=0;s--)v[s].dp+=i;for(g.push.apply(g,v),p.splice(o+1,1),c=0,s=g.length-1;s>=0;s--)c+=g[s].dp;for(a=c/g.length,s=g.length-1;s>=0;s--)g[s].dp-=a;n=!1}else o++}p.forEach(m)}for(o=p.length-1;o>=0;o--){var b=p[o];for(s=b.length-1;s>=0;s--){var _=b[s],w=_.datum;w.offset=_.dp,w.del=_.del}}}(Xt,Yt?\"xa\":\"ya\",u),z(Xt,Yt,u._invScaleX,u._invScaleY));if(s&&s.tagName){var Zt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,It);f(n.select(s),Zt?\"pointer\":\"\")}if(!s||a||!function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt))return;Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt});t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:V,yvals:H})}(t,e,r,a,s)}))},r.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=j(i),o=U(i),s=I(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,c=t.eventData;if(c){var u=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),m=t.trace;if(g.traceIs(m,\"gl3d\")){var v=i._fullLayout[m.scene]._scene.container,y=v.offsetLeft,x=v.offsetTop;u+=y,f+=y,h+=x,d+=x}c.bbox={x0:u+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(c.bbox)}else c=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:c}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}),l=0,c=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5<l?l-n+5:0,l=n+t.by+t.offset,r===e.anchorIndex&&(c=t.offset)})).each((function(t){t.offset-=c})),z(s,!1,i._fullLayout._invScaleX,i._fullLayout._invScaleY),r?s:s.node()};var P=/<extra>([\\s\\S]*)<\\/extra>/;function I(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,c=e.rotateLabels,f=e.bgColor,d=e.container,m=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||y.HOVERFONT,k=e.fontSize||y.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),I=A[P+\"Label\"],z=V(r,m),D=z.top,R=z.width,F=z.height,B=void 0!==I&&A.distance<=e.hoverdistance&&(\"x\"===a||\"y\"===a);if(B){var N,j,U=!0;for(N=0;N<t.length;N++)if(U&&void 0===t[N].zLabel&&(U=!1),j=t[N].hoverinfo||t[N].trace.hoverinfo){var H=Array.isArray(j)?j:j.split(\"+\");if(-1===H.indexOf(\"all\")&&-1===H.indexOf(a)){B=!1;break}}U&&(B=!1)}var q=d.selectAll(\"g.axistext\").data(B?[0]:[]);if(q.enter().append(\"g\").classed(\"axistext\",!0),q.exit().remove(),q.each((function(){var t=n.select(this),e=o.ensureSingle(t,\"path\",\"\",(function(t){t.style({\"stroke-width\":\"1px\"})})),l=o.ensureSingle(t,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),c=w.bgcolor||p.defaultLine,f=w.bordercolor||p.contrast(c),d=p.contrast(c),m={family:w.font.family||T,size:w.font.size||k,color:w.font.color||d};e.style({fill:c,stroke:f}),l.text(I).call(h.font,m).call(u.positionText,0,0).call(u.convertToTspans,r),t.attr(\"transform\",\"\");var g,v,y=V(r,l.node());if(\"x\"===a){var x=\"top\"===E.side?\"-\":\"\";l.attr(\"text-anchor\",\"middle\").call(u.positionText,0,\"top\"===E.side?D-y.bottom-M-S:D-y.top+M+S),g=E._offset+(A.x0+A.x1)/2,v=L._offset+(\"top\"===E.side?0:L._length);var b=y.width/2+S;g<b?(g=b,e.attr(\"d\",\"M-\"+(b-M)+\",0L-\"+(b-2*M)+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"Z\")):g>i.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+y.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+(S+y.width/2)+\"v\"+x+(2*S+y.height)+\"H-\"+(S+y.width/2)+\"V\"+x+M+\"H-\"+M+\"Z\")}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),v=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+y.height/2)+\"h\"+P+(2*S+y.width)+\"V-\"+(S+y.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\");var O,z=y.height/2,R=D-y.top-z,F=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g<y.width+2*S+M){O=\"M-\"+(M+S)+\"-\"+z+\"h-\"+(y.width-S)+\"V\"+z+\"h\"+(y.width-S)+\"Z\";var B=y.width-g+S;u.positionText(l,B,R),\"end\"===_&&l.selectAll(\"tspan\").each((function(){var t=n.select(this),e=h.tester.append(\"text\").text(t.text()).call(h.font,m),i=V(r,e.node());Math.round(i.width)<Math.round(y.width)&&t.attr(\"x\",B-i.width),e.remove()}))}else u.positionText(l,C*(S+M),R),O=null;var N=i._topclips.selectAll(\"#\"+F).data(O?[0]:[]);N.enter().append(\"clipPath\").attr(\"id\",F).append(\"path\"),N.exit().remove(),N.select(\"path\").attr(\"d\",O),h.setClipUrl(l,O?F:null,r)}t.attr(\"transform\",s(g,v))})),v.isUnifiedHover(a)){d.selectAll(\"g.hovertext\").remove();var G=t.filter((function(t){return\"none\"!==t.hoverinfo}));if(0===G.length)return;var Y=i.hoverlabel,W=Y.font,X={showlegend:!0,legend:{title:{text:I,font:W},font:W,bgcolor:Y.bgcolor,bordercolor:Y.bordercolor,borderwidth:1,tracegroupgap:7,traceorder:i.legend?i.legend.traceorder:void 0,orientation:\"v\"}},Z={font:W};x(X,Z,r._fullData);var J=Z.legend;J.entries=[];for(var K=0;K<G.length;K++){var Q=G[K];if(\"none\"!==Q.hoverinfo){var $=O(Q,!0,a,i,I),tt=$[0],et=$[1];Q.name=et,Q.text=\"\"!==et?et+\" : 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../plots/plots\"),o=t(\"../../registry\"),s=t(\"../../lib/events\"),l=t(\"../dragelement\"),c=t(\"../drawing\"),u=t(\"../color\"),f=t(\"../../lib/svg_text_utils\"),h=t(\"./handle_click\"),p=t(\"./constants\"),d=t(\"../../constants/alignment\"),m=d.LINE_SPACING,g=d.FROM_TL,v=d.FROM_BR,y=t(\"./get_legend_data\"),x=t(\"./style\"),b=t(\"./helpers\");function _(t,e,r,n,i){var a=r.data()[0][0].trace,l={event:i,node:r.node(),curveNumber:a.index,expandedIndex:a._expandedIndex,data:t.data,layout:t.layout,frames:t._transitionData._frames,config:t._context,fullData:t._fullData,fullLayout:t._fullLayout};if(a._group&&(l.group=a._group),o.traceIs(a,\"pie-like\")&&(l.label=r.datum()[0].label),!1!==s.triggerHandler(t,\"plotly_legendclick\",l))if(1===n)e._clickTimeout=setTimeout((function(){t._fullLayout&&h(r,t,n)}),t._context.doubleClickDelay);else 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r=e.currentTarget;r.setAttribute(\"data-attr\",\"zoom\"),r.setAttribute(\"data-val\",\"reset\"),f(t,e),r.setAttribute(\"data-attr\",\"resetLastSave\"),p(t,e),x(t,\"geo\"),x(t,\"mapbox\")}},u.toggleSpikelines={name:\"toggleSpikelines\",title:function(t){return c(t,\"Toggle Spike Lines\")},icon:o.spikeline,attr:\"_cartesianSpikesEnabled\",val:\"on\",click:function(t){var e=t._fullLayout,r=e._cartesianSpikesEnabled;e._cartesianSpikesEnabled=\"on\"===r?\"off\":\"on\",n.call(\"_guiRelayout\",t,function(t){for(var e=\"on\"===t._fullLayout._cartesianSpikesEnabled,r=a.list(t,null,!0),n={},i=0;i<r.length;i++){var o=r[i];n[o._name+\".showspikes\"]=!!e||o._showSpikeInitial}return n}(t))}},u.resetViewMapbox={name:\"resetViewMapbox\",_cat:\"resetView\",title:function(t){return c(t,\"Reset view\")},attr:\"reset\",icon:o.home,click:function(t){x(t,\"mapbox\")}},u.zoomInMapbox={name:\"zoomInMapbox\",_cat:\"zoomin\",title:function(t){return c(t,\"Zoom 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n=t(\"../../lib\"),i=t(\"../color\"),a=t(\"../../plot_api/plot_template\"),o=t(\"./attributes\");e.exports=function(t,e){var r=t.modebar||{},s=a.newContainer(e,\"modebar\");function l(t,e){return n.coerce(r,s,o,t,e)}l(\"orientation\"),l(\"bgcolor\",i.addOpacity(e.paper_bgcolor,.5));var c=i.contrast(i.rgb(e.modebar.bgcolor));l(\"color\",i.addOpacity(c,.3)),l(\"activecolor\",i.addOpacity(c,.7)),l(\"uirevision\",e.uirevision),l(\"add\"),l(\"remove\")}},{\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../color\":366,\"./attributes\":425}],429:[function(t,e,r){\"use strict\";e.exports={moduleType:\"component\",name:\"modebar\",layoutAttributes:t(\"./attributes\"),supplyLayoutDefaults:t(\"./defaults\"),manage:t(\"./manage\")}},{\"./attributes\":425,\"./defaults\":428,\"./manage\":430}],430:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axis_ids\"),i=t(\"../../traces/scatter/subtypes\"),a=t(\"../../registry\"),o=t(\"../fx/helpers\").isUnifiedHover,s=t(\"./modebar\"),l=t(\"./buttons\"),c=t(\"./constants\").DRAW_MODES;e.exports=function(t){var e=t._fullLayout,r=t._context,u=e._modeBar;if(r.displayModeBar||r.watermark){if(!Array.isArray(r.modeBarButtonsToRemove))throw new Error([\"*modeBarButtonsToRemove* configuration options\",\"must be an array.\"].join(\" \"));if(!Array.isArray(r.modeBarButtonsToAdd))throw new Error([\"*modeBarButtonsToAdd* configuration options\",\"must be an array.\"].join(\" \"));var f,h=r.modeBarButtons;f=Array.isArray(h)&&h.length?function(t){for(var e=0;e<t.length;e++)for(var r=t[e],n=0;n<r.length;n++){var i=r[n];if(\"string\"==typeof i){if(void 0===l[i])throw new Error([\"*modeBarButtons* configuration options\",\"invalid button name\"].join(\" \"));t[e][n]=l[i]}}return t}(h):!r.displayModeBar&&r.watermark?[]:function(t){var e=t._fullLayout,r=t._fullData,s=t._context;function u(t,e){if(\"string\"==typeof e){if(e.toLowerCase()===t.toLowerCase())return!0}else{var r=e.name,n=e._cat||e.name;if(r===t||n===t.toLowerCase())return!0}return!1}var f=e.modebar.add;\"string\"==typeof f&&(f=[f]);var h=e.modebar.remove;\"string\"==typeof h&&(h=[h]);var p=s.modeBarButtonsToAdd.concat(f.filter((function(t){for(var e=0;e<s.modeBarButtonsToRemove.length;e++)if(u(t,s.modeBarButtonsToRemove[e]))return!1;return!0}))),d=s.modeBarButtonsToRemove.concat(h.filter((function(t){for(var e=0;e<s.modeBarButtonsToAdd.length;e++)if(u(t,s.modeBarButtonsToAdd[e]))return!1;return!0}))),m=e._has(\"cartesian\"),g=e._has(\"gl3d\"),v=e._has(\"geo\"),y=e._has(\"pie\"),x=e._has(\"funnelarea\"),b=e._has(\"gl2d\"),_=e._has(\"ternary\"),w=e._has(\"mapbox\"),T=e._has(\"polar\"),k=e._has(\"smith\"),A=e._has(\"sankey\"),M=function(t){for(var e=n.list({_fullLayout:t},null,!0),r=0;r<e.length;r++)if(!e[r].fixedrange)return!1;return!0}(e),S=o(e.hovermode),E=[];function L(t){if(t.length){for(var e=[],r=0;r<t.length;r++){for(var n=t[r],i=l[n],a=i.name.toLowerCase(),o=(i._cat||i.name).toLowerCase(),s=!1,c=0;c<d.length;c++){var u=d[c].toLowerCase();if(u===a||u===o){s=!0;break}}s||e.push(l[n])}E.push(e)}}var C=[\"toImage\"];s.showEditInChartStudio?C.push(\"editInChartStudio\"):s.showSendToCloud&&C.push(\"sendDataToCloud\");L(C);var P=[],I=[],O=[],z=[];(m||b||y||x||_)+v+g+w+T+k>1?(I=[\"toggleHover\"],O=[\"resetViews\"]):v?(P=[\"zoomInGeo\",\"zoomOutGeo\"],I=[\"hoverClosestGeo\"],O=[\"resetGeo\"]):g?(I=[\"hoverClosest3d\"],O=[\"resetCameraDefault3d\",\"resetCameraLastSave3d\"]):w?(P=[\"zoomInMapbox\",\"zoomOutMapbox\"],I=[\"toggleHover\"],O=[\"resetViewMapbox\"]):b?I=[\"hoverClosestGl2d\"]:y?I=[\"hoverClosestPie\"]:A?(I=[\"hoverClosestCartesian\",\"hoverCompareCartesian\"],O=[\"resetViewSankey\"]):I=[\"toggleHover\"];m&&(I=[\"toggleSpikelines\",\"hoverClosestCartesian\",\"hoverCompareCartesian\"]);(function(t){for(var e=0;e<t.length;e++)if(!a.traceIs(t[e],\"noHover\"))return!1;return!0}(r)||S)&&(I=[]);!m&&!b||M||(P=[\"zoomIn2d\",\"zoomOut2d\",\"autoScale2d\"],\"resetViews\"!==O[0]&&(O=[\"resetScale2d\"]));g?z=[\"zoom3d\",\"pan3d\",\"orbitRotation\",\"tableRotation\"]:(m||b)&&!M||_?z=[\"zoom2d\",\"pan2d\"]:w||v?z=[\"pan2d\"]:T&&(z=[\"zoom2d\"]);(function(t){for(var e=!1,r=0;r<t.length&&!e;r++){var 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a=n._fullLayout._zoomlayer.selectAll(\".select-outline-\"+r.id);if(a&&n._fullLayout._drawing){var o=v(a,t);o&&i.call(\"_guiRelayout\",n,{shapes:o}),n._fullLayout._drawing=!1}}r.selection={},r.selection.selectionDefs=t.selectionDefs=[],r.selection.mergedPolygons=t.mergedPolygons=[]}function N(t,e,r,n){var i,a,o,s=[],l=e.map((function(t){return t._id})),c=r.map((function(t){return t._id}));for(o=0;o<t.calcdata.length;o++)if(!0===(a=(i=t.calcdata[o])[0].trace).visible&&a._module&&a._module.selectPoints)if(!n||a.subplot!==n&&a.geo!==n)if(\"splom\"===a.type&&a._xaxes[l[0]]&&a._yaxes[c[0]]){var u=h(a._module,i,e[0],r[0]);u.scene=t._fullLayout._splomScenes[a.uid],s.push(u)}else if(\"sankey\"===a.type){var f=h(a._module,i,e[0],r[0]);s.push(f)}else{if(-1===l.indexOf(a.xaxis))continue;if(-1===c.indexOf(a.yaxis))continue;s.push(h(a._module,i,_(t,a.xaxis),_(t,a.yaxis)))}else s.push(h(a._module,i,e[0],r[0]));return s;function h(t,e,r,n){return{_module:t,cd:e,xaxis:r,yaxis:n}}}function j(t){var 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n=t(\"@plotly/d3\"),i=t(\"d3-geo\"),a=i.geoPath,o=i.geoDistance,s=t(\"d3-geo-projection\"),l=t(\"../../registry\"),c=t(\"../../lib\"),u=c.strTranslate,f=t(\"../../components/color\"),h=t(\"../../components/drawing\"),p=t(\"../../components/fx\"),d=t(\"../plots\"),m=t(\"../cartesian/axes\"),g=t(\"../cartesian/autorange\").getAutoRange,v=t(\"../../components/dragelement\"),y=t(\"../cartesian/select\").prepSelect,x=t(\"../cartesian/select\").clearSelect,b=t(\"../cartesian/select\").selectOnClick,_=t(\"./zoom\"),w=t(\"./constants\"),T=t(\"../../lib/geo_location_utils\"),k=t(\"../../lib/topojson_utils\"),A=t(\"topojson-client\").feature;function 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i=t[n],a=i[0].trace;\"choropleth\"===a.type&&(this.hasChoropleth=!0),!0===a.visible&&a._length>0&&a._module.calcGeoJSON(i,e)}if(!this.updateProjection(t,e)){this.viewInitial&&this.scope===r.scope||this.saveViewInitial(r),this.scope=r.scope,this.updateBaseLayers(e,r),this.updateDims(e,r),this.updateFx(e,r),d.generalUpdatePerTraceModule(this.graphDiv,this,t,r);var o=this.layers.frontplot.select(\".scatterlayer\");this.dataPoints.point=o.selectAll(\".point\"),this.dataPoints.text=o.selectAll(\"text\"),this.dataPaths.line=o.selectAll(\".js-line\");var s=this.layers.backplot.select(\".choroplethlayer\");this.dataPaths.choropleth=s.selectAll(\"path\"),this.render()}},S.updateProjection=function(t,e){var r=this.graphDiv,n=e[this.id],l=e._size,u=n.domain,f=n.projection,h=n.lonaxis,p=n.lataxis,d=h._ax,m=p._ax,v=this.projection=function(t){var e=t.projection,r=e.type,n=w.projNames[r];n=\"geo\"+c.titleCase(n);for(var l=(i[n]||s[n])(),u=t._isSatellite?180*Math.acos(1/e.distance)/Math.PI:t._isClipped?w.lonaxisSpan[r]/2:null,f=[\"center\",\"rotate\",\"parallels\",\"clipExtent\"],h=function(t){return t?l:[]},p=0;p<f.length;p++){var d=f[p];\"function\"!=typeof l[d]&&(l[d]=h)}l.isLonLatOverEdges=function(t){if(null===l(t))return!0;if(u){var e=l.rotate();return o(t,[-e[0],-e[1]])>u*Math.PI/180}return!1},l.getPath=function(){return a().projection(l)},l.getBounds=function(t){return l.getPath().bounds(t)},l.precision(w.precision),t._isSatellite&&l.tilt(e.tilt).distance(e.distance);u&&l.clipAngle(u-w.clipPad);return l}(n),y=[[l.l+l.w*u.x[0],l.t+l.h*(1-u.y[1])],[l.l+l.w*u.x[1],l.t+l.h*(1-u.y[0])]],x=n.center||{},b=f.rotation||{},_=h.range||[],T=p.range||[];if(n.fitbounds){d._length=y[1][0]-y[0][0],m._length=y[1][1]-y[0][1],d.range=g(r,d),m.range=g(r,m);var k=(d.range[0]+d.range[1])/2,A=(m.range[0]+m.range[1])/2;if(n._isScoped)x={lon:k,lat:A};else if(n._isClipped){x={lon:k,lat:A},b={lon:k,lat:A,roll:b.roll};var 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Boolean(w.lineLayers[t])}function c(t){return Boolean(w.fillLayers[t])}var u=(this.hasChoropleth?w.layersForChoropleth:w.layers).filter((function(t){return l(t)||c(t)?e[\"show\"+t]:!s(t)||e[t].showgrid})),p=r.framework.selectAll(\".layer\").data(u,String);p.exit().each((function(t){delete a[t],delete o[t],n.select(this).remove()})),p.enter().append(\"g\").attr(\"class\",(function(t){return\"layer \"+t})).each((function(t){var e=a[t]=n.select(this);\"bg\"===t?r.bgRect=e.append(\"rect\").style(\"pointer-events\",\"all\"):s(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\"):\"backplot\"===t?e.append(\"g\").classed(\"choroplethlayer\",!0):\"frontplot\"===t?e.append(\"g\").classed(\"scatterlayer\",!0):l(t)?o[t]=e.append(\"path\").style(\"fill\",\"none\").style(\"stroke-miterlimit\",2):c(t)&&(o[t]=e.append(\"path\").style(\"stroke\",\"none\"))})),p.order(),p.each((function(r){var 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r=this.bounds,n=(e.framewidth||0)/2,i=r[0][0]-n,a=r[0][1]-n,o=r[1][0]-i+n,s=r[1][1]-a+n;h.setRect(this.clipRect,i,a,o,s),this.bgRect.call(h.setRect,i,a,o,s).call(f.fill,e.bgcolor),this.xaxis._offset=i,this.xaxis._length=o,this.yaxis._offset=a,this.yaxis._length=s},S.updateFx=function(t,e){var r=this,i=r.graphDiv,a=r.bgRect,o=t.dragmode,s=t.clickmode;if(!r.isStatic){var u;\"select\"===o?u=function(t,e){(t.range={})[r.id]=[h([e.xmin,e.ymin]),h([e.xmax,e.ymax])]}:\"lasso\"===o&&(u=function(t,e,n){(t.lassoPoints={})[r.id]=n.filtered.map(h)});var f={element:r.bgRect.node(),gd:i,plotinfo:{id:r.id,xaxis:r.xaxis,yaxis:r.yaxis,fillRangeItems:u},xaxes:[r.xaxis],yaxes:[r.yaxis],subplot:r.id,clickFn:function(t){2===t&&x(i)}};\"pan\"===o?(a.node().onmousedown=null,a.call(_(r,e)),a.on(\"dblclick.zoom\",(function(){var t=r.viewInitial,e={};for(var n in 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t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var 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_=(l[3]-l[1])/(l[2]-l[0]);Math.abs(a*_)>Math.abs(s)?(c.boxEnd[1]=c.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),c.boxEnd[1]<l[1]?(c.boxEnd[1]=l[1],c.boxEnd[0]=c.boxStart[0]+(l[1]-c.boxStart[1])/Math.abs(_)):c.boxEnd[1]>l[3]&&(c.boxEnd[1]=l[3],c.boxEnd[0]=c.boxStart[0]+(l[3]-c.boxStart[1])/Math.abs(_))):(c.boxEnd[0]=c.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),c.boxEnd[0]<l[0]?(c.boxEnd[0]=l[0],c.boxEnd[1]=c.boxStart[1]+(l[0]-c.boxStart[0])*Math.abs(_)):c.boxEnd[0]>l[2]&&(c.boxEnd[0]=l[2],c.boxEnd[1]=c.boxStart[1]+(l[2]-c.boxStart[0])*Math.abs(_)))}}else c.boxEnabled?(a=c.boxStart[0]!==c.boxEnd[0],s=c.boxStart[1]!==c.boxEnd[1],a||s?(a&&(g(0,c.boxStart[0],c.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,c.boxStart[1],c.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),c.boxEnabled=!1,c.boxInited=!1):c.boxInited&&(c.boxInited=!1);break;case\"pan\":c.boxEnabled=!1,c.boxInited=!1,e?(c.panning||(c.dragStart[0]=n,c.dragStart[1]=i),Math.abs(c.dragStart[0]-n)<d&&(n=c.dragStart[0]),Math.abs(c.dragStart[1]-i)<d&&(i=c.dragStart[1]),a=(h-n)*(l[2]-l[0])/(r.viewBox[2]-r.viewBox[0]),s=(p-i)*(l[3]-l[1])/(r.viewBox[3]-r.viewBox[1]),l[0]+=a,l[2]+=a,l[1]+=s,l[3]+=s,t.setRanges(l),c.panning=!0,c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations()):c.panning&&(c.panning=!1,t.relayoutCallback())}c.lastPos[0]=n,c.lastPos[1]=i}return c.mouseListener=n(e,f),e.addEventListener(\"touchstart\",(function(t){var r=a(t.changedTouches[0],e);f(0,r[0],r[1]),f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchmove\",(function(t){t.preventDefault();var r=a(t.changedTouches[0],e);f(1,r[0],r[1]),t.preventDefault()}),!!s&&{passive:!1}),e.addEventListener(\"touchend\",(function(t){f(0,c.lastPos[0],c.lastPos[1]),t.preventDefault()}),!!s&&{passive:!1}),c.wheelListener=i(e,(function(e,n){if(!t.scrollZoom)return!1;var i=t.calcDataBox(),a=r.viewBox,o=c.lastPos[0],s=c.lastPos[1],l=Math.exp(5*n/(a[3]-a[1])),f=o/(a[2]-a[0])*(i[2]-i[0])+i[0],h=s/(a[3]-a[1])*(i[3]-i[1])+i[1];return i[0]=(i[0]-f)*l+f,i[2]=(i[2]-f)*l+f,i[1]=(i[1]-h)*l+h,i[3]=(i[3]-h)*l+h,t.setRanges(i),c.lastInputTime=Date.now(),u(),t.cameraChanged(),t.handleAnnotations(),t.relayoutCallback(),!0}),!0),c}},{\"../cartesian/constants\":561,\"has-passive-events\":229,\"mouse-change\":241,\"mouse-event-offset\":242,\"mouse-wheel\":244}],595:[function(t,e,r){\"use strict\";var n=t(\"../cartesian/axes\"),i=t(\"../../lib/str2rgbarray\");function a(t){this.scene=t,this.gl=t.gl,this.pixelRatio=t.pixelRatio,this.screenBox=[0,0,1,1],this.viewBox=[0,0,1,1],this.dataBox=[-1,-1,1,1],this.borderLineEnable=[!1,!1,!1,!1],this.borderLineWidth=[1,1,1,1],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.ticks=[[],[]],this.tickEnable=[!0,!0,!1,!1],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labels=[\"x\",\"y\"],this.labelEnable=[!0,!0,!1,!1],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelPad=[15,15,15,15],this.labelSize=[12,12],this.labelFont=[\"sans-serif\",\"sans-serif\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.title=\"\",this.titleEnable=!0,this.titleCenter=[0,0,0,0],this.titleAngle=0,this.titleColor=[0,0,0,1],this.titleFont=\"sans-serif\",this.titleSize=18,this.gridLineEnable=[!0,!0],this.gridLineColor=[[0,0,0,.5],[0,0,0,.5]],this.gridLineWidth=[1,1],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[1,1],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.static=this.scene.staticPlot}var o=a.prototype,s=[\"xaxis\",\"yaxis\"];o.merge=function(t){var e,r,n,a,o,l,c,u,f,h,p;for(this.titleEnable=!1,this.backgroundColor=i(t.plot_bgcolor),h=0;h<2;++h){var d=(e=s[h]).charAt(0);for(n=(r=t[this.scene[e]._name]).title.text===this.scene.fullLayout._dfltTitle[d]?\"\":r.title.text,p=0;p<=2;p+=2)this.labelEnable[h+p]=!1,this.labels[h+p]=n,this.labelColor[h+p]=i(r.title.font.color),this.labelFont[h+p]=r.title.font.family,this.labelSize[h+p]=r.title.font.size,this.labelPad[h+p]=this.getLabelPad(e,r),this.tickEnable[h+p]=!1,this.tickColor[h+p]=i((r.tickfont||{}).color),this.tickAngle[h+p]=\"auto\"===r.tickangle?0:Math.PI*-r.tickangle/180,this.tickPad[h+p]=this.getTickPad(r),this.tickMarkLength[h+p]=0,this.tickMarkWidth[h+p]=r.tickwidth||0,this.tickMarkColor[h+p]=i(r.tickcolor),this.borderLineEnable[h+p]=!1,this.borderLineColor[h+p]=i(r.linecolor),this.borderLineWidth[h+p]=r.linewidth||0;c=this.hasSharedAxis(r),o=this.hasAxisInDfltPos(e,r)&&!c,l=this.hasAxisInAltrPos(e,r)&&!c,a=r.mirror||!1,u=c?-1!==String(a).indexOf(\"all\"):!!a,f=c?\"allticks\"===a:-1!==String(a).indexOf(\"ticks\"),o?this.labelEnable[h]=!0:l&&(this.labelEnable[h+2]=!0),o?this.tickEnable[h]=r.showticklabels:l&&(this.tickEnable[h+2]=r.showticklabels),(o||u)&&(this.borderLineEnable[h]=r.showline),(l||u)&&(this.borderLineEnable[h+2]=r.showline),(o||f)&&(this.tickMarkLength[h]=this.getTickMarkLength(r)),(l||f)&&(this.tickMarkLength[h+2]=this.getTickMarkLength(r)),this.gridLineEnable[h]=r.showgrid,this.gridLineColor[h]=i(r.gridcolor),this.gridLineWidth[h]=r.gridwidth,this.zeroLineEnable[h]=r.zeroline,this.zeroLineColor[h]=i(r.zerolinecolor),this.zeroLineWidth[h]=r.zerolinewidth}},o.hasSharedAxis=function(t){var e=this.scene,r=e.fullLayout._subplots.gl2d;return 0!==n.findSubplotsWithAxis(r,t).indexOf(e.id)},o.hasAxisInDfltPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"bottom\"===r:\"yaxis\"===t?\"left\"===r:void 0},o.hasAxisInAltrPos=function(t,e){var r=e.side;return\"xaxis\"===t?\"top\"===r:\"yaxis\"===t?\"right\"===r:void 0},o.getLabelPad=function(t,e){var r=e.title.font.size,n=e.showticklabels;return\"xaxis\"===t?\"top\"===e.side?r*(1.5+(n?1:0))-10:r*(1.5+(n?.5:0))-10:\"yaxis\"===t?\"right\"===e.side?10+r*(1.5+(n?1:.5)):10+r*(1.5+(n?.5:0)):void 0},o.getTickPad=function(t){return\"outside\"===t.ticks?10+t.ticklen:15},o.getTickMarkLength=function(t){if(!t.ticks)return 0;var e=t.ticklen;return\"inside\"===t.ticks?-e:e},e.exports=function(t){return new a(t)}},{\"../../lib/str2rgbarray\":528,\"../cartesian/axes\":554}],596:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"./scene2d\"),a=t(\"../layout_attributes\"),o=t(\"../../constants/xmlns_namespaces\"),s=t(\"../cartesian/constants\"),l=t(\"../cartesian\"),c=t(\"../../components/fx/layout_attributes\"),u=t(\"../get_data\").getSubplotData;r.name=\"gl2d\",r.attr=[\"xaxis\",\"yaxis\"],r.idRoot=[\"x\",\"y\"],r.idRegex=s.idRegex,r.attrRegex=s.attrRegex,r.attributes=t(\"../cartesian/attributes\"),r.supplyLayoutDefaults=function(t,e,r){e._has(\"cartesian\")||l.supplyLayoutDefaults(t,e,r)},r.layoutAttrOverrides=n(l.layoutAttributes,\"plot\",\"from-root\"),r.baseLayoutAttrOverrides=n({plot_bgcolor:a.plot_bgcolor,hoverlabel:c.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl2d,a=0;a<n.length;a++){var o=n[a],s=e._plots[o],l=u(r,\"gl2d\",o),c=s._scene2d;void 0===c&&(c=new i({id:o,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio},e),s._scene2d=c),c.plot(l,t.calcdata,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl2d||[],a=0;a<i.length;a++){var o=i[a],s=n._plots[o];if(s._scene2d){var c=u(t,\"gl2d\",o);0===c.length&&(s._scene2d.destroy(),delete n._plots[o])}}l.clean.apply(this,arguments)},r.drawFramework=function(t){t._context.staticPlot||l.drawFramework(t)},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){var i=e._plots[r[n]]._scene2d,a=i.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":a,x:0,y:0,width:\"100%\",height:\"100%\",preserveAspectRatio:\"none\"}),i.destroy()}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl2d,n=0;n<r.length;n++){e._plots[r[n]]._scene2d.updateFx(e.dragmode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../plot_api/edit_types\":536,\"../cartesian\":568,\"../cartesian/attributes\":552,\"../cartesian/constants\":561,\"../get_data\":593,\"../layout_attributes\":610,\"./scene2d\":597}],597:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../registry\"),o=t(\"../../plots/cartesian/axes\"),s=t(\"../../components/fx\"),l=t(\"../../../stackgl_modules\").gl_plot2d,c=t(\"../../../stackgl_modules\").gl_spikes2d,u=t(\"../../../stackgl_modules\").gl_select_box,f=t(\"webgl-context\"),h=t(\"./convert\"),p=t(\"./camera\"),d=t(\"../../lib/show_no_webgl_msg\"),m=t(\"../cartesian/constraints\"),g=m.enforce,v=m.clean,y=t(\"../cartesian/autorange\").doAutoRange,x=t(\"../../components/dragelement/helpers\"),b=x.drawMode,_=x.selectMode,w=[\"xaxis\",\"yaxis\"],T=t(\"../cartesian/constants\").SUBPLOT_PATTERN;function k(t,e){this.container=t.container,this.graphDiv=t.graphDiv,this.pixelRatio=t.plotGlPixelRatio||window.devicePixelRatio,this.id=t.id,this.staticPlot=!!t.staticPlot,this.scrollZoom=this.graphDiv._context._scrollZoom.cartesian,this.fullData=null,this.updateRefs(e),this.makeFramework(),this.stopped||(this.glplotOptions=h(this),this.glplotOptions.merge(e),this.glplot=l(this.glplotOptions),this.camera=p(this),this.traces={},this.spikes=c(this.glplot),this.selectBox=u(this.glplot,{innerFill:!1,outerFill:!0}),this.lastButtonState=0,this.pickResult=null,this.isMouseOver=!0,this.stopped=!1,this.redraw=this.draw.bind(this),this.redraw())}e.exports=k;var A=k.prototype;A.makeFramework=function(){if(this.staticPlot){if(!(i||(n=document.createElement(\"canvas\"),i=f({canvas:n,preserveDrawingBuffer:!1,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"Error creating static canvas/context for image server\");this.canvas=n,this.gl=i}else{var t=this.container.querySelector(\".gl-canvas-focus\"),e=f({canvas:t,preserveDrawingBuffer:!0,premultipliedAlpha:!0});if(!e)return d(this),void(this.stopped=!0);this.canvas=t,this.gl=e}var r=this.canvas;r.style.width=\"100%\",r.style.height=\"100%\",r.style.position=\"absolute\",r.style.top=\"0px\",r.style.left=\"0px\",r.style[\"pointer-events\"]=\"none\",this.updateSize(r);var a=this.svgContainer=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");a.style.position=\"absolute\",a.style.top=a.style.left=\"0px\",a.style.width=a.style.height=\"100%\",a.style[\"z-index\"]=20,a.style[\"pointer-events\"]=\"none\";var o=this.mouseContainer=document.createElement(\"div\");o.style.position=\"absolute\",o.style[\"pointer-events\"]=\"auto\",this.pickCanvas=this.container.querySelector(\".gl-canvas-pick\");var s=this.container;s.appendChild(a),s.appendChild(o);var l=this;o.addEventListener(\"mouseout\",(function(){l.isMouseOver=!1,l.unhover()})),o.addEventListener(\"mouseover\",(function(){l.isMouseOver=!0}))},A.toImage=function(t){t||(t=\"png\"),this.stopped=!0,this.staticPlot&&this.container.appendChild(n),this.updateSize(this.canvas);var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.clearColor(1,1,1,0),e.clear(e.COLOR_BUFFER_BIT|e.DEPTH_BUFFER_BIT),this.glplot.setDirty(),this.glplot.draw(),e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a);for(var o=0,s=i-1;o<s;++o,--s)for(var l=0;l<r;++l)for(var c=0;c<4;++c){var u=a[4*(r*o+l)+c];a[4*(r*o+l)+c]=a[4*(r*s+l)+c],a[4*(r*s+l)+c]=u}var f=document.createElement(\"canvas\");f.width=r,f.height=i;var h,p=f.getContext(\"2d\"),d=p.createImageData(r,i);switch(d.data.set(a),p.putImageData(d,0,0),t){case\"jpeg\":h=f.toDataURL(\"image/jpeg\");break;case\"webp\":h=f.toDataURL(\"image/webp\");break;default:h=f.toDataURL(\"image/png\")}return this.staticPlot&&this.container.removeChild(n),h},A.updateSize=function(t){t||(t=this.canvas);var e=this.pixelRatio,r=this.fullLayout,n=r.width,i=r.height,a=0|Math.ceil(e*n),o=0|Math.ceil(e*i);return t.width===a&&t.height===o||(t.width=a,t.height=o),t},A.computeTickMarks=function(){this.xaxis.setScale(),this.yaxis.setScale();for(var t=[o.calcTicks(this.xaxis),o.calcTicks(this.yaxis)],e=0;e<2;++e)for(var r=0;r<t[e].length;++r)t[e][r].text=t[e][r].text+\"\";return t},A.updateRefs=function(t){this.fullLayout=t;var e=this.id.match(T),r=\"xaxis\"+e[1],n=\"yaxis\"+e[2];this.xaxis=this.fullLayout[r],this.yaxis=this.fullLayout[n]},A.relayoutCallback=function(){var t=this.graphDiv,e=this.xaxis,r=this.yaxis,n=t.layout,i={},o=i[e._name+\".range\"]=e.range.slice(),s=i[r._name+\".range\"]=r.range.slice();i[e._name+\".autorange\"]=e.autorange,i[r._name+\".autorange\"]=r.autorange,a.call(\"_storeDirectGUIEdit\",t.layout,t._fullLayout._preGUI,i);var l=n[e._name];l.range=o,l.autorange=e.autorange;var c=n[r._name];c.range=s,c.autorange=r.autorange,i.lastInputTime=this.camera.lastInputTime,t.emit(\"plotly_relayout\",i)},A.cameraChanged=function(){var t=this.camera;this.glplot.setDataBox(this.calcDataBox());var e=this.computeTickMarks();(function(t,e){for(var r=0;r<2;++r){var n=t[r],i=e[r];if(n.length!==i.length)return!0;for(var a=0;a<n.length;++a)if(n[a].x!==i[a].x)return!0}return!1})(e,this.glplotOptions.ticks)&&(this.glplotOptions.ticks=e,this.glplotOptions.dataBox=t.dataBox,this.glplot.update(this.glplotOptions),this.handleAnnotations())},A.handleAnnotations=function(){for(var t=this.graphDiv,e=this.fullLayout.annotations,r=0;r<e.length;r++){var n=e[r];n.xref===this.xaxis._id&&n.yref===this.yaxis._id&&a.getComponentMethod(\"annotations\",\"drawOne\")(t,r)}},A.destroy=function(){if(this.glplot){var t=this.traces;t&&Object.keys(t).map((function(e){t[e].dispose(),delete t[e]})),this.glplot.dispose(),this.container.removeChild(this.svgContainer),this.container.removeChild(this.mouseContainer),this.fullData=null,this.glplot=null,this.stopped=!0,this.camera.mouseListener.enabled=!1,this.mouseContainer.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null}},A.plot=function(t,e,r){var n=this.glplot;this.updateRefs(r),this.xaxis.clearCalc(),this.yaxis.clearCalc(),this.updateTraces(t,e),this.updateFx(r.dragmode);var i=r.width,a=r.height;this.updateSize(this.canvas);var o=this.glplotOptions;o.merge(r),o.screenBox=[0,0,i,a];var s={_fullLayout:{_axisConstraintGroups:r._axisConstraintGroups,xaxis:this.xaxis,yaxis:this.yaxis,_size:r._size}};v(s,this.xaxis),v(s,this.yaxis);var l,c,u=r._size,f=this.xaxis.domain,h=this.yaxis.domain;for(o.viewBox=[u.l+f[0]*u.w,u.b+h[0]*u.h,i-u.r-(1-f[1])*u.w,a-u.t-(1-h[1])*u.h],this.mouseContainer.style.width=u.w*(f[1]-f[0])+\"px\",this.mouseContainer.style.height=u.h*(h[1]-h[0])+\"px\",this.mouseContainer.height=u.h*(h[1]-h[0]),this.mouseContainer.style.left=u.l+f[0]*u.w+\"px\",this.mouseContainer.style.top=u.t+(1-h[1])*u.h+\"px\",c=0;c<2;++c)(l=this[w[c]])._length=o.viewBox[c+2]-o.viewBox[c],y(this.graphDiv,l),l.setScale();g(s),o.ticks=this.computeTickMarks(),o.dataBox=this.calcDataBox(),o.merge(r),n.update(o),this.glplot.draw()},A.calcDataBox=function(){var t=this.xaxis,e=this.yaxis,r=t.range,n=e.range,i=t.r2l,a=e.r2l;return[i(r[0]),a(n[0]),i(r[1]),a(n[1])]},A.setRanges=function(t){var e=this.xaxis,r=this.yaxis,n=e.l2r,i=r.l2r;e.range=[n(t[0]),n(t[2])],r.range=[i(t[1]),i(t[3])]},A.updateTraces=function(t,e){var r,n,i,a=Object.keys(this.traces);this.fullData=t;t:for(r=0;r<a.length;r++){var o=a[r],s=this.traces[o];for(n=0;n<t.length;n++)if((i=t[n]).uid===o&&i.type===s.type)continue t;s.dispose(),delete this.traces[o]}for(r=0;r<t.length;r++){i=t[r];var l=e[r],c=this.traces[i.uid];c?c.update(i,l):(c=i._module.plot(this,i,l),this.traces[i.uid]=c)}this.glplot.objects.sort((function(t,e){return t._trace.index-e._trace.index}))},A.updateFx=function(t){_(t)||b(t)?(this.pickCanvas.style[\"pointer-events\"]=\"none\",this.mouseContainer.style[\"pointer-events\"]=\"none\"):(this.pickCanvas.style[\"pointer-events\"]=\"auto\",this.mouseContainer.style[\"pointer-events\"]=\"auto\"),this.mouseContainer.style.cursor=\"pan\"===t?\"move\":\"zoom\"===t?\"crosshair\":null},A.emitPointAction=function(t,e){for(var r,n=t.trace.uid,i=t.pointIndex,a=0;a<this.fullData.length;a++)this.fullData[a].uid===n&&(r=this.fullData[a]);var o={x:t.traceCoord[0],y:t.traceCoord[1],curveNumber:r.index,pointNumber:i,data:r._input,fullData:this.fullData,xaxis:this.xaxis,yaxis:this.yaxis};s.appendArrayPointValue(o,r,i),this.graphDiv.emit(e,{points:[o]})},A.draw=function(){if(!this.stopped){requestAnimationFrame(this.redraw);var t=this.glplot,e=this.camera,r=e.mouseListener,n=1===this.lastButtonState&&0===r.buttons,i=this.fullLayout;this.lastButtonState=r.buttons,this.cameraChanged();var a,o=r.x*t.pixelRatio,l=this.canvas.height-t.pixelRatio*r.y;if(e.boxEnabled&&\"zoom\"===i.dragmode){this.selectBox.enabled=!0;for(var c=this.selectBox.selectBox=[Math.min(e.boxStart[0],e.boxEnd[0]),Math.min(e.boxStart[1],e.boxEnd[1]),Math.max(e.boxStart[0],e.boxEnd[0]),Math.max(e.boxStart[1],e.boxEnd[1])],u=0;u<2;u++)e.boxStart[u]===e.boxEnd[u]&&(c[u]=t.dataBox[u],c[u+2]=t.dataBox[u+2]);t.setDirty()}else if(!e.panning&&this.isMouseOver){this.selectBox.enabled=!1;var f=i._size,h=this.xaxis.domain,p=this.yaxis.domain,d=(a=t.pick(o/t.pixelRatio+f.l+h[0]*f.w,l/t.pixelRatio-(f.t+(1-p[1])*f.h)))&&a.object._trace.handlePick(a);if(d&&n&&this.emitPointAction(d,\"plotly_click\"),a&&\"skip\"!==a.object._trace.hoverinfo&&i.hovermode&&d&&(!this.lastPickResult||this.lastPickResult.traceUid!==d.trace.uid||this.lastPickResult.dataCoord[0]!==d.dataCoord[0]||this.lastPickResult.dataCoord[1]!==d.dataCoord[1])){var m=d;this.lastPickResult={traceUid:d.trace?d.trace.uid:null,dataCoord:d.dataCoord.slice()},this.spikes.update({center:a.dataCoord}),m.screenCoord=[((t.viewBox[2]-t.viewBox[0])*(a.dataCoord[0]-t.dataBox[0])/(t.dataBox[2]-t.dataBox[0])+t.viewBox[0])/t.pixelRatio,(this.canvas.height-(t.viewBox[3]-t.viewBox[1])*(a.dataCoord[1]-t.dataBox[1])/(t.dataBox[3]-t.dataBox[1])-t.viewBox[1])/t.pixelRatio],this.emitPointAction(d,\"plotly_hover\");var g=this.fullData[m.trace.index]||{},v=m.pointIndex,y=s.castHoverinfo(g,i,v);if(y&&\"all\"!==y){var x=y.split(\"+\");-1===x.indexOf(\"x\")&&(m.traceCoord[0]=void 0),-1===x.indexOf(\"y\")&&(m.traceCoord[1]=void 0),-1===x.indexOf(\"z\")&&(m.traceCoord[2]=void 0),-1===x.indexOf(\"text\")&&(m.textLabel=void 0),-1===x.indexOf(\"name\")&&(m.name=void 0)}s.loneHover({x:m.screenCoord[0],y:m.screenCoord[1],xLabel:this.hoverFormatter(\"xaxis\",m.traceCoord[0]),yLabel:this.hoverFormatter(\"yaxis\",m.traceCoord[1]),zLabel:m.traceCoord[2],text:m.textLabel,name:m.name,color:s.castHoverOption(g,v,\"bgcolor\")||m.color,borderColor:s.castHoverOption(g,v,\"bordercolor\"),fontFamily:s.castHoverOption(g,v,\"font.family\"),fontSize:s.castHoverOption(g,v,\"font.size\"),fontColor:s.castHoverOption(g,v,\"font.color\"),nameLength:s.castHoverOption(g,v,\"namelength\"),textAlign:s.castHoverOption(g,v,\"align\")},{container:this.svgContainer,gd:this.graphDiv})}}a||this.unhover(),t.draw()}},A.unhover=function(){this.lastPickResult&&(this.spikes.update({}),this.lastPickResult=null,this.graphDiv.emit(\"plotly_unhover\"),s.loneUnhover(this.svgContainer))},A.hoverFormatter=function(t,e){if(void 0!==e){var r=this[t];return o.tickText(r,r.c2l(e),\"hover\").text}}},{\"../../../stackgl_modules\":1120,\"../../components/dragelement/helpers\":384,\"../../components/fx\":406,\"../../lib/show_no_webgl_msg\":525,\"../../plots/cartesian/axes\":554,\"../../registry\":638,\"../cartesian/autorange\":553,\"../cartesian/constants\":561,\"../cartesian/constraints\":562,\"./camera\":594,\"./convert\":595,\"webgl-context\":331}],598:[function(t,e,r){\"use strict\";var n=t(\"../../plot_api/edit_types\").overrideAll,i=t(\"../../components/fx/layout_attributes\"),a=t(\"./scene\"),o=t(\"../get_data\").getSubplotData,s=t(\"../../lib\"),l=t(\"../../constants/xmlns_namespaces\");r.name=\"gl3d\",r.attr=\"scene\",r.idRoot=\"scene\",r.idRegex=r.attrRegex=s.counterRegex(\"scene\"),r.attributes=t(\"./layout/attributes\"),r.layoutAttributes=t(\"./layout/layout_attributes\"),r.baseLayoutAttrOverrides=n({hoverlabel:i.hoverlabel},\"plot\",\"nested\"),r.supplyLayoutDefaults=t(\"./layout/defaults\"),r.plot=function(t){for(var e=t._fullLayout,r=t._fullData,n=e._subplots.gl3d,i=0;i<n.length;i++){var s=n[i],l=o(r,\"gl3d\",s),c=e[s],u=c.camera,f=c._scene;f||(f=new a({id:s,graphDiv:t,container:t.querySelector(\".gl-container\"),staticPlot:t._context.staticPlot,plotGlPixelRatio:t._context.plotGlPixelRatio,camera:u},e),c._scene=f),f.viewInitial||(f.viewInitial={up:{x:u.up.x,y:u.up.y,z:u.up.z},eye:{x:u.eye.x,y:u.eye.y,z:u.eye.z},center:{x:u.center.x,y:u.center.y,z:u.center.z}}),f.plot(l,e,t.layout)}},r.clean=function(t,e,r,n){for(var i=n._subplots.gl3d||[],a=0;a<i.length;a++){var o=i[a];!e[o]&&n[o]._scene&&(n[o]._scene.destroy(),n._infolayer&&n._infolayer.selectAll(\".annotation-\"+o).remove())}},r.toSVG=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=e._size,i=0;i<r.length;i++){var a=e[r[i]],o=a.domain,s=a._scene,c=s.toImage(\"png\");e._glimages.append(\"svg:image\").attr({xmlns:l.svg,\"xlink:href\":c,x:n.l+n.w*o.x[0],y:n.t+n.h*(1-o.y[1]),width:n.w*(o.x[1]-o.x[0]),height:n.h*(o.y[1]-o.y[0]),preserveAspectRatio:\"none\"}),s.destroy()}},r.cleanId=function(t){if(t.match(/^scene[0-9]*$/)){var e=t.substr(5);return\"1\"===e&&(e=\"\"),\"scene\"+e}},r.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots.gl3d,n=0;n<r.length;n++){e[r[n]]._scene.updateFx(e.dragmode,e.hovermode)}}},{\"../../components/fx/layout_attributes\":407,\"../../constants/xmlns_namespaces\":480,\"../../lib\":503,\"../../plot_api/edit_types\":536,\"../get_data\":593,\"./layout/attributes\":599,\"./layout/defaults\":603,\"./layout/layout_attributes\":604,\"./scene\":608}],599:[function(t,e,r){\"use strict\";e.exports={scene:{valType:\"subplotid\",dflt:\"scene\",editType:\"calc+clearAxisTypes\"}}},{}],600:[function(t,e,r){\"use strict\";var n=t(\"../../../components/color\"),i=t(\"../../cartesian/layout_attributes\"),a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../plot_api/edit_types\").overrideAll;e.exports=o({visible:i.visible,showspikes:{valType:\"boolean\",dflt:!0},spikesides:{valType:\"boolean\",dflt:!0},spikethickness:{valType:\"number\",min:0,dflt:2},spikecolor:{valType:\"color\",dflt:n.defaultLine},showbackground:{valType:\"boolean\",dflt:!1},backgroundcolor:{valType:\"color\",dflt:\"rgba(204, 204, 204, 0.5)\"},showaxeslabels:{valType:\"boolean\",dflt:!0},color:i.color,categoryorder:i.categoryorder,categoryarray:i.categoryarray,title:{text:i.title.text,font:i.title.font},type:a({},i.type,{values:[\"-\",\"linear\",\"log\",\"date\",\"category\"]}),autotypenumbers:i.autotypenumbers,autorange:i.autorange,rangemode:i.rangemode,range:a({},i.range,{items:[{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}},{valType:\"any\",editType:\"plot\",impliedEdits:{\"^autorange\":!1}}],anim:!1}),tickmode:i.tickmode,nticks:i.nticks,tick0:i.tick0,dtick:i.dtick,tickvals:i.tickvals,ticktext:i.ticktext,ticks:i.ticks,mirror:i.mirror,ticklen:i.ticklen,tickwidth:i.tickwidth,tickcolor:i.tickcolor,showticklabels:i.showticklabels,tickfont:i.tickfont,tickangle:i.tickangle,tickprefix:i.tickprefix,showtickprefix:i.showtickprefix,ticksuffix:i.ticksuffix,showticksuffix:i.showticksuffix,showexponent:i.showexponent,exponentformat:i.exponentformat,minexponent:i.minexponent,separatethousands:i.separatethousands,tickformat:i.tickformat,tickformatstops:i.tickformatstops,hoverformat:i.hoverformat,showline:i.showline,linecolor:i.linecolor,linewidth:i.linewidth,showgrid:i.showgrid,gridcolor:a({},i.gridcolor,{dflt:\"rgb(204, 204, 204)\"}),gridwidth:i.gridwidth,zeroline:i.zeroline,zerolinecolor:i.zerolinecolor,zerolinewidth:i.zerolinewidth,_deprecated:{title:i._deprecated.title,titlefont:i._deprecated.titlefont}},\"plot\",\"from-root\")},{\"../../../components/color\":366,\"../../../lib/extend\":493,\"../../../plot_api/edit_types\":536,\"../../cartesian/layout_attributes\":569}],601:[function(t,e,r){\"use strict\";var n=t(\"tinycolor2\").mix,i=t(\"../../../lib\"),a=t(\"../../../plot_api/plot_template\"),o=t(\"./axis_attributes\"),s=t(\"../../cartesian/type_defaults\"),l=t(\"../../cartesian/axis_defaults\"),c=[\"xaxis\",\"yaxis\",\"zaxis\"];e.exports=function(t,e,r){var u,f;function h(t,e){return i.coerce(u,f,o,t,e)}for(var p=0;p<c.length;p++){var d=c[p];u=t[d]||{},(f=a.newContainer(e,d))._id=d[0]+r.scene,f._name=d,s(u,f,h,r),l(u,f,h,{font:r.font,letter:d[0],data:r.data,showGrid:!0,noTickson:!0,noTicklabelmode:!0,noTicklabelposition:!0,noTicklabeloverflow:!0,bgColor:r.bgColor,calendar:r.calendar},r.fullLayout),h(\"gridcolor\",n(f.color,r.bgColor,13600/187).toRgbString()),h(\"title.text\",d[0]),f.setScale=i.noop,h(\"showspikes\")&&(h(\"spikesides\"),h(\"spikethickness\"),h(\"spikecolor\",f.color)),h(\"showaxeslabels\"),h(\"showbackground\")&&h(\"backgroundcolor\")}}},{\"../../../lib\":503,\"../../../plot_api/plot_template\":543,\"../../cartesian/axis_defaults\":556,\"../../cartesian/type_defaults\":582,\"./axis_attributes\":600,tinycolor2:312}],602:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"];function o(){this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[18,18,18],this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"Open Sans\",\"Open Sans\",\"Open Sans\"],this.labelSize=[20,20,20],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[30,30,30],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[10,10,10],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!0,!0,!0],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._defaultTickPad=this.tickPad.slice(),this._defaultLabelPad=this.labelPad.slice(),this._defaultLineTickLength=this.lineTickLength.slice()}o.prototype.merge=function(t,e){for(var r=0;r<3;++r){var o=e[a[r]];o.visible?(this.labels[r]=t._meta?i.templateString(o.title.text,t._meta):o.title.text,\"font\"in o.title&&(o.title.font.color&&(this.labelColor[r]=n(o.title.font.color)),o.title.font.family&&(this.labelFont[r]=o.title.font.family),o.title.font.size&&(this.labelSize[r]=o.title.font.size)),\"showline\"in o&&(this.lineEnable[r]=o.showline),\"linecolor\"in o&&(this.lineColor[r]=n(o.linecolor)),\"linewidth\"in o&&(this.lineWidth[r]=o.linewidth),\"showgrid\"in o&&(this.gridEnable[r]=o.showgrid),\"gridcolor\"in o&&(this.gridColor[r]=n(o.gridcolor)),\"gridwidth\"in o&&(this.gridWidth[r]=o.gridwidth),\"log\"===o.type?this.zeroEnable[r]=!1:\"zeroline\"in o&&(this.zeroEnable[r]=o.zeroline),\"zerolinecolor\"in o&&(this.zeroLineColor[r]=n(o.zerolinecolor)),\"zerolinewidth\"in o&&(this.zeroLineWidth[r]=o.zerolinewidth),\"ticks\"in o&&o.ticks?this.lineTickEnable[r]=!0:this.lineTickEnable[r]=!1,\"ticklen\"in o&&(this.lineTickLength[r]=this._defaultLineTickLength[r]=o.ticklen),\"tickcolor\"in o&&(this.lineTickColor[r]=n(o.tickcolor)),\"tickwidth\"in o&&(this.lineTickWidth[r]=o.tickwidth),\"tickangle\"in o&&(this.tickAngle[r]=\"auto\"===o.tickangle?-3600:Math.PI*-o.tickangle/180),\"showticklabels\"in o&&(this.tickEnable[r]=o.showticklabels),\"tickfont\"in o&&(o.tickfont.color&&(this.tickColor[r]=n(o.tickfont.color)),o.tickfont.family&&(this.tickFont[r]=o.tickfont.family),o.tickfont.size&&(this.tickSize[r]=o.tickfont.size)),\"mirror\"in o?-1!==[\"ticks\",\"all\",\"allticks\"].indexOf(o.mirror)?(this.lineTickMirror[r]=!0,this.lineMirror[r]=!0):!0===o.mirror?(this.lineTickMirror[r]=!1,this.lineMirror[r]=!0):(this.lineTickMirror[r]=!1,this.lineMirror[r]=!1):this.lineMirror[r]=!1,\"showbackground\"in o&&!1!==o.showbackground?(this.backgroundEnable[r]=!0,this.backgroundColor[r]=n(o.backgroundcolor)):this.backgroundEnable[r]=!1):(this.tickEnable[r]=!1,this.labelEnable[r]=!1,this.lineEnable[r]=!1,this.lineTickEnable[r]=!1,this.gridEnable[r]=!1,this.zeroEnable[r]=!1,this.backgroundEnable[r]=!1)}},e.exports=function(t,e){var r=new o;return r.merge(t,e),r}},{\"../../../lib\":503,\"../../../lib/str2rgbarray\":528}],603:[function(t,e,r){\"use strict\";var n=t(\"../../../lib\"),i=t(\"../../../components/color\"),a=t(\"../../../registry\"),o=t(\"../../subplot_defaults\"),s=t(\"./axis_defaults\"),l=t(\"./layout_attributes\"),c=t(\"../../get_data\").getSubplotData;function u(t,e,r,n){for(var o=r(\"bgcolor\"),l=i.combine(o,n.paper_bgcolor),u=[\"up\",\"center\",\"eye\"],f=0;f<u.length;f++)r(\"camera.\"+u[f]+\".x\"),r(\"camera.\"+u[f]+\".y\"),r(\"camera.\"+u[f]+\".z\");r(\"camera.projection.type\");var h=!!r(\"aspectratio.x\")&&!!r(\"aspectratio.y\")&&!!r(\"aspectratio.z\"),p=r(\"aspectmode\",h?\"manual\":\"auto\");h||(t.aspectratio=e.aspectratio={x:1,y:1,z:1},\"manual\"===p&&(e.aspectmode=\"auto\"),t.aspectmode=e.aspectmode);var d=c(n.fullData,\"gl3d\",n.id);s(t,e,{font:n.font,scene:n.id,data:d,bgColor:l,calendar:n.calendar,autotypenumbersDflt:n.autotypenumbersDflt,fullLayout:n.fullLayout}),a.getComponentMethod(\"annotations3d\",\"handleDefaults\")(t,e,n);var m=n.getDfltFromLayout(\"dragmode\");if(!1!==m&&!m)if(m=\"orbit\",t.camera&&t.camera.up){var g=t.camera.up.x,v=t.camera.up.y,y=t.camera.up.z;0!==y&&(g&&v&&y?y/Math.sqrt(g*g+v*v+y*y)>.999&&(m=\"turntable\"):m=\"turntable\")}else m=\"turntable\";r(\"dragmode\",m),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}e.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:\"gl3d\",attributes:l,handleDefaults:u,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},{\"../../../components/color\":366,\"../../../lib\":503,\"../../../registry\":638,\"../../get_data\":593,\"../../subplot_defaults\":632,\"./axis_defaults\":601,\"./layout_attributes\":604}],604:[function(t,e,r){\"use strict\";var n=t(\"./axis_attributes\"),i=t(\"../../domain\").attributes,a=t(\"../../../lib/extend\").extendFlat,o=t(\"../../../lib\").counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}e.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},{\"../../../lib\":503,\"../../../lib/extend\":493,\"../../domain\":584,\"./axis_attributes\":600}],605:[function(t,e,r){\"use strict\";var n=t(\"../../../lib/str2rgbarray\"),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},e.exports=function(t){var e=new a;return e.merge(t),e}},{\"../../../lib/str2rgbarray\":528}],606:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var f=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var h=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/h)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d<p.length;++d)p[d].x=p[d].x*t.dataScale[c],\"date\"===u.type&&(p[d].text=p[d].text.replace(/\\<br\\>/g,\" \"));l[c]=p,u.tickmode=f}}e.ticks=l;for(c=0;c<3;++c){o[c]=.5*(t.glplot.bounds[0][c]+t.glplot.bounds[1][c]);for(d=0;d<2;++d)e.bounds[d][c]=t.glplot.bounds[d][c]}t.contourLevels=function(t){for(var e=new Array(3),r=0;r<3;++r){for(var n=t[r],i=new Array(n.length),a=0;a<n.length;++a)i[a]=n[a].x;e[r]=i}return e}(l)};var n=t(\"../../cartesian/axes\"),i=t(\"../../../lib\"),a=[\"xaxis\",\"yaxis\",\"zaxis\"],o=[0,0,0]},{\"../../../lib\":503,\"../../cartesian/axes\":554}],607:[function(t,e,r){\"use strict\";function n(t,e){var r,n,i=[0,0,0,0];for(r=0;r<4;++r)for(n=0;n<4;++n)i[n]+=t[4*r+n]*e[r];return i}e.exports=function(t,e){return n(t.projection,n(t.view,n(t.model,[e[0],e[1],e[2],1])))}},{}],608:[function(t,e,r){\"use strict\";var n,i,a=t(\"../../../stackgl_modules\").gl_plot3d,o=a.createCamera,s=a.createScene,l=t(\"webgl-context\"),c=t(\"has-passive-events\"),u=t(\"../../registry\"),f=t(\"../../lib\"),h=f.preserveDrawingBuffer(),p=t(\"../../plots/cartesian/axes\"),d=t(\"../../components/fx\"),m=t(\"../../lib/str2rgbarray\"),g=t(\"../../lib/show_no_webgl_msg\"),v=t(\"./project\"),y=t(\"./layout/convert\"),x=t(\"./layout/spikes\"),b=t(\"./layout/tick_marks\");function _(t,e){var r=document.createElement(\"div\"),n=t.container;this.graphDiv=t.graphDiv;var i=document.createElementNS(\"http://www.w3.org/2000/svg\",\"svg\");i.style.position=\"absolute\",i.style.top=i.style.left=\"0px\",i.style.width=i.style.height=\"100%\",i.style[\"z-index\"]=20,i.style[\"pointer-events\"]=\"none\",r.appendChild(i),this.svgContainer=i,r.id=t.id,r.style.position=\"absolute\",r.style.top=r.style.left=\"0px\",r.style.width=r.style.height=\"100%\",n.appendChild(r),this.fullLayout=e,this.id=t.id||\"scene\",this.fullSceneLayout=e[this.id],this.plotArgs=[[],{},{}],this.axesOptions=y(e,e[this.id]),this.spikeOptions=x(e[this.id]),this.container=r,this.staticMode=!!t.staticPlot,this.pixelRatio=this.pixelRatio||t.plotGlPixelRatio||2,this.dataScale=[1,1,1],this.contourLevels=[[],[],[]],this.convertAnnotations=u.getComponentMethod(\"annotations3d\",\"convert\"),this.drawAnnotations=u.getComponentMethod(\"annotations3d\",\"draw\"),this.initializeGLPlot()}var w=_.prototype;w.prepareOptions=function(){var t={canvas:this.canvas,gl:this.gl,glOptions:{preserveDrawingBuffer:h,premultipliedAlpha:!0,antialias:!0},container:this.container,axes:this.axesOptions,spikes:this.spikeOptions,pickRadius:10,snapToData:!0,autoScale:!0,autoBounds:!1,cameraObject:this.camera,pixelRatio:this.pixelRatio};if(this.staticMode){if(!(i||(n=document.createElement(\"canvas\"),i=l({canvas:n,preserveDrawingBuffer:!0,premultipliedAlpha:!0,antialias:!0}))))throw new Error(\"error creating static canvas/context for image server\");t.gl=i,t.canvas=n}return t};var T=!0;w.tryCreatePlot=function(){var t=this.prepareOptions(),e=!0;try{this.glplot=s(t)}catch(r){if(this.staticMode||!T||h)e=!1;else{f.warn([\"webgl setup failed possibly due to\",\"false preserveDrawingBuffer config.\",\"The mobile/tablet device may not be detected by is-mobile module.\",\"Enabling preserveDrawingBuffer in second attempt to create webgl scene...\"].join(\" \"));try{h=t.glOptions.preserveDrawingBuffer=!0,this.glplot=s(t)}catch(r){h=t.glOptions.preserveDrawingBuffer=!1,e=!1}}}return T=!1,e},w.initializeGLCamera=function(){var t=this.fullSceneLayout.camera,e=\"orthographic\"===t.projection.type;this.camera=o(this.container,{center:[t.center.x,t.center.y,t.center.z],eye:[t.eye.x,t.eye.y,t.eye.z],up:[t.up.x,t.up.y,t.up.z],_ortho:e,zoomMin:.01,zoomMax:100,mode:\"orbit\"})},w.initializeGLPlot=function(){var t=this;if(t.initializeGLCamera(),!t.tryCreatePlot())return g(t);t.traces={},t.make4thDimension();var e=t.graphDiv,r=e.layout,n=function(){var e={};return t.isCameraChanged(r)&&(e[t.id+\".camera\"]=t.getCamera()),t.isAspectChanged(r)&&(e[t.id+\".aspectratio\"]=t.glplot.getAspectratio(),\"manual\"!==r[t.id].aspectmode&&(t.fullSceneLayout.aspectmode=r[t.id].aspectmode=e[t.id+\".aspectmode\"]=\"manual\")),e},i=function(t){if(!1!==t.fullSceneLayout.dragmode){var e=n();t.saveLayout(r),t.graphDiv.emit(\"plotly_relayout\",e)}};return t.glplot.canvas&&(t.glplot.canvas.addEventListener(\"mouseup\",(function(){i(t)})),t.glplot.canvas.addEventListener(\"wheel\",(function(r){if(e._context._scrollZoom.gl3d){if(t.camera._ortho){var n=r.deltaX>r.deltaY?1.1:1/1.1,a=t.glplot.getAspectratio();t.glplot.setAspectratio({x:n*a.x,y:n*a.y,z:n*a.z})}i(t)}}),!!c&&{passive:!1}),t.glplot.canvas.addEventListener(\"mousemove\",(function(){if(!1!==t.fullSceneLayout.dragmode&&0!==t.camera.mouseListener.buttons){var e=n();t.graphDiv.emit(\"plotly_relayouting\",e)}})),t.staticMode||t.glplot.canvas.addEventListener(\"webglcontextlost\",(function(r){e&&e.emit&&e.emit(\"plotly_webglcontextlost\",{event:r,layer:t.id})}),!1)),t.glplot.oncontextloss=function(){t.recoverContext()},t.glplot.onrender=function(){t.render()},!0},w.render=function(){var t,e=this,r=e.graphDiv,n=e.svgContainer,i=e.container.getBoundingClientRect();r._fullLayout._calcInverseTransform(r);var a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,h=e.glplot.selection,m=0;m<c.length;++m)\"skip\"!==(t=e.traces[c[m]]).data.hoverinfo&&t.handlePick(h)&&(u=t),t.setContourLevels&&t.setContourLevels();function g(t,r,n){var i=e.fullSceneLayout[t+\"axis\"];return\"log\"!==i.type&&(r=i.d2l(r)),p.hoverLabelText(i,r,n)}if(null!==u){var y=v(e.glplot.cameraParams,h.dataCoordinate);t=u.data;var x,_=r._fullData[t.index],w=h.index,T={xLabel:g(\"x\",h.traceCoordinate[0],t.xhoverformat),yLabel:g(\"y\",h.traceCoordinate[1],t.yhoverformat),zLabel:g(\"z\",h.traceCoordinate[2],t.zhoverformat)},k=d.castHoverinfo(_,e.fullLayout,w),A=(k||\"\").split(\"+\"),M=k&&\"all\"===k;_.hovertemplate||M||(-1===A.indexOf(\"x\")&&(T.xLabel=void 0),-1===A.indexOf(\"y\")&&(T.yLabel=void 0),-1===A.indexOf(\"z\")&&(T.zLabel=void 0),-1===A.indexOf(\"text\")&&(h.textLabel=void 0),-1===A.indexOf(\"name\")&&(u.name=void 0));var S=[];\"cone\"===t.type||\"streamtube\"===t.type?(T.uLabel=g(\"x\",h.traceCoordinate[3],t.uhoverformat),(M||-1!==A.indexOf(\"u\"))&&S.push(\"u: \"+T.uLabel),T.vLabel=g(\"y\",h.traceCoordinate[4],t.vhoverformat),(M||-1!==A.indexOf(\"v\"))&&S.push(\"v: \"+T.vLabel),T.wLabel=g(\"z\",h.traceCoordinate[5],t.whoverformat),(M||-1!==A.indexOf(\"w\"))&&S.push(\"w: \"+T.wLabel),T.normLabel=h.traceCoordinate[6].toPrecision(3),(M||-1!==A.indexOf(\"norm\"))&&S.push(\"norm: \"+T.normLabel),\"streamtube\"===t.type&&(T.divergenceLabel=h.traceCoordinate[7].toPrecision(3),(M||-1!==A.indexOf(\"divergence\"))&&S.push(\"divergence: \"+T.divergenceLabel)),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"<br>\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*y[0]/y[3])*s,y:(.5-.5*y[1]/y[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:u.name,color:d.castHoverOption(_,w,\"bgcolor\")||u.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],c=e[o+\"calendar\"],u=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(u||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;d<l[p].length;++d)h=s.d2l(l[p][d],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else h=s.d2l(l[p],0,c),!isNaN(h)&&isFinite(h)&&(r[0][i]=Math.min(r[0][i],h),r[1][i]=Math.max(r[1][i],h));else r[0][i]=Math.min(r[0][i],0),r[1][i]=Math.max(r[1][i],u-1)}}w.plot=function(t,e,r){if(this.plotArgs=[t,e,r],!this.glplot.contextLost){var n,i,a,o,s,l,c=e[this.id],u=r[this.id];this.fullLayout=e,this.fullSceneLayout=c,this.axesOptions.merge(e,c),this.spikeOptions.merge(c),this.setViewport(c),this.updateFx(c.dragmode,c.hovermode),this.camera.enableWheel=this.graphDiv._context._scrollZoom.gl3d,this.glplot.setClearColor(m(c.bgcolor)),this.setConvert(s),t?Array.isArray(t)||(t=[t]):t=[];var f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&A(this,n,f);!function(t,e){for(var r=t.fullSceneLayout,n=r.annotations||[],i=0;i<3;i++)for(var a=k[i],o=a.charAt(0),s=r[a],l=0;l<n.length;l++){var c=n[l];if(c.visible){var u=s.r2l(c[o]);!isNaN(u)&&isFinite(u)&&(e[0][i]=Math.min(e[0][i],u),e[1][i]=Math.max(e[1][i],u))}}}(this,f);var h=[1,1,1];for(o=0;o<3;++o)f[1][o]===f[0][o]?h[o]=1:h[o]=1/(f[1][o]-f[0][o]);for(this.dataScale=h,this.convertAnnotations(this),a=0;a<t.length;++a)!0===(n=t[a]).visible&&0!==n._length&&((i=this.traces[n.uid])?i.data.type===n.type?i.update(n):(i.dispose(),i=n._module.plot(this,n),this.traces[n.uid]=i):(i=n._module.plot(this,n),this.traces[n.uid]=i),i.name=n.name);var p=Object.keys(this.traces);t:for(a=0;a<p.length;++a){for(o=0;o<t.length;++o)if(t[o].uid===p[a]&&!0===t[o].visible&&0!==t[o]._length)continue t;(i=this.traces[p[a]]).dispose(),delete this.traces[p[a]]}this.glplot.objects.sort((function(t,e){return t._trace.data.index-e._trace.data.index}));var d,g=[[0,0,0],[0,0,0]],v=[],y={};for(a=0;a<3;++a){if((l=(s=c[k[a]]).type)in y?(y[l].acc*=h[a],y[l].count+=1):y[l]={acc:h[a],count:1},s.autorange){g[0][a]=1/0,g[1][a]=-1/0;var x=this.glplot.objects,b=this.fullSceneLayout.annotations||[],_=s._name.charAt(0);for(o=0;o<x.length;o++){var w=x[o],T=w.bounds,M=w._trace.data._pad||0;\"ErrorBars\"===w.constructor.name&&s._lowerLogErrorBound?g[0][a]=Math.min(g[0][a],s._lowerLogErrorBound):g[0][a]=Math.min(g[0][a],T[0][a]/h[a]-M),g[1][a]=Math.max(g[1][a],T[1][a]/h[a]+M)}for(o=0;o<b.length;o++){var S=b[o];if(S.visible){var E=s.r2l(S[_]);g[0][a]=Math.min(g[0][a],E),g[1][a]=Math.max(g[1][a],E)}}if(\"rangemode\"in s&&\"tozero\"===s.rangemode&&(g[0][a]=Math.min(g[0][a],0),g[1][a]=Math.max(g[1][a],0)),g[0][a]>g[1][a])g[0][a]=-1,g[1][a]=1;else{var L=g[1][a]-g[0][a];g[0][a]-=L/32,g[1][a]+=L/32}if(\"reversed\"===s.autorange){var C=g[0][a];g[0][a]=g[1][a],g[1][a]=C}}else{var P=s.range;g[0][a]=s.r2l(P[0]),g[1][a]=s.r2l(P[1])}g[0][a]===g[1][a]&&(g[0][a]-=1,g[1][a]+=1),v[a]=g[1][a]-g[0][a],this.glplot.setBounds(a,{min:g[0][a]*h[a],max:g[1][a]*h[a]})}var I=c.aspectmode;if(\"cube\"===I)d=[1,1,1];else if(\"manual\"===I){var O=c.aspectratio;d=[O.x,O.y,O.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(a=0;a<3;++a){var D=y[l=(s=c[k[a]]).type];z[a]=Math.pow(D.acc,1/D.count)/h[a]}d=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=u.aspectratio.x=d[0],c.aspectratio.y=u.aspectratio.y=d[1],c.aspectratio.z=u.aspectratio.z=d[2],this.glplot.setAspectratio(c.aspectratio),this.viewInitial.aspectratio||(this.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),this.viewInitial.aspectmode||(this.viewInitial.aspectmode=c.aspectmode);var R=c.domain||null,F=e._size||null;if(R&&F){var B=this.container.style;B.position=\"absolute\",B.left=F.l+R.x[0]*F.w+\"px\",B.top=F.t+(1-R.y[1])*F.h+\"px\",B.width=F.w*(R.x[1]-R.x[0])+\"px\",B.height=F.h*(R.y[1]-R.y[0])+\"px\"}this.glplot.redraw()}},w.destroy=function(){this.glplot&&(this.camera.mouseListener.enabled=!1,this.container.removeEventListener(\"wheel\",this.camera.wheelListener),this.camera=null,this.glplot.dispose(),this.container.parentNode.removeChild(this.container),this.glplot=null)},w.getCamera=function(){var t;return this.camera.view.recalcMatrix(this.camera.view.lastT()),{up:{x:(t=this.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=t.camera;this.camera.lookAt.apply(this,[[(e=r).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),this.glplot.setAspectratio(t.aspectratio),\"orthographic\"===r.projection.type!==this.camera._ortho&&(this.glplot.redraw(),this.glplot.clearRGBA(),this.glplot.dispose(),this.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this.fullLayout,l=this.isCameraChanged(t),c=this.isAspectChanged(t),h=l||c;if(h){var p={};if(l&&(e=this.getCamera(),n=(r=f.nestedProperty(t,this.id+\".camera\")).get(),p[this.id+\".camera\"]=n),c&&(i=this.glplot.getAspectratio(),o=(a=f.nestedProperty(t,this.id+\".aspectratio\")).get(),p[this.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,s._preGUI,p),l)r.set(e),f.nestedProperty(s,this.id+\".camera\").set(e);if(c)a.set(i),f.nestedProperty(s,this.id+\".aspectratio\").set(i),this.glplot.redraw()}return h},w.updateFx=function(t,e){var r=this.camera;if(r)if(\"orbit\"===t)r.mode=\"orbit\",r.keyBindingMode=\"rotate\";else if(\"turntable\"===t){r.up=[0,0,1],r.mode=\"turntable\",r.keyBindingMode=\"rotate\";var n=this.graphDiv,i=n._fullLayout,a=this.fullSceneLayout.camera,o=a.up.x,s=a.up.y,l=a.up.z;if(l/Math.sqrt(o*o+s*s+l*l)<.999){var c=this.id+\".camera.up\",h={x:0,y:0,z:1},p={};p[c]=h;var d=n.layout;u.call(\"_storeDirectGUIEdit\",d,i._preGUI,p),a.up=h,f.nestedProperty(d,c).set(h)}}else r.keyBindingMode=t;this.fullSceneLayout.hovermode=e},w.toImage=function(t){t||(t=\"png\"),this.staticMode&&this.container.appendChild(n),this.glplot.redraw();var e=this.glplot.gl,r=e.drawingBufferWidth,i=e.drawingBufferHeight;e.bindFramebuffer(e.FRAMEBUFFER,null);var a=new Uint8Array(r*i*4);e.readPixels(0,0,r,i,e.RGBA,e.UNSIGNED_BYTE,a),function(t,e,r){for(var n=0,i=r-1;n<i;++n,--i)for(var a=0;a<e;++a)for(var o=0;o<4;++o){var s=4*(e*n+a)+o,l=4*(e*i+a)+o,c=t[s];t[s]=t[l],t[l]=c}}(a,r,i),function(t,e,r){for(var n=0;n<r;++n)for(var i=0;i<e;++i){var a=4*(e*n+i),o=t[a+3];if(o>0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(a,r,i);var o=document.createElement(\"canvas\");o.width=r,o.height=i;var 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n,i=this,a=e[i.id];i.map&&a.accesstoken!==i.accessToken&&(i.map.remove(),i.map=null,i.styleObj=null,i.traceHash={},i.layerList=[]),n=i.map?new Promise((function(r,n){i.updateMap(t,e,r,n)})):new Promise((function(r,n){i.createMap(t,e,r,n)})),r.push(n)},_.createMap=function(t,e,r,i){var o=this,s=e[o.id],l=o.styleObj=T(s.style);o.accessToken=s.accesstoken;var c=o.map=new n.Map({container:o.div,style:l.style,center:A(s.center),zoom:s.zoom,bearing:s.bearing,pitch:s.pitch,interactive:!o.isStatic,preserveDrawingBuffer:o.isStatic,doubleClickZoom:!1,boxZoom:!1,attributionControl:!1}).addControl(new n.AttributionControl({compact:!0}));c._canvas.style.left=\"0px\",c._canvas.style.top=\"0px\",o.rejectOnError(i),o.isStatic||o.initFx(t,e);var u=[];u.push(new Promise((function(t){c.once(\"load\",t)}))),u=u.concat(a.fetchTraceGeoData(t)),Promise.all(u).then((function(){o.fillBelowLookup(t,e),o.updateData(t),o.updateLayout(e),o.resolveOnRender(r)})).catch(i)},_.updateMap=function(t,e,r,n){var 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e[o.findIndexOfMin(e,(function(e){return o.angleDist(t,e)}))]}function V(t,e,r){return e?(t.attr(\"display\",null),t.attr(r)):t&&t.attr(\"display\",\"none\"),t}e.exports=function(t,e,r){return new B(t,e,r)},N.plot=function(t,e){for(var r=e[this.id],n=!1,i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){n=!0;break}}this._hasClipOnAxisFalse=n,this.updateLayers(e,r),this.updateLayout(e,r),f.generalUpdatePerTraceModule(this.gd,this,t,r),this.updateFx(e,r),this.isSmith&&(delete r.realaxis.range,delete r.imaginaryaxis.range)},N.updateLayers=function(t,e){var r=this.isSmith,i=this.layers,a=this.getRadial(e),o=this.getAngular(e),s=S.layerNames,l=s.indexOf(\"frontplot\"),c=s.slice(0,l),u=\"below traces\"===o.layer,f=\"below traces\"===a.layer;u&&c.push(\"angular-line\"),f&&c.push(\"radial-line\"),u&&c.push(\"angular-axis\"),f&&c.push(\"radial-axis\"),c.push(\"frontplot\"),u||c.push(\"angular-line\"),f||c.push(\"radial-line\"),u||c.push(\"angular-axis\"),f||c.push(\"radial-axis\");var h=(r?\"smith\":\"polar\")+\"sublayer\",p=this.framework.selectAll(\".\"+h).data(c,String);p.enter().append(\"g\").attr(\"class\",(function(t){return h+\" \"+t})).each((function(t){var e=i[t]=n.select(this);switch(t){case\"frontplot\":r||e.append(\"g\").classed(\"barlayer\",!0),e.append(\"g\").classed(\"scatterlayer\",!0);break;case\"backplot\":e.append(\"g\").classed(\"maplayer\",!0);break;case\"plotbg\":i.bg=e.append(\"path\");break;case\"radial-grid\":case\"angular-grid\":e.style(\"fill\",\"none\");break;case\"radial-line\":e.append(\"line\").style(\"fill\",\"none\");break;case\"angular-line\":e.append(\"path\").style(\"fill\",\"none\")}})),p.order()},N.updateLayout=function(t,e){var r=this.layers,n=t._size,i=this.getRadial(e),a=this.getAngular(e),o=e.domain.x,s=e.domain.y;this.xOffset=n.l+n.w*o[0],this.yOffset=n.t+n.h*(1-s[1]);var f=this.xLength=n.w*(o[1]-o[0]),h=this.yLength=n.h*(s[1]-s[0]),p=this.getSector(e);this.sectorInRad=p.map(R);var d,m,g,v,y,x=this.sectorBBox=function(t){var e,r,n,i,a=t[0],o=t[1]-a,s=D(a,360),l=s+o,c=Math.cos(R(s)),u=Math.sin(R(s)),f=Math.cos(R(l)),h=Math.sin(R(l));i=s<=90&&l>=90||s>90&&l>=450?1:u<=0&&h<=0?0:Math.max(u,h);e=s<=180&&l>=180||s>180&&l>=540?-1:c>=0&&f>=0?0:Math.min(c,f);r=s<=270&&l>=270||s>270&&l>=630?-1:u>=0&&h>=0?0:Math.min(u,h);n=l>=360?1:c<=0&&f<=0?0:Math.max(c,f);return[e,r,n,i]}(p),b=x[2]-x[0],_=x[3]-x[1],w=h/f,T=Math.abs(_/b);w>T?(d=f,y=(h-(m=f*T))/n.h/2,g=[o[0],o[1]],v=[s[0]+y,s[1]-y]):(m=h,y=(f-(d=h/T))/n.w/2,g=[o[0]+y,o[1]-y],v=[s[0],s[1]]),this.xLength2=d,this.yLength2=m,this.xDomain2=g,this.yDomain2=v;var k,A=this.xOffset2=n.l+n.w*g[0],M=this.yOffset2=n.t+n.h*(1-v[1]),S=this.radius=d/b,E=this.innerRadius=this.getHole(e)*S,L=this.cx=A-S*x[0],C=this.cy=M+S*x[3],P=this.cxx=L-A,I=this.cyy=C-M,O=i.side;\"counterclockwise\"===O?(k=O,O=\"top\"):\"clockwise\"===O&&(k=O,O=\"bottom\"),this.radialAxis=this.mockAxis(t,e,i,{_id:\"x\",side:O,_trueSide:k,domain:[E/n.w,S/n.w]}),this.angularAxis=this.mockAxis(t,e,a,{side:\"right\",domain:[0,Math.PI],autorange:!1}),this.doAutoRange(t,e),this.updateAngularAxis(t,e),this.updateRadialAxis(t,e),this.updateRadialAxisTitle(t,e),this.xaxis=this.mockCartesianAxis(t,e,{_id:\"x\",domain:g}),this.yaxis=this.mockCartesianAxis(t,e,{_id:\"y\",domain:v});var z=this.pathSubplot();this.clipPaths.forTraces.select(\"path\").attr(\"d\",z).attr(\"transform\",l(P,I)),r.frontplot.attr(\"transform\",l(A,M)).call(u.setClipUrl,this._hasClipOnAxisFalse?null:this.clipIds.forTraces,this.gd),r.bg.attr(\"d\",z).attr(\"transform\",l(L,C)).call(c.fill,e.bgcolor)},N.mockAxis=function(t,e,r,n){var i=o.extendFlat({},r,n);return d(i,e,t),i},N.mockCartesianAxis=function(t,e,r){var n=this,i=n.isSmith,a=r._id,s=o.extendFlat({type:\"linear\"},r);p(s,t);var l={x:[0,2],y:[1,3]};return s.setRange=function(){var t=n.sectorBBox,r=l[a],i=n.radialAxis._rl,o=(i[1]-i[0])/(1-n.getHole(e));s.range=[t[r[0]]*o,t[r[1]]*o]},s.isPtWithinRange=\"x\"!==a||i?function(){return!0}:function(t){return n.isPtInside(t)},s.setRange(),s.setScale(),s},N.doAutoRange=function(t,e){var r=this.gd,n=this.radialAxis,i=this.getRadial(e);m(r,n);var 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h.tickText(g,t,!0,!1)})):h.calcTicks(g),k=y?T:h.clipEnds(g,T),A=h.getTickSigns(g)[2];y&&((\"top\"===g.ticks&&\"bottom\"===g.side||\"bottom\"===g.ticks&&\"top\"===g.side)&&(A=-A),\"top\"===g.ticks&&\"top\"===g.side&&(w=-g.ticklen),\"bottom\"===g.ticks&&\"bottom\"===g.side&&(w=g.ticklen)),h.drawTicks(n,g,{vals:T,layer:i[\"radial-axis\"],path:h.makeTickPath(g,0,A),transFn:x,crisp:!1}),h.drawGrid(n,g,{vals:k,layer:i[\"radial-grid\"],path:b,transFn:o.noop,crisp:!1}),h.drawLabels(n,g,{vals:T,layer:i[\"radial-axis\"],transFn:x,labelFns:h.makeLabelFns(g,w)})}var M=r.radialAxisAngle=r.vangles?F(U(R(d.angle),r.vangles)):d.angle,S=l(f,p),E=S+s(-M);V(i[\"radial-axis\"],v&&(d.showticklabels||d.ticks),{transform:E}),V(i[\"radial-grid\"],v&&d.showgrid,{transform:y?\"\":S}),V(i[\"radial-line\"].select(\"line\"),v&&d.showline,{x1:y?-a:u,y1:0,x2:a,y2:0,transform:E}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateRadialAxisTitle=function(t,e,r){if(!this.isSmith){var n=this.gd,i=this.radius,a=this.cx,o=this.cy,s=this.getRadial(e),l=this.id+\"title\",c=0;if(s.title){var f=u.bBox(this.layers[\"radial-axis\"].node()).height,h=s.title.font.size,p=s.side;c=\"top\"===p?h:\"counterclockwise\"===p?-(f+.4*h):f+.8*h}var d=void 0!==r?r:this.radialAxisAngle,m=R(d),g=Math.cos(m),v=Math.sin(m),y=a+i/2*g+c*v,b=o-i/2*v+c*g;this.layers[\"radial-axis-title\"]=x.draw(n,l,{propContainer:s,propName:this.id+\".radialaxis.title\",placeholder:z(n,\"Click to enter radial axis title\"),attributes:{x:y,y:b,\"text-anchor\":\"middle\"},transform:{rotate:-d}})}},N.updateAngularAxis=function(t,e){var r=this,n=r.gd,i=r.layers,a=r.radius,u=r.innerRadius,f=r.cx,p=r.cy,d=r.getAngular(e),m=r.angularAxis,g=r.isSmith;g||(r.fillViewInitialKey(\"angularaxis.rotation\",d.rotation),m.setGeometry(),m.setScale());var v=g?function(t){var e=O(r,C([0,t.x]));return Math.atan2(e[0]-f,e[1]-p)-Math.PI/2}:function(t){return m.t2g(t.x)};\"linear\"===m.type&&\"radians\"===m.thetaunit&&(m.tick0=F(m.tick0),m.dtick=F(m.dtick));var y=function(t){return l(f+a*Math.cos(t),p-a*Math.sin(t))},x=g?function(t){var e=O(r,C([0,t.x]));return l(e[0],e[1])}:function(t){return y(v(t))},b=g?function(t){var e=O(r,C([0,t.x])),n=Math.atan2(e[0]-f,e[1]-p)-Math.PI/2;return l(e[0],e[1])+s(-F(n))}:function(t){var e=v(t);return y(e)+s(-F(e))},_=g?function(t){return P(r,t.x,0,1/0)}:function(t){var e=v(t),r=Math.cos(e),n=Math.sin(e);return\"M\"+[f+u*r,p-u*n]+\"L\"+[f+a*r,p-a*n]},w=h.makeLabelFns(m,0).labelStandoff,T={xFn:function(t){var e=v(t);return Math.cos(e)*w},yFn:function(t){var e=v(t),r=Math.sin(e)>0?.2:1;return-Math.sin(e)*(w+t.fontSize*r)+Math.abs(Math.cos(e))*(t.fontSize*M)},anchorFn:function(t){var e=v(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},heightFn:function(t,e,r){var n=v(t);return-.5*(1+Math.sin(n))*r}},k=j(d);r.angularTickLayout!==k&&(i[\"angular-axis\"].selectAll(\".\"+m._id+\"tick\").remove(),r.angularTickLayout=k);var A,S=g?[1/0].concat(m.tickvals||[]).map((function(t){return h.tickText(m,t,!0,!1)})):h.calcTicks(m);if(g&&(S[0].text=\"\\u221e\",S[0].fontSize*=1.75),\"linear\"===e.gridshape?(A=S.map(v),o.angleDelta(A[0],A[1])<0&&(A=A.slice().reverse())):A=null,r.vangles=A,\"category\"===m.type&&(S=S.filter((function(t){return o.isAngleInsideSector(v(t),r.sectorInRad)}))),m.visible){var E=\"inside\"===m.ticks?-1:1,L=(m.linewidth||1)/2;h.drawTicks(n,m,{vals:S,layer:i[\"angular-axis\"],path:\"M\"+E*L+\",0h\"+E*m.ticklen,transFn:b,crisp:!1}),h.drawGrid(n,m,{vals:S,layer:i[\"angular-grid\"],path:_,transFn:o.noop,crisp:!1}),h.drawLabels(n,m,{vals:S,layer:i[\"angular-axis\"],repositionOnUpdate:!0,transFn:x,labelFns:T})}V(i[\"angular-line\"].select(\"path\"),d.showline,{d:r.pathSubplot(),transform:l(f,p)}).attr(\"stroke-width\",d.linewidth).call(c.stroke,d.linecolor)},N.updateFx=function(t,e){this.gd._context.staticPlot||(!this.isSmith&&(this.updateAngularDrag(t),this.updateRadialDrag(t,e,0),this.updateRadialDrag(t,e,1)),this.updateHoverAndMainDrag(t))},N.updateHoverAndMainDrag=function(t){var e,r,s=this,c=s.isSmith,u=s.gd,f=s.layers,h=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,L=s.sectorInRad,C=s.vangles,P=s.radialAxis,I=E.clampTiny,O=E.findXYatLength,z=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(f,\"path\",\"maindrag\",\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){y.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||v.unhover(u,t)};var B,N,j,U,V,H,q,G,Y,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function X(t,e){return Math.sqrt(t*t+e*e)}function Z(t,e){return X(t-A,e-M)}function J(t,e){return Math.atan2(M-e,t-A)}function K(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function Q(t,e){if(0===t)return s.pathSector(2*D);var r=R/t,n=e-r,i=e+r,a=Math.max(0,Math.min(t,m)),o=a-D,l=a+D;return\"M\"+K(o,n)+\"A\"+[o,o]+\" 0,0,0 \"+K(o,i)+\"L\"+K(l,i)+\"A\"+[l,l]+\" 0,0,1 \"+K(l,n)+\"Z\"}function $(t,e,r){if(0===t)return s.pathSector(2*D);var n,i,a=K(t,e),o=K(t,r),l=I((a[0]+o[0])/2),c=I((a[1]+o[1])/2);if(l&&c){var u=c/l,f=-1/u,h=O(D,u,l,c);n=O(R,f,h[0][0],h[0][1]),i=O(R,f,h[1][0],h[1][1])}else{var p,d;c?(p=R,d=D):(p=D,d=R),n=[[l-p,c-d],[l+p,c-d]],i=[[l-p,c+d],[l+p,c+d]]}return\"M\"+n.join(\"L\")+\"L\"+i.reverse().join(\"L\")+\"Z\"}function tt(t,e){return e=Math.max(Math.min(e,m),x),t<d?t=0:m-t<d?t=m:e<d?e=0:m-e<d&&(e=m),Math.abs(e-t)>p?(t<e?(j=t,U=e):(j=e,U=t),!0):(j=null,U=null,!1)}function et(t,e){t=t||V,e=e||\"M0,0Z\",G.attr(\"d\",t),Y.attr(\"d\",e),g.transitionZoombox(G,Y,H,q),H=!0;var r={};ot(r),u.emit(\"plotly_relayouting\",r)}function rt(t,n){var i,a,o=B+(t*=e),l=N+(n*=r),c=Z(B,N),u=Math.min(Z(o,l),m),f=J(B,N);tt(c,u)&&(i=V+s.pathSector(U),j&&(i+=s.pathSector(j)),a=Q(j,f)+Q(U,f)),et(i,a)}function 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n=t(\"../../components/color/attributes\"),i=t(\"../domain\").attributes,a=t(\"../cartesian/layout_attributes\"),o=t(\"../../plot_api/edit_types\").overrideAll,s=t(\"../../lib/extend\").extendFlat,l={title:{text:a.title.text,font:a.title.font},color:a.color,tickmode:a.tickmode,nticks:s({},a.nticks,{dflt:6,min:1}),tick0:a.tick0,dtick:a.dtick,tickvals:a.tickvals,ticktext:a.ticktext,ticks:a.ticks,ticklen:a.ticklen,tickwidth:a.tickwidth,tickcolor:a.tickcolor,showticklabels:a.showticklabels,showtickprefix:a.showtickprefix,tickprefix:a.tickprefix,showticksuffix:a.showticksuffix,ticksuffix:a.ticksuffix,showexponent:a.showexponent,exponentformat:a.exponentformat,minexponent:a.minexponent,separatethousands:a.separatethousands,tickfont:a.tickfont,tickangle:a.tickangle,tickformat:a.tickformat,tickformatstops:a.tickformatstops,hoverformat:a.hoverformat,showline:s({},a.showline,{dflt:!0}),linecolor:a.linecolor,linewidth:a.linewidth,showgrid:s({},a.showgrid,{dflt:!0}),gridcolor:a.gridcolor,gridwidth:a.gridwidth,layer:a.layer,min:{valType:\"number\",dflt:0,min:0},_deprecated:{title:a._deprecated.title,titlefont:a._deprecated.titlefont}},c=e.exports=o({domain:i({name:\"ternary\"}),bgcolor:{valType:\"color\",dflt:n.background},sum:{valType:\"number\",dflt:1,min:0},aaxis:l,baxis:l,caxis:l},\"plot\",\"from-root\");c.uirevision={valType:\"any\",editType:\"none\"},c.aaxis.uirevision=c.baxis.uirevision=c.caxis.uirevision={valType:\"any\",editType:\"none\"}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../cartesian/layout_attributes\":569,\"../domain\":584}],636:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../plot_api/plot_template\"),a=t(\"../../lib\"),o=t(\"../subplot_defaults\"),s=t(\"../cartesian/tick_label_defaults\"),l=t(\"../cartesian/prefix_suffix_defaults\"),c=t(\"../cartesian/tick_mark_defaults\"),u=t(\"../cartesian/tick_value_defaults\"),f=t(\"../cartesian/line_grid_defaults\"),h=t(\"./layout_attributes\"),p=[\"aaxis\",\"baxis\",\"caxis\"];function d(t,e,r,a){var o,s,l,c=r(\"bgcolor\"),u=r(\"sum\");a.bgColor=n.combine(c,a.paper_bgcolor);for(var f=0;f<p.length;f++)s=t[o=p[f]]||{},(l=i.newContainer(e,o))._name=o,m(s,l,a,e);var h=e.aaxis,d=e.baxis,g=e.caxis;h.min+d.min+g.min>=u&&(h.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function m(t,e,r,n){var i=h[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,m=e._name.charAt(0).toUpperCase(),g=\"Component \"+m,v=o(\"title.text\",g);e._hovertitle=v===g?v:m,a.coerceFont(o,\"title.font\",{family:r.font.family,size:a.bigFont(r.font.size),color:d}),o(\"min\"),u(t,e,o,\"linear\"),l(t,e,o,\"linear\"),s(t,e,o,\"linear\"),c(t,e,o,{outerTicks:!0}),o(\"showticklabels\")&&(a.coerceFont(o,\"tickfont\",{family:r.font.family,size:r.font.size,color:d}),o(\"tickangle\"),o(\"tickformat\")),f(t,e,o,{dfltColor:p,bgColor:r.bgColor,blend:60,showLine:!0,showGrid:!0,noZeroLine:!0,attributes:i}),o(\"hoverformat\"),o(\"layer\")}e.exports=function(t,e,r){o(t,e,r,{type:\"ternary\",attributes:h,handleDefaults:d,font:e.font,paper_bgcolor:e.paper_bgcolor})}},{\"../../components/color\":366,\"../../lib\":503,\"../../plot_api/plot_template\":543,\"../cartesian/line_grid_defaults\":571,\"../cartesian/prefix_suffix_defaults\":573,\"../cartesian/tick_label_defaults\":578,\"../cartesian/tick_mark_defaults\":579,\"../cartesian/tick_value_defaults\":580,\"../subplot_defaults\":632,\"./layout_attributes\":635}],637:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"tinycolor2\"),a=t(\"../../registry\"),o=t(\"../../lib\"),s=o.strTranslate,l=o._,c=t(\"../../components/color\"),u=t(\"../../components/drawing\"),f=t(\"../cartesian/set_convert\"),h=t(\"../../lib/extend\").extendFlat,p=t(\"../plots\"),d=t(\"../cartesian/axes\"),m=t(\"../../components/dragelement\"),g=t(\"../../components/fx\"),v=t(\"../../components/dragelement/helpers\"),y=v.freeMode,x=v.rectMode,b=t(\"../../components/titles\"),_=t(\"../cartesian/select\").prepSelect,w=t(\"../cartesian/select\").selectOnClick,T=t(\"../cartesian/select\").clearSelect,k=t(\"../cartesian/select\").clearSelectionsCache,A=t(\"../cartesian/constants\");function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}e.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=e[this.id],n=e._size;this._hasClipOnAxisFalse=!1;for(var i=0;i<t.length;i++){if(!1===t[i][0].trace.cliponaxis){this._hasClipOnAxisFalse=!0;break}}this.updateLayers(r),this.adjustLayout(r,n),p.generalUpdatePerTraceModule(this.graphDiv,this,t,r),this.layers.plotbg.select(\"path\").call(c.fill,r.bgcolor)},S.makeFramework=function(t){var e=this.graphDiv,r=t[this.id],n=this.clipId=\"clip\"+this.layoutId+this.id,i=this.clipIdRelative=\"clip-relative\"+this.layoutId+this.id;this.clipDef=o.ensureSingleById(t._clips,\"clipPath\",n,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.clipDefRelative=o.ensureSingleById(t._clips,\"clipPath\",i,(function(t){t.append(\"path\").attr(\"d\",\"M0,0Z\")})),this.plotContainer=o.ensureSingle(this.container,\"g\",this.id),this.updateLayers(r),u.setClipUrl(this.layers.backplot,n,e),u.setClipUrl(this.layers.grids,n,e)},S.updateLayers=function(t){var e=this.layers,r=[\"draglayer\",\"plotbg\",\"backplot\",\"grids\"];\"below traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"below traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"below traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\"),r.push(\"frontplot\"),\"above traces\"===t.aaxis.layer&&r.push(\"aaxis\",\"aline\"),\"above traces\"===t.baxis.layer&&r.push(\"baxis\",\"bline\"),\"above traces\"===t.caxis.layer&&r.push(\"caxis\",\"cline\");var i=this.plotContainer.selectAll(\"g.toplevel\").data(r,String),a=[\"agrid\",\"bgrid\",\"cgrid\"];i.enter().append(\"g\").attr(\"class\",(function(t){return\"toplevel \"+t})).each((function(t){var r=n.select(this);e[t]=r,\"frontplot\"===t?r.append(\"g\").classed(\"scatterlayer\",!0):\"backplot\"===t?r.append(\"g\").classed(\"maplayer\",!0):\"plotbg\"===t?r.append(\"path\").attr(\"d\",\"M0,0Z\"):\"aline\"===t||\"bline\"===t||\"cline\"===t?r.append(\"path\"):\"grids\"===t&&a.forEach((function(t){e[t]=r.append(\"g\").classed(\"grid \"+t,!0)}))})),i.order()};var E=Math.sqrt(4/3);S.adjustLayout=function(t,e){var r,n,i,a,o,l,p=this,d=t.domain,m=(d.x[0]+d.x[1])/2,g=(d.y[0]+d.y[1])/2,v=d.x[1]-d.x[0],y=d.y[1]-d.y[0],x=v*e.w,b=y*e.h,_=t.sum,w=t.aaxis.min,T=t.baxis.min,k=t.caxis.min;x>E*b?i=(a=b)*E:a=(i=x)/E,o=v*i/x,l=y*a/b,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var I=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",I),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var O=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",O),p.layers.bgrid.attr(\"transform\",O);var z=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",z),p.layers.agrid.attr(\"transform\",z);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this.graphDiv,r=this.id.substr(7)+\"title\",n=this.layers,i=this.aaxis,a=this.baxis,o=this.caxis;if(this.drawAx(i),this.drawAx(a),this.drawAx(o),t){var s=Math.max(i.showticklabels?i.tickfont.size/2:0,(o.showticklabels?.75*o.tickfont.size:0)+(\"outside\"===o.ticks?.87*o.ticklen:0)),c=(a.showticklabels?a.tickfont.size:0)+(\"outside\"===a.ticks?a.ticklen:0)+3;n[\"a-title\"]=b.draw(e,\"a\"+r,{propContainer:i,propName:this.id+\".aaxis.title\",placeholder:l(e,\"Click to enter Component A title\"),attributes:{x:this.x0+this.w/2,y:this.y0-i.title.font.size/3-s,\"text-anchor\":\"middle\"}}),n[\"b-title\"]=b.draw(e,\"b\"+r,{propContainer:a,propName:this.id+\".baxis.title\",placeholder:l(e,\"Click to enter Component B title\"),attributes:{x:this.x0-c,y:this.y0+this.h+.83*a.title.font.size+c,\"text-anchor\":\"middle\"}}),n[\"c-title\"]=b.draw(e,\"c\"+r,{propContainer:o,propName:this.id+\".caxis.title\",placeholder:l(e,\"Click to enter Component C title\"),attributes:{x:this.x0+this.w+c,y:this.y0+this.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this.graphDiv,n=t._name,i=n.charAt(0),a=t._id,s=this.layers[n],l=i+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);this[l]!==c&&(s.selectAll(\".\"+a+\"tick\").remove(),this[l]=c),t.setScale();var u=d.calcTicks(t),f=d.clipEnds(t,u),h=d.makeTransTickFn(t),p=d.getTickSigns(t)[2],m=o.deg2rad(30),g=p*(t.linewidth||1)/2,v=p*t.ticklen,y=this.w,x=this.h,b=\"b\"===i?\"M0,\"+g+\"l\"+Math.sin(m)*v+\",\"+Math.cos(m)*v:\"M\"+g+\",0l\"+Math.cos(m)*v+\",\"+-Math.sin(m)*v,_={a:\"M0,0l\"+x+\",-\"+y/2,b:\"M0,0l-\"+y/2+\",-\"+x,c:\"M0,0l-\"+x+\",\"+y/2}[i];d.drawTicks(r,t,{vals:\"inside\"===t.ticks?f:u,layer:s,path:b,transFn:h,crisp:!1}),d.drawGrid(r,t,{vals:f,layer:this.layers[i+\"grid\"],path:_,transFn:h,crisp:!1}),d.drawLabels(r,t,{vals:u,layer:s,transFn:h,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",I=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",O=!0;function z(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearSelect=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,v,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,D=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;z(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),m=(l/2+u)*M.w,g=(1-l/2-c)*M.w,y=(m+g)/2,x=g-m,_=(1-l)*M.h,w=_-x/E;x<A.MINZOOM?(p=f,T.attr(\"d\",v),k.attr(\"d\",\"M0,0Z\")):(p={a:f.a+l*h,b:f.b+c*h,c:f.c+u*h},T.attr(\"d\",v+\"M\"+m+\",\"+_+\"H\"+g+\"L\"+y+\",\"+w+\"L\"+m+\",\"+_+\"Z\"),k.attr(\"d\",\"M\"+r+\",\"+n+\"m0.5,0.5h5v-2h-5v-5h-2v5h-5v2h5v5h2ZM\"+m+\",\"+_+C+\"M\"+g+\",\"+_+P+\"M\"+y+\",\"+w+I)),b||(T.transition().style(\"fill\",d>.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){z(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),O&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),O=!1))}function H(t,e){var 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n=t(\"../heatmap/attributes\"),i=t(\"../scatter/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\"),o=a.axisHoverFormat,s=a.descriptionOnlyNumbers,l=t(\"../../components/colorscale/attributes\"),c=t(\"../../components/drawing/attributes\").dash,u=t(\"../../plots/font_attributes\"),f=t(\"../../lib/extend\").extendFlat,h=t(\"../../constants/filter_ops\"),p=h.COMPARISON_OPS2,d=h.INTERVAL_OPS,m=i.line;e.exports=f({z:n.z,x:n.x,x0:n.x0,dx:n.dx,y:n.y,y0:n.y0,dy:n.dy,xperiod:n.xperiod,yperiod:n.yperiod,xperiod0:i.xperiod0,yperiod0:i.yperiod0,xperiodalignment:n.xperiodalignment,yperiodalignment:n.yperiodalignment,text:n.text,hovertext:n.hovertext,transpose:n.transpose,xtype:n.xtype,ytype:n.ytype,xhoverformat:o(\"x\"),yhoverformat:o(\"y\"),zhoverformat:o(\"z\",1),hovertemplate:n.hovertemplate,texttemplate:f({},n.texttemplate,{}),textfont:f({},n.textfont,{}),hoverongaps:n.hoverongaps,connectgaps:f({},n.connectgaps,{}),fillcolor:{valType:\"color\",editType:\"calc\"},autocontour:{valType:\"boolean\",dflt:!0,editType:\"calc\",impliedEdits:{\"contours.start\":void 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a}(u,h),[l]}}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/hover\":655}],778:[function(t,e,r){\"use 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s=r||n.select(t).selectAll(\"g.funnellayer\").selectAll(\"g.trace\");l(t,s,\"funnel\"),s.style(\"opacity\",(function(t){return t[0].trace.opacity})),s.each((function(e){var r=n.select(this),s=e[0].trace;r.selectAll(\".point > path\").each((function(t){if(!t.isBlank){var e=s.marker;n.select(this).call(a.fill,t.mc||e.color).call(a.stroke,t.mlc||e.line.color).call(i.dashLine,e.line.dash,t.mlw||e.line.width).style(\"opacity\",s.selectedpoints&&!t.selected?o:1)}})),c(r,s,t),r.selectAll(\".regions\").each((function(){n.select(this).selectAll(\"path\").style(\"stroke-width\",0).call(a.fill,s.connector.fillcolor)})),r.selectAll(\".lines\").each((function(){var t=s.connector.line;i.lineGroupStyle(n.select(this).selectAll(\"path\"),t.width,t.color,t.dash)}))}))}}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/interactions\":478,\"../bar/style\":662,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],783:[function(t,e,r){\"use strict\";var n=t(\"../pie/attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../plots/domain\").attributes,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/template_attributes\").texttemplateAttrs,l=t(\"../../lib/extend\").extendFlat;e.exports={labels:n.labels,label0:n.label0,dlabel:n.dlabel,values:n.values,marker:{colors:n.marker.colors,line:{color:l({},n.marker.line.color,{dflt:null}),width:l({},n.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},text:n.text,hovertext:n.hovertext,scalegroup:l({},n.scalegroup,{}),textinfo:l({},n.textinfo,{flags:[\"label\",\"text\",\"value\",\"percent\"]}),texttemplate:s({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),hoverinfo:l({},i.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:o({},{keys:[\"label\",\"color\",\"value\",\"text\",\"percent\"]}),textposition:l({},n.textposition,{values:[\"inside\",\"none\"],dflt:\"inside\"}),textfont:n.textfont,insidetextfont:n.insidetextfont,title:{text:n.title.text,font:n.title.font,position:l({},n.title.position,{values:[\"top 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n=t(\"../../lib\"),i=t(\"./attributes\"),a=t(\"../../plots/domain\").defaults,o=t(\"../bar/defaults\").handleText,s=t(\"../pie/defaults\").handleLabelsAndValues;e.exports=function(t,e,r,l){function c(r,a){return n.coerce(t,e,i,r,a)}var u=c(\"labels\"),f=c(\"values\"),h=s(u,f),p=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),p){e._length=p,c(\"marker.line.width\")&&c(\"marker.line.color\",l.paper_bgcolor),c(\"marker.colors\"),c(\"scalegroup\");var d,m=c(\"text\"),g=c(\"texttemplate\");if(g||(d=c(\"textinfo\",Array.isArray(m)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),g||d&&\"none\"!==d){var v=c(\"textposition\");o(t,e,l,c,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1})}a(e,l,c),c(\"title.text\")&&(c(\"title.position\"),n.coerceFont(c,\"title.font\",l.font)),c(\"aspectratio\"),c(\"baseratio\")}else e.visible=!1}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"../pie/defaults\":903,\"./attributes\":783}],787:[function(t,e,r){\"use strict\";e.exports={moduleType:\"trace\",name:\"funnelarea\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"funnelarea\",\"showLegend\"],attributes:t(\"./attributes\"),layoutAttributes:t(\"./layout_attributes\"),supplyDefaults:t(\"./defaults\"),supplyLayoutDefaults:t(\"./layout_defaults\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\"),style:t(\"./style\"),styleOne:t(\"../pie/style_one\"),meta:{}}},{\"../pie/style_one\":911,\"./attributes\":783,\"./base_plot\":784,\"./calc\":785,\"./defaults\":786,\"./layout_attributes\":788,\"./layout_defaults\":789,\"./plot\":790,\"./style\":791}],788:[function(t,e,r){\"use strict\";var n=t(\"../pie/layout_attributes\").hiddenlabels;e.exports={hiddenlabels:n,funnelareacolorway:{valType:\"colorlist\",editType:\"calc\"},extendfunnelareacolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{\"../pie/layout_attributes\":907}],789:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"funnelareacolorway\",e.colorway),r(\"extendfunnelareacolors\")}},{\"../../lib\":503,\"./layout_attributes\":788}],790:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/drawing\"),a=t(\"../../lib\"),o=a.strScale,s=a.strTranslate,l=t(\"../../lib/svg_text_utils\"),c=t(\"../bar/plot\").toMoveInsideBar,u=t(\"../bar/uniform_text\"),f=u.recordMinTextSize,h=u.clearMinTextSize,p=t(\"../pie/helpers\"),d=t(\"../pie/plot\"),m=d.attachFxHandlers,g=d.determineInsideTextFont,v=d.layoutAreas,y=d.prerenderTitles,x=d.positionTitleOutside,b=d.formatSliceLabel;function _(t,e){return\"l\"+(e[0]-t[0])+\",\"+(e[1]-t[1])}e.exports=function(t,e){var r=t._fullLayout;h(\"funnelarea\",r),y(e,t),v(e,r._size),a.makeTraceGroups(r._funnelarealayer,e,\"trace\").each((function(e){var u=n.select(this),h=e[0],d=h.trace;!function(t){if(!t.length)return;var e=t[0],r=e.trace,n=r.aspectratio,i=r.baseratio;i>.999&&(i=.999);var a,o=Math.pow(i,2),s=e.vTotal,l=s,c=s*o/(1-o)/s;function u(){var t,e={x:t=Math.sqrt(c),y:-t};return[e.x,e.y]}var f,h,p=[];for(p.push(u()),f=t.length-1;f>-1;f--)if(!(h=t[f]).hidden){var d=h.v/l;c+=d,p.push(u())}var 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w=\"M\"+(u+o.TR[0])+\",\"+(v+o.TR[1])+_(o.TR,o.BR)+_(o.BR,o.BL)+_(o.BL,o.TL)+\"Z\";x.attr(\"d\",w),b(t,o,h);var T=p.castOption(d.textposition,o.pts),k=y.selectAll(\"g.slicetext\").data(o.text&&\"none\"!==T?[0]:[]);k.enter().append(\"g\").classed(\"slicetext\",!0),k.exit().remove(),k.each((function(){var h=a.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),p=a.ensureUniformFontSize(t,g(d,o,r.font));h.text(o.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(i.font,p).call(l.convertToTspans,t);var m,y,x,b=i.bBox(h.node()),_=Math.min(o.BL[1],o.BR[1])+v,w=Math.max(o.TL[1],o.TR[1])+v;y=Math.max(o.TL[0],o.BL[0])+u,x=Math.min(o.TR[0],o.BR[0])+u,(m=c(y,x,_,w,b,{isHorizontal:!0,constrained:!0,angle:0,anchor:\"middle\"})).fontSize=p.size,f(d.type,m,r),e[s].transform=m,h.attr(\"transform\",a.getTextTransform(m))}))}}));var 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n=t._fullLayout,i=o.extendFlat({type:\"linear\",ticks:\"outside\",range:r,showline:!0},e),a={type:\"linear\",_id:\"x\"+e._id},s={letter:\"x\",font:n.font,noHover:!0,noTickson:!0};function l(t,e){return o.coerce(i,a,v,t,e)}return m(i,a,l,s,n),g(i,a,l,s),a}function A(t,e,r){return[Math.min(e/t.width,r/t.height),t,e+\"x\"+r]}function M(t,e,r,i){var a=document.createElementNS(\"http://www.w3.org/2000/svg\",\"text\"),o=n.select(a);return o.text(t).attr(\"x\",0).attr(\"y\",0).attr(\"text-anchor\",r).attr(\"data-unformatted\",t).call(p.convertToTspans,i).call(f.font,e),f.bBox(o.node())}function S(t,e,r,n,i,a){var s=\"_cache\"+e;t[s]&&t[s].key===i||(t[s]={key:i,value:r});var l=o.aggNums(a,null,[t[s].value,n],2);return t[s].value=l,l}e.exports=function(t,e,r,m){var g,v=t._fullLayout;w(r)&&m&&(g=m()),o.makeTraceGroups(v._indicatorlayer,e,\"trace\").each((function(e){var 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e=I(t),r=Math.cos(e);return Math.abs(r)<.1?\"middle\":r>0?\"start\":\"end\"},O.heightFn=function(t,e,r){var n=I(t);return-.5*(1+Math.sin(n))*r};var D=function(t){return l(b[0]+g*Math.cos(t),b[1]-g*Math.sin(t))};f=function(t){return D(I(t))};if(s=d.calcTicks(o),h=d.getTickSigns(o)[2],o.visible){h=\"inside\"===o.ticks?-1:1;var R=(o.linewidth||1)/2;d.drawTicks(t,o,{vals:s,layer:A,path:\"M\"+h*R+\",0h\"+h*o.ticklen,transFn:function(t){var e=I(t);return D(e)+\"rotate(\"+-c(e)+\")\"}}),d.drawLabels(t,o,{vals:s,layer:A,transFn:f,labelFns:O})}var F=[y].concat(p.gauge.steps),B=_.selectAll(\"g.bg-arc\").data(F);B.enter().append(\"g\").classed(\"bg-arc\",!0).append(\"path\"),B.select(\"path\").call(P).call(T),B.exit().remove();var N=C(p.gauge.bar.thickness),j=_.selectAll(\"g.value-arc\").data([p.gauge.bar]);j.enter().append(\"g\").classed(\"value-arc\",!0).append(\"path\");var U=j.select(\"path\");w(M)?(U.transition().duration(M.duration).ease(M.easing).each(\"end\",(function(){S&&S()})).each(\"interrupt\",(function(){S&&S()})).attrTween(\"d\",(V=N,H=L(r[0].lastY),q=L(r[0].y),function(){var t=i(H,q);return function(e){return V.endAngle(t(e))()}})),p._lastValue=r[0].y):U.attr(\"d\",\"number\"==typeof r[0].y?N.endAngle(L(r[0].y)):\"M0,0Z\");var V,H,q;U.call(T),j.exit().remove(),F=[];var G=p.gauge.threshold.value;(G||0===G)&&F.push({range:[G,G],color:p.gauge.threshold.color,line:{color:p.gauge.threshold.line.color,width:p.gauge.threshold.line.width},thickness:p.gauge.threshold.thickness});var Y=_.selectAll(\"g.threshold-arc\").data(F);Y.enter().append(\"g\").classed(\"threshold-arc\",!0).append(\"path\"),Y.select(\"path\").call(P).call(T),Y.exit().remove();var W=_.selectAll(\"g.gauge-outline\").data([x]);W.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"path\"),W.select(\"path\").call(P).call(T),W.exit().remove()}(t,0,e,{radius:U,innerRadius:V,gauge:Y,layer:W,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var X=O.selectAll(\"g.bullet\").data(R?e:[]);X.exit().remove();var Z=O.selectAll(\"g.bulletaxis\").data(R?e:[]);Z.exit().remove(),R&&function(t,e,r,n){var i,a,o,s,c,u=r[0].trace,f=n.gauge,p=n.layer,m=n.gaugeBg,g=n.gaugeOutline,v=n.size,x=u.domain,b=n.transitionOpts,_=n.onComplete;f.enter().append(\"g\").classed(\"bullet\",!0),f.attr(\"transform\",l(v.l,v.t)),p.enter().append(\"g\").classed(\"bulletaxis\",!0).classed(\"crisp\",!0),p.selectAll(\"g.xbulletaxistick,path,text\").remove();var A=v.h,M=u.gauge.bar.thickness*A,S=x.x[0],E=x.x[0]+(x.x[1]-x.x[0])*(u._hasNumber||u._hasDelta?1-h.bulletNumberDomainSize:1);(i=k(t,u.gauge.axis))._id=\"xbulletaxis\",i.domain=[S,E],i.setScale(),a=d.calcTicks(i),o=d.makeTransTickFn(i),s=d.getTickSigns(i)[2],c=v.t+v.h,i.visible&&(d.drawTicks(t,i,{vals:\"inside\"===i.ticks?d.clipEnds(i,a):a,layer:p,path:d.makeTickPath(i,c,s),transFn:o}),d.drawLabels(t,i,{vals:a,layer:p,transFn:o,labelFns:d.makeLabelFns(i,c)}));function L(t){t.attr(\"width\",(function(t){return Math.max(0,i.c2p(t.range[1])-i.c2p(t.range[0]))})).attr(\"x\",(function(t){return i.c2p(t.range[0])})).attr(\"y\",(function(t){return.5*(1-t.thickness)*A})).attr(\"height\",(function(t){return t.thickness*A}))}var C=[m].concat(u.gauge.steps),P=f.selectAll(\"g.bg-bullet\").data(C);P.enter().append(\"g\").classed(\"bg-bullet\",!0).append(\"rect\"),P.select(\"rect\").call(L).call(T),P.exit().remove();var I=f.selectAll(\"g.value-bullet\").data([u.gauge.bar]);I.enter().append(\"g\").classed(\"value-bullet\",!0).append(\"rect\"),I.select(\"rect\").attr(\"height\",M).attr(\"y\",(A-M)/2).call(T),w(b)?I.select(\"rect\").transition().duration(b.duration).ease(b.easing).each(\"end\",(function(){_&&_()})).each(\"interrupt\",(function(){_&&_()})).attr(\"width\",Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y)))):I.select(\"rect\").attr(\"width\",\"number\"==typeof r[0].y?Math.max(0,i.c2p(Math.min(u.gauge.axis.range[1],r[0].y))):0);I.exit().remove();var O=r.filter((function(){return u.gauge.threshold.value||0===u.gauge.threshold.value})),z=f.selectAll(\"g.threshold-bullet\").data(O);z.enter().append(\"g\").classed(\"threshold-bullet\",!0).append(\"line\"),z.select(\"line\").attr(\"x1\",i.c2p(u.gauge.threshold.value)).attr(\"x2\",i.c2p(u.gauge.threshold.value)).attr(\"y1\",(1-u.gauge.threshold.thickness)/2*A).attr(\"y2\",(1-(1-u.gauge.threshold.thickness)/2)*A).call(y.stroke,u.gauge.threshold.line.color).style(\"stroke-width\",u.gauge.threshold.line.width),z.exit().remove();var D=f.selectAll(\"g.gauge-outline\").data([g]);D.enter().append(\"g\").classed(\"gauge-outline\",!0).append(\"rect\"),D.select(\"rect\").call(L).call(T),D.exit().remove()}(t,0,e,{gauge:X,layer:Z,size:B,gaugeBg:C,gaugeOutline:P,transitionOpts:r,onComplete:g});var J=O.selectAll(\"text.title\").data(e);J.exit().remove(),J.enter().append(\"text\").classed(\"title\",!0),J.attr(\"text-anchor\",(function(){return 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n=t(\"../../components/colorscale/attributes\"),i=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,a=t(\"../../plots/template_attributes\").hovertemplateAttrs,o=t(\"../mesh3d/attributes\"),s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;var 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0===e.isomax||null===e.isomin?s:e.isomax,n(t,e,{vals:[e._vMin,e._vMax],containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374,\"../streamtube/calc\":1041}],864:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_mesh3d,i=t(\"../../lib/gl_format_color\").parseColorScale,a=t(\"../../lib/str2rgbarray\"),o=t(\"../../components/colorscale\").extractOpts,s=t(\"../../plots/gl3d/zip3\"),l=function(t,e){for(var r=e.length-1;r>0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n<t&&t<=i)return{id:r,distRatio:(i-t)/(i-n)}}return{id:0,distRatio:0}};function c(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.data=null,this.showContour=!1}var u=c.prototype;u.handlePick=function(t){if(t.object===this.mesh){var e=t.data.index,r=this.data._meshX[e],n=this.data._meshY[e],i=this.data._meshZ[e],a=this.data._Ys.length,o=this.data._Zs.length,s=l(r,this.data._Xs).id,c=l(n,this.data._Ys).id,u=l(i,this.data._Zs).id,f=t.index=u+o*c+o*a*s;t.traceCoordinate=[this.data._meshX[f],this.data._meshY[f],this.data._meshZ[f],this.data._value[f]];var h=this.data.hovertext||this.data.text;return Array.isArray(h)&&void 0!==h[f]?t.textLabel=h[f]:h&&(t.textLabel=h),!0}},u.update=function(t){var e=this.scene,r=e.fullSceneLayout;function n(t,e,r,n){return e.map((function(e){return t.d2l(e,0,n)*r}))}this.data=h(t);var l={positions:s(n(r.xaxis,t._meshX,e.dataScale[0],t.xcalendar),n(r.yaxis,t._meshY,e.dataScale[1],t.ycalendar),n(r.zaxis,t._meshZ,e.dataScale[2],t.zcalendar)),cells:s(t._meshI,t._meshJ,t._meshK),lightPosition:[t.lightposition.x,t.lightposition.y,t.lightposition.z],ambient:t.lighting.ambient,diffuse:t.lighting.diffuse,specular:t.lighting.specular,roughness:t.lighting.roughness,fresnel:t.lighting.fresnel,vertexNormalsEpsilon:t.lighting.vertexnormalsepsilon,faceNormalsEpsilon:t.lighting.facenormalsepsilon,opacity:t.opacity,contourEnable:t.contour.show,contourColor:a(t.contour.color).slice(0,3),contourWidth:t.contour.width,useFacetNormals:t.flatshading},c=o(t);l.vertexIntensity=t._meshIntensity,l.vertexIntensityBounds=[c.min,c.max],l.colormap=i(t),this.mesh.update(l)},u.dispose=function(){this.scene.glplot.remove(this.mesh),this.mesh.dispose()};var f=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"];function h(t){t._meshI=[],t._meshJ=[],t._meshK=[];var e,r,n,i,a,o,s,c=t.surface.show,u=t.spaceframe.show,h=t.surface.fill,p=t.spaceframe.fill,d=!1,m=!1,g=0,v=t._Xs,y=t._Ys,x=t._Zs,b=v.length,_=y.length,w=x.length,T=f.indexOf(t._gridFill.replace(/-/g,\"\").replace(/\\+/g,\"\")),k=function(t,e,r){switch(T){case 5:return r+w*e+w*_*t;case 4:return r+w*t+w*b*e;case 3:return e+_*r+_*w*t;case 2:return e+_*t+_*b*r;case 1:return t+b*r+b*w*e;default:return t+b*e+b*_*r}},A=t._minValues,M=t._maxValues,S=t._vMin,E=t._vMax;function L(t,e,s){for(var l=o.length,c=r;c<l;c++)if(t===n[c]&&e===i[c]&&s===a[c])return c;return-1}function C(){r=e}function P(){n=[],i=[],a=[],o=[],e=0,C()}function I(t,r,s,l){return n.push(t),i.push(r),a.push(s),o.push(l),++e-1}function O(t,e,r){for(var n=[],i=0;i<t.length;i++)n[i]=t[i]*(1-r)+r*e[i];return n}function z(t){s=t}function D(t,e){return\"all\"===t||null===t||t.indexOf(e)>-1}function R(t,e){return null===t?e:t}function F(e,r,n){C();var i,a,o,l=[r],c=[n];if(s>=1)l=[r],c=[n];else if(s>0){var u=function(t,e){var 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i=r(\"values\"),a=r(\"visible\");if(i&&i.length||(a=e.visible=!1),a){r(\"label\"),r(\"displayindex\",e._index);var o,s=t.categoryarray,c=Array.isArray(s)&&s.length>0;c&&(o=\"array\");var u=r(\"categoryorder\",o);\"array\"===u?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==u||(e.categoryorder=\"trace\")}}e.exports=function(t,e,r,f){function h(r,i){return n.coerce(t,e,l,r,i)}var p=s(t,e,{name:\"dimensions\",handleItemDefaults:u}),d=function(t,e,r,o,s){s(\"line.shape\"),s(\"line.hovertemplate\");var l=s(\"line.color\",o.colorway[0]);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,f,h);o(e,f,h),Array.isArray(p)&&p.length||(e.visible=!1),c(e,p,\"values\",d),h(\"hoveron\"),h(\"hovertemplate\"),h(\"arrangement\"),h(\"bundlecolors\"),h(\"sortpaths\"),h(\"counts\");var m={family:f.font.family,size:Math.round(f.font.size),color:f.font.color};n.coerceFont(h,\"labelfont\",m);var g={family:f.font.family,size:Math.round(f.font.size/1.2),color:f.font.color};n.coerceFont(h,\"tickfont\",g)}},{\"../../components/colorscale/defaults\":376,\"../../components/colorscale/helpers\":377,\"../../lib\":503,\"../../plots/array_container_defaults\":549,\"../../plots/domain\":584,\"../parcoords/merge_length\":897,\"./attributes\":881}],885:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":881,\"./base_plot\":882,\"./calc\":883,\"./defaults\":884,\"./plot\":887}],886:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"d3-interpolate\").interpolateNumber,a=t(\"../../plot_api/plot_api\"),o=t(\"../../components/fx\"),s=t(\"../../lib\"),l=s.strTranslate,c=t(\"../../components/drawing\"),u=t(\"tinycolor2\"),f=t(\"../../lib/svg_text_utils\");function h(t,e,r,i){var a=t.map(F.bind(0,e,r)),o=i.selectAll(\"g.parcatslayer\").data([null]);o.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",\"all\");var u=o.selectAll(\"g.trace.parcats\").data(a,p),h=u.enter().append(\"g\").attr(\"class\",\"trace parcats\");u.attr(\"transform\",(function(t){return l(t.x,t.y)})),h.append(\"g\").attr(\"class\",\"paths\");var y=u.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),p);y.attr(\"fill\",(function(t){return t.model.color}));var x=y.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);_(x),y.attr(\"d\",(function(t){return t.svgD})),x.empty()||y.sort(m),y.exit().remove(),y.on(\"mouseover\",g).on(\"mouseout\",v).on(\"click\",b),h.append(\"g\").attr(\"class\",\"dimensions\");var w=u.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),p);w.enter().append(\"g\").attr(\"class\",\"dimension\"),w.attr(\"transform\",(function(t){return l(t.x,0)})),w.exit().remove();var A=w.selectAll(\"g.category\").data((function(t){return t.categories}),p),M=A.enter().append(\"g\").attr(\"class\",\"category\");A.attr(\"transform\",(function(t){return l(0,t.y)})),M.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),A.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),T(M);var S=A.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p);S.each((function(){s.raiseToTop(this)})),S.attr(\"fill\",(function(t){return t.color}));var E=S.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);S.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(E),S.exit().remove(),M.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\");var z=e._fullLayout.paper_bgcolor;A.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"text-shadow\",f.makeTextShadow(z)).style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),f.convertToTspans(n.select(this),e)})),M.append(\"text\").attr(\"class\",\"dimlabel\"),A.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),A.selectAll(\"rect.bandrect\").on(\"mouseover\",L).on(\"mouseout\",C),A.exit().remove(),w.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",P).on(\"drag\",I).on(\"dragend\",O)),u.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),u.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return t.model.rawColor>e.model.rawColor?1:t.model.rawColor<e.model.rawColor?-1:0}function g(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){s.raiseToTop(this),w(n.select(this));var e=y(t),r=x(t);if(t.parcatsViewModel.graphDiv.emit(\"plotly_hover\",{points:e,event:n.event,constraints:r}),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\")){var i,a,l,c=n.mouse(this)[0],f=t.parcatsViewModel.graphDiv,h=t.parcatsViewModel.trace,p=f._fullLayout,d=p._paperdiv.node().getBoundingClientRect(),m=t.parcatsViewModel.graphDiv.getBoundingClientRect();for(l=0;l<t.leftXs.length-1;l++)if(t.leftXs[l]+t.dimWidths[l]-2<=c&&c<=t.leftXs[l+1]+2){var g=t.parcatsViewModel.dimensions[l],v=t.parcatsViewModel.dimensions[l+1];i=(g.x+g.width+v.x)/2,a=(t.topYs[l]+t.topYs[l+1]+t.height)/2;break}var b=t.parcatsViewModel.x+i,_=t.parcatsViewModel.y+a,T=u.mostReadable(t.model.color,[\"black\",\"white\"]),k=t.model.count,A=k/t.parcatsViewModel.model.count,M={countLabel:k,probabilityLabel:A.toFixed(3)},S=[];-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&S.push([\"Count:\",M.countLabel].join(\" \")),-1!==t.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&S.push([\"P:\",M.probabilityLabel].join(\" \"));var E=S.join(\"<br>\"),L=n.mouse(f)[0];o.loneHover({trace:h,x:b-d.left+m.left,y:_-d.top+m.top,text:E,color:t.model.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:10,fontColor:T,idealAlign:L<b?\"right\":\"left\",hovertemplate:(h.line||{}).hovertemplate,hovertemplateLabels:M,eventData:[{data:h._input,fullData:h,count:k,probability:A}]},{container:p._hoverlayer.node(),outerContainer:p._paper.node(),gd:f})}}}function v(t){if(!t.parcatsViewModel.dragDimension&&(_(n.select(this)),o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()),t.parcatsViewModel.pathSelection.sort(m),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\"))){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_unhover\",{points:e,event:n.event,constraints:r})}}function y(t){for(var e=[],r=z(t.parcatsViewModel),n=0;n<t.model.valueInds.length;n++){var i=t.model.valueInds[n];e.push({curveNumber:r,pointNumber:i})}return e}function x(t){for(var e={},r=t.parcatsViewModel.model.dimensions,n=0;n<r.length;n++){var i=r[n],a=i.categories[t.model.categoryInds[n]];e[i.containerInd]=a.categoryValue}return void 0!==t.model.rawColor&&(e.color=t.model.rawColor),e}function b(t){if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){var e=y(t),r=x(t);t.parcatsViewModel.graphDiv.emit(\"plotly_click\",{points:e,event:n.event,constraints:r})}}function _(t){t.attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",.6).attr(\"stroke\",\"lightgray\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1)}function w(t){t.attr(\"fill-opacity\",.8).attr(\"stroke\",(function(t){return u.mostReadable(t.model.color,[\"black\",\"white\"])})).attr(\"stroke-width\",.3)}function T(t){t.select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",1).attr(\"stroke-opacity\",1)}function k(t){t.attr(\"stroke\",\"black\").attr(\"stroke-width\",.2).attr(\"stroke-opacity\",1).attr(\"fill-opacity\",1)}function A(t){var e=t.parcatsViewModel.pathSelection,r=t.categoryViewModel.model.dimensionInd,n=t.categoryViewModel.model.categoryInd;return e.filter((function(e){return e.model.categoryInds[r]===n&&e.model.color===t.color}))}function M(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=n.select(t.parentNode).selectAll(\"rect.bandrect\"),l=[];s.each((function(t){A(t).each((function(t){Array.prototype.push.apply(l,y(t))}))}));var c={};c[a.dimensionInd]=a.categoryValue,o.emit(e,{points:l,event:r,constraints:c})}function S(t,e,r){var i=n.select(t).datum(),a=i.categoryViewModel.model,o=i.parcatsViewModel.graphDiv,s=A(i),l=[];s.each((function(t){Array.prototype.push.apply(l,y(t))}));var c={};c[a.dimensionInd]=a.categoryValue,void 0!==i.rawColor&&(c.color=i.rawColor),o.emit(e,{points:l,event:r,constraints:c})}function E(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=n.select(r.parentNode).select(\"rect.catrect\"),c=l.node().getBoundingClientRect(),u=l.datum(),f=u.parcatsViewModel,h=f.model.dimensions[u.model.dimensionInd],p=f.trace,d=c.top+c.height/2;f.dimensions.length>1&&h.displayInd===f.dimensions.length-1?(i=c.left,a=\"left\"):(i=c.left+c.width,a=\"right\");var m=u.model.count,g=u.model.categoryLabel,v=m/u.parcatsViewModel.model.count,y={countLabel:m,categoryLabel:g,probabilityLabel:v.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",y.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+y.categoryLabel+\"):\",y.probabilityLabel].join(\" \"));var b=x.join(\"<br>\");return{trace:p,x:o*(i-e.left),y:s*(d-e.top),text:b,color:\"lightgray\",borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontSize:12,fontColor:\"black\",idealAlign:a,hovertemplate:p.hovertemplate,hovertemplateLabels:y,eventData:[{data:p._input,fullData:p,count:m,category:g,probability:v}]}}function L(t){if(!t.parcatsViewModel.dragDimension&&-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")){if(n.mouse(this)[1]<-1)return;var e,r=t.parcatsViewModel.graphDiv,i=r._fullLayout,a=i._paperdiv.node().getBoundingClientRect(),l=t.parcatsViewModel.hoveron;if(\"color\"===l?(!function(t){var e=n.select(t).datum(),r=A(e);w(r),r.each((function(){s.raiseToTop(this)})),n.select(t.parentNode).selectAll(\"rect.bandrect\").filter((function(t){return t.color===e.color})).each((function(){s.raiseToTop(this),n.select(this).attr(\"stroke\",\"black\").attr(\"stroke-width\",1.5)}))}(this),S(this,\"plotly_hover\",n.event)):(!function(t){n.select(t.parentNode).selectAll(\"rect.bandrect\").each((function(t){var e=A(t);w(e),e.each((function(){s.raiseToTop(this)}))})),n.select(t.parentNode).select(\"rect.catrect\").attr(\"stroke\",\"black\").attr(\"stroke-width\",2.5)}(this),M(this,\"plotly_hover\",n.event)),-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"none\"))\"category\"===l?e=E(r,a,this):\"color\"===l?e=function(t,e,r){t._fullLayout._calcInverseTransform(t);var i,a,o=t._fullLayout._invScaleX,s=t._fullLayout._invScaleY,l=r.getBoundingClientRect(),c=n.select(r).datum(),f=c.categoryViewModel,h=f.parcatsViewModel,p=h.model.dimensions[f.model.dimensionInd],d=h.trace,m=l.y+l.height/2;h.dimensions.length>1&&p.displayInd===h.dimensions.length-1?(i=l.left,a=\"left\"):(i=l.left+l.width,a=\"right\");var g=f.model.categoryLabel,v=c.parcatsViewModel.model.count,y=0;c.categoryViewModel.bands.forEach((function(t){t.color===c.color&&(y+=t.count)}));var x=f.model.count,b=0;h.pathSelection.each((function(t){t.model.color===c.color&&(b+=t.model.count)}));var _=y/v,w=y/b,T=y/x,k={countLabel:v,categoryLabel:g,probabilityLabel:_.toFixed(3)},A=[];-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&A.push([\"Count:\",k.countLabel].join(\" \")),-1!==f.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&(A.push(\"P(color \\u2229 \"+g+\"): \"+k.probabilityLabel),A.push(\"P(\"+g+\" | color): \"+w.toFixed(3)),A.push(\"P(color | \"+g+\"): \"+T.toFixed(3)));var M=A.join(\"<br>\"),S=u.mostReadable(c.color,[\"black\",\"white\"]);return{trace:d,x:o*(i-e.left),y:s*(m-e.top),text:M,color:c.color,borderColor:\"black\",fontFamily:'Monaco, \"Courier New\", monospace',fontColor:S,fontSize:10,idealAlign:a,hovertemplate:d.hovertemplate,hovertemplateLabels:k,eventData:[{data:d._input,fullData:d,category:g,count:v,probability:_,categorycount:x,colorcount:b,bandcolorcount:y}]}}(r,a,this):\"dimension\"===l&&(e=function(t,e,r){var i=[];return n.select(r.parentNode.parentNode).selectAll(\"g.category\").select(\"rect.catrect\").each((function(){i.push(E(t,e,this))})),i}(r,a,this)),e&&o.loneHover(e,{container:i._hoverlayer.node(),outerContainer:i._paper.node(),gd:r})}}function C(t){var e=t.parcatsViewModel;if(!e.dragDimension&&(_(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1===e.hoverinfoItems.indexOf(\"skip\"))){\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event)}}function P(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.y<i&&i<=e.y+e.height&&(t.potentialClickBand=this)})))})),t.parcatsViewModel.dragDimension=t,o.loneUnhover(t.parcatsViewModel.graphDiv._fullLayout._hoverlayer.node()))}function I(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragHasMoved=!0,null!==t.dragDimensionDisplayInd)){var e=t.dragDimensionDisplayInd,r=e-1,i=e+1,a=t.parcatsViewModel.dimensions[e];if(null!==t.dragCategoryDisplayInd){var o=a.categories[t.dragCategoryDisplayInd];o.model.dragY+=n.event.dy;var s=o.model.dragY,l=o.model.displayInd,c=a.categories,u=c[l-1],f=c[l+1];void 0!==u&&s<u.y+u.height/2&&(o.model.displayInd=u.model.displayInd,u.model.displayInd=l),void 0!==f&&s+o.height>f.y+f.height/2&&(o.model.displayInd=f.model.displayInd,f.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var h=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==h&&a.model.dragX<h.x+h.width&&(a.model.displayInd=h.model.displayInd,h.model.displayInd=e),void 0!==p&&a.model.dragX+a.width>p.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function O(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=z(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),f=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[f],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}if(-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd)t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null;t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function 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e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},r.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},r.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":t,preserveAspectRatio:\"none\",x:0,y:0,width:this.style.width,height:this.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},{\"../../constants/xmlns_namespaces\":480,\"../../plots/get_data\":593,\"./plot\":899,\"@plotly/d3\":58}],891:[function(t,e,r){\"use strict\";var 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n=t(\"../../lib\").isTypedArray;r.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},r.isOrdinal=function(t){return!!t.tickvals},r.isVisible=function(t){return t.visible||!(\"visible\"in t)}},{\"../../lib\":503}],895:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:t(\"./base_plot\"),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},{\"./attributes\":888,\"./base_plot\":890,\"./calc\":891,\"./defaults\":893,\"./plot\":899}],896:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nattribute vec4 p01_04, p05_08, p09_12, p13_16,\\n               p17_20, p21_24, p25_28, p29_32,\\n               p33_36, p37_40, p41_44, p45_48,\\n               p49_52, p53_56, p57_60, 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hi) {\\n    return (lo < hi) && (lo > p || p > hi);\\n}\\n\\nbool vOutside(vec4 p, vec4 lo, vec4 hi) {\\n    return (\\n        fOutside(p[0], lo[0], hi[0]) ||\\n        fOutside(p[1], lo[1], hi[1]) ||\\n        fOutside(p[2], lo[2], hi[2]) ||\\n        fOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool mOutside(mat4 p, mat4 lo, mat4 hi) {\\n    return (\\n        vOutside(p[0], lo[0], hi[0]) ||\\n        vOutside(p[1], lo[1], hi[1]) ||\\n        vOutside(p[2], lo[2], hi[2]) ||\\n        vOutside(p[3], lo[3], hi[3])\\n    );\\n}\\n\\nbool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    return mOutside(A, loA, hiA) ||\\n           mOutside(B, loB, hiB) ||\\n           mOutside(C, loC, hiC) ||\\n           mOutside(D, loD, hiD);\\n}\\n\\nbool outsideRasterMask(mat4 A, mat4 B, mat4 C, mat4 D) {\\n    mat4 pnts[4];\\n    pnts[0] = A;\\n    pnts[1] = B;\\n    pnts[2] = C;\\n    pnts[3] = D;\\n\\n    for(int i = 0; i < 4; ++i) {\\n        for(int j = 0; j < 4; ++j) {\\n            for(int k = 0; k < 4; ++k) {\\n                if(0 == iMod(\\n                    int(255.0 * texture2D(maskTexture,\\n                        vec2(\\n                            (float(i * 2 + j / 2) + 0.5) / 8.0,\\n                            (pnts[i][j][k] * (maskHeight - 1.0) + 1.0) / maskHeight\\n                        ))[3]\\n                    ) / int(pow(2.0, float(iMod(j * 4 + k, 8)))),\\n                    2\\n                )) return true;\\n            }\\n        }\\n    }\\n    return false;\\n}\\n\\nvec4 position(bool isContext, float v, mat4 A, mat4 B, mat4 C, mat4 D) {\\n    float x = 0.5 * sign(v) + 0.5;\\n    float y = axisY(x, A, B, C, D);\\n    float z = 1.0 - abs(v);\\n\\n    z += isContext ? 0.0 : 2.0 * float(\\n        outsideBoundingBox(A, B, C, D) ||\\n        outsideRasterMask(A, B, C, D)\\n    );\\n\\n    return vec4(\\n        2.0 * (vec2(x, y) * viewBoxSize + viewBoxPos) / resolution - 1.0,\\n        z,\\n        1.0\\n    );\\n}\\n\\nvoid main() 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right\"],editType:\"calc\"}}}},{\"../../components/color/attributes\":365,\"../../lib/extend\":493,\"../../plots/attributes\":550,\"../../plots/domain\":584,\"../../plots/font_attributes\":585,\"../../plots/template_attributes\":633}],901:[function(t,e,r){\"use strict\";var n=t(\"../../plots/plots\");r.name=\"pie\",r.plot=function(t,e,i,a){n.plotBasePlot(r.name,t,e,i,a)},r.clean=function(t,e,i,a){n.cleanBasePlot(r.name,t,e,i,a)}},{\"../../plots/plots\":619}],902:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"tinycolor2\"),a=t(\"../../components/color\"),o={};function s(t){return function(e,r){return!!e&&(!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e))}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r<t.length;r++)a.push(i(t[r]).lighten(20).toHexString());for(r=0;r<t.length;r++)a.push(i(t[r]).darken(20).toHexString());e[n]=a}return a}e.exports={calc:function(t,e){var r,i,a=[],o=t._fullLayout,l=o.hiddenlabels||[],c=e.labels,u=e.marker.colors||[],f=e.values,h=e._length,p=e._hasValues&&h;if(e.dlabel)for(c=new Array(h),r=0;r<h;r++)c[r]=String(e.label0+r*e.dlabel);var d={},m=s(o[\"_\"+e.type+\"colormap\"]),g=0,v=!1;for(r=0;r<h;r++){var y,x,b;if(p){if(y=f[r],!n(y))continue;y=+y}else y=1;void 0!==(x=c[r])&&\"\"!==x||(x=r);var _=d[x=String(x)];void 0===_?(d[x]=a.length,(b=-1!==l.indexOf(x))||(g+=y),a.push({v:y,label:x,color:m(u[r],x),i:r,pts:[r],hidden:b})):(v=!0,(i=a[_]).v+=y,i.pts.push(r),i.hidden||(g+=y),!1===i.color&&u[r]&&(i.color=m(u[r],x)))}return a=a.filter((function(t){return t.v>=0})),(\"funnelarea\"===e.type?v:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u<i.length;u++){var f=i[u];if(f[0].trace.type===r)for(var h=0;h<f.length;h++){var p=f[h];!1===p.color&&(s[p.label]?p.color=s[p.label]:(s[p.label]=p.color=a[c%a.length],c++))}}},makePullColorFn:s,generateExtendedColors:l}},{\"../../components/color\":366,\"fast-isnumeric\":190,tinycolor2:312}],903:[function(t,e,r){\"use strict\";var n=t(\"fast-isnumeric\"),i=t(\"../../lib\"),a=t(\"./attributes\"),o=t(\"../../plots/domain\").defaults,s=t(\"../bar/defaults\").handleText;function l(t,e){var r=Array.isArray(t),a=i.isArrayOrTypedArray(e),o=Math.min(r?t.length:1/0,a?e.length:1/0);if(isFinite(o)||(o=0),o&&a){for(var s,l=0;l<o;l++){var c=e[l];if(n(c)&&c>0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}e.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function c(r,n){return i.coerce(t,e,a,r,n)}var u=l(c(\"labels\"),c(\"values\")),f=u.len;if(e._hasLabels=u.hasLabels,e._hasValues=u.hasValues,!e._hasLabels&&e._hasValues&&(c(\"label0\"),c(\"dlabel\")),f){e._length=f,c(\"marker.line.width\")&&c(\"marker.line.color\"),c(\"marker.colors\"),c(\"scalegroup\");var h,p=c(\"text\"),d=c(\"texttemplate\");if(d||(h=c(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),c(\"hovertext\"),c(\"hovertemplate\"),d||h&&\"none\"!==h){var m=c(\"textposition\");s(t,e,n,c,m,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(m)||\"auto\"===m||\"outside\"===m)&&c(\"automargin\"),(\"inside\"===m||\"auto\"===m||Array.isArray(m))&&c(\"insidetextorientation\")}o(e,n,c);var g=c(\"hole\");if(c(\"title.text\")){var v=c(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(c,\"title.font\",n.font)}c(\"sort\"),c(\"direction\"),c(\"rotation\"),c(\"pull\")}else e.visible=!1}}},{\"../../lib\":503,\"../../plots/domain\":584,\"../bar/defaults\":652,\"./attributes\":900,\"fast-isnumeric\":190}],904:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx/helpers\").appendArrayMultiPointValues;e.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},{\"../../components/fx/helpers\":402}],905:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}r.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},r.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},r.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r<e.length;r++){var n=t[e[r]];if(n||0===n||\"\"===n)return n}},r.castOption=function(t,e){return Array.isArray(t)?r.getFirstFilled(t,e):t||void 0},r.getRotationAngle=function(t){return(\"auto\"===t?0:t)*Math.PI/180}},{\"../../lib\":503}],906:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\").supplyDefaults,supplyLayoutDefaults:t(\"./layout_defaults\"),layoutAttributes:t(\"./layout_attributes\"),calc:t(\"./calc\").calc,crossTraceCalc:t(\"./calc\").crossTraceCalc,plot:t(\"./plot\").plot,style:t(\"./style\"),styleOne:t(\"./style_one\"),moduleType:\"trace\",name:\"pie\",basePlotModule:t(\"./base_plot\"),categories:[\"pie-like\",\"pie\",\"showLegend\"],meta:{}}},{\"./attributes\":900,\"./base_plot\":901,\"./calc\":902,\"./defaults\":903,\"./layout_attributes\":907,\"./layout_defaults\":908,\"./plot\":909,\"./style\":910,\"./style_one\":911}],907:[function(t,e,r){\"use strict\";e.exports={hiddenlabels:{valType:\"data_array\",editType:\"calc\"},piecolorway:{valType:\"colorlist\",editType:\"calc\"},extendpiecolors:{valType:\"boolean\",dflt:!0,editType:\"calc\"}}},{}],908:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./layout_attributes\");e.exports=function(t,e){function r(r,a){return n.coerce(t,e,i,r,a)}r(\"hiddenlabels\"),r(\"piecolorway\",e.colorway),r(\"extendpiecolors\")}},{\"../../lib\":503,\"./layout_attributes\":907}],909:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../plots/plots\"),a=t(\"../../components/fx\"),o=t(\"../../components/color\"),s=t(\"../../components/drawing\"),l=t(\"../../lib\"),c=l.strScale,u=l.strTranslate,f=t(\"../../lib/svg_text_utils\"),h=t(\"../bar/uniform_text\"),p=h.recordMinTextSize,d=h.clearMinTextSize,m=t(\"../bar/constants\").TEXTPAD,g=t(\"./helpers\"),v=t(\"./event_data\"),y=t(\"../../lib\").isValidTextValue;function x(t,e,r){var i=r[0],o=i.cx,s=i.cy,c=i.trace,u=\"funnelarea\"===c.type;\"_hasHoverLabel\"in c||(c._hasHoverLabel=!1),\"_hasHoverEvent\"in c||(c._hasHoverEvent=!1),t.on(\"mouseover\",(function(t){var r=e._fullLayout,f=e._fullData[c.index];if(!e._dragging&&!1!==r.hovermode){var h=f.hoverinfo;if(Array.isArray(h)&&(h=a.castHoverinfo({hoverinfo:[g.castOption(h,t.pts)],_module:c._module},r,0)),\"all\"===h&&(h=\"label+text+value+percent+name\"),f.hovertemplate||\"none\"!==h&&\"skip\"!==h&&h){var p=t.rInscribed||0,d=o+t.pxmid[0]*(1-p),m=s+t.pxmid[1]*(1-p),y=r.separators,x=[];if(h&&-1!==h.indexOf(\"label\")&&x.push(t.label),t.text=g.castOption(f.hovertext||f.text,t.pts),h&&-1!==h.indexOf(\"text\")){var b=t.text;l.isValidTextValue(b)&&x.push(b)}t.value=t.v,t.valueLabel=g.formatPieValue(t.v,y),h&&-1!==h.indexOf(\"value\")&&x.push(t.valueLabel),t.percent=t.v/i.vTotal,t.percentLabel=g.formatPiePercent(t.percent,y),h&&-1!==h.indexOf(\"percent\")&&x.push(t.percentLabel);var _=f.hoverlabel,w=_.font,T=[];a.loneHover({trace:c,x0:d-p*i.r,x1:d+p*i.r,y:m,_x0:u?o+t.TL[0]:d-p*i.r,_x1:u?o+t.TR[0]:d+p*i.r,_y0:u?s+t.TL[1]:m-p*i.r,_y1:u?s+t.BL[1]:m+p*i.r,text:x.join(\"<br>\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[v(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[v(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[v(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[v(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;i<t.length;i++)if((n=(r=t[i][0]).trace).title.text){var a=n.title.text;n._meta&&(a=l.templateString(a,n._meta));var o=s.tester.append(\"text\").attr(\"data-notex\",1).text(a).call(s.font,n.title.font).call(f.convertToTspans,e),c=s.bBox(o.node(),!0);r.titleBox={width:c.width,height:c.height},o.remove()}}function w(t,e,r){var n=r.r||e.rpx1,i=e.rInscribed;if(e.startangle===e.stopangle)return{rCenter:1-i,scale:0,rotate:0,textPosAngle:0};var a,o=e.ring,s=1===o&&Math.abs(e.startangle-e.stopangle)===2*Math.PI,l=e.halfangle,c=e.midangle,u=r.trace.insidetextorientation,f=\"horizontal\"===u,h=\"tangential\"===u,p=\"radial\"===u,d=\"auto\"===u,m=[];if(!d){var g,v=function(r,i){if(function(t,e){var r=t.startangle,n=t.stopangle;return r>e&&e>n||r<e&&e<n}(e,r)){var s=Math.abs(r-e.startangle),l=Math.abs(r-e.stopangle),c=s<l?s:l;(a=\"tan\"===i?k(t,n,o,c,0):T(t,n,o,c,Math.PI/2)).textPosAngle=r,m.push(a)}};if(f||h){for(g=4;g>=-4;g-=2)v(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)v(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)v(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var y=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/y,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||h)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,b=0,_=0;_<m.length;_++){var w=m[_].scale;if(b<w&&(b=w,x=_),!d&&b>=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=I(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,c,u=(l=t.r,c=t.trace.aspectratio,l/(void 0===c?1:c)),f=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(f+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?f*=2:-1!==a.title.position.indexOf(\"right\")&&(f+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=f/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function I(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;e<t.pull.length;e++)t.pull[e]>r&&(r=t.pull[e]);return r}function O(t,e){for(var r=[],n=0;n<t.length;n++){var i=t[n][0],a=i.trace,o=a.domain,s=e.w*(o.x[1]-o.x[0]),l=e.h*(o.y[1]-o.y[0]);a.title.text&&\"middle center\"!==a.title.position&&(l-=P(i,e));var c=s/2,u=l/2;\"funnelarea\"!==a.type||a.scalegroup||(u/=a.aspectratio),i.r=Math.min(c,u)/(1+I(a)),i.cx=e.l+e.w*(a.domain.x[1]+a.domain.x[0])/2,i.cy=e.t+e.h*(1-a.domain.y[0])-l/2,a.title.text&&-1!==a.title.position.indexOf(\"bottom\")&&(i.cy-=P(i,e)),a.scalegroup&&-1===r.indexOf(a.scalegroup)&&r.push(a.scalegroup)}!function(t,e){for(var r,n,i,a=0;a<e.length;a++){var o=1/0,s=e[a];for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var l;if(\"pie\"===i.type)l=r.r*r.r;else if(\"funnelarea\"===i.type){var c,u;i.aspectratio>1?(c=r.r,u=c/i.aspectratio):(u=r.r,c=u*i.aspectratio),c*=(1+i.baseratio)/2,l=c*u}o=Math.min(o,l/r.vTotal)}for(n=0;n<t.length;n++)if(r=t[n][0],(i=r.trace).scalegroup===s){var f=o*r.vTotal;\"funnelarea\"===i.type&&(f/=(1+i.baseratio)/2,f/=i.aspectratio),r.r=Math.sqrt(f)}}}(t,r)}function z(t,e){return[t*Math.sin(e),-t*Math.cos(e)]}function D(t,e,r){var n=t._fullLayout,i=r.trace,a=i.texttemplate,o=i.textinfo;if(!a&&o&&\"none\"!==o){var s,c=o.split(\"+\"),u=function(t){return-1!==c.indexOf(t)},f=u(\"label\"),h=u(\"text\"),p=u(\"value\"),d=u(\"percent\"),m=n.separators;if(s=f?[e.label]:[],h){var v=g.getFirstFilled(i.text,e.pts);y(v)&&s.push(v)}p&&s.push(g.formatPieValue(e.v,m)),d&&s.push(g.formatPiePercent(e.v/r.vTotal,m)),e.text=s.join(\"<br>\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(y(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}e.exports={plot:function(t,e){var r=t._fullLayout,a=r._size;d(\"pie\",r),_(e,t),O(e,a);var h=l.makeTraceGroups(r._pielayer,e,\"trace\").each((function(e){var h=n.select(this),d=e[0],m=d.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;e<t.length&&t[e].hidden;e++);if(e===t.length)return;s+=l*t[e].v,l*=-1,c=\"px1\",u=\"px0\"}for(n=z(a,s),e=0;e<t.length;e++)(r=t[e]).hidden||(r[c]=n,r.startangle=s,s+=l*r.v/2,r.pxmid=z(a,s),r.midangle=s,s+=l*r.v/2,n=z(a,s),r.stopangle=s,r[u]=n,r.largeArc=r.v>i.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),h.attr(\"stroke-linejoin\",\"round\"),h.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var y=[[[],[]],[[],[]]],_=!1;v.each((function(i,a){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=m.index,y[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var o=d.cx,c=d.cy,u=n.select(this),h=u.selectAll(\"path.surface\").data([i]);if(h.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":\"all\"}),u.call(x,t,e),m.pull){var v=+g.castOption(m.pull,i.pts)||0;v>0&&(o+=v*i.pxmid[0],c+=v*i.pxmid[1])}i.cxFinal=o,i.cyFinal=c;var T=m.hole;if(i.v===d.vTotal){var k=\"M\"+(o+i.px0[0])+\",\"+(c+i.px0[1])+C(i.px0,i.pxmid,!0,1)+C(i.pxmid,i.px0,!0,1)+\"Z\";T?h.attr(\"d\",\"M\"+(o+T*i.px0[0])+\",\"+(c+T*i.px0[1])+C(i.px0,i.pxmid,!1,T)+C(i.pxmid,i.px0,!1,T)+\"Z\"+k):h.attr(\"d\",k)}else{var A=C(i.px0,i.px1,!0,1);if(T){var M=1-T;h.attr(\"d\",\"M\"+(o+T*i.px1[0])+\",\"+(c+T*i.px1[1])+C(i.px1,i.px0,!1,T)+\"l\"+M*i.px0[0]+\",\"+M*i.px0[1]+A+\"Z\")}else h.attr(\"d\",\"M\"+o+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+A+\"Z\")}D(t,i,d);var S=g.castOption(m.textposition,i.pts),E=u.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==S?[0]:[]);E.enter().append(\"g\").classed(\"slicetext\",!0),E.exit().remove(),E.each((function(){var u=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===S?function(t,e,r){var n=g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,i=g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n,family:i,size:a}}(m,i,r.font):b(m,i,r.font));u.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var v,y=s.bBox(u.node());if(\"outside\"===S)v=L(y,i);else if(v=w(y,i,d),\"auto\"===S&&v.scale<1){var x=l.ensureUniformFontSize(t,m.outsidetextfont);u.call(s.font,x),v=L(y=s.bBox(u.node()),i)}var T=v.textPosAngle,k=void 0===T?i.pxmid:z(d.r,T);if(v.targetX=o+k[0]*v.rCenter+(v.x||0),v.targetY=c+k[1]*v.rCenter+(v.y||0),R(v,y),v.outside){var A=v.targetY;i.yLabelMin=A-y.height/2,i.yLabelMid=A,i.yLabelMax=A+y.height/2,i.labelExtraX=0,i.labelExtraY=0,_=!0}v.fontSize=h.size,p(m.type,v,r),e[a].transform=v,u.attr(\"transform\",l.getTextTransform(v))}))}function C(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*d.r+\",\"+n*d.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var T=n.select(this).selectAll(\"g.titletext\").data(m.title.text?[0]:[]);if(T.enter().append(\"g\").classed(\"titletext\",!0),T.exit().remove(),T.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=m.title.text;m._meta&&(i=l.templateString(i,m._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,m.title.font).call(f.convertToTspans,t),e=\"middle center\"===m.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(d):C(d,a),r.attr(\"transform\",u(e.x,e.y)+c(Math.min(1,e.scale))+u(e.tx,e.ty))})),_&&function(t,e){var r,n,i,a,o,s,l,c,u,f,h,p,d;function m(t,e){return t.pxmid[1]-e.pxmid[1]}function v(t,e){return e.pxmid[1]-t.pxmid[1]}function y(t,r){r||(r={});var i,c,u,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,m=n?t.yLabelMax:t.yLabelMin,v=t.cyFinal+o(t.px0[1],t.px1[1]),y=p-d;if(y*l>0&&(t.labelExtraY=y),Array.isArray(e.pull))for(c=0;c<f.length;c++)(u=f[c])===t||(g.castOption(e.pull,t.pts)||0)>=(g.castOption(e.pull,u.pts)||0)||((t.pxmid[1]-u.pxmid[1])*l>0?(y=u.cyFinal+o(u.px0[1],u.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=y):(m+t.labelExtraY-v)*l>0&&(i=3*s*Math.abs(c-f.indexOf(t)),(h=u.cxFinal+a(u.px0[0],u.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?m:v,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(c=t[n][r]).sort(i),u=t[1-n][r],f=u.concat(c),p=[],h=0;h<c.length;h++)void 0!==c[h].yLabelMid&&p.push(c[h]);for(d=!1,h=0;n&&h<u.length;h++)if(void 0!==u[h].yLabelMid){d=u[h];break}for(h=0;h<p.length;h++){var x=h&&p[h-1];d&&!h&&(x=d),y(p[h],x)}}}(y,m),function(t,e){t.each((function(t){var r=n.select(this);if(t.labelExtraX||t.labelExtraY){var i=r.select(\"g.slicetext text\");t.transform.targetX+=t.labelExtraX,t.transform.targetY+=t.labelExtraY,i.attr(\"transform\",l.getTextTransform(t.transform));var a=t.cxFinal+t.pxmid[0],s=\"M\"+a+\",\"+(t.cyFinal+t.pxmid[1]),c=(t.yLabelMax-t.yLabelMin)*(t.pxmid[0]<0?-1:1)/4;if(t.labelExtraX){var u=t.labelExtraX*t.pxmid[1]/t.pxmid[0],f=t.yLabelMid+t.labelExtraY-(t.cyFinal+t.pxmid[1]);Math.abs(u)>Math.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+c):s+=\"l\"+t.labelExtraX+\",\"+u+\"v\"+(f-u)+\"h\"+c}else s+=\"V\"+(t.yLabelMid+t.labelExtraY)+\"h\"+c;l.ensureSingle(r,\"path\",\"textline\").call(o.stroke,e.outsidetextfont.color).attr({\"stroke-width\":Math.min(2,e.outsidetextfont.size/8),d:s,fill:\"none\"})}else r.select(\"path.textline\").remove()}))}(v,m),_&&m.automargin){var k=s.bBox(h.node()),A=m.domain,M=a.w*(A.x[1]-A.x[0]),S=a.h*(A.y[1]-A.y[0]),E=(.5*M-d.r)/a.w,P=(.5*S-d.r)/a.h;i.autoMargin(t,\"pie.\"+m.uid+\".automargin\",{xl:A.x[0]-E,xr:A.x[1]+E,yb:A.y[0]-P,yt:A.y[1]+P,l:Math.max(d.cx-d.r-k.left,0),r:Math.max(k.right-(d.cx+d.r),0),b:Math.max(k.bottom-(d.cy+d.r),0),t:Math.max(d.cy-d.r-k.top,0),pad:5})}}))}));setTimeout((function(){h.selectAll(\"tspan\").each((function(){var t=n.select(this);t.attr(\"dy\")&&t.attr(\"dy\",t.attr(\"dy\"))}))}),0)},formatSliceLabel:D,transformInsideText:w,determineInsideTextFont:b,positionTitleOutside:C,prerenderTitles:_,layoutAreas:O,attachFxHandlers:x,computeTransform:R}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../components/fx\":406,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../../plots/plots\":619,\"../bar/constants\":650,\"../bar/uniform_text\":664,\"./event_data\":904,\"./helpers\":905,\"@plotly/d3\":58}],910:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"./style_one\"),a=t(\"../bar/uniform_text\").resizeText;e.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},{\"../bar/uniform_text\":664,\"./style_one\":911,\"@plotly/d3\":58}],911:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"./helpers\").castOption;e.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},{\"../../components/color\":366,\"./helpers\":905}],912:[function(t,e,r){\"use strict\";var n=t(\"../scatter/attributes\");e.exports={x:n.x,y:n.y,xy:{valType:\"data_array\",editType:\"calc\"},indices:{valType:\"data_array\",editType:\"calc\"},xbounds:{valType:\"data_array\",editType:\"calc\"},ybounds:{valType:\"data_array\",editType:\"calc\"},text:n.text,marker:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},opacity:{valType:\"number\",min:0,max:1,dflt:1,arrayOk:!1,editType:\"calc\"},blend:{valType:\"boolean\",dflt:null,editType:\"calc\"},sizemin:{valType:\"number\",min:.1,max:2,dflt:.5,editType:\"calc\"},sizemax:{valType:\"number\",min:.1,dflt:20,editType:\"calc\"},border:{color:{valType:\"color\",arrayOk:!1,editType:\"calc\"},arearatio:{valType:\"number\",min:0,max:1,dflt:0,editType:\"calc\"},editType:\"calc\"},editType:\"calc\"},transforms:void 0}},{\"../scatter/attributes\":926}],913:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_pointcloud2d,i=t(\"../../lib/str2rgbarray\"),a=t(\"../../plots/cartesian/autorange\").findExtremes,o=t(\"../scatter/get_trace_color\");function s(t,e){this.scene=t,this.uid=e,this.type=\"pointcloud\",this.pickXData=[],this.pickYData=[],this.xData=[],this.yData=[],this.textLabels=[],this.color=\"rgb(0, 0, 0)\",this.name=\"\",this.hoverinfo=\"all\",this.idToIndex=new Int32Array(0),this.bounds=[0,0,0,0],this.pointcloudOptions={positions:new Float32Array(0),idToIndex:this.idToIndex,sizemin:.5,sizemax:12,color:[0,0,0,1],areaRatio:1,borderColor:[0,0,0,1]},this.pointcloud=n(t.glplot,this.pointcloudOptions),this.pointcloud._trace=this}var l=s.prototype;l.handlePick=function(t){var e=this.idToIndex[t.pointId];return{trace:this,dataCoord:t.dataCoord,traceCoord:this.pickXYData?[this.pickXYData[2*e],this.pickXYData[2*e+1]]:[this.pickXData[e],this.pickYData[e]],textLabel:Array.isArray(this.textLabels)?this.textLabels[e]:this.textLabels,color:this.color,name:this.name,pointIndex:e,hoverinfo:this.hoverinfo}},l.update=function(t){this.index=t.index,this.textLabels=t.text,this.name=t.name,this.hoverinfo=t.hoverinfo,this.bounds=[1/0,1/0,-1/0,-1/0],this.updateFast(t),this.color=o(t,{})},l.updateFast=function(t){var e,r,n,o,s,l,c=this.xData=this.pickXData=t.x,u=this.yData=this.pickYData=t.y,f=this.pickXYData=t.xy,h=t.xbounds&&t.ybounds,p=t.indices,d=this.bounds;if(f){if(n=f,e=f.length>>>1,h)d[0]=t.xbounds[0],d[2]=t.xbounds[1],d[1]=t.ybounds[0],d[3]=t.ybounds[1];else for(l=0;l<e;l++)o=n[2*l],s=n[2*l+1],o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);if(p)r=p;else for(r=new Int32Array(e),l=0;l<e;l++)r[l]=l}else for(e=c.length,n=new Float32Array(2*e),r=new Int32Array(e),l=0;l<e;l++)o=c[l],s=u[l],r[l]=l,n[2*l]=o,n[2*l+1]=s,o<d[0]&&(d[0]=o),o>d[2]&&(d[2]=o),s<d[1]&&(d[1]=s),s>d[3]&&(d[3]=s);this.idToIndex=r,this.pointcloudOptions.idToIndex=r,this.pointcloudOptions.positions=n;var m=i(t.marker.color),g=i(t.marker.border.color),v=t.opacity*t.marker.opacity;m[3]*=v,this.pointcloudOptions.color=m;var y=t.marker.blend;if(null===y){y=c.length<100||u.length<100}this.pointcloudOptions.blend=y,g[3]*=v,this.pointcloudOptions.borderColor=g;var x=t.marker.sizemin,b=Math.max(t.marker.sizemax,t.marker.sizemin);this.pointcloudOptions.sizeMin=x,this.pointcloudOptions.sizeMax=b,this.pointcloudOptions.areaRatio=t.marker.border.arearatio,this.pointcloud.update(this.pointcloudOptions);var _=this.scene.xaxis,w=this.scene.yaxis,T=b/2||.5;t._extremes[_._id]=a(_,[d[0],d[2]],{ppad:T}),t._extremes[w._id]=a(w,[d[1],d[3]],{ppad:T})},l.dispose=function(){this.pointcloud.dispose()},e.exports=function(t,e){var r=new s(t,e.uid);return r.update(e),r}},{\"../../../stackgl_modules\":1120,\"../../lib/str2rgbarray\":528,\"../../plots/cartesian/autorange\":553,\"../scatter/get_trace_color\":936}],914:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"./attributes\");e.exports=function(t,e,r){function a(r,a){return n.coerce(t,e,i,r,a)}a(\"x\"),a(\"y\"),a(\"xbounds\"),a(\"ybounds\"),t.xy&&t.xy instanceof Float32Array&&(e.xy=t.xy),t.indices&&t.indices instanceof Int32Array&&(e.indices=t.indices),a(\"text\"),a(\"marker.color\",r),a(\"marker.opacity\"),a(\"marker.blend\"),a(\"marker.sizemin\"),a(\"marker.sizemax\"),a(\"marker.border.color\",r),a(\"marker.border.arearatio\"),e._length=null}},{\"../../lib\":503,\"./attributes\":912}],915:[function(t,e,r){\"use strict\";[\"*pointcloud* trace is deprecated!\",\"Please consider switching to the *scattergl* trace type.\"].join(\" \");e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"../scatter3d/calc\"),plot:t(\"./convert\"),moduleType:\"trace\",name:\"pointcloud\",basePlotModule:t(\"../../plots/gl2d\"),categories:[\"gl\",\"gl2d\",\"showLegend\"],meta:{}}},{\"../../plots/gl2d\":596,\"../scatter3d/calc\":955,\"./attributes\":912,\"./convert\":913,\"./defaults\":914}],916:[function(t,e,r){\"use strict\";var n=t(\"../../plots/font_attributes\"),i=t(\"../../plots/attributes\"),a=t(\"../../components/color/attributes\"),o=t(\"../../components/fx/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../../plots/template_attributes\").hovertemplateAttrs,c=t(\"../../components/colorscale/attributes\"),u=t(\"../../plot_api/plot_template\").templatedArray,f=t(\"../../plots/cartesian/axis_format_attributes\").descriptionOnlyNumbers,h=t(\"../../lib/extend\").extendFlat,p=t(\"../../plot_api/edit_types\").overrideAll;(e.exports=p({hoverinfo:h({},i.hoverinfo,{flags:[],arrayOk:!1}),hoverlabel:o.hoverlabel,domain:s({name:\"sankey\",trace:!0}),orientation:{valType:\"enumerated\",values:[\"v\",\"h\"],dflt:\"h\"},valueformat:{valType:\"string\",dflt:\".3s\",description:f(\"value\")},valuesuffix:{valType:\"string\",dflt:\"\"},arrangement:{valType:\"enumerated\",values:[\"snap\",\"perpendicular\",\"freeform\",\"fixed\"],dflt:\"snap\"},textfont:n({}),customdata:void 0,node:{label:{valType:\"data_array\",dflt:[]},groups:{valType:\"info_array\",impliedEdits:{x:[],y:[]},dimensions:2,freeLength:!0,dflt:[],items:{valType:\"number\",editType:\"calc\"}},x:{valType:\"data_array\",dflt:[]},y:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:.5,arrayOk:!0}},pad:{valType:\"number\",arrayOk:!1,min:0,dflt:20},thickness:{valType:\"number\",arrayOk:!1,min:1,dflt:20},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]})},link:{label:{valType:\"data_array\",dflt:[]},color:{valType:\"color\",arrayOk:!0},customdata:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:a.defaultLine,arrayOk:!0},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0}},source:{valType:\"data_array\",dflt:[]},target:{valType:\"data_array\",dflt:[]},value:{valType:\"data_array\",dflt:[]},hoverinfo:{valType:\"enumerated\",values:[\"all\",\"none\",\"skip\"],dflt:\"all\"},hoverlabel:o.hoverlabel,hovertemplate:l({},{keys:[\"value\",\"label\"]}),colorscales:u(\"concentrationscales\",{editType:\"calc\",label:{valType:\"string\",editType:\"calc\",dflt:\"\"},cmax:{valType:\"number\",editType:\"calc\",dflt:1},cmin:{valType:\"number\",editType:\"calc\",dflt:0},colorscale:h(c().colorscale,{dflt:[[0,\"white\"],[1,\"black\"]]})})}},\"calc\",\"nested\")).transforms=void 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h={_id:\"x\",c2p:u.identity,_offset:r._sankey.translateX,_length:r._sankey.width},p={_id:\"y\",c2p:u.identity,_offset:r._sankey.translateY,_length:r._sankey.height},d={gd:t,element:o.node(),plotinfo:{id:e,xaxis:h,yaxis:p,fillRangeItems:u.noop},subplot:e,xaxes:[h],yaxes:[p],doneFnCompleted:function(r){var n,i=t._fullData[e],a=i.node.groups.slice(),o=[];function s(t){for(var e=i._sankey.graph.nodes,r=0;r<e.length;r++)if(e[r].pointNumber===t)return e[r]}for(var l=0;l<r.length;l++){var c=s(r[l].pointNumber);if(c)if(c.group){for(var u=0;u<c.childrenNodes.length;u++)o.push(c.childrenNodes[u].pointNumber);a[c.pointNumber-i.node._count]=!1}else o.push(c.pointNumber)}n=a.filter(Boolean).concat([o]),f.call(\"_guiRestyle\",t,{\"node.groups\":[n]},e)},prepFn:function(t,e,r){c(t,e,r,d,i)}};l.init(d)}}r.name=\"sankey\",r.baseLayoutAttrOverrides=n({hoverlabel:o.hoverlabel},\"plot\",\"nested\"),r.plot=function(t){var e=i(t.calcdata,\"sankey\")[0];a(t,e),r.updateFx(t)},r.clean=function(t,e,r,n){var 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n,i=y(e),a=i.trace,u=a.domain,f=\"h\"===a.orientation,p=a.node.pad,d=a.node.thickness,m=t.width*(u.x[1]-u.x[0]),g=t.height*(u.y[1]-u.y[0]),v=i._nodes,x=i._links,b=i.circular;(n=b?s.sankeyCircular().circularLinkGap(0):o.sankey()).iterations(l.sankeyIterations).size(f?[m,g]:[g,m]).nodeWidth(d).nodePadding(p).nodeId((function(t){return t.pointNumber})).nodes(v).links(x);var _,w,T,k=n();for(var A in n.nodePadding()<p&&h.warn(\"node.pad was reduced to \",n.nodePadding(),\" to fit within the figure.\"),i._groupLookup){var M,S=parseInt(i._groupLookup[A]);for(_=0;_<k.nodes.length;_++)if(k.nodes[_].pointNumber===S){M=k.nodes[_];break}if(M){var E={pointNumber:parseInt(A),x0:M.x0,x1:M.x1,y0:M.y0,y1:M.y1,partOfGroup:!0,sourceLinks:[],targetLinks:[]};k.nodes.unshift(E),M.childrenNodes.unshift(E)}}if(function(){for(_=0;_<k.nodes.length;_++){var t,e,r=k.nodes[_],n={};for(w=0;w<r.targetLinks.length;w++)t=(e=r.targetLinks[w]).source.pointNumber+\":\"+e.target.pointNumber,n.hasOwnProperty(t)||(n[t]=[]),n[t].push(e);var i=Object.keys(n);for(w=0;w<i.length;w++){var a=n[t=i[w]],o=0,s={};for(T=0;T<a.length;T++)s[(e=a[T]).label]||(s[e.label]=0),s[e.label]+=e.value,o+=e.value;for(T=0;T<a.length;T++)(e=a[T]).flow={value:o,labelConcentration:s[e.label]/o,concentration:e.value/o,links:a},e.concentrationscale&&(e.color=c(e.concentrationscale(e.flow.labelConcentration)))}var l=0;for(w=0;w<r.sourceLinks.length;w++)l+=r.sourceLinks[w].value;for(w=0;w<r.sourceLinks.length;w++)(e=r.sourceLinks[w]).concentrationOut=e.value/l;var u=0;for(w=0;w<r.targetLinks.length;w++)u+=r.targetLinks[w].value;for(w=0;w<r.targetLinks.length;w++)(e=r.targetLinks[w]).concenrationIn=e.value/u}}(),a.node.x.length&&a.node.y.length){for(_=0;_<Math.min(a.node.x.length,a.node.y.length,k.nodes.length);_++)if(a.node.x[_]&&a.node.y[_]){var L=[a.node.x[_]*m,a.node.y[_]*g];k.nodes[_].x0=L[0]-d/2,k.nodes[_].x1=L[0]+d/2;var C=k.nodes[_].y1-k.nodes[_].y0;k.nodes[_].y0=L[1]-C/2,k.nodes[_].y1=L[1]+C/2}if(\"snap\"===a.arrangement)!function(t){t.forEach((function(t){var e,r,n,i=0,a=t.length;for(t.sort((function(t,e){return t.y0-e.y0})),n=0;n<a;++n)(e=t[n]).y0>=i||(r=i-e.y0)>1e-6&&(e.y0+=r,e.y1+=r),i=e.y1+p}))}(function(t){var e,r,n=t.map((function(t,e){return{x0:t.x0,index:e}})).sort((function(t,e){return t.x0-e.x0})),i=[],a=-1,o=-1/0;for(_=0;_<n.length;_++){var s=t[n[_].index];s.x0>o+d&&(a+=1,e=s.x0),o=s.x0,i[a]||(i[a]=[]),i[a].push(s),r=e-s.x0,s.x0+=r,s.x1+=r}return i}(v=k.nodes));n.update(k)}return{circular:b,key:r,trace:a,guid:h.randstr(),horizontal:f,width:m,height:g,nodePad:a.node.pad,nodeLineColor:a.node.line.color,nodeLineWidth:a.node.line.width,linkLineColor:a.link.line.color,linkLineWidth:a.link.line.width,valueFormat:a.valueformat,valueSuffix:a.valuesuffix,textFont:a.textfont,translateX:u.x[0]*t.width+t.margin.l,translateY:t.height-u.y[1]*t.height+t.margin.t,dragParallel:f?g:m,dragPerpendicular:f?m:g,arrangement:a.arrangement,sankey:n,graph:k,forceLayouts:{},interactionState:{dragInProgress:!1,hovered:!1}}}function A(t,e,r){var n=c(e.color),i=e.source.label+\"|\"+e.target.label+\"__\"+r;return e.trace=t.trace,e.curveNumber=t.trace.index,{circular:t.circular,key:i,traceId:t.key,pointNumber:e.pointNumber,link:e,tinyColorHue:u.tinyRGB(n),tinyColorAlpha:n.getAlpha(),linkPath:M,linkLineColor:t.linkLineColor,linkLineWidth:t.linkLineWidth,valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,parent:t,interactionState:t.interactionState,flow:e.flow}}function M(){return function(t){if(t.link.circular)return e=t.link,r=e.width/2,n=e.circularPathData,\"top\"===e.circularLinkType?\"M \"+n.targetX+\" \"+(n.targetY+r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 1 \"+(n.rightFullExtent-r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 1 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 0 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY-n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 0 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY-n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"L\"+n.targetX+\" \"+(n.targetY-r)+\"Z\":\"M \"+n.targetX+\" \"+(n.targetY-r)+\" L\"+n.rightInnerExtent+\" \"+(n.targetY-r)+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightSmallArcRadius+r)+\" 0 0 0 \"+(n.rightFullExtent-r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"L\"+(n.rightFullExtent-r)+\" \"+n.verticalRightInnerExtent+\"A\"+(n.rightLargeArcRadius+r)+\" \"+(n.rightLargeArcRadius+r)+\" 0 0 0 \"+n.rightInnerExtent+\" \"+(n.verticalFullExtent+r)+\"L\"+n.leftInnerExtent+\" \"+(n.verticalFullExtent+r)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftLargeArcRadius+r)+\" 0 0 0 \"+(n.leftFullExtent+r)+\" \"+n.verticalLeftInnerExtent+\"L\"+(n.leftFullExtent+r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"A\"+(n.leftLargeArcRadius+r)+\" \"+(n.leftSmallArcRadius+r)+\" 0 0 0 \"+n.leftInnerExtent+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY-r)+\"L\"+n.sourceX+\" \"+(n.sourceY+r)+\"L\"+n.leftInnerExtent+\" \"+(n.sourceY+r)+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftSmallArcRadius-r)+\" 0 0 1 \"+(n.leftFullExtent-r)+\" \"+(n.sourceY+n.leftSmallArcRadius)+\"L\"+(n.leftFullExtent-r)+\" \"+n.verticalLeftInnerExtent+\"A\"+(n.leftLargeArcRadius-r)+\" \"+(n.leftLargeArcRadius-r)+\" 0 0 1 \"+n.leftInnerExtent+\" \"+(n.verticalFullExtent-r)+\"L\"+n.rightInnerExtent+\" \"+(n.verticalFullExtent-r)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightLargeArcRadius-r)+\" 0 0 1 \"+(n.rightFullExtent+r)+\" \"+n.verticalRightInnerExtent+\"L\"+(n.rightFullExtent+r)+\" \"+(n.targetY+n.rightSmallArcRadius)+\"A\"+(n.rightLargeArcRadius-r)+\" \"+(n.rightSmallArcRadius-r)+\" 0 0 1 \"+n.rightInnerExtent+\" \"+(n.targetY+r)+\"L\"+n.targetX+\" \"+(n.targetY+r)+\"Z\";var e,r,n,a=t.link.source.x1,o=t.link.target.x0,s=i(a,o),l=s(.5),c=s(.5),u=t.link.y0-t.link.width/2,f=t.link.y0+t.link.width/2,h=t.link.y1-t.link.width/2,p=t.link.y1+t.link.width/2;return\"M\"+a+\",\"+u+\"C\"+l+\",\"+u+\" \"+c+\",\"+h+\" \"+o+\",\"+h+\"L\"+o+\",\"+p+\"C\"+c+\",\"+p+\" \"+l+\",\"+f+\" \"+a+\",\"+f+\"Z\"}}function S(t,e){var r=c(e.color),n=l.nodePadAcross,i=t.nodePad/2;e.dx=e.x1-e.x0,e.dy=e.y1-e.y0;var a=e.dx,o=Math.max(.5,e.dy),s=\"node_\"+e.pointNumber;return e.group&&(s=h.randstr()),e.trace=t.trace,e.curveNumber=t.trace.index,{index:e.pointNumber,key:s,partOfGroup:e.partOfGroup||!1,group:e.group,traceId:t.key,trace:t.trace,node:e,nodePad:t.nodePad,nodeLineColor:t.nodeLineColor,nodeLineWidth:t.nodeLineWidth,textFont:t.textFont,size:t.horizontal?t.height:t.width,visibleWidth:Math.ceil(a),visibleHeight:o,zoneX:-n,zoneY:-i,zoneWidth:a+2*n,zoneHeight:o+2*i,labelY:t.horizontal?e.dy/2+1:e.dx/2+1,left:1===e.originalLayer,sizeAcross:t.width,forceLayouts:t.forceLayouts,horizontal:t.horizontal,darkBackground:r.getBrightness()<=128,tinyColorHue:u.tinyRGB(r),tinyColorAlpha:r.getAlpha(),valueFormat:t.valueFormat,valueSuffix:t.valueSuffix,sankey:t.sankey,graph:t.graph,arrangement:t.arrangement,uniqueNodeLabelPathId:[t.guid,t.key,s].join(\"_\"),interactionState:t.interactionState,figure:t}}function E(t){t.attr(\"transform\",(function(t){return p(t.node.x0.toFixed(3),t.node.y0.toFixed(3))}))}function L(t){t.call(E)}function C(t,e){t.call(L),e.attr(\"d\",M())}function P(t){t.attr(\"width\",(function(t){return t.node.x1-t.node.x0})).attr(\"height\",(function(t){return t.visibleHeight}))}function I(t){return t.link.width>1||t.linkLineWidth>0}function O(t){return p(t.translateX,t.translateY)+(t.horizontal?\"matrix(1 0 0 1 0 0)\":\"matrix(0 1 1 0 0 0)\")}function z(t,e,r){t.on(\".basic\",null).on(\"mouseover.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.hover(this,t,e),t.interactionState.hovered=[this,t])})).on(\"mousemove.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.follow(this,t),t.interactionState.hovered=[this,t])})).on(\"mouseout.basic\",(function(t){t.interactionState.dragInProgress||t.partOfGroup||(r.unhover(this,t,e),t.interactionState.hovered=!1)})).on(\"click.basic\",(function(t){t.interactionState.hovered&&(r.unhover(this,t,e),t.interactionState.hovered=!1),t.interactionState.dragInProgress||t.partOfGroup||r.select(this,t,e)}))}function D(t,e,r,i){var o=a.behavior.drag().origin((function(t){return{x:t.node.x0+t.visibleWidth/2,y:t.node.y0+t.visibleHeight/2}})).on(\"dragstart\",(function(a){if(\"fixed\"!==a.arrangement&&(h.ensureSingle(i._fullLayout._infolayer,\"g\",\"dragcover\",(function(t){i._fullLayout._dragCover=t})),h.raiseToTop(this),a.interactionState.dragInProgress=a.node,F(a.node),a.interactionState.hovered&&(r.nodeEvents.unhover.apply(0,a.interactionState.hovered),a.interactionState.hovered=!1),\"snap\"===a.arrangement)){var o=a.traceId+\"|\"+a.key;a.forceLayouts[o]?a.forceLayouts[o].alpha(1):function(t,e,r,i){!function(t){for(var e=0;e<t.length;e++)t[e].y=(t[e].y0+t[e].y1)/2,t[e].x=(t[e].x0+t[e].x1)/2}(r.graph.nodes);var a=r.graph.nodes.filter((function(t){return t.originalX===r.node.originalX})).filter((function(t){return!t.partOfGroup}));r.forceLayouts[e]=n.forceSimulation(a).alphaDecay(0).force(\"collide\",n.forceCollide().radius((function(t){return t.dy/2+r.nodePad/2})).strength(1).iterations(l.forceIterations)).force(\"constrain\",function(t,e,r,n){return function(){for(var t=0,i=0;i<r.length;i++){var a=r[i];a===n.interactionState.dragInProgress?(a.x=a.lastDraggedX,a.y=a.lastDraggedY):(a.vx=(a.originalX-a.x)/l.forceTicksPerFrame,a.y=Math.min(n.size-a.dy/2,Math.max(a.dy/2,a.y))),t=Math.max(t,Math.abs(a.vx),Math.abs(a.vy))}!n.interactionState.dragInProgress&&t<.1&&n.forceLayouts[e].alpha()>0&&n.forceLayouts[e].alpha(0)}}(0,e,a,r)).stop()}(0,o,a),function(t,e,r,n,i){window.requestAnimationFrame((function a(){var o;for(o=0;o<l.forceTicksPerFrame;o++)r.forceLayouts[n].tick();if(function(t){for(var e=0;e<t.length;e++)t[e].y0=t[e].y-t[e].dy/2,t[e].y1=t[e].y0+t[e].dy,t[e].x0=t[e].x-t[e].dx/2,t[e].x1=t[e].x0+t[e].dx}(r.graph.nodes),r.sankey.update(r.graph),C(t.filter(B(r)),e),r.forceLayouts[n].alpha()>0)window.requestAnimationFrame(a);else{var s=r.node.originalX;r.node.x0=s-r.visibleWidth/2,r.node.x1=s+r.visibleWidth/2,R(r,i)}}))}(t,e,a,o,i)}})).on(\"drag\",(function(r){if(\"fixed\"!==r.arrangement){var n=a.event.x,i=a.event.y;\"snap\"===r.arrangement?(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2,r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2):(\"freeform\"===r.arrangement&&(r.node.x0=n-r.visibleWidth/2,r.node.x1=n+r.visibleWidth/2),i=Math.max(0,Math.min(r.size-r.visibleHeight/2,i)),r.node.y0=i-r.visibleHeight/2,r.node.y1=i+r.visibleHeight/2),F(r.node),\"snap\"!==r.arrangement&&(r.sankey.update(r.graph),C(t.filter(B(r)),e))}})).on(\"dragend\",(function(t){if(\"fixed\"!==t.arrangement){t.interactionState.dragInProgress=!1;for(var e=0;e<t.node.childrenNodes.length;e++)t.node.childrenNodes[e].x=t.node.x,t.node.childrenNodes[e].y=t.node.y;\"snap\"!==t.arrangement&&R(t,i)}}));t.on(\".drag\",null).call(o)}function R(t,e){for(var r=[],n=[],i=0;i<t.graph.nodes.length;i++){var a=(t.graph.nodes[i].x0+t.graph.nodes[i].x1)/2,o=(t.graph.nodes[i].y0+t.graph.nodes[i].y1)/2;r.push(a/t.figure.width),n.push(o/t.figure.height)}b.call(\"_guiRestyle\",e,{\"node.x\":[r],\"node.y\":[n]},t.trace.index).then((function(){e._fullLayout._dragCover&&e._fullLayout._dragCover.remove()}))}function F(t){t.lastDraggedX=t.x0+t.dx/2,t.lastDraggedY=t.y0+t.dy/2}function B(t){return function(e){return e.node.originalX===t.node.originalX}}e.exports=function(t,e,r,n,i){var o=!1;h.ensureSingle(t._fullLayout._infolayer,\"g\",\"first-render\",(function(){o=!0}));var s=t._fullLayout._dragCover,m=r.filter((function(t){return y(t).trace.visible})).map(k.bind(null,n)),b=e.selectAll(\".\"+l.cn.sankey).data(m,g);b.exit().remove(),b.enter().append(\"g\").classed(l.cn.sankey,!0).style(\"box-sizing\",\"content-box\").style(\"position\",\"absolute\").style(\"left\",0).style(\"shape-rendering\",\"geometricPrecision\").style(\"pointer-events\",\"auto\").attr(\"transform\",O),b.each((function(e,r){t._fullData[r]._sankey=e;var n=\"bgsankey-\"+e.trace.uid+\"-\"+r;h.ensureSingle(t._fullLayout._draggers,\"rect\",n),t._fullData[r]._bgRect=a.select(\".\"+n),t._fullData[r]._bgRect.style(\"pointer-events\",\"all\").attr(\"width\",e.width).attr(\"height\",e.height).attr(\"x\",e.translateX).attr(\"y\",e.translateY).classed(\"bgsankey\",!0).style({fill:\"transparent\",\"stroke-width\":0})})),b.transition().ease(l.ease).duration(l.duration).attr(\"transform\",O);var _=b.selectAll(\".\"+l.cn.sankeyLinks).data(v,g);_.enter().append(\"g\").classed(l.cn.sankeyLinks,!0).style(\"fill\",\"none\");var L=_.selectAll(\".\"+l.cn.sankeyLink).data((function(t){return t.graph.links.filter((function(t){return t.value})).map(A.bind(null,t))}),g);L.enter().append(\"path\").classed(l.cn.sankeyLink,!0).call(z,b,i.linkEvents),L.style(\"stroke\",(function(t){return I(t)?u.tinyRGB(c(t.linkLineColor)):t.tinyColorHue})).style(\"stroke-opacity\",(function(t){return I(t)?u.opacity(t.linkLineColor):t.tinyColorAlpha})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})).style(\"stroke-width\",(function(t){return I(t)?t.linkLineWidth:1})).attr(\"d\",M()),L.style(\"opacity\",(function(){return t._context.staticPlot||o||s?1:0})).transition().ease(l.ease).duration(l.duration).style(\"opacity\",1),L.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var C=b.selectAll(\".\"+l.cn.sankeyNodeSet).data(v,g);C.enter().append(\"g\").classed(l.cn.sankeyNodeSet,!0),C.style(\"cursor\",(function(t){switch(t.arrangement){case\"fixed\":return\"default\";case\"perpendicular\":return\"ns-resize\";default:return\"move\"}}));var R=C.selectAll(\".\"+l.cn.sankeyNode).data((function(t){var e=t.graph.nodes;return function(t){var e,r=[];for(e=0;e<t.length;e++)t[e].originalX=(t[e].x0+t[e].x1)/2,t[e].originalY=(t[e].y0+t[e].y1)/2,-1===r.indexOf(t[e].originalX)&&r.push(t[e].originalX);for(r.sort((function(t,e){return t-e})),e=0;e<t.length;e++)t[e].originalLayerIndex=r.indexOf(t[e].originalX),t[e].originalLayer=t[e].originalLayerIndex/(r.length-1)}(e),e.map(S.bind(null,t))}),g);R.enter().append(\"g\").classed(l.cn.sankeyNode,!0).call(E).style(\"opacity\",(function(e){return!t._context.staticPlot&&!o||e.partOfGroup?0:1})),R.call(z,b,i.nodeEvents).call(D,L,i,t),R.transition().ease(l.ease).duration(l.duration).call(E).style(\"opacity\",(function(t){return t.partOfGroup?0:1})),R.exit().transition().ease(l.ease).duration(l.duration).style(\"opacity\",0).remove();var F=R.selectAll(\".\"+l.cn.nodeRect).data(v);F.enter().append(\"rect\").classed(l.cn.nodeRect,!0).call(P),F.style(\"stroke-width\",(function(t){return t.nodeLineWidth})).style(\"stroke\",(function(t){return u.tinyRGB(c(t.nodeLineColor))})).style(\"stroke-opacity\",(function(t){return u.opacity(t.nodeLineColor)})).style(\"fill\",(function(t){return t.tinyColorHue})).style(\"fill-opacity\",(function(t){return t.tinyColorAlpha})),F.transition().ease(l.ease).duration(l.duration).call(P);var B=R.selectAll(\".\"+l.cn.nodeLabel).data(v);B.enter().append(\"text\").classed(l.cn.nodeLabel,!0).style(\"cursor\",\"default\"),B.attr(\"data-notex\",1).text((function(t){return t.node.label})).each((function(e){var r=a.select(this);f.font(r,e.textFont),x.convertToTspans(r,t)})).style(\"text-shadow\",x.makeTextShadow(t._fullLayout.paper_bgcolor)).attr(\"text-anchor\",(function(t){return t.horizontal&&t.left?\"end\":\"start\"})).attr(\"transform\",(function(t){var e=a.select(this),r=x.lineCount(e),n=t.textFont.size*((r-1)*T-w),i=t.nodeLineWidth/2+3,o=((t.horizontal?t.visibleHeight:t.visibleWidth)-n)/2;t.horizontal&&(t.left?i=-i:i+=t.visibleWidth);var s=t.horizontal?\"\":\"scale(-1,1)\"+d(90);return p(t.horizontal?i:o,t.horizontal?o:i)+s})),B.transition().ease(l.ease).duration(l.duration)}},{\"../../components/color\":366,\"../../components/drawing\":388,\"../../constants/alignment\":471,\"../../lib\":503,\"../../lib/gup\":500,\"../../lib/svg_text_utils\":529,\"../../registry\":638,\"./constants\":919,\"@plotly/d3\":58,\"@plotly/d3-sankey\":57,\"@plotly/d3-sankey-circular\":56,\"d3-force\":111,\"d3-interpolate\":116,tinycolor2:312}],924:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=[],n=t.cd[0].trace,i=n._sankey.graph.nodes,a=0;a<i.length;a++){var o=i[a];if(!o.partOfGroup){var s=[(o.x0+o.x1)/2,(o.y0+o.y1)/2];\"v\"===n.orientation&&s.reverse(),e&&e.contains(s,!1,a,t)&&r.push({pointNumber:o.pointNumber})}}return r}},{}],925:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){for(var r=0;r<t.length;r++)t[r].i=r;n.mergeArray(e.text,t,\"tx\"),n.mergeArray(e.texttemplate,t,\"txt\"),n.mergeArray(e.hovertext,t,\"htx\"),n.mergeArray(e.customdata,t,\"data\"),n.mergeArray(e.textposition,t,\"tp\"),e.textfont&&(n.mergeArrayCastPositive(e.textfont.size,t,\"ts\"),n.mergeArray(e.textfont.color,t,\"tc\"),n.mergeArray(e.textfont.family,t,\"tf\"));var i=e.marker;if(i){n.mergeArrayCastPositive(i.size,t,\"ms\"),n.mergeArrayCastPositive(i.opacity,t,\"mo\"),n.mergeArray(i.symbol,t,\"mx\"),n.mergeArray(i.color,t,\"mc\");var a=i.line;i.line&&(n.mergeArray(a.color,t,\"mlc\"),n.mergeArrayCastPositive(a.width,t,\"mlw\"));var o=i.gradient;o&&\"none\"!==o.type&&(n.mergeArray(o.type,t,\"mgt\"),n.mergeArray(o.color,t,\"mgc\"))}}},{\"../../lib\":503}],926:[function(t,e,r){\"use strict\";var 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n=t(\"../../lib\"),i=t(\"../scatter/constants\"),a=t(\"../scatter/subtypes\"),o=t(\"../scatter/marker_defaults\"),s=t(\"../scatter/line_defaults\"),l=t(\"../scatter/line_shape_defaults\"),c=t(\"../scatter/text_defaults\"),u=t(\"../scatter/fillcolor_defaults\"),f=t(\"./attributes\");e.exports=function(t,e,r,h){function p(r,i){return n.coerce(t,e,f,r,i)}p(\"carpet\"),e.xaxis=\"x\",e.yaxis=\"y\";var d=p(\"a\"),m=p(\"b\"),g=Math.min(d.length,m.length);if(g){e._length=g,p(\"text\"),p(\"texttemplate\"),p(\"hovertext\"),p(\"mode\",g<i.PTS_LINESONLY?\"lines+markers\":\"lines\"),a.hasLines(e)&&(s(t,e,r,h,p),l(t,e,p),p(\"connectgaps\")),a.hasMarkers(e)&&o(t,e,r,h,p,{gradient:!0}),a.hasText(e)&&c(t,e,h,p);var v=[];(a.hasMarkers(e)||a.hasText(e))&&(p(\"marker.maxdisplayed\"),v.push(\"points\")),p(\"fill\"),\"none\"!==e.fill&&(u(t,e,r,p),a.hasLines(e)||l(t,e,p)),\"tonext\"!==e.fill&&\"toself\"!==e.fill||v.push(\"fills\"),\"fills\"!==p(\"hoveron\",v.join(\"+\")||\"points\")&&p(\"hovertemplate\"),n.coerceSelectionMarkerOpacity(e,p)}else e.visible=!1}},{\"../../lib\":503,\"../scatter/constants\":930,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/line_shape_defaults\":941,\"../scatter/marker_defaults\":945,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"./attributes\":960}],963:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){var a=n[i];return t.a=a.a,t.b=a.b,t.y=a.y,t}},{}],964:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r={},n=e._carpet,i=n.ab2ij([t.a,t.b]),a=Math.floor(i[0]),o=i[0]-a,s=Math.floor(i[1]),l=i[1]-s,c=n.evalxy([],a,s,o,l);return r.yLabel=c[1].toFixed(3),r}},{}],965:[function(t,e,r){\"use strict\";var n=t(\"../scatter/hover\"),i=t(\"../../lib\").fillText;e.exports=function(t,e,r,a){var o=n(t,e,r,a);if(o&&!1!==o[0].index){var s=o[0];if(void 0===s.index){var l=1-s.y0/t.ya._length,c=t.xa._length,u=c*l/2,f=c-u;return s.x0=Math.max(Math.min(s.x0,f),u),s.x1=Math.max(Math.min(s.x1,f),u),o}var h=s.cd[s.index];s.a=h.a,s.b=h.b,s.xLabelVal=void 0,s.yLabelVal=void 0;var p=s.trace,d=p._carpet,m=p._module.formatLabels(h,p);s.yLabel=m.yLabel,delete s.text;var g=[];if(!p.hovertemplate){var v=(h.hi||p.hoverinfo).split(\"+\");-1!==v.indexOf(\"all\")&&(v=[\"a\",\"b\",\"text\"]),-1!==v.indexOf(\"a\")&&y(d.aaxis,h.a),-1!==v.indexOf(\"b\")&&y(d.baxis,h.b),g.push(\"y: \"+s.yLabel),-1!==v.indexOf(\"text\")&&i(h,p,g),s.extraText=g.join(\"<br>\")}return o}function y(t,e){var r;r=t.labelprefix&&t.labelprefix.length>0?t.labelprefix.replace(/ = $/,\"\"):t._hovertitle,g.push(r+\": \"+e.toFixed(3)+t.labelsuffix)}}},{\"../../lib\":503,\"../scatter/hover\":937}],966:[function(t,e,r){\"use 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s,l,c,u=r[0][0].carpet,f={xaxis:i.getFromId(t,u.xaxis||\"x\"),yaxis:i.getFromId(t,u.yaxis||\"y\"),plot:e.plot};for(n(t,f,r,o),s=0;s<r.length;s++)l=r[s][0].trace,c=o.selectAll(\"g.trace\"+l.uid+\" .js-line\"),a.setClipUrl(c,r[s][0].carpet._clipPathId,t)}},{\"../../components/drawing\":388,\"../../plots/cartesian/axes\":554,\"../scatter/plot\":947}],968:[function(t,e,r){\"use strict\";var n=t(\"../../plots/template_attributes\").hovertemplateAttrs,i=t(\"../../plots/template_attributes\").texttemplateAttrs,a=t(\"../scatter/attributes\"),o=t(\"../../plots/attributes\"),s=t(\"../../components/colorscale/attributes\"),l=t(\"../../components/drawing/attributes\").dash,c=t(\"../../lib/extend\").extendFlat,u=t(\"../../plot_api/edit_types\").overrideAll,f=a.marker,h=a.line,p=f.line;e.exports=u({lon:{valType:\"data_array\"},lat:{valType:\"data_array\"},locations:{valType:\"data_array\"},locationmode:{valType:\"enumerated\",values:[\"ISO-3\",\"USA-states\",\"country 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n=t(\"../../lib\"),i=t(\"../scatter/subtypes\"),a=t(\"../scatter/marker_defaults\"),o=t(\"../scatter/line_defaults\"),s=t(\"../scatter/text_defaults\"),l=t(\"../scatter/fillcolor_defaults\"),c=t(\"./attributes\");e.exports=function(t,e,r,u){function f(r,i){return n.coerce(t,e,c,r,i)}var h,p=f(\"locations\");if(p&&p.length){var d,m=f(\"geojson\");(\"string\"==typeof m&&\"\"!==m||n.isPlainObject(m))&&(d=\"geojson-id\"),\"geojson-id\"===f(\"locationmode\",d)&&f(\"featureidkey\"),h=p.length}else{var g=f(\"lon\")||[],v=f(\"lat\")||[];h=Math.min(g.length,v.length)}h?(e._length=h,f(\"text\"),f(\"hovertext\"),f(\"hovertemplate\"),f(\"mode\"),i.hasLines(e)&&(o(t,e,r,u,f),f(\"connectgaps\")),i.hasMarkers(e)&&a(t,e,r,u,f,{gradient:!0}),i.hasText(e)&&(f(\"texttemplate\"),s(t,e,u,f)),f(\"fill\"),\"none\"!==e.fill&&l(t,e,r,f),n.coerceSelectionMarkerOpacity(e,f)):e.visible=!1}},{\"../../lib\":503,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"./attributes\":968}],971:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i){t.lon=e.lon,t.lat=e.lat,t.location=e.loc?e.loc:null;var a=n[i];return a.fIn&&a.fIn.properties&&(t.properties=a.fIn.properties),t}},{}],972:[function(t,e,r){\"use strict\";var n=t(\"../../plots/cartesian/axes\");e.exports=function(t,e,r){var i={},a=r[e.geo]._subplot.mockAxis,o=t.lonlat;return i.lonLabel=n.tickText(a,a.c2l(o[0]),!0).text,i.latLabel=n.tickText(a,a.c2l(o[1]),!0).text,i}},{\"../../plots/cartesian/axes\":554}],973:[function(t,e,r){\"use strict\";var n=t(\"../../components/fx\"),i=t(\"../../constants/numerical\").BADNUM,a=t(\"../scatter/get_trace_color\"),o=t(\"../../lib\").fillText,s=t(\"./attributes\");e.exports=function(t,e,r){var l=t.cd,c=l[0].trace,u=t.xa,f=t.ya,h=t.subplot,p=h.projection.isLonLatOverEdges,d=h.project;if(n.getClosest(l,(function(t){var n=t.lonlat;if(n[0]===i)return 1/0;if(p(n))return 1/0;var a=d(n),o=d([e,r]),s=Math.abs(a[0]-o[0]),l=Math.abs(a[1]-o[1]),c=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(s*s+l*l)-c,1-3/c)}),t),!1!==t.index){var m=l[t.index],g=m.lonlat,v=[u.c2p(g),f.c2p(g)],y=m.mrc||1;t.x0=v[0]-y,t.x1=v[0]+y,t.y0=v[1]-y,t.y1=v[1]+y,t.loc=m.loc,t.lon=g[0],t.lat=g[1];var x={};x[c.geo]={_subplot:h};var b=c._module.formatLabels(m,c,x);return t.lonLabel=b.lonLabel,t.latLabel=b.latLabel,t.color=a(c,m),t.extraText=function(t,e,r,n){if(t.hovertemplate)return;var i=e.hi||t.hoverinfo,a=\"all\"===i?s.hoverinfo.flags:i.split(\"+\"),l=-1!==a.indexOf(\"location\")&&Array.isArray(t.locations),c=-1!==a.indexOf(\"lon\"),u=-1!==a.indexOf(\"lat\"),f=-1!==a.indexOf(\"text\"),h=[];function p(t){return t+\"\\xb0\"}l?h.push(e.loc):c&&u?h.push(\"(\"+p(r.latLabel)+\", \"+p(r.lonLabel)+\")\"):c?h.push(n.lon+p(r.lonLabel)):u&&h.push(n.lat+p(r.latLabel));f&&o(e,t,h);return h.join(\"<br>\")}(c,m,t,l[0].t.labels),t.hovertemplate=c.hovertemplate,[t]}}},{\"../../components/fx\":406,\"../../constants/numerical\":479,\"../../lib\":503,\"../scatter/get_trace_color\":936,\"./attributes\":968}],974:[function(t,e,r){\"use 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n=t(\"@plotly/d3\"),i=t(\"../../lib\"),a=t(\"../../lib/topojson_utils\").getTopojsonFeatures,o=t(\"../../lib/geojson_utils\"),s=t(\"../../lib/geo_location_utils\"),l=t(\"../../plots/cartesian/autorange\").findExtremes,c=t(\"../../constants/numerical\").BADNUM,u=t(\"../scatter/calc\").calcMarkerSize,f=t(\"../scatter/subtypes\"),h=t(\"./style\");e.exports={calcGeoJSON:function(t,e){var r,n,i=t[0].trace,o=e[i.geo],f=o._subplot,h=i._length;if(Array.isArray(i.locations)){var p=i.locationmode,d=\"geojson-id\"===p?s.extractTraceFeature(t):a(i,f.topojson);for(r=0;r<h;r++){n=t[r];var m=\"geojson-id\"===p?n.fOut:s.locationToFeature(p,n.loc,d);n.lonlat=m?m.properties.ct:[c,c]}}var g,v,y={padded:!0};if(\"geojson\"===o.fitbounds&&\"geojson-id\"===i.locationmode){var x=s.computeBbox(s.getTraceGeojson(i));g=[x[0],x[2]],v=[x[1],x[3]]}else{for(g=new Array(h),v=new 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n=t(\"../../lib\"),i=t(\"../../registry\"),a=t(\"./helpers\"),o=t(\"./attributes\"),s=t(\"../scatter/constants\"),l=t(\"../scatter/subtypes\"),c=t(\"../scatter/xy_defaults\"),u=t(\"../scatter/period_defaults\"),f=t(\"../scatter/marker_defaults\"),h=t(\"../scatter/line_defaults\"),p=t(\"../scatter/fillcolor_defaults\"),d=t(\"../scatter/text_defaults\");e.exports=function(t,e,r,m){function g(r,i){return n.coerce(t,e,o,r,i)}var v=!!t.marker&&a.isOpenSymbol(t.marker.symbol),y=l.isBubble(t),x=c(t,e,m,g);if(x){u(t,e,m,g),g(\"xhoverformat\"),g(\"yhoverformat\");var b=x<s.PTS_LINESONLY?\"lines+markers\":\"lines\";g(\"text\"),g(\"hovertext\"),g(\"hovertemplate\"),g(\"mode\",b),l.hasLines(e)&&(g(\"connectgaps\"),h(t,e,r,m,g),g(\"line.shape\")),l.hasMarkers(e)&&(f(t,e,r,m,g),g(\"marker.line.width\",v||y?1:0)),l.hasText(e)&&(g(\"texttemplate\"),d(t,e,m,g));var _=(e.line||{}).color,w=(e.marker||{}).color;g(\"fill\"),\"none\"!==e.fill&&p(t,e,r,g);var T=i.getComponentMethod(\"errorbars\",\"supplyDefaults\");T(t,e,_||w||r,{axis:\"y\"}),T(t,e,_||w||r,{axis:\"x\",inherit:\"y\"}),n.coerceSelectionMarkerOpacity(e,g)}else e.visible=!1}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/constants\":930,\"../scatter/fillcolor_defaults\":934,\"../scatter/line_defaults\":939,\"../scatter/marker_defaults\":945,\"../scatter/period_defaults\":946,\"../scatter/subtypes\":951,\"../scatter/text_defaults\":952,\"../scatter/xy_defaults\":953,\"./attributes\":978,\"./helpers\":985}],983:[function(t,e,r){\"use strict\";var n=t(\"../../lib\"),i=t(\"../../components/color\"),a=t(\"../../constants/interactions\").DESELECTDIM;e.exports={styleTextSelection:function(t){var e,r,o=t[0],s=o.trace,l=o.t,c=l._scene,u=l.index,f=c.selectBatch[u],h=c.unselectBatch[u],p=c.textOptions[u],d=c.textSelectedOptions[u]||{},m=c.textUnselectedOptions[u]||{},g=n.extendFlat({},p);if(f.length||h.length){var v=d.color,y=m.color,x=p.color,b=Array.isArray(x);for(g.color=new Array(s._length),e=0;e<f.length;e++)r=f[e],g.color[r]=v||(b?x[r]:x);for(e=0;e<h.length;e++){r=h[e];var _=b?x[r]:x;g.color[r]=y||(v?_:i.addOpacity(_,a))}}c.glText[u].update(g)}}},{\"../../components/color\":366,\"../../constants/interactions\":478,\"../../lib\":503}],984:[function(t,e,r){\"use strict\";var n=t(\"../scatter/format_labels\");e.exports=function(t,e,r){var i=t.i;return\"x\"in t||(t.x=e._x[i]),\"y\"in t||(t.y=e._y[i]),n(t,e,r)}},{\"../scatter/format_labels\":935}],985:[function(t,e,r){\"use strict\";var n=t(\"./constants\");r.isOpenSymbol=function(t){return\"string\"==typeof t?n.OPEN_RE.test(t):t%200>100},r.isDotSymbol=function(t){return\"string\"==typeof t?n.DOT_RE.test(t):t>200}},{\"./constants\":980}],986:[function(t,e,r){\"use strict\";var n=t(\"../../registry\"),i=t(\"../../lib\"),a=t(\"../scatter/get_trace_color\");function o(t,e,r,o){var s=t.xa,l=t.ya,c=t.distance,u=t.dxy,f=t.index,h={pointNumber:f,x:e[f],y:r[f]};h.tx=Array.isArray(o.text)?o.text[f]:o.text,h.htx=Array.isArray(o.hovertext)?o.hovertext[f]:o.hovertext,h.data=Array.isArray(o.customdata)?o.customdata[f]:o.customdata,h.tp=Array.isArray(o.textposition)?o.textposition[f]:o.textposition;var p=o.textfont;p&&(h.ts=i.isArrayOrTypedArray(p.size)?p.size[f]:p.size,h.tc=Array.isArray(p.color)?p.color[f]:p.color,h.tf=Array.isArray(p.family)?p.family[f]:p.family);var d=o.marker;d&&(h.ms=i.isArrayOrTypedArray(d.size)?d.size[f]:d.size,h.mo=i.isArrayOrTypedArray(d.opacity)?d.opacity[f]:d.opacity,h.mx=i.isArrayOrTypedArray(d.symbol)?d.symbol[f]:d.symbol,h.mc=i.isArrayOrTypedArray(d.color)?d.color[f]:d.color);var m=d&&d.line;m&&(h.mlc=Array.isArray(m.color)?m.color[f]:m.color,h.mlw=i.isArrayOrTypedArray(m.width)?m.width[f]:m.width);var g=d&&d.gradient;g&&\"none\"!==g.type&&(h.mgt=Array.isArray(g.type)?g.type[f]:g.type,h.mgc=Array.isArray(g.color)?g.color[f]:g.color);var v=s.c2p(h.x,!0),y=l.c2p(h.y,!0),x=h.mrc||1,b=o.hoverlabel;b&&(h.hbg=Array.isArray(b.bgcolor)?b.bgcolor[f]:b.bgcolor,h.hbc=Array.isArray(b.bordercolor)?b.bordercolor[f]:b.bordercolor,h.hts=i.isArrayOrTypedArray(b.font.size)?b.font.size[f]:b.font.size,h.htc=Array.isArray(b.font.color)?b.font.color[f]:b.font.color,h.htf=Array.isArray(b.font.family)?b.font.family[f]:b.font.family,h.hnl=i.isArrayOrTypedArray(b.namelength)?b.namelength[f]:b.namelength);var _=o.hoverinfo;_&&(h.hi=Array.isArray(_)?_[f]:_);var w=o.hovertemplate;w&&(h.ht=Array.isArray(w)?w[f]:w);var T={};T[t.index]=h;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,h),x0:v-x,x1:v+x,xLabelVal:k?k[f]:h.x,y0:y-x,y1:y+x,yLabelVal:A?A[f]:h.y,cd:T,distance:c,spikeDistance:u,hovertemplate:h.ht});return h.htx?M.text=h.htx:h.tx?M.text=h.tx:o.text&&(M.text=o.text),i.fillText(h,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(h,o,M),M}e.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,f,h,p,d,m=t.cd,g=m[0].t,v=m[0].trace,y=t.xa,x=t.ya,b=g.x,_=g.y,w=y.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=y.p2c(w-k),M=y.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var L=k;if(\"x\"===n){var C=!!v.xperiodalignment,P=!!v.yperiodalignment;for(u=0;u<i.length;u++){if(l=b[a=i[u]],f=Math.abs(y.c2p(l)-w),C){var I=y.c2p(v._xStarts[a]),O=y.c2p(v._xEnds[a]);f=w>=Math.min(I,O)&&w<=Math.max(I,O)?0:1/0}if(f<L){if(L=f,c=_[a],h=x.c2p(c)-T,P){var z=x.c2p(v._yStarts[a]),D=x.c2p(v._yEnds[a]);h=T>=Math.min(z,D)&&T<=Math.max(z,D)?0:1/0}d=Math.sqrt(f*f+h*h),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=b[a=i[u]],c=_[a],f=y.c2p(l)-w,h=x.c2p(c)-T,(p=Math.sqrt(f*f+h*h))<L&&(L=d=p,s=a);return t.index=s,t.distance=L,t.dxy=d,void 0===s?[t]:[o(t,b,_,v)]},calcHover:o}},{\"../../lib\":503,\"../../registry\":638,\"../scatter/get_trace_color\":936}],987:[function(t,e,r){\"use strict\";var n=t(\"./hover\");e.exports={moduleType:\"trace\",name:\"scattergl\",basePlotModule:t(\"../../plots/cartesian\"),categories:[\"gl\",\"regl\",\"cartesian\",\"symbols\",\"errorBarsOK\",\"showLegend\",\"scatter-like\"],attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),crossTraceDefaults:t(\"../scatter/cross_trace_defaults\"),colorbar:t(\"../scatter/marker_colorbar\"),formatLabels:t(\"./format_labels\"),calc:t(\"./calc\"),plot:t(\"./plot\"),hoverPoints:n.hoverPoints,selectPoints:t(\"./select\"),meta:{}}},{\"../../plots/cartesian\":568,\"../scatter/cross_trace_defaults\":932,\"../scatter/marker_colorbar\":944,\"./attributes\":978,\"./calc\":979,\"./defaults\":982,\"./format_labels\":984,\"./hover\":986,\"./plot\":988,\"./select\":990}],988:[function(t,e,r){\"use strict\";var n=t(\"regl-scatter2d\"),i=t(\"regl-line2d\"),a=t(\"regl-error2d\"),o=t(\"gl-text\"),s=t(\"../../lib\"),l=t(\"../../components/dragelement/helpers\").selectMode,c=t(\"../../lib/prepare_regl\"),u=t(\"../scatter/subtypes\"),f=t(\"../scatter/link_traces\"),h=t(\"./edit_style\").styleTextSelection;function p(t,e,r,n){var i=t._size,a=t.width*n,o=t.height*n,s=i.l*n,l=i.b*n,c=i.r*n,u=i.t*n,f=i.w*n,h=i.h*n;return[s+e.domain[0]*f,l+r.domain[0]*h,a-c-(1-e.domain[1])*f,o-u-(1-r.domain[1])*h]}e.exports=function(t,e,r){if(r.length){var d,m,g=t._fullLayout,v=e._scene,y=e.xaxis,x=e.yaxis;if(v)if(c(t,[\"ANGLE_instanced_arrays\",\"OES_element_index_uint\"])){var b=v.count,_=g._glcanvas.data()[0].regl;if(f(t,e,r),v.dirty){if(!0===v.error2d&&(v.error2d=a(_)),!0===v.line2d&&(v.line2d=i(_)),!0===v.scatter2d&&(v.scatter2d=n(_)),!0===v.fill2d&&(v.fill2d=i(_)),!0===v.glText)for(v.glText=new Array(b),d=0;d<b;d++)v.glText[d]=new o(_);if(v.glText){if(b>v.glText.length){var w=b-v.glText.length;for(d=0;d<w;d++)v.glText.push(new o(_))}else if(b<v.glText.length){var T=v.glText.length-b;v.glText.splice(b,T).forEach((function(t){t.destroy()}))}for(d=0;d<b;d++)v.glText[d].update(v.textOptions[d])}if(v.line2d&&(v.line2d.update(v.lineOptions),v.lineOptions=v.lineOptions.map((function(t){if(t&&t.positions){for(var e=t.positions,r=0;r<e.length&&(isNaN(e[r])||isNaN(e[r+1]));)r+=2;for(var n=e.length-2;n>r&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var k=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(k)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var f,h,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(f=0;f<d.length&&isNaN(d[f+1]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h+1]);)h-=2;0!==d[f+1]&&(p=[d[f],0]),p=p.concat(d.slice(f,h+2)),0!==d[h+1]&&(p=p.concat([d[h],0]))}else if(\"tozerox\"===s.fill){for(f=0;f<d.length&&isNaN(d[f]);)f+=2;for(h=d.length-2;h>f&&isNaN(d[h]);)h-=2;0!==d[f]&&(p=[0,d[f+1]]),p=p.concat(d.slice(f,h+2)),0!==d[h]&&(p=p.concat([0,d[h+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a<d.length;a+=2)(isNaN(d[a])||isNaN(d[a+1]))&&((p=p.concat(d.slice(i,a))).push(d[i],d[i+1]),p.push(null,null),i=a+2);p=p.concat(d.slice(i)),i&&p.push(d[i],d[i+1])}else{var m=s._nexttrace;if(m){var g=v.lineOptions[e+1];if(g){var y=g.positions;if(\"tonexty\"===s.fill){for(p=d.slice(),e=Math.floor(y.length/2);e--;){var x=y[2*e],b=y[2*e+1];isNaN(x)||isNaN(b)||p.push(x,b)}t.fill=m.fillcolor}}}}if(s._prevtrace&&\"tonext\"===s._prevtrace.fill){var _=v.lineOptions[e-1].positions,w=p.length/2,T=[i=w];for(a=0;a<_.length;a+=2)(isNaN(_[a])||isNaN(_[a+1]))&&(T.push(a/2+w+1),i=a+2);p=p.concat(_),t.hole=T}return t.fillmode=s.fill,t.opacity=s.opacity,t.positions=p,t}})),v.fill2d.update(v.fillOptions))}var A=g.dragmode,M=l(A),S=g.clickmode.indexOf(\"select\")>-1;for(d=0;d<b;d++){var E=r[d][0],L=E.trace,C=E.t,P=C.index,I=L._length,O=C.x,z=C.y;if(L.selectedpoints||M||S){if(M||(M=!0),L.selectedpoints){var D=v.selectBatch[P]=s.selIndices2selPoints(L),R={};for(m=0;m<D.length;m++)R[D[m]]=1;var F=[];for(m=0;m<I;m++)R[m]||F.push(m);v.unselectBatch[P]=F}var B=C.xpx=new Array(I),N=C.ypx=new Array(I);for(m=0;m<I;m++)B[m]=y.c2p(O[m]),N[m]=x.c2p(z[m])}else C.xpx=C.ypx=null}if(M){if(v.select2d||(v.select2d=n(g._glcanvas.data()[1].regl)),v.scatter2d){var j=new Array(b);for(d=0;d<b;d++)j[d]=v.selectBatch[d].length||v.unselectBatch[d].length?v.markerUnselectedOptions[d]:{};v.scatter2d.update(j)}v.select2d&&(v.select2d.update(v.markerOptions),v.select2d.update(v.markerSelectedOptions)),v.glText&&r.forEach((function(t){var e=((t||[])[0]||{}).trace||{};u.hasText(e)&&h(t)}))}else v.scatter2d&&v.scatter2d.update(v.markerOptions);var U={viewport:p(g,y,x,t._context.plotGlPixelRatio),range:[(y._rl||y.range)[0],(x._rl||x.range)[0],(y._rl||y.range)[1],(x._rl||x.range)[1]]},V=s.repeat(U,v.count);v.fill2d&&v.fill2d.update(V),v.line2d&&v.line2d.update(V),v.error2d&&v.error2d.update(V.concat(V)),v.scatter2d&&v.scatter2d.update(V),v.select2d&&v.select2d.update(V),v.glText&&v.glText.forEach((function(t){t.update(U)}))}else v.init()}}},{\"../../components/dragelement/helpers\":384,\"../../lib\":503,\"../../lib/prepare_regl\":516,\"../scatter/link_traces\":942,\"../scatter/subtypes\":951,\"./edit_style\":983,\"gl-text\":225,\"regl-error2d\":279,\"regl-line2d\":280,\"regl-scatter2d\":281}],989:[function(t,e,r){\"use strict\";var n=t(\"../../lib\");e.exports=function(t,e){var r=e._scene,i={count:0,dirty:!0,lineOptions:[],fillOptions:[],markerOptions:[],markerSelectedOptions:[],markerUnselectedOptions:[],errorXOptions:[],errorYOptions:[],textOptions:[],textSelectedOptions:[],textUnselectedOptions:[],selectBatch:[],unselectBatch:[]},a={fill2d:!1,scatter2d:!1,error2d:!1,line2d:!1,glText:!1,select2d:!1};return e._scene||((r=e._scene={}).init=function(){n.extendFlat(r,a,i)},r.init(),r.update=function(t){var e=n.repeat(t,r.count);if(r.fill2d&&r.fill2d.update(e),r.scatter2d&&r.scatter2d.update(e),r.line2d&&r.line2d.update(e),r.error2d&&r.error2d.update(e.concat(e)),r.select2d&&r.select2d.update(e),r.glText)for(var i=0;i<r.count;i++)r.glText[i].update(t)},r.draw=function(){for(var t=r.count,e=r.fill2d,i=r.error2d,a=r.line2d,o=r.scatter2d,s=r.glText,l=r.select2d,c=r.selectBatch,u=r.unselectBatch,f=0;f<t;f++){if(e&&r.fillOrder[f]&&e.draw(r.fillOrder[f]),a&&r.lineOptions[f]&&a.draw(f),i&&(r.errorXOptions[f]&&i.draw(f),r.errorYOptions[f]&&i.draw(f+t)),o&&r.markerOptions[f])if(u[f].length){var h=n.repeat([],r.count);h[f]=u[f],o.draw(h)}else 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e,r,i,a,s,l,c,u,f,h,p,d,m=t._x,g=t._y,v=t._z,y=t._len,x=-1/0,b=1/0,_=-1/0,w=1/0,T=-1/0,k=1/0,A=\"\";for(y&&(c=m[0],f=g[0],p=v[0]),y>1&&(u=m[y-1],h=g[y-1],d=v[y-1]),e=0;e<y;e++)x=Math.max(x,m[e]),b=Math.min(b,m[e]),_=Math.max(_,g[e]),w=Math.min(w,g[e]),T=Math.max(T,v[e]),k=Math.min(k,v[e]),a||m[e]===c||(a=!0,A+=\"x\"),s||g[e]===f||(s=!0,A+=\"y\"),l||v[e]===p||(l=!0,A+=\"z\");a||(A+=\"x\"),s||(A+=\"y\"),l||(A+=\"z\");var M=o(t._x),S=o(t._y),E=o(t._z);A=(A=(A=A.replace(\"x\",(c>u?\"-\":\"+\")+\"x\")).replace(\"y\",(f>h?\"-\":\"+\")+\"y\")).replace(\"z\",(p>d?\"-\":\"+\")+\"z\");var L=function(){y=0,M=[],S=[],E=[]};(!y||y<M.length*S.length*E.length)&&L();var C=function(t){return\"x\"===t?m:\"y\"===t?g:v},P=function(t){return\"x\"===t?M:\"y\"===t?S:E},I=function(t){return t[y-1]<t[0]?-1:1},O=C(A[1]),z=C(A[3]),D=C(A[5]),R=P(A[1]).length,F=P(A[3]).length,B=P(A[5]).length,N=!1,j=function(t,e,r){return 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n=t(\"../../plots/attributes\"),i=t(\"../../plots/template_attributes\").hovertemplateAttrs,a=t(\"../../plots/template_attributes\").texttemplateAttrs,o=t(\"../../components/colorscale/attributes\"),s=t(\"../../plots/domain\").attributes,l=t(\"../pie/attributes\"),c=t(\"./constants\"),u=t(\"../../lib/extend\").extendFlat;e.exports={labels:{valType:\"data_array\",editType:\"calc\"},parents:{valType:\"data_array\",editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},branchvalues:{valType:\"enumerated\",values:[\"remainder\",\"total\"],dflt:\"remainder\",editType:\"calc\"},count:{valType:\"flaglist\",flags:[\"branches\",\"leaves\"],dflt:\"leaves\",editType:\"calc\"},level:{valType:\"any\",editType:\"plot\",anim:!0},maxdepth:{valType:\"integer\",editType:\"plot\",dflt:-1},marker:u({colors:{valType:\"data_array\",editType:\"calc\"},line:{color:u({},l.marker.line.color,{dflt:null}),width:u({},l.marker.line.width,{dflt:1}),editType:\"calc\"},editType:\"calc\"},o(\"marker\",{colorAttr:\"colors\",anim:!1})),leaf:{opacity:{valType:\"number\",editType:\"style\",min:0,max:1},editType:\"plot\"},text:l.text,textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"current 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c=n.select(this),f=s.ensureSingle(c,\"path\",\"surface\",(function(t){t.style(\"pointer-events\",\"all\")}));i.rpx0=H(i.y0),i.rpx1=H(i.y1),i.xmid=(i.x0+i.x1)/2,i.pxmid=q(i.rpx1,i.xmid),i.midangle=-(i.xmid-Math.PI/2),i.startangle=-(i.x0-Math.PI/2),i.stopangle=-(i.x1-Math.PI/2),i.halfangle=.5*Math.min(s.angleDelta(i.x0,i.x1)||Math.PI,Math.PI),i.ring=1-i.rpx0/i.rpx1,i.rInscribed=function(t){return 0===t.rpx0&&s.isFullCircle([t.x0,t.x1])?1:Math.max(0,Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2))}(i),v?f.transition().attrTween(\"d\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n={x0:t.x0,x1:t.x1,rpx0:t.rpx0,rpx1:t.rpx1};if(r)e=r;else if(D)if(t.parent)if(X){var i=(t.x1>X?2*Math.PI:0)+U;e={x0:i,x1:i}}else e={rpx0:I,rpx1:I},s.extendFlat(e,K(t));else e={rpx0:0,rpx1:0};else e={x0:U,x1:U};return a(e,n)}(t);return function(t){return G(e(t))}})):f.attr(\"d\",G),c.call(y,M,t,e,{eventDataKeys:x.eventDataKeys,transitionTime:x.CLICK_TRANSITION_TIME,transitionEasing:x.CLICK_TRANSITION_EASING}).call(b.setSliceCursor,t,{hideOnRoot:!0,hideOnLeaves:!0,isTransitioning:t._transitioning}),f.call(g,i,k);var p=s.ensureSingle(c,\"g\",\"slicetext\"),_=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),w=s.ensureUniformFontSize(t,b.determineTextFont(k,i,h.font));_.text(r.formatSliceLabel(i,M,k,e,h)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,w).call(l.convertToTspans,t);var A=o.bBox(_.node());i.transform=m(A,i,T),i.transform.targetX=Y(i),i.transform.targetY=W(i);var S=function(t,e){var r=t.transform;return d(r,e),r.fontSize=w.size,u(k.type,r,h),s.getTextTransform(r)};v?_.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=R[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},D)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,K(t));else e.x0=e.x1=U;else e.x0=e.x1=U;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),c=a(e.x0,t.x0),f=a(e.x1,t.x1),p=a(e.transform.scale,n.scale),d=a(e.transform.rotate,n.rotate),m=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,g=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=c(t),i=f(t),a=function(t){return g(Math.pow(t,m))}(t),s={pxmid:q(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return u(k.type,n,h),{transform:{targetX:Y(s),targetY:W(s),scale:p(t),rotate:d(t),rCenter:a}}}}(t);return function(t){return S(e(t),A)}})):_.attr(\"transform\",S(i,A))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}r.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&b.hasTransition(r);(f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),u)?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){_(t,e,this,r)}))}))):(a.each((function(e){_(t,e,this,r)})),s.uniformtext.mode&&v(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\"));c&&a.exit().remove()},r.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,c=n[0],u=t.data.data,f=c.hierarchy,h=b.isHierarchyRoot(t),p=b.getParent(f,t),d=b.getValue(t);if(!a){var m,g=o.split(\"+\"),v=function(t){return-1!==g.indexOf(t)},y=[];if(v(\"label\")&&u.label&&y.push(u.label),u.hasOwnProperty(\"v\")&&v(\"value\")&&y.push(b.formatValue(u.v,l)),!h){v(\"current path\")&&y.push(b.getPath(t.data));var x=0;v(\"percent parent\")&&x++,v(\"percent entry\")&&x++,v(\"percent root\")&&x++;var _=x>1;if(x){var w,T=function(t){m=b.formatPercent(w,l),_&&(m+=\" of \"+t),y.push(m)};v(\"percent parent\")&&!h&&(w=d/b.getValue(p),T(\"parent\")),v(\"percent entry\")&&(w=d/b.getValue(e),T(\"entry\")),v(\"percent root\")&&(w=d/b.getValue(f),T(\"root\"))}}return v(\"text\")&&(m=s.castOption(r,u.i,\"text\"),s.isValidTextValue(m)&&y.push(m)),y.join(\"<br>\")}var k=s.castOption(r,u.i,\"texttemplate\");if(!k)return\"\";var A={};u.label&&(A.label=u.label),u.hasOwnProperty(\"v\")&&(A.value=u.v,A.valueLabel=b.formatValue(u.v,l)),A.currentPath=b.getPath(t.data),h||(A.percentParent=d/b.getValue(p),A.percentParentLabel=b.formatPercent(A.percentParent,l),A.parent=b.getPtLabel(p)),A.percentEntry=d/b.getValue(e),A.percentEntryLabel=b.formatPercent(A.percentEntry,l),A.entry=b.getPtLabel(e),A.percentRoot=d/b.getValue(f),A.percentRootLabel=b.formatPercent(A.percentRoot,l),A.root=b.getPtLabel(f),u.hasOwnProperty(\"color\")&&(A.color=u.color);var M=s.castOption(r,u.i,\"text\");return(s.isValidTextValue(M)||\"\"===M)&&(A.text=M),A.customdata=s.castOption(r,u.i,\"customdata\"),s.texttemplateString(k,A,i._d3locale,A,r._meta||{})}},{\"../../components/drawing\":388,\"../../lib\":503,\"../../lib/svg_text_utils\":529,\"../bar/style\":662,\"../bar/uniform_text\":664,\"../pie/helpers\":905,\"../pie/plot\":909,\"./constants\":1048,\"./fx\":1050,\"./helpers\":1051,\"./style\":1056,\"@plotly/d3\":58,\"d3-hierarchy\":115,\"d3-interpolate\":116}],1056:[function(t,e,r){\"use strict\";var n=t(\"@plotly/d3\"),i=t(\"../../components/color\"),a=t(\"../../lib\"),o=t(\"../bar/uniform_text\").resizeText;function s(t,e,r){var n=e.data.data,o=!e.children,s=n.i,l=a.castOption(r,s,\"marker.line.color\")||i.defaultLine,c=a.castOption(r,s,\"marker.line.width\")||0;t.style(\"stroke-width\",c).call(i.fill,n.color).call(i.stroke,l).style(\"opacity\",o?r.leaf.opacity:null)}e.exports={style:function(t){var e=t._fullLayout._sunburstlayer.selectAll(\".trace\");o(t,e,\"sunburst\"),e.each((function(t){var e=n.select(this),r=t[0].trace;e.style(\"opacity\",r.opacity),e.selectAll(\"path.surface\").each((function(t){n.select(this).call(s,t,r)}))}))},styleOne:s}},{\"../../components/color\":366,\"../../lib\":503,\"../bar/uniform_text\":664,\"@plotly/d3\":58}],1057:[function(t,e,r){\"use strict\";var n=t(\"../../components/color\"),i=t(\"../../components/colorscale/attributes\"),a=t(\"../../plots/cartesian/axis_format_attributes\").axisHoverFormat,o=t(\"../../plots/template_attributes\").hovertemplateAttrs,s=t(\"../../plots/attributes\"),l=t(\"../../lib/extend\").extendFlat,c=t(\"../../plot_api/edit_types\").overrideAll;function u(t){return{show:{valType:\"boolean\",dflt:!1},start:{valType:\"number\",dflt:null,editType:\"plot\"},end:{valType:\"number\",dflt:null,editType:\"plot\"},size:{valType:\"number\",dflt:null,min:0,editType:\"plot\"},project:{x:{valType:\"boolean\",dflt:!1},y:{valType:\"boolean\",dflt:!1},z:{valType:\"boolean\",dflt:!1}},color:{valType:\"color\",dflt:n.defaultLine},usecolormap:{valType:\"boolean\",dflt:!1},width:{valType:\"number\",min:1,max:16,dflt:2},highlight:{valType:\"boolean\",dflt:!0},highlightcolor:{valType:\"color\",dflt:n.defaultLine},highlightwidth:{valType:\"number\",min:1,max:16,dflt:2}}}var f=e.exports=c(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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0},{\"../../components/color\":366,\"../../components/colorscale/attributes\":373,\"../../lib/extend\":493,\"../../plot_api/edit_types\":536,\"../../plots/attributes\":550,\"../../plots/cartesian/axis_format_attributes\":557,\"../../plots/template_attributes\":633}],1058:[function(t,e,r){\"use strict\";var n=t(\"../../components/colorscale/calc\");e.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},{\"../../components/colorscale/calc\":374}],1059:[function(t,e,r){\"use strict\";var n=t(\"../../../stackgl_modules\").gl_surface3d,i=t(\"../../../stackgl_modules\").ndarray,a=t(\"../../../stackgl_modules\").ndarray_linear_interpolate.d2,o=t(\"../heatmap/interp2d\"),s=t(\"../heatmap/find_empties\"),l=t(\"../../lib\").isArrayOrTypedArray,c=t(\"../../lib/gl_format_color\").parseColorScale,u=t(\"../../lib/str2rgbarray\"),f=t(\"../../components/colorscale\").extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){var o=t.dataCoordinate[a];null!=o&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var s=this.data.hovertext||this.data.text;return Array.isArray(s)&&s[i]&&void 0!==s[i][n]?t.textLabel=s[i][n]:t.textLabel=s||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:y,columnOrder:M,groupHeight:y,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:m}),gdColumns:m.map((function(t){return t[0]})),gdColumnsOriginalOrder:m.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:m.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:u,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=u(t)})),C}},{\"../../lib/extend\":493,\"./constants\":1065,\"fast-isnumeric\":190}],1067:[function(t,e,r){\"use strict\";var n=t(\"../../lib/extend\").extendFlat;r.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},r.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0,n=e?r+e.rows.length:0;return[r,n]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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a.indexOf(t)})),s=o.length;s<n;s++)o.push(s);e(\"columnorder\",o)}(e,s),s(\"cells.values\"),s(\"cells.format\"),s(\"cells.align\"),s(\"cells.prefix\"),s(\"cells.suffix\"),s(\"cells.height\"),s(\"cells.line.width\"),s(\"cells.line.color\"),s(\"cells.fill.color\"),n.coerceFont(s,\"cells.font\",n.extendFlat({},o.font)),e._length=null}},{\"../../lib\":503,\"../../plots/domain\":584,\"./attributes\":1062}],1069:[function(t,e,r){\"use strict\";e.exports={attributes:t(\"./attributes\"),supplyDefaults:t(\"./defaults\"),calc:t(\"./calc\"),plot:t(\"./plot\"),moduleType:\"trace\",name:\"table\",basePlotModule:t(\"./base_plot\"),categories:[\"noOpacity\"],meta:{}}},{\"./attributes\":1062,\"./base_plot\":1063,\"./calc\":1064,\"./defaults\":1068,\"./plot\":1070}],1070:[function(t,e,r){\"use strict\";var n=t(\"./constants\"),i=t(\"@plotly/d3\"),a=t(\"../../lib\").numberFormat,o=t(\"../../lib/gup\"),s=t(\"../../components/drawing\"),l=t(\"../../lib/svg_text_utils\"),c=t(\"../../lib\").raiseToTop,u=t(\"../../lib\").strTranslate,f=t(\"../../lib\").cancelTransition,h=t(\"./data_preparation_helper\"),p=t(\"./data_split_helpers\"),d=t(\"../../components/color\");function m(t){return Math.ceil(t.calcdata.maxLineWidth/2)}function g(t,e){return\"clip\"+t._fullLayout._uid+\"_scrollAreaBottomClip_\"+e.key}function v(t,e){return\"clip\"+t._fullLayout._uid+\"_columnBoundaryClippath_\"+e.calcdata.key+\"_\"+e.specIndex}function y(t){return[].concat.apply([],t.map((function(t){return t}))).map((function(t){return t.__data__}))}function x(t,e,r){var a=t.selectAll(\".\"+n.cn.scrollbarKit).data(o.repeat,o.keyFun);a.enter().append(\"g\").classed(n.cn.scrollbarKit,!0).style(\"shape-rendering\",\"geometricPrecision\"),a.each((function(t){var e=t.scrollbarState;e.totalHeight=function(t){var e=t.rowBlocks;return D(e,e.length-1)+(e.length?R(e[e.length-1],1/0):1)}(t),e.scrollableAreaHeight=t.groupHeight-S(t),e.currentlyVisibleHeight=Math.min(e.totalHeight,e.scrollableAreaHeight),e.ratio=e.currentlyVisibleHeight/e.totalHeight,e.barLength=Math.max(e.ratio*e.currentlyVisibleHeight,n.goldenRatio*n.scrollbarWidth),e.barWiggleRoom=e.currentlyVisibleHeight-e.barLength,e.wiggleRoom=Math.max(0,e.totalHeight-e.scrollableAreaHeight),e.topY=0===e.barWiggleRoom?0:t.scrollY/e.wiggleRoom*e.barWiggleRoom,e.bottomY=e.topY+e.barLength,e.dragMultiplier=e.wiggleRoom/e.barWiggleRoom})).attr(\"transform\",(function(t){var e=t.width+n.scrollbarWidth/2+n.scrollbarOffset;return u(e,S(t))}));var s=a.selectAll(\".\"+n.cn.scrollbar).data(o.repeat,o.keyFun);s.enter().append(\"g\").classed(n.cn.scrollbar,!0);var l=s.selectAll(\".\"+n.cn.scrollbarSlider).data(o.repeat,o.keyFun);l.enter().append(\"g\").classed(n.cn.scrollbarSlider,!0),l.attr(\"transform\",(function(t){return u(0,t.scrollbarState.topY||0)}));var c=l.selectAll(\".\"+n.cn.scrollbarGlyph).data(o.repeat,o.keyFun);c.enter().append(\"line\").classed(n.cn.scrollbarGlyph,!0).attr(\"stroke\",\"black\").attr(\"stroke-width\",n.scrollbarWidth).attr(\"stroke-linecap\",\"round\").attr(\"y1\",n.scrollbarWidth/2),c.attr(\"y2\",(function(t){return t.scrollbarState.barLength-n.scrollbarWidth/2})).attr(\"stroke-opacity\",(function(t){return t.columnDragInProgress||!t.scrollbarState.barWiggleRoom||r?0:.4})),c.transition().delay(0).duration(0),c.transition().delay(n.scrollbarHideDelay).duration(n.scrollbarHideDuration).attr(\"stroke-opacity\",0);var f=s.selectAll(\".\"+n.cn.scrollbarCaptureZone).data(o.repeat,o.keyFun);f.enter().append(\"line\").classed(n.cn.scrollbarCaptureZone,!0).attr(\"stroke\",\"white\").attr(\"stroke-opacity\",.01).attr(\"stroke-width\",n.scrollbarCaptureWidth).attr(\"stroke-linecap\",\"butt\").attr(\"y1\",0).on(\"mousedown\",(function(r){var n=i.event.y,a=this.getBoundingClientRect(),o=r.scrollbarState,s=n-a.top,l=i.scale.linear().domain([0,o.scrollableAreaHeight]).range([0,o.totalHeight]).clamp(!0);o.topY<=s&&s<=o.bottomY||L(e,t,null,l(s-o.barLength/2))(r)})).call(i.behavior.drag().origin((function(t){return i.event.stopPropagation(),t.scrollbarState.scrollbarScrollInProgress=!0,t})).on(\"drag\",L(e,t)).on(\"dragend\",(function(){}))),f.attr(\"y2\",(function(t){return t.scrollbarState.scrollableAreaHeight})),e._context.staticPlot&&(c.remove(),f.remove())}function b(t,e,r,a){var l=function(t){var e=t.selectAll(\".\"+n.cn.columnCell).data(p.splitToCells,(function(t){return t.keyWithinBlock}));return e.enter().append(\"g\").classed(n.cn.columnCell,!0),e.exit().remove(),e}(function(t){var e=t.selectAll(\".\"+n.cn.columnCells).data(o.repeat,o.keyFun);return e.enter().append(\"g\").classed(n.cn.columnCells,!0),e.exit().remove(),e}(r));!function(t){t.each((function(t,e){var r=t.calcdata.cells.font,n=t.column.specIndex,i={size:T(r.size,n,e),color:T(r.color,n,e),family:T(r.family,n,e)};t.rowNumber=t.key,t.align=T(t.calcdata.cells.align,n,e),t.cellBorderWidth=T(t.calcdata.cells.line.width,n,e),t.font=i}))}(l),function(t){t.attr(\"width\",(function(t){return t.column.columnWidth})).attr(\"stroke-width\",(function(t){return t.cellBorderWidth})).each((function(t){var e=i.select(this);d.stroke(e,T(t.calcdata.cells.line.color,t.column.specIndex,t.rowNumber)),d.fill(e,T(t.calcdata.cells.fill.color,t.column.specIndex,t.rowNumber))}))}(function(t){var e=t.selectAll(\".\"+n.cn.cellRect).data(o.repeat,(function(t){return t.keyWithinBlock}));return e.enter().append(\"rect\").classed(n.cn.cellRect,!0),e}(l));var c=function(t){var e=t.selectAll(\".\"+n.cn.cellText).data(o.repeat,(function(t){return t.keyWithinBlock}));return 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n=t[Math.min(e,t.length-1)];return Array.isArray(n)?n[Math.min(r,n.length-1)]:n}return t}function k(t,e,r){t.transition().ease(n.releaseTransitionEase).duration(n.releaseTransitionDuration).attr(\"transform\",u(e.x,r))}function A(t){return\"cells\"===t.type}function M(t){return\"header\"===t.type}function S(t){return(t.rowBlocks.length?t.rowBlocks[0].auxiliaryBlocks:[]).reduce((function(t,e){return t+R(e,1/0)}),0)}function E(t,e,r){var n=y(e)[0];if(void 0!==n){var i=n.rowBlocks,a=n.calcdata,o=D(i,i.length),s=n.calcdata.groupHeight-S(n),l=a.scrollY=Math.max(0,Math.min(o-s,a.scrollY)),c=function(t,e,r){for(var n=[],i=0,a=0;a<t.length;a++){for(var o=t[a],s=o.rows,l=0,c=0;c<s.length;c++)l+=s[c].rowHeight;o.allRowsHeight=l;e<i+l&&e+r>i&&n.push(a),i+=l}return n}(i,l,s);1===c.length&&(c[0]===i.length-1?c.unshift(c[0]-1):c.push(c[0]+1)),c[0]%2&&c.reverse(),e.each((function(t,e){t.page=c[e],t.scrollY=l})),e.attr(\"transform\",(function(t){var e=D(t.rowBlocks,t.page)-t.scrollY;return 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0}},{}],6:[function(t,e,r){e.exports={alpha_shape:t(\"alpha-shape\"),convex_hull:t(\"convex-hull\"),delaunay_triangulate:t(\"delaunay-triangulate\"),gl_cone3d:t(\"gl-cone3d\"),gl_error3d:t(\"gl-error3d\"),gl_heatmap2d:t(\"gl-heatmap2d\"),gl_line3d:t(\"gl-line3d\"),gl_mesh3d:t(\"gl-mesh3d\"),gl_plot2d:t(\"gl-plot2d\"),gl_plot3d:t(\"gl-plot3d\"),gl_pointcloud2d:t(\"gl-pointcloud2d\"),gl_scatter3d:t(\"gl-scatter3d\"),gl_select_box:t(\"gl-select-box\"),gl_spikes2d:t(\"gl-spikes2d\"),gl_streamtube3d:t(\"gl-streamtube3d\"),gl_surface3d:t(\"gl-surface3d\"),ndarray:t(\"ndarray\"),ndarray_linear_interpolate:t(\"ndarray-linear-interpolate\")}},{\"alpha-shape\":12,\"convex-hull\":58,\"delaunay-triangulate\":63,\"gl-cone3d\":79,\"gl-error3d\":84,\"gl-heatmap2d\":88,\"gl-line3d\":91,\"gl-mesh3d\":112,\"gl-plot2d\":118,\"gl-plot3d\":121,\"gl-pointcloud2d\":123,\"gl-scatter3d\":128,\"gl-select-box\":130,\"gl-spikes2d\":139,\"gl-streamtube3d\":143,\"gl-surface3d\":145,ndarray:259,\"ndarray-linear-interpolate\":253}],7:[function(t,e,r){\"use 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i=0|t.words[0],a=0|e.words[0],o=i*a,s=67108863&o,l=o/67108864|0;r.words[0]=s;for(var c=1;c<n;c++){for(var u=l>>>26,f=67108863&l,h=Math.min(c,e.length-1),p=Math.max(0,c-t.length+1);p<=h;p++){var d=c-p|0;u+=(o=(i=0|t.words[d])*(a=0|e.words[p])+f)/67108864|0,f=67108863&o}r.words[c]=0|f,l=0|u}return 0!==l?r.words[c]=0|l:r.length--,r.strip()}a.prototype.toString=function(t,e){var r;if(e=0|e||1,16===(t=t||10)||\"hex\"===t){r=\"\";for(var i=0,a=0,o=0;o<this.length;o++){var s=this.words[o],l=(16777215&(s<<i|a)).toString(16);r=0!==(a=s>>>24-i&16777215)||o!==this.length-1?u[6-l.length]+l+r:l+r,(i+=2)>=26&&(i-=26,o--)}for(0!==a&&(r=a.toString(16)+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}if(t===(0|t)&&t>=2&&t<=36){var c=f[t],p=h[t];r=\"\";var d=this.clone();for(d.negative=0;!d.isZero();){var m=d.modn(p).toString(t);r=(d=d.idivn(p)).isZero()?m+r:u[c-m.length]+m+r}for(this.isZero()&&(r=\"0\"+r);r.length%e!=0;)r=\"0\"+r;return 0!==this.negative&&(r=\"-\"+r),r}n(!1,\"Base 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32-Math.clz32(t)}:a.prototype._countBits=function(t){var e=t,r=0;return e>=4096&&(r+=13,e>>>=13),e>=64&&(r+=7,e>>>=7),e>=8&&(r+=4,e>>>=4),e>=2&&(r+=2,e>>>=2),r+e},a.prototype._zeroBits=function(t){if(0===t)return 26;var e=t,r=0;return 0==(8191&e)&&(r+=13,e>>>=13),0==(127&e)&&(r+=7,e>>>=7),0==(15&e)&&(r+=4,e>>>=4),0==(3&e)&&(r+=2,e>>>=2),0==(1&e)&&r++,r},a.prototype.bitLength=function(){var t=this.words[this.length-1],e=this._countBits(t);return 26*(this.length-1)+e},a.prototype.zeroBits=function(){if(this.isZero())return 0;for(var t=0,e=0;e<this.length;e++){var r=this._zeroBits(this.words[e]);if(t+=r,26!==r)break}return t},a.prototype.byteLength=function(){return Math.ceil(this.bitLength()/8)},a.prototype.toTwos=function(t){return 0!==this.negative?this.abs().inotn(t).iaddn(1):this.clone()},a.prototype.fromTwos=function(t){return this.testn(t-1)?this.notn(t).iaddn(1).ineg():this.clone()},a.prototype.isNeg=function(){return 0!==this.negative},a.prototype.neg=function(){return this.clone().ineg()},a.prototype.ineg=function(){return this.isZero()||(this.negative^=1),this},a.prototype.iuor=function(t){for(;this.length<t.length;)this.words[this.length++]=0;for(var e=0;e<t.length;e++)this.words[e]=this.words[e]|t.words[e];return this.strip()},a.prototype.ior=function(t){return n(0==(this.negative|t.negative)),this.iuor(t)},a.prototype.or=function(t){return this.length>t.length?this.clone().ior(t):t.clone().ior(this)},a.prototype.uor=function(t){return this.length>t.length?this.clone().iuor(t):t.clone().iuor(this)},a.prototype.iuand=function(t){var e;e=this.length>t.length?t:this;for(var r=0;r<e.length;r++)this.words[r]=this.words[r]&t.words[r];return this.length=e.length,this.strip()},a.prototype.iand=function(t){return n(0==(this.negative|t.negative)),this.iuand(t)},a.prototype.and=function(t){return this.length>t.length?this.clone().iand(t):t.clone().iand(this)},a.prototype.uand=function(t){return this.length>t.length?this.clone().iuand(t):t.clone().iuand(this)},a.prototype.iuxor=function(t){var e,r;this.length>t.length?(e=this,r=t):(e=t,r=this);for(var n=0;n<r.length;n++)this.words[n]=e.words[n]^r.words[n];if(this!==e)for(;n<e.length;n++)this.words[n]=e.words[n];return this.length=e.length,this.strip()},a.prototype.ixor=function(t){return n(0==(this.negative|t.negative)),this.iuxor(t)},a.prototype.xor=function(t){return this.length>t.length?this.clone().ixor(t):t.clone().ixor(this)},a.prototype.uxor=function(t){return this.length>t.length?this.clone().iuxor(t):t.clone().iuxor(this)},a.prototype.inotn=function(t){n(\"number\"==typeof t&&t>=0);var e=0|Math.ceil(t/26),r=t%26;this._expand(e),r>0&&e--;for(var i=0;i<e;i++)this.words[i]=67108863&~this.words[i];return r>0&&(this.words[i]=~this.words[i]&67108863>>26-r),this.strip()},a.prototype.notn=function(t){return this.clone().inotn(t)},a.prototype.setn=function(t,e){n(\"number\"==typeof t&&t>=0);var r=t/26|0,i=t%26;return this._expand(r+1),this.words[r]=e?this.words[r]|1<<i:this.words[r]&~(1<<i),this.strip()},a.prototype.iadd=function(t){var e,r,n;if(0!==this.negative&&0===t.negative)return this.negative=0,e=this.isub(t),this.negative^=1,this._normSign();if(0===this.negative&&0!==t.negative)return t.negative=0,e=this.isub(t),t.negative=1,e._normSign();this.length>t.length?(r=this,n=t):(r=t,n=this);for(var i=0,a=0;a<n.length;a++)e=(0|r.words[a])+(0|n.words[a])+i,this.words[a]=67108863&e,i=e>>>26;for(;0!==i&&a<r.length;a++)e=(0|r.words[a])+i,this.words[a]=67108863&e,i=e>>>26;if(this.length=r.length,0!==i)this.words[this.length]=i,this.length++;else if(r!==this)for(;a<r.length;a++)this.words[a]=r.words[a];return this},a.prototype.add=function(t){var e;return 0!==t.negative&&0===this.negative?(t.negative=0,e=this.sub(t),t.negative^=1,e):0===t.negative&&0!==this.negative?(this.negative=0,e=t.sub(this),this.negative=1,e):this.length>t.length?this.clone().iadd(t):t.clone().iadd(this)},a.prototype.isub=function(t){if(0!==t.negative){t.negative=0;var e=this.iadd(t);return t.negative=1,e._normSign()}if(0!==this.negative)return this.negative=0,this.iadd(t),this.negative=1,this._normSign();var r,n,i=this.cmp(t);if(0===i)return this.negative=0,this.length=1,this.words[0]=0,this;i>0?(r=this,n=t):(r=t,n=this);for(var a=0,o=0;o<n.length;o++)a=(e=(0|r.words[o])-(0|n.words[o])+a)>>26,this.words[o]=67108863&e;for(;0!==a&&o<r.length;o++)a=(e=(0|r.words[o])+a)>>26,this.words[o]=67108863&e;if(0===a&&o<r.length&&r!==this)for(;o<r.length;o++)this.words[o]=r.words[o];return this.length=Math.max(this.length,o),r!==this&&(this.negative=1),this.strip()},a.prototype.sub=function(t){return this.clone().isub(t)};var d=function(t,e,r){var n,i,a,o=t.words,s=e.words,l=r.words,c=0,u=0|o[0],f=8191&u,h=u>>>13,p=0|o[1],d=8191&p,m=p>>>13,g=0|o[2],v=8191&g,y=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],I=8191&P,O=P>>>13,z=0|o[8],D=8191&z,R=z>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Y=0|s[2],W=8191&Y,X=Y>>>13,Z=0|s[3],J=8191&Z,K=Z>>>13,Q=0|s[4],$=8191&Q,tt=Q>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ct=st>>>13,ut=0|s[8],ft=8191&ut,ht=ut>>>13,pt=0|s[9],dt=8191&pt,mt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(c+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;c=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(m,U)|0,a=Math.imul(m,V);var vt=(c+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;c=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(vt>>>26)|0,vt&=67108863,n=Math.imul(v,U),i=(i=Math.imul(v,V))+Math.imul(y,U)|0,a=Math.imul(y,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0;var yt=(c+(n=n+Math.imul(f,W)|0)|0)+((8191&(i=(i=i+Math.imul(f,X)|0)+Math.imul(h,W)|0))<<13)|0;c=((a=a+Math.imul(h,X)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(v,q)|0,i=(i=i+Math.imul(v,G)|0)+Math.imul(y,q)|0,a=a+Math.imul(y,G)|0,n=n+Math.imul(d,W)|0,i=(i=i+Math.imul(d,X)|0)+Math.imul(m,W)|0,a=a+Math.imul(m,X)|0;var xt=(c+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;c=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(v,W)|0,i=(i=i+Math.imul(v,X)|0)+Math.imul(y,W)|0,a=a+Math.imul(y,X)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0;var bt=(c+(n=n+Math.imul(f,$)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,$)|0))<<13)|0;c=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,W)|0,i=(i=i+Math.imul(b,X)|0)+Math.imul(_,W)|0,a=a+Math.imul(_,X)|0,n=n+Math.imul(v,J)|0,i=(i=i+Math.imul(v,K)|0)+Math.imul(y,J)|0,a=a+Math.imul(y,K)|0,n=n+Math.imul(d,$)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,$)|0,a=a+Math.imul(m,tt)|0;var _t=(c+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;c=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,X)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,X)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(v,$)|0,i=(i=i+Math.imul(v,tt)|0)+Math.imul(y,$)|0,a=a+Math.imul(y,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;c=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(I,U),i=(i=Math.imul(I,V))+Math.imul(O,U)|0,a=Math.imul(O,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,X)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,X)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,$)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,$)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(v,rt)|0,i=(i=i+Math.imul(v,nt)|0)+Math.imul(y,rt)|0,a=a+Math.imul(y,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ct)|0)+Math.imul(h,lt)|0))<<13)|0;c=((a=a+Math.imul(h,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(I,q)|0,i=(i=i+Math.imul(I,G)|0)+Math.imul(O,q)|0,a=a+Math.imul(O,G)|0,n=n+Math.imul(L,W)|0,i=(i=i+Math.imul(L,X)|0)+Math.imul(C,W)|0,a=a+Math.imul(C,X)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(v,at)|0,i=(i=i+Math.imul(v,ot)|0)+Math.imul(y,at)|0,a=a+Math.imul(y,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;c=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,q)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(I,W)|0,i=(i=i+Math.imul(I,X)|0)+Math.imul(O,W)|0,a=a+Math.imul(O,X)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(v,lt)|0,i=(i=i+Math.imul(v,ct)|0)+Math.imul(y,lt)|0,a=a+Math.imul(y,ct)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var At=(c+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,mt)|0)+Math.imul(h,dt)|0))<<13)|0;c=((a=a+Math.imul(h,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,X)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,X)|0,n=n+Math.imul(I,J)|0,i=(i=i+Math.imul(I,K)|0)+Math.imul(O,J)|0,a=a+Math.imul(O,K)|0,n=n+Math.imul(L,$)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,$)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ct)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ct)|0,n=n+Math.imul(v,ft)|0,i=(i=i+Math.imul(v,ht)|0)+Math.imul(y,ft)|0,a=a+Math.imul(y,ht)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,X))+Math.imul(N,W)|0,a=Math.imul(N,X),n=n+Math.imul(D,J)|0,i=(i=i+Math.imul(D,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(I,$)|0,i=(i=i+Math.imul(I,tt)|0)+Math.imul(O,$)|0,a=a+Math.imul(O,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(c+(n=n+Math.imul(v,dt)|0)|0)+((8191&(i=(i=i+Math.imul(v,mt)|0)+Math.imul(y,dt)|0))<<13)|0;c=((a=a+Math.imul(y,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(I,rt)|0,i=(i=i+Math.imul(I,nt)|0)+Math.imul(O,rt)|0,a=a+Math.imul(O,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(c+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,mt)|0)+Math.imul(_,dt)|0))<<13)|0;c=((a=a+Math.imul(_,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,tt))+Math.imul(N,$)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(I,at)|0,i=(i=i+Math.imul(I,ot)|0)+Math.imul(O,at)|0,a=a+Math.imul(O,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ct)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ct)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(I,lt)|0,i=(i=i+Math.imul(I,ct)|0)+Math.imul(O,lt)|0,a=a+Math.imul(O,ct)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(I,ft)|0,i=(i=i+Math.imul(I,ht)|0)+Math.imul(O,ft)|0,a=a+Math.imul(O,ht)|0;var Pt=(c+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,mt)|0)+Math.imul(C,dt)|0))<<13)|0;c=((a=a+Math.imul(C,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ft)|0,i=(i=i+Math.imul(D,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var It=(c+(n=n+Math.imul(I,dt)|0)|0)+((8191&(i=(i=i+Math.imul(I,mt)|0)+Math.imul(O,dt)|0))<<13)|0;c=((a=a+Math.imul(O,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var Ot=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863;var zt=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863,l[0]=gt,l[1]=vt,l[2]=yt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=It,l[17]=Ot,l[18]=zt,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r=this.length+t.length;return 10===this.length&&10===t.length?d(this,t,e):r<63?p(this,t,e):r<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a<r.length-1;a++){var o=i;i=0;for(var s=67108863&n,l=Math.min(a,e.length-1),c=Math.max(0,a-t.length+1);c<=l;c++){var u=a-c,f=(0|t.words[u])*(0|e.words[c]),h=67108863&f;s=67108863&(h=h+s|0),i+=(o=(o=o+(f/67108864|0)|0)+(h>>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e)},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n<t;n++)e[n]=this.revBin(n,r,t);return e},g.prototype.revBin=function(t,e,r){if(0===t||t===r-1)return t;for(var n=0,i=0;i<e;i++)n|=(1&t)<<e-i-1,t>>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o<a;o++)n[o]=e[t[o]],i[o]=r[t[o]]},g.prototype.transform=function(t,e,r,n,i,a){this.permute(a,t,e,r,n,i);for(var o=1;o<i;o<<=1)for(var s=o<<1,l=Math.cos(2*Math.PI/s),c=Math.sin(2*Math.PI/s),u=0;u<i;u+=s)for(var f=l,h=c,p=0;p<o;p++){var d=r[u+p],m=n[u+p],g=r[u+p+o],v=n[u+p+o],y=f*g-h*v;v=f*v+h*g,g=y,r[u+p]=d+g,n[u+p]=m+v,r[u+p+o]=d-g,n[u+p+o]=m-v,p!==s&&(y=l*f-c*h,h=l*h+c*f,f=y)}},g.prototype.guessLen13b=function(t,e){var r=1|Math.max(e,t),n=1&r,i=0;for(r=r/2|0;r;r>>>=1)i++;return 1<<i+1+n},g.prototype.conjugate=function(t,e,r){if(!(r<=1))for(var n=0;n<r/2;n++){var i=t[n];t[n]=t[r-n-1],t[r-n-1]=i,i=e[n],e[n]=-e[r-n-1],e[r-n-1]=-i}},g.prototype.normalize13b=function(t,e){for(var r=0,n=0;n<e/2;n++){var i=8192*Math.round(t[2*n+1]/e)+Math.round(t[2*n]/e)+r;t[n]=67108863&i,r=i<67108864?0:i/67108864|0}return t},g.prototype.convert13b=function(t,e,r,i){for(var a=0,o=0;o<e;o++)a+=0|t[o],r[2*o]=8191&a,a>>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o<i;++o)r[o]=0;n(0===a),n(0==(-8192&a))},g.prototype.stub=function(t){for(var e=new Array(t),r=0;r<t;r++)e[r]=0;return e},g.prototype.mulp=function(t,e,r){var n=2*this.guessLen13b(t.length,e.length),i=this.makeRBT(n),a=this.stub(n),o=new Array(n),s=new Array(n),l=new Array(n),c=new Array(n),u=new Array(n),f=new Array(n),h=r.words;h.length=n,this.convert13b(t.words,t.length,o,n),this.convert13b(e.words,e.length,c,n),this.transform(o,a,s,l,n,i),this.transform(c,a,u,f,n,i);for(var p=0;p<n;p++){var d=s[p]*u[p]-l[p]*f[p];l[p]=s[p]*f[p]+l[p]*u[p],s[p]=d}return this.conjugate(s,l,n),this.transform(s,l,h,a,n,i),this.conjugate(h,a,n),this.normalize13b(h,n),r.negative=t.negative^e.negative,r.length=t.length+e.length,r.strip()},a.prototype.mul=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),this.mulTo(t,e)},a.prototype.mulf=function(t){var e=new a(null);return e.words=new Array(this.length+t.length),m(this,t,e)},a.prototype.imul=function(t){return this.clone().mulTo(t,this)},a.prototype.imuln=function(t){n(\"number\"==typeof t),n(t<67108864);for(var e=0,r=0;r<this.length;r++){var i=(0|this.words[r])*t,a=(67108863&i)+(67108863&e);e>>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r<e.length;r++){var n=r/26|0,i=r%26;e[r]=(t.words[n]&1<<i)>>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n<e.length&&0===e[n];n++,r=r.sqr());if(++n<e.length)for(var i=r.sqr();n<e.length;n++,i=i.sqr())0!==e[n]&&(r=r.mul(i));return r},a.prototype.iushln=function(t){n(\"number\"==typeof t&&t>=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e<this.length;e++){var s=this.words[e]&a,l=(0|this.words[e])-s<<r;this.words[e]=l|o,o=s>>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e<i;e++)this.words[e]=0;this.length+=i}return this.strip()},a.prototype.ishln=function(t){return n(0===this.negative),this.iushln(t)},a.prototype.iushrn=function(t,e,r){var i;n(\"number\"==typeof t&&t>=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<<a,l=r;if(i-=o,i=Math.max(0,i),l){for(var c=0;c<o;c++)l.words[c]=this.words[c];l.length=o}if(0===o);else if(this.length>o)for(this.length-=o,c=0;c<this.length;c++)this.words[c]=this.words[c+o];else this.words[0]=0,this.length=1;var u=0;for(c=this.length-1;c>=0&&(0!==u||c>=i);c--){var f=0|this.words[c];this.words[c]=u<<26-a|f>>>a,u=f&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<<e;return!(this.length<=r)&&!!(this.words[r]&i)},a.prototype.imaskn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<<e;this.words[this.length-1]&=i}return this.strip()},a.prototype.maskn=function(t){return this.clone().imaskn(t)},a.prototype.iaddn=function(t){return n(\"number\"==typeof t),n(t<67108864),t<0?this.isubn(-t):0!==this.negative?1===this.length&&(0|this.words[0])<t?(this.words[0]=t-(0|this.words[0]),this.negative=0,this):(this.negative=0,this.isubn(t),this.negative=1,this):this._iaddn(t)},a.prototype._iaddn=function(t){this.words[0]+=t;for(var e=0;e<this.length&&this.words[e]>=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e<this.length&&this.words[e]<0;e++)this.words[e]+=67108864,this.words[e+1]-=1;return this.strip()},a.prototype.addn=function(t){return this.clone().iaddn(t)},a.prototype.subn=function(t){return this.clone().isubn(t)},a.prototype.iabs=function(){return this.negative=0,this},a.prototype.abs=function(){return this.clone().iabs()},a.prototype._ishlnsubmul=function(t,e,r){var i,a,o=t.length+r;this._expand(o);var s=0;for(i=0;i<t.length;i++){a=(0|this.words[i+r])+s;var l=(0|t.words[i])*e;s=((a-=67108863&l)>>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i<this.length-r;i++)s=(a=(0|this.words[i+r])+s)>>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i<this.length;i++)s=(a=-(0|this.words[i])+s)>>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!==(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c<s.length;c++)s.words[c]=0}var u=n.clone()._ishlnsubmul(i,1,l);0===u.negative&&(n=u,s&&(s.words[l]=1));for(var f=l-1;f>=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<<e;if(this.length<=r)return this._expand(r+1),this.words[r]|=i,this;for(var a=i,o=r;0!==a&&o<this.length;o++){var s=0|this.words[o];a=(s+=a)>>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:i<t?-1:1}return 0!==this.negative?0|-e:e},a.prototype.cmp=function(t){if(0!==this.negative&&0===t.negative)return-1;if(0===this.negative&&0!==t.negative)return 1;var e=this.ucmp(t);return 0!==this.negative?0|-e:e},a.prototype.ucmp=function(t){if(this.length>t.length)return 1;if(this.length<t.length)return-1;for(var e=0,r=this.length-1;r>=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){n<i?e=-1:n>i&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var v={k256:null,p224:null,p192:null,p25519:null};function y(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){y.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){y.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){y.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){y.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}y.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},y.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e<this.n?-1:r.ucmp(this.p);return 0===n?(r.words[0]=0,r.length=1):n>0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},y.prototype.split=function(t,e){t.iushrn(this.n,0,e)},y.prototype.imulK=function(t){return t.imul(this.k)},i(x,y),x.prototype.split=function(t,e){for(var r=Math.min(t.length,9),n=0;n<r;n++)e.words[n]=t.words[n];if(e.length=r,t.length<=9)return t.words[0]=0,void(t.length=1);var i=t.words[9];for(e.words[e.length++]=4194303&i,n=10;n<t.length;n++){var a=0|t.words[n];t.words[n-10]=(4194303&a)<<4|i>>>22,i=a}i>>>=22,t.words[n-10]=i,0===i&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r<t.length;r++){var n=0|t.words[r];e+=977*n,t.words[r]=67108863&e,e=64*n+(e/67108864|0)}return 0===t.words[t.length-1]&&(t.length--,0===t.words[t.length-1]&&t.length--),t},i(b,y),i(_,y),i(w,y),w.prototype.imulK=function(t){for(var e=0,r=0;r<t.length;r++){var n=19*(0|t.words[r])+e,i=67108863&n;n>>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(v[t])return v[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return v[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var f=this.pow(u,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g<d);var v=this.pow(f,new a(1).iushln(d-g-1));h=h.redMul(v),f=v.redSqr(),p=p.redMul(f),d=g}return h},T.prototype.invm=function(t){var e=t._invmp(this.m);return 0!==e.negative?(e.negative=0,this.imod(e).redNeg()):this.imod(e)},T.prototype.pow=function(t,e){if(e.isZero())return new a(1).toRed(this);if(0===e.cmpn(1))return t.clone();var r=new Array(16);r[0]=new a(1).toRed(this),r[1]=t;for(var n=2;n<r.length;n++)r[n]=this.mul(r[n-1],t);var i=r[0],o=0,s=0,l=e.bitLength()%26;for(0===l&&(l=26),n=e.length-1;n>=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var f=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4===++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var 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p=h.prototype;function d(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function m(t,e){return t<e?-1:t>e?1:0}Object.defineProperty(p,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(p,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),p.clone=function(){return new h(this.tree,this._stack.slice())},p.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var e=new Array(t.length),r=t[t.length-1];e[e.length-1]=new n(r._color,r.key,r.value,r.left,r.right,r._count);for(var l=t.length-2;l>=0;--l){(r=t[l]).left===t[l+1]?e[l]=new n(r._color,r.key,r.value,e[l+1],r.right,r._count):e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count)}if((r=e[e.length-1]).left&&r.right){var c=e.length;for(r=r.left;r.right;)e.push(r),r=r.right;var u=e[c-1];e.push(new n(r._color,u.key,u.value,r.left,r.right,r._count)),e[c-1].key=r.key,e[c-1].value=r.value;for(l=e.length-2;l>=c;--l)r=e[l],e[l]=new n(r._color,r.key,r.value,r.left,e[l+1],r._count);e[c-1].left=e[c]}if(0===(r=e[e.length-1])._color){var f=e[e.length-2];f.left===r?f.left=null:f.right===r&&(f.right=null),e.pop();for(l=0;l<e.length;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(r.left||r.right){r.left?d(r,r.left):r.right&&d(r,r.right),r._color=1;for(l=0;l<e.length-1;++l)e[l]._count--;return new s(this.tree._compare,e[0])}if(1===e.length)return new s(this.tree._compare,null);for(l=0;l<e.length;++l)e[l]._count--;var h=e[e.length-2];return function(t){for(var e,r,n,s,l=t.length-1;l>=0;--l){if(e=t[l],0===l)return void(e._color=1);if((r=t[l-1]).left===e){if((n=r.right).right&&0===n.right._color){if(s=(n=r.right=i(n)).right=i(n.right),r.right=n.left,n.left=r,n.right=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).left===r?c.left=n:c.right=n;return void(t[l-1]=n)}if(n.left&&0===n.left._color){if(s=(n=r.right=i(n)).left=i(n.left),r.right=s.left,n.left=s.right,s.left=r,s.right=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).left===r?c.left=s:c.right=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.right=a(0,n));r.right=a(0,n);continue}n=i(n),r.right=n.left,n.left=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).left===r?c.left=n:c.right=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}else{if((n=r.left).left&&0===n.left._color){if(s=(n=r.left=i(n)).left=i(n.left),r.left=n.right,n.right=r,n.left=s,n._color=r._color,e._color=1,r._color=1,s._color=1,o(r),o(n),l>1)(c=t[l-2]).right===r?c.right=n:c.left=n;return void(t[l-1]=n)}if(n.right&&0===n.right._color){if(s=(n=r.left=i(n)).right=i(n.right),r.left=s.right,n.right=s.left,s.right=r,s.left=n,s._color=r._color,r._color=1,n._color=1,e._color=1,o(r),o(n),o(s),l>1)(c=t[l-2]).right===r?c.right=s:c.left=s;return void(t[l-1]=s)}if(1===n._color){if(0===r._color)return r._color=1,void(r.left=a(0,n));r.left=a(0,n);continue}var c;n=i(n),r.left=n.right,n.right=r,n._color=r._color,r._color=0,o(r),o(n),l>1&&((c=t[l-2]).right===r?c.right=n:c.left=n),t[l-1]=n,t[l]=r,l+1<t.length?t[l+1]=e:t.push(e),l+=2}}}(e),h.left===r?h.left=null:h.right=null,new s(this.tree._compare,e[0])},Object.defineProperty(p,\"key\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(p,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(p,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),p.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),p.update=function(t){var e=this._stack;if(0===e.length)throw new Error(\"Can't update empty node!\");var r=new Array(e.length),i=e[e.length-1];r[r.length-1]=new n(i._color,i.key,t,i.left,i.right,i._count);for(var a=e.length-2;a>=0;--a)(i=e[a]).left===e[a+1]?r[a]=new n(i._color,i.key,i.value,r[a+1],i.right,i._count):r[a]=new n(i._color,i.key,i.value,i.left,r[a+1],i._count);return new s(this.tree._compare,r[0])},p.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(p,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},{}],70:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=new u(t);return r.update(e),r};var n=t(\"./lib/text.js\"),i=t(\"./lib/lines.js\"),a=t(\"./lib/background.js\"),o=t(\"./lib/cube.js\"),s=t(\"./lib/ticks.js\"),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function c(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function u(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=u.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t){r=t.ticks,u=!0,this.autoTicks=!1;for(p=0;p<3;++p)this.tickSpacing[p]=0}else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(p=0;p<3;++p)r[p].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var m=l(\"labels\");l(\"labelFont\")&&(m=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\"),this._text?this._text&&(m||u)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var f=a,h=s,p=o,d=l;c&1<<u&&(f=s,h=a,p=l,d=o),f[u]=r[0][u],h[u]=r[1][u],i[u]>0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var m=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var v=[0,0,0],y=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,u=o(r,n,i,a,s),f=u.cubeEdges,h=u.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E=m;for(A=0;A<3;++A)this.backgroundEnable[A]?E[A]=h[A]:E[A]=0;this._background.draw(r,n,i,a,E,this.backgroundColor),this._lines.bind(r,n,i,this);for(A=0;A<3;++A){var L=[0,0,0];h[A]>0?L[A]=a[1][A]:L[A]=a[0][A];for(var C=0;C<2;++C){var P=(A+1+C)%3,I=(A+1+(1^C))%3;this.gridEnable[P]&&this._lines.drawGrid(P,I,this.bounds,L,this.gridColor[P],this.gridWidth[P]*this.pixelRatio)}for(C=0;C<2;++C){P=(A+1+C)%3,I=(A+1+(1^C))%3;this.zeroEnable[I]&&Math.min(a[0][I],a[1][I])<=0&&Math.max(a[0][I],a[1][I])>=0&&this._lines.drawZero(P,I,this.bounds,L,this.zeroLineColor[I],this.zeroLineWidth[I]*this.pixelRatio)}}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var O=c(v,M[A].primalMinor),z=c(y,M[A].mirrorMinor),D=this.lineTickLength;for(C=0;C<3;++C){var R=k/r[5*C];O[C]*=D[C]*R,z[C]*=D[C]*R}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,O,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio);var F,B;function N(t){(B=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=c(x,M[A].primalOffset);for(C=0;C<3;++C)this.lineTickEnable[A]&&(H[C]+=k*U[C]*Math.max(this.lineTickLength[C],0)/r[5*C]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,F=1,\"auto\"===(S=[this.tickAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),B=[0,0,0],j(A,U,V);for(C=0;C<3;++C)H[C]+=k*U[C]*this.tickPad[C]/r[5*C];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,B,S)}if(this.labelEnable[A]){F=0,B=[0,0,0],this.labels[A].length>4&&(N(A),F=1),\"auto\"===(S=[this.labelAlign[A],.5,F])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]);for(C=0;C<3;++C)H[C]+=k*U[C]*this.labelPad[C]/r[5*C];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],B,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},{\"./lib/background.js\":71,\"./lib/cube.js\":72,\"./lib/lines.js\":73,\"./lib/text.js\":75,\"./lib/ticks.js\":76}],71:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[c]=d;for(var m=-1;m<=1;m+=2)f[u]=m,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=c;c=u,u=g}var v=n(t,new Float32Array(e)),y=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:v,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:v,type:t.FLOAT,size:3,offset:12,stride:24}],y),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,v,x,b)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],72:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var y=0,x=0;x<2;++x){u[2]=a[x][2];for(var b=0;b<2;++b){u[1]=a[b][1];for(var _=0;_<2;++_)u[0]=a[_][0],h(l[y],u,s),y+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]<c[w][2])&&(w=x)}if(w<0){w=0;for(var A=0;A<3;++A){for(var M=(A+2)%3,S=(A+1)%3,E=-1,L=-1,C=0;C<2;++C){var P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);o(c[O],c[P],c[I],f)<0||(C?E=1:L=1)}if(E<0||L<0)L>E&&(w|=1<<A);else{for(C=0;C<2;++C){P=(O=C<<A)+(C<<M)+(1-C<<S),I=O+(1-C<<M)+(C<<S);var O,z=d([l[O],l[P],l[I],l[O+(1<<M)+(1<<S)]]);C?E=z:L=z}L>E&&(w|=1<<A)}}}var D=7^w,R=-1;for(x=0;x<8;++x)x!==w&&x!==D&&(R<0||c[R][1]>c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x){if((N=R^1<<x)!==w&&N!==D)F<0&&(F=N),(S=c[N])[0]<c[F][0]&&(F=N)}var B=-1;for(x=0;x<3;++x){var N;if((N=R^1<<x)!==w&&N!==D&&N!==F)B<0&&(B=N),(S=c[N])[0]>c[B][0]&&(B=N)}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1<<A?-1:1;return v};var n=t(\"bit-twiddle\"),i=t(\"gl-mat4/multiply\"),a=t(\"split-polygon\"),o=t(\"robust-orientation\"),s=new Array(16),l=new Array(8),c=new Array(8),u=new Array(3),f=[0,0,0];function h(t,e,r){for(var n=0;n<4;++n){t[n]=r[12+n];for(var i=0;i<3;++i)t[n]+=e[i]*r[4*i+n]}}!function(){for(var t=0;t<8;++t)l[t]=[1,1,1,1],c[t]=[1,1,1]}();var p=[[0,0,1,0,0],[0,0,-1,1,0],[0,-1,0,1,0],[0,1,0,1,0],[-1,0,0,1,0],[1,0,0,1,0]];function d(t){for(var e=0;e<p.length;++e)if((t=a.positive(t,p[e])).length<3)return 0;var r=t[0],n=r[0]/r[3],i=r[1]/r[3],o=0;for(e=1;e+1<t.length;++e){var s=t[e],l=t[e+1],c=s[0]/s[3]-n,u=s[1]/s[3]-i,f=l[0]/l[3]-n,h=l[1]/l[3]-i;o+=Math.abs(c*h-u*f)}return o}var m=[1,1,1],g=[0,0,0],v={cubeEdges:m,axis:g}},{\"bit-twiddle\":32,\"gl-mat4/multiply\":100,\"robust-orientation\":284,\"split-polygon\":300}],73:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var o=[],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[0,0,0];o.push(0,0,1,0,1,1,0,0,-1,0,0,-1,0,1,1,0,1,-1);for(var f=0;f<3;++f){for(var h=o.length/3|0,d=0;d<r[f].length;++d){var m=+r[f][d].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}var g=o.length/3|0;s[f]=h,l[f]=g-h;h=o.length/3|0;for(var v=0;v<r[f].length;++v){m=+r[f][v].x;o.push(m,0,1,m,1,1,m,0,-1,m,0,-1,m,1,1,m,1,-1)}g=o.length/3|0;c[f]=h,u[f]=g-h}var y=n(t,new Float32Array(o)),x=i(t,[{buffer:y,type:t.FLOAT,size:3,stride:0,offset:0}]),b=a(t);return b.attributes.position.location=0,new p(t,y,x,b,l,s,u,c)};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders\").line,o=[0,0,0],s=[0,0,0],l=[0,0,0],c=[0,0,0],u=[1,1];function f(t){return t[0]=t[1]=t[2]=0,t}function h(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t,e,r,n,i,a,o,s){this.gl=t,this.vertBuffer=e,this.vao=r,this.shader=n,this.tickCount=i,this.tickOffset=a,this.gridCount=o,this.gridOffset=s}var d=p.prototype;d.bind=function(t,e,r){this.shader.bind(),this.shader.uniforms.model=t,this.shader.uniforms.view=e,this.shader.uniforms.projection=r,u[0]=this.gl.drawingBufferWidth,u[1]=this.gl.drawingBufferHeight,this.shader.uniforms.screenShape=u,this.vao.bind()},d.unbind=function(){this.vao.unbind()},d.drawAxisLine=function(t,e,r,n,i){var a=f(s);this.shader.uniforms.majorAxis=s,a[t]=e[1][t]-e[0][t],this.shader.uniforms.minorAxis=a;var o,u=h(c,r);u[t]+=e[0][t],this.shader.uniforms.offset=u,this.shader.uniforms.lineWidth=i,this.shader.uniforms.color=n,(o=f(l))[(t+2)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6),(o=f(l))[(t+1)%3]=1,this.shader.uniforms.screenAxis=o,this.vao.draw(this.gl.TRIANGLES,6)},d.drawAxisTicks=function(t,e,r,n,i){if(this.tickCount[t]){var a=f(o);a[t]=1,this.shader.uniforms.majorAxis=a,this.shader.uniforms.offset=e,this.shader.uniforms.minorAxis=r,this.shader.uniforms.color=n,this.shader.uniforms.lineWidth=i;var s=f(l);s[t]=1,this.shader.uniforms.screenAxis=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t])}},d.drawGrid=function(t,e,r,n,i,a){if(this.gridCount[t]){var u=f(s);u[e]=r[1][e]-r[0][e],this.shader.uniforms.minorAxis=u;var p=h(c,n);p[e]+=r[0][e],this.shader.uniforms.offset=p;var d=f(o);d[t]=1,this.shader.uniforms.majorAxis=d;var m=f(l);m[t]=1,this.shader.uniforms.screenAxis=m,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,this.gridCount[t],this.gridOffset[t])}},d.drawZero=function(t,e,r,n,i,a){var o=f(s);this.shader.uniforms.majorAxis=o,o[t]=r[1][t]-r[0][t],this.shader.uniforms.minorAxis=o;var u=h(c,n);u[t]+=r[0][t],this.shader.uniforms.offset=u;var p=f(l);p[e]=1,this.shader.uniforms.screenAxis=p,this.shader.uniforms.lineWidth=a,this.shader.uniforms.color=i,this.vao.draw(this.gl.TRIANGLES,6)},d.dispose=function(){this.vao.dispose(),this.vertBuffer.dispose(),this.shader.dispose()}},{\"./shaders\":74,\"gl-buffer\":78,\"gl-vao\":150}],74:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, majorAxis, minorAxis, screenAxis;\\nuniform float lineWidth;\\nuniform vec2 screenShape;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nvoid main() {\\n  vec3 major = position.x * majorAxis;\\n  vec3 minor = position.y * minorAxis;\\n\\n  vec3 vPosition = major + minor + offset;\\n  vec3 pPosition = project(vPosition);\\n  vec3 offset = project(vPosition + screenAxis * position.z);\\n\\n  vec2 screen = normalize((offset - pPosition).xy * screenShape) / screenShape;\\n\\n  gl_Position = vec4(pPosition + vec3(0.5 * screen * lineWidth, 0), 1.0);\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.line=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"}])};var s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 offset, axis, alignDir, alignOpt;\\nuniform float scale, angle, pixelScale;\\nuniform vec2 resolution;\\n\\nvec3 project(vec3 p) {\\n  vec4 pp = projection * view * model * vec4(p, 1.0);\\n  return pp.xyz / max(pp.w, 0.0001);\\n}\\n\\nfloat computeViewAngle(vec3 a, vec3 b) {\\n  vec3 A = project(a);\\n  vec3 B = project(b);\\n\\n  return atan(\\n    (B.y - A.y) * resolution.y,\\n    (B.x - A.x) * resolution.x\\n  );\\n}\\n\\nconst float PI = 3.141592;\\nconst float TWO_PI = 2.0 * PI;\\nconst float HALF_PI = 0.5 * PI;\\nconst float ONE_AND_HALF_PI = 1.5 * PI;\\n\\nint option = int(floor(alignOpt.x + 0.001));\\nfloat hv_ratio =       alignOpt.y;\\nbool enableAlign =    (alignOpt.z != 0.0);\\n\\nfloat mod_angle(float a) {\\n  return mod(a, PI);\\n}\\n\\nfloat positive_angle(float a) {\\n  return mod_angle((a < 0.0) ?\\n    a + TWO_PI :\\n    a\\n  );\\n}\\n\\nfloat look_upwards(float a) {\\n  float b = positive_angle(a);\\n  return ((b > HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n    b - PI :\\n    b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n  // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n  // if ratio is set to 0.5 then it is 50%, 50%.\\n  // when using a higher ratio e.g. 0.75 the result would\\n  // likely be more horizontal than vertical.\\n\\n  float b = positive_angle(a);\\n\\n  return\\n    (b < (      ratio) * HALF_PI) ? 0.0 :\\n    (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n    (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n    (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n                                    0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n  return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n  float b = positive_angle(a);\\n  float div = TWO_PI / float(n);\\n  float c = roundTo(b, div);\\n  return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n  return\\n    (option >  2) ? look_round_n_directions(rawAngle + delta, option) :       // option 3-n: round to n directions\\n    (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n    (option == 1) ? rawAngle + delta :       // use free angle, and flip to align with one direction of the axis\\n    (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n    (option ==-1) ? 0.0 :                    // useful for backward compatibility, all texts remains horizontal\\n                    rawAngle;                // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n                   (axis.y == 0.0) &&\\n                   (axis.z == 0.0);\\n\\nvoid main() {\\n  //Compute world offset\\n  float axisDistance = position.z;\\n  vec3 dataPosition = axisDistance * axis + offset;\\n\\n  float beta = angle; // i.e. user defined attributes for each tick\\n\\n  float axisAngle;\\n  float clipAngle;\\n  float flip;\\n\\n  if (enableAlign) {\\n    axisAngle = (isAxisTitle) ? HALF_PI :\\n                      computeViewAngle(dataPosition, dataPosition + axis);\\n    clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n    axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n    clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n    flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n                vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n    beta += applyAlignOption(clipAngle, flip * PI);\\n  }\\n\\n  //Compute plane offset\\n  vec2 planeCoord = position.xy * pixelScale;\\n\\n  mat2 planeXform = scale * mat2(\\n     cos(beta), sin(beta),\\n    -sin(beta), cos(beta)\\n  );\\n\\n  vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n  //Compute clip position\\n  vec3 clipPosition = project(dataPosition);\\n\\n  //Apply text offset in clip coordinates\\n  clipPosition += vec3(viewOffset, 0.0);\\n\\n  //Done\\n  gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = color;\\n}\"]);r.text=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n  vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n  vec3 realNormal = signAxis * normal;\\n\\n  if(dot(realNormal, enable) > 0.0) {\\n    vec3 minRange = min(bounds[0], bounds[1]);\\n    vec3 maxRange = max(bounds[0], bounds[1]);\\n    vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n    gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n  } else {\\n    gl_Position = vec4(0,0,0,0);\\n  }\\n\\n  colorChannel = abs(realNormal);\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n  gl_FragColor = colorChannel.x * colors[0] +\\n                 colorChannel.y * colors[1] +\\n                 colorChannel.z * colors[2];\\n}\"]);r.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],75:[function(t,e,r){(function(r){(function(){\"use strict\";e.exports=function(t,e,r,a,s,l){var u=n(t),f=i(t,[{buffer:u,size:3}]),h=o(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,a,s,l),p};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"vectorize-text\"),o=t(\"./shaders\").text,s=window||r.global||{},l=s.__TEXT_CACHE||{};s.__TEXT_CACHE={};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}var u=c.prototype,f=[0,0];u.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},u.unbind=function(){this.vao.unbind()},u.update=function(t,e,r,n,i){var o=[];function s(t,e,r,n,i,s){var c=l[r];c||(c=l[r]={});var u=c[e];u||(u=c[e]=function(t,e){try{return a(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=u.positions,p=u.cells,d=0,m=p.length;d<m;++d)for(var g=p[d],v=2;v>=0;--v){var y=h[g[v]];o.push(f*y[0],-f*y[1],t)}}for(var c=[0,0,0],u=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=o.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(o.length/3|0)-f[d],c[d]=o.length/3|0;for(var m=0;m<n[d].length;++m)n[d][m].text&&s(n[d][m].x,n[d][m].text,n[d][m].font||i,n[d][m].fontSize||12,1.25,p);u[d]=(o.length/3|0)-c[d]}this.buffer.update(o),this.tickOffset=c,this.tickCount=u,this.labelOffset=f,this.labelCount=h},u.drawTicks=function(t,e,r,n,i,a,o,s){this.tickCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.tickCount[t],this.tickOffset[t]))},u.drawLabel=function(t,e,r,n,i,a,o,s){this.labelCount[t]&&(this.shader.uniforms.axis=a,this.shader.uniforms.color=i,this.shader.uniforms.angle=r,this.shader.uniforms.scale=e,this.shader.uniforms.offset=n,this.shader.uniforms.alignDir=o,this.shader.uniforms.alignOpt=s,this.vao.draw(this.gl.TRIANGLES,this.labelCount[t],this.labelOffset[t]))},u.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()}}).call(this)}).call(this,t(\"_process\"))},{\"./shaders\":74,_process:5,\"gl-buffer\":78,\"gl-vao\":150,\"vectorize-text\":311}],76:[function(t,e,r){\"use strict\";function n(t,e){var r=t+\"\",n=r.indexOf(\".\"),i=0;n>=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var f=\"\"+c;f.length<i;)f=\"0\"+f;return u+\".\"+f}return u}r.create=function(t,e){for(var r=[],i=0;i<3;++i){for(var a=[],o=(t[0][i],t[1][i],0);o*e[i]<=t[1][i];++o)a.push({x:o*e[i],text:n(e[i],o)});for(o=-1;o*e[i]>=t[0][i];--o)a.push({x:o*e[i],text:n(e[i],o)});r.push(a)}return r},r.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;n<t[r].length;++n){var i=t[r][n],a=e[r][n];if(i.x!==a.x||i.text!==a.text||i.font!==a.font||i.fontColor!==a.fontColor||i.fontSize!==a.fontSize||i.dx!==a.dx||i.dy!==a.dy)return!1}}return!0}},{}],77:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,l,f){var h=e.model||c,p=e.view||c,v=e.projection||c,y=e._ortho||!1,x=t.bounds,b=(f=f||a(h,p,v,x,y)).axis;o(u,p,h),o(u,v,u);for(var _=m,w=0;w<3;++w)_[w].lo=1/0,_[w].hi=-1/0,_[w].pixelsPerDataUnit=1/0;var T=n(s(u,u));s(u,u);for(var k=0;k<3;++k){var A=(k+1)%3,M=(k+2)%3,S=g;t:for(w=0;w<2;++w){var E=[];if(b[k]<0!=!!w){S[k]=x[w][k];for(var L=0;L<2;++L){S[A]=x[L^w][A];for(var C=0;C<2;++C)S[M]=x[C^L^w][M],E.push(S.slice())}var P=y?5:4;for(L=P;L===P;++L){if(0===E.length)continue t;E=i.positive(E,T[L])}for(L=0;L<E.length;++L){M=E[L];var I=d(g,u,M,r,l);for(C=0;C<3;++C)_[C].lo=Math.min(_[C].lo,M[C]),_[C].hi=Math.max(_[C].hi,M[C]),C!==k&&(_[C].pixelsPerDataUnit=Math.min(_[C].pixelsPerDataUnit,Math.abs(I[C])))}}}}return _};var n=t(\"extract-frustum-planes\"),i=t(\"split-polygon\"),a=t(\"./lib/cube.js\"),o=t(\"gl-mat4/multiply\"),s=t(\"gl-mat4/transpose\"),l=t(\"gl-vec4/transformMat4\"),c=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),u=new Float32Array(16);function f(t,e,r){this.lo=t,this.hi=e,this.pixelsPerDataUnit=r}var h=[0,0,0,1],p=[0,0,0,1];function d(t,e,r,n,i){for(var a=0;a<3;++a){for(var o=h,s=p,c=0;c<3;++c)s[c]=o[c]=r[c];s[3]=o[3]=1,s[a]+=1,l(s,s,e),s[3]<0&&(t[a]=1/0),o[a]-=1,l(o,o,e),o[3]<0&&(t[a]=1/0);var u=(o[0]/o[3]-s[0]/s[3])*n,f=(o[1]/o[3]-s[1]/s[3])*i;t[a]=.25*Math.sqrt(u*u+f*f)}return t}var m=[new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0),new f(1/0,-1/0,1/0)],g=[0,0,0]},{\"./lib/cube.js\":72,\"extract-frustum-planes\":67,\"gl-mat4/multiply\":100,\"gl-mat4/transpose\":109,\"gl-vec4/transformMat4\":221,\"split-polygon\":300}],78:[function(t,e,r){\"use strict\";var n=t(\"typedarray-pool\"),i=t(\"ndarray-ops\"),a=t(\"ndarray\"),o=[\"uint8\",\"uint8_clamped\",\"uint16\",\"uint32\",\"int8\",\"int16\",\"int32\",\"float32\"];function s(t,e,r,n,i){this.gl=t,this.type=e,this.handle=r,this.length=n,this.usage=i}var l=s.prototype;function c(t,e,r,n,i,a){var o=i.length*i.BYTES_PER_ELEMENT;if(a<0)return t.bufferData(e,i,n),o;if(o+a>r)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a<i;++a)r[a]=t[a];return r}l.bind=function(){this.gl.bindBuffer(this.type,this.handle)},l.unbind=function(){this.gl.bindBuffer(this.type,null)},l.dispose=function(){this.gl.deleteBuffer(this.handle)},l.update=function(t,e){if(\"number\"!=typeof e&&(e=-1),this.bind(),\"object\"==typeof t&&void 0!==t.shape){var r=t.dtype;if(o.indexOf(r)<0&&(r=\"float32\"),this.type===this.gl.ELEMENT_ARRAY_BUFFER)r=gl.getExtension(\"OES_element_index_uint\")&&\"uint16\"!==r?\"uint32\":\"uint16\";if(r===t.dtype&&function(t,e){for(var r=1,n=e.length-1;n>=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},e.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},{ndarray:259,\"ndarray-ops\":254,\"typedarray-pool\":308}],79:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\");e.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,f=1/0,h=-1/0,p=null,d=null,m=[],g=1/0,v=!1,y=0;y<r.length;y++){var x=r[y];s=Math.min(x[0],s),l=Math.max(x[0],l),c=Math.min(x[1],c),u=Math.max(x[1],u),f=Math.min(x[2],f),h=Math.max(x[2],h);var b=i[y];if(n.length(b)>o&&(o=n.length(b)),y){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),v=!1):v=!0}v||(p=x,d=b),m.push(b)}var w=[s,c,f],T=[l,u,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A;y=0;for(var M=0;y<r.length;y++)for(var S=(x=r[y])[0],E=x[1],L=x[2],C=m[y],P=n.length(C)*k,I=0;I<8;I++){a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.positions.push([S,E,L,M++]),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vectors.push(C),a.vertexIntensity.push(P,P,P),a.vertexIntensity.push(P,P,P);var O=a.positions.length;a.cells.push([O-6,O-5,O-4],[O-3,O-2,O-1])}return a};var i=t(\"./lib/shaders\");e.exports.createMesh=t(\"./create_mesh\"),e.exports.createConeMesh=function(t,r){return e.exports.createMesh(t,r,{shaders:i,traceType:\"cone\"})}},{\"./create_mesh\":80,\"./lib/shaders\":81,\"gl-vec3\":169}],80:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"gl-mat4/multiply\"),l=t(\"gl-mat4/invert\"),c=t(\"ndarray\"),u=t(\"colormap\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e,r,n,i,a,o,s,l,c,u){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.pickShader=n,this.trianglePositions=i,this.triangleVectors=a,this.triangleColors=s,this.triangleUVs=l,this.triangleIds=o,this.triangleVAO=c,this.triangleCount=0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.traceType=u,this.tubeScale=1,this.coneScale=2,this.vectorScale=1,this.coneOffset=.25,this._model=f,this._view=f,this._projection=f,this._resolution=[1,1]}var p=h.prototype;function d(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}function m(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}p.isOpaque=function(){return this.opacity>=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g<p.length;++g){var v=p[g];d=Math.min(d,v),m=Math.max(m,v)}else for(g=0;g<n.length;++g){v=n[g][2];d=Math.min(d,v),m=Math.max(m,v)}this.intensity=p||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n),this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(g=0;g<n.length;++g)for(var y=n[g],x=0;x<3;++x)!isNaN(y[x])&&isFinite(y[x])&&(this.bounds[0][x]=Math.min(this.bounds[0][x],y[x]),this.bounds[1][x]=Math.max(this.bounds[1][x],y[x]));var b=0;t:for(g=0;g<r.length;++g){var _=r[g];switch(_.length){case 3:for(x=0;x<3;++x){y=n[T=_[x]];for(var w=0;w<3;++w)if(isNaN(y[w])||!isFinite(y[w]))continue t}for(x=0;x<3;++x){var T;y=n[T=_[2-x]];a.push(y[0],y[1],y[2],y[3]);var k=i[T];o.push(k[0],k[1],k[2],k[3]||0);var A,M=h;3===M.length?s.push(M[0],M[1],M[2],1):s.push(M[0],M[1],M[2],M[3]),A=p?[(p[T]-d)/(m-d),0]:[(y[2]-d)/(m-d),0],l.push(A[0],A[1]),f.push(g)}b+=1}}this.triangleCount=b,this.trianglePositions.update(a),this.triangleVectors.update(o),this.triangleColors.update(s),this.triangleUVs.update(l),this.triangleIds.update(new Uint32Array(f))}},p.drawTransparent=p.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var c={model:r,view:n,projection:i,inverseModel:f.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],opacity:this.opacity,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,texture:0};c.inverseModel=l(c.inverseModel,c.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);s(u,c.view,c.model),s(u,c.projection,u),l(u,u);for(o=0;o<3;++o)c.eyePosition[o]=u[12+o]/u[15];var h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];c.lightPosition[o]=p/h}if(this.triangleCount>0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},e.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=m(t,n),u=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));u.generateMipmap(),u.minFilter=t.LINEAR_MIPMAP_LINEAR,u.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:v,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,u,s,l,f,p,y,g,v,x,r.traceType||\"cone\");return b.update(e),b}},{colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],81:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * conePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(conePosition, 1.0);\\n  vec4 t_position  = view * conePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = conePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n//   segment + 0 top vertex\\n//   segment + 1 perimeter vertex a+1\\n//   segment + 2 perimeter vertex a\\n//   segment + 3 center base vertex\\n//   segment + 4 perimeter vertex a\\n//   segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n  const float segmentCount = 8.0;\\n\\n  float index = rawIndex - floor(rawIndex /\\n    (segmentCount * 6.0)) *\\n    (segmentCount * 6.0);\\n\\n  float segment = floor(0.001 + index/6.0);\\n  float segmentIndex = index - (segment*6.0);\\n\\n  normal = -normalize(d);\\n\\n  if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n    return mix(vec3(0.0), -d, coneOffset);\\n  }\\n\\n  float nextAngle = (\\n    (segmentIndex > 0.99 &&  segmentIndex < 1.01) ||\\n    (segmentIndex > 4.99 &&  segmentIndex < 5.01)\\n  ) ? 1.0 : 0.0;\\n  float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n  vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n  vec3 v2 = v1 - d;\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d)*0.25;\\n  vec3 y = v * sin(angle) * length(d)*0.25;\\n  vec3 v3 = v2 + x + y;\\n  if (segmentIndex < 3.0) {\\n    vec3 tx = u * sin(angle);\\n    vec3 ty = v * -cos(angle);\\n    vec3 tangent = tx + ty;\\n    normal = normalize(cross(v3 - v1, tangent));\\n  }\\n\\n  if (segmentIndex == 0.0) {\\n    return mix(d, vec3(0.0), coneOffset);\\n  }\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n  vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n  gl_Position = projection * view * conePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},{glslify:231}],82:[function(t,e,r){e.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},{}],83:[function(t,e,r){var n=t(\"./1.0/numbers\");e.exports=function(t){return n[t]}},{\"./1.0/numbers\":82}],84:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\"),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho||!1?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var f=0;f<3;++f)e.lineWidth(this.lineWidth[f]*this.pixelRatio),r.capSize=this.capSize[f]*u,this.lineCount[f]&&e.drawArrays(e.LINES,this.lineOffset[f],this.lineCount[f]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function f(t,e,r,n){for(var i=u[n],a=0;a<i.length;++a){var o=i[a];t.push(e[0],e[1],e[2],r[0],r[1],r[2],r[3],o[0],o[1],o[2])}return i.length}l.update=function(t){\"lineWidth\"in(t=t||{})&&(this.lineWidth=t.lineWidth,Array.isArray(this.lineWidth)||(this.lineWidth=[this.lineWidth,this.lineWidth,this.lineWidth])),\"capSize\"in t&&(this.capSize=t.capSize,Array.isArray(this.capSize)||(this.capSize=[this.capSize,this.capSize,this.capSize])),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var e=t.color||[[0,0,0],[0,0,0],[0,0,0]],r=t.position,n=t.error;if(Array.isArray(e[0])||(e=[e,e,e]),r&&n){var i=[],a=r.length,o=0;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.lineCount=[0,0,0];for(var s=0;s<3;++s){this.lineOffset[s]=o;t:for(var l=0;l<a;++l){for(var u=r[l],h=0;h<3;++h)if(isNaN(u[h])||!isFinite(u[h]))continue t;var p=n[l],d=e[s];if(Array.isArray(d[0])&&(d=e[l]),3===d.length?d=[d[0],d[1],d[2],1]:4===d.length&&(d=[d[0],d[1],d[2],d[3]],!this.hasAlpha&&d[3]<1&&(this.hasAlpha=!0)),!isNaN(p[0][s])&&!isNaN(p[1][s])){var m;if(p[0][s]<0)(m=u.slice())[s]+=p[0][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s);if(p[1][s]>0)(m=u.slice())[s]+=p[1][s],i.push(u[0],u[1],u[2],d[0],d[1],d[2],d[3],0,0,0,m[0],m[1],m[2],d[0],d[1],d[2],d[3],0,0,0),c(this.bounds,m),o+=2+f(i,m,d,s)}}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},{\"./shaders/index\":85,\"gl-buffer\":78,\"gl-vao\":150}],85:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n  vec4 worldPosition  = model * vec4(position, 1.0);\\n  worldPosition       = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n  gl_Position         = projection * view * worldPosition;\\n  fragColor           = color;\\n  fragPosition        = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  gl_FragColor = opacity * fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},{\"gl-shader\":132,glslify:231}],86:[function(t,e,r){\"use strict\";var n=t(\"gl-texture2d\");e.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;a<n;++a)i[a]=t.COLOR_ATTACHMENT0+a;for(a=n;a<r;++a)i[a]=t.NONE;l[n]=i}}(t,c);Array.isArray(e)&&(n=r,r=0|e[1],e=0|e[0]);if(\"number\"!=typeof e)throw new Error(\"gl-fbo: Missing shape parameter\");var u=t.getParameter(t.MAX_RENDERBUFFER_SIZE);if(e<0||e>u||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else n.preferFloat&&f>0&&p&&(h=t.FLOAT);var m=!0;\"depth\"in n&&(m=!!n.depth);var g=!1;\"stencil\"in n&&(g=!!n.stencil);return new d(t,e,r,h,f,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function f(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function h(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d<i;++d)this.color[d]=null;this._color_rb=null,this.depth=null,this._depth_rb=null,this._colorType=n,this._useDepth=a,this._useStencil=o;var m=this,g=[0|e,0|r];Object.defineProperties(g,{0:{get:function(){return m._shape[0]},set:function(t){return m.width=t}},1:{get:function(){return m._shape[1]},set:function(t){return m.height=t}}}),this._shapeVector=g,function(t){var e=c(t.gl),r=t.gl,n=t.handle=r.createFramebuffer(),i=t._shape[0],a=t._shape[1],o=t.color.length,s=t._ext,d=t._useStencil,m=t._useDepth,g=t._colorType;r.bindFramebuffer(r.FRAMEBUFFER,n);for(var v=0;v<o;++v)t.color[v]=h(r,i,a,g,r.RGBA,r.COLOR_ATTACHMENT0+v);0===o?(t._color_rb=p(r,i,a,r.RGBA4,r.COLOR_ATTACHMENT0),s&&s.drawBuffersWEBGL(l[0])):o>1&&s.drawBuffersWEBGL(l[o]);var y=r.getExtension(\"WEBGL_depth_texture\");y?d?t.depth=h(r,i,a,y.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=h(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null);for(v=0;v<t.color.length;++v)t.color[v].dispose(),t.color[v]=null;t._color_rb&&(r.deleteRenderbuffer(t._color_rb),t._color_rb=null),u(r,e),f(x)}u(r,e)}(this)}var m=d.prototype;function g(t,e,r){if(t._destroyed)throw new Error(\"gl-fbo: Can't resize destroyed FBO\");if(t._shape[0]!==e||t._shape[1]!==r){var n=t.gl,i=n.getParameter(n.MAX_RENDERBUFFER_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o<t.color.length;++o)t.color[o].shape=t._shape;t._color_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._color_rb),n.renderbufferStorage(n.RENDERBUFFER,n.RGBA4,t._shape[0],t._shape[1])),t.depth&&(t.depth.shape=t._shape),t._depth_rb&&(n.bindRenderbuffer(n.RENDERBUFFER,t._depth_rb),t._useDepth&&t._useStencil?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_STENCIL,t._shape[0],t._shape[1]):t._useDepth?n.renderbufferStorage(n.RENDERBUFFER,n.DEPTH_COMPONENT16,t._shape[0],t._shape[1]):t._useStencil&&n.renderbufferStorage(n.RENDERBUFFER,n.STENCIL_INDEX,t._shape[0],t._shape[1])),n.bindFramebuffer(n.FRAMEBUFFER,t.handle);var s=n.checkFramebufferStatus(n.FRAMEBUFFER);s!==n.FRAMEBUFFER_COMPLETE&&(t.dispose(),u(n,a),f(s)),u(n,a)}}Object.defineProperties(m,{shape:{get:function(){return this._destroyed?[0,0]:this._shapeVector},set:function(t){if(Array.isArray(t)||(t=[0|t,0|t]),2!==t.length)throw new Error(\"gl-fbo: Shape vector must be length 2\");var e=0|t[0],r=0|t[1];return g(this,e,r),[e,r]},enumerable:!1},width:{get:function(){return this._destroyed?0:this._shape[0]},set:function(t){return g(this,t|=0,this._shape[1]),t},enumerable:!1},height:{get:function(){return this._destroyed?0:this._shape[1]},set:function(t){return t|=0,g(this,this._shape[0],t),t},enumerable:!1}}),m.bind=function(){if(!this._destroyed){var t=this.gl;t.bindFramebuffer(t.FRAMEBUFFER,this.handle),t.viewport(0,0,this._shape[0],this._shape[1])}},m.dispose=function(){if(!this._destroyed){this._destroyed=!0;var t=this.gl;t.deleteFramebuffer(this.handle),this.handle=null,this.depth&&(this.depth.dispose(),this.depth=null),this._depth_rb&&(t.deleteRenderbuffer(this._depth_rb),this._depth_rb=null);for(var e=0;e<this.color.length;++e)this.color[e].dispose(),this.color[e]=null;this._color_rb&&(t.deleteRenderbuffer(this._color_rb),this._color_rb=null)}}},{\"gl-texture2d\":146}],87:[function(t,e,r){var n=t(\"sprintf-js\").sprintf,i=t(\"gl-constants/lookup\"),a=t(\"glsl-shader-name\"),o=t(\"add-line-numbers\");e.exports=function(t,e,r){\"use strict\";var s=a(e)||\"of unknown name (see npm glsl-shader-name)\",l=\"unknown type\";void 0!==r&&(l=r===i.FRAGMENT_SHADER?\"fragment\":\"vertex\");for(var c=n(\"Error compiling %s shader %s:\\n\",l,s),u=n(\"%s%s\",c,t),f=t.split(\"\\n\"),h={},p=0;p<f.length;p++){var d=f[p];if(\"\"!==d&&\"\\0\"!==d){var m=parseInt(d.split(\":\")[2]);if(isNaN(m))throw new Error(n(\"Could not parse error: %s\",d));h[m]=d}}var g=o(e).split(\"\\n\");for(p=0;p<g.length;p++)if(h[p+3]||h[p+2]||h[p+1]){var v=g[p];if(c+=v+\"\\n\",h[p+1]){var y=h[p+1];y=y.substr(y.split(\":\",3).join(\":\").length+1).trim(),c+=n(\"^^^ %s\\n\\n\",y)}}return{long:c.trim(),short:u.trim()}}},{\"add-line-numbers\":9,\"gl-constants/lookup\":83,\"glsl-shader-name\":223,\"sprintf-js\":301}],88:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.gl,n=o(r,l.vertex,l.fragment),i=o(r,l.pickVertex,l.pickFragment),a=s(r),u=s(r),f=s(r),h=s(r),p=new c(t,n,i,a,u,f,h);return p.update(e),t.addObject(p),p};var n=t(\"binary-search-bounds\"),i=t(\"iota-array\"),a=t(\"typedarray-pool\"),o=t(\"gl-shader\"),s=t(\"gl-buffer\"),l=t(\"./lib/shaders\");function c(t,e,r,n,i,a,o){this.plot=t,this.shader=e,this.pickShader=r,this.positionBuffer=n,this.weightBuffer=i,this.colorBuffer=a,this.idBuffer=o,this.xData=[],this.yData=[],this.shape=[0,0],this.bounds=[1/0,1/0,-1/0,-1/0],this.pickOffset=0}var u,f=c.prototype,h=[0,0,1,0,0,1,1,0,1,1,0,1];f.draw=(u=[1,0,0,0,1,0,0,0,1],function(){var t=this.plot,e=this.shader,r=this.bounds,n=this.numVertices;if(!(n<=0)){var i=t.gl,a=t.dataBox,o=r[2]-r[0],s=r[3]-r[1],l=a[2]-a[0],c=a[3]-a[1];u[0]=2*o/l,u[4]=2*s/c,u[6]=2*(r[0]-a[0])/l-1,u[7]=2*(r[1]-a[1])/c-1,e.bind();var f=e.uniforms;f.viewTransform=u,f.shape=this.shape;var h=e.attributes;this.positionBuffer.bind(),h.position.pointer(),this.weightBuffer.bind(),h.weight.pointer(i.UNSIGNED_BYTE,!1),this.colorBuffer.bind(),h.color.pointer(i.UNSIGNED_BYTE,!0),i.drawArrays(i.TRIANGLES,0,n)}}),f.drawPick=function(){var t=[1,0,0,0,1,0,0,0,1],e=[0,0,0,0];return function(r){var n=this.plot,i=this.pickShader,a=this.bounds,o=this.numVertices;if(!(o<=0)){var s=n.gl,l=n.dataBox,c=a[2]-a[0],u=a[3]-a[1],f=l[2]-l[0],h=l[3]-l[1];t[0]=2*c/f,t[4]=2*u/h,t[6]=2*(a[0]-l[0])/f-1,t[7]=2*(a[1]-l[1])/h-1;for(var p=0;p<4;++p)e[p]=r>>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r<n||r>=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,f,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,v=this.bounds;l?(c=v[0]=r[0],u=v[1]=o[0],f=v[2]=r[r.length-1],p=v[3]=o[o.length-1]):(c=v[0]=r[0]+(r[1]-r[0])/2,u=v[1]=o[0]+(o[1]-o[0])/2,f=v[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=v[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var y=1/(f-c),x=1/(p-u),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C<L;++C){var P,I;l?(P=x*(o[C]-u),I=x*(o[C+1]-u)):(P=C<_-1?x*(o[C]-(o[C+1]-o[C])/2-u):x*(o[C]-(o[C]-o[C-1])/2-u),I=C<_-1?x*(o[C]+(o[C+1]-o[C])/2-u):x*(o[C]+(o[C]-o[C-1])/2-u));for(var O=0;O<E;++O){var z,D;l?(z=y*(r[O]-c),D=y*(r[O+1]-c)):(z=O<b-1?y*(r[O]-(r[O+1]-r[O])/2-c):y*(r[O]-(r[O]-r[O-1])/2-c),D=O<b-1?y*(r[O]+(r[O+1]-r[O])/2-c):y*(r[O]+(r[O]-r[O-1])/2-c));for(var R=0;R<h.length;R+=2){var F,B,N,j,U=h[R],V=h[R+1],H=s[l?(C+V)*b+(O+U):C*b+O],q=n.le(d,H);if(q<0)F=m[0],B=m[1],N=m[2],j=m[3];else if(q===g-1)F=m[4*g-4],B=m[4*g-3],N=m[4*g-2],j=m[4*g-1];else{var G=(H-d[q])/(d[q+1]-d[q]),Y=1-G,W=4*q,X=4*(q+1);F=Y*m[W]+G*m[X],B=Y*m[W+1]+G*m[X+1],N=Y*m[W+2]+G*m[X+2],j=Y*m[W+3]+G*m[X+3]}T[4*S]=255*F,T[4*S+1]=255*B,T[4*S+2]=255*N,T[4*S+3]=255*j,k[2*S]=.5*z+.5*D,k[2*S+1]=.5*P+.5*I,A[2*S]=U,A[2*S+1]=V,M[S]=C*b+O,S+=1}}}this.positionBuffer.update(k),this.weightBuffer.update(A),this.colorBuffer.update(T),this.idBuffer.update(M),a.free(k),a.free(T),a.free(A),a.free(M)},f.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.positionBuffer.dispose(),this.weightBuffer.dispose(),this.colorBuffer.dispose(),this.idBuffer.dispose(),this.plot.removeObject(this)}},{\"./lib/shaders\":89,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"iota-array\":235,\"typedarray-pool\":308}],89:[function(t,e,r){\"use strict\";var n=t(\"glslify\");e.exports={fragment:n([\"precision lowp float;\\n#define GLSLIFY 1\\nvarying vec4 fragColor;\\nvoid main() {\\n  gl_FragColor = vec4(fragColor.rgb * fragColor.a, fragColor.a);\\n}\\n\"]),vertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 color;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  fragColor = color;\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"]),pickFragment:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nuniform vec2 shape;\\nuniform vec4 pickOffset;\\n\\nvoid main() {\\n  vec2 d = step(.5, vWeight);\\n  vec4 id = fragId + pickOffset;\\n  id.x += d.x + d.y*shape.x;\\n\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  gl_FragColor = id/255.;\\n}\\n\"]),pickVertex:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\nattribute vec2 weight;\\n\\nuniform vec2 shape;\\nuniform mat3 viewTransform;\\n\\nvarying vec4 fragId;\\nvarying vec2 vWeight;\\n\\nvoid main() {\\n  vWeight = weight;\\n\\n  fragId = pickId;\\n\\n  vec3 vPosition = viewTransform * vec3( position + (weight-.5)/(shape-1.) , 1.0);\\n  gl_Position = vec4(vPosition.xy, 0, vPosition.z);\\n}\\n\"])}},{glslify:231}],90:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, nextPosition;\\nattribute float arcLength, lineWidth;\\nattribute vec4 color;\\n\\nuniform vec2 screenShape;\\nuniform float pixelRatio;\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 fragColor;\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  vec4 startPoint = project(position);\\n  vec4 endPoint   = project(nextPosition);\\n\\n  vec2 A = startPoint.xy / startPoint.w;\\n  vec2 B =   endPoint.xy /   endPoint.w;\\n\\n  float clipAngle = atan(\\n    (B.y - A.y) * screenShape.y,\\n    (B.x - A.x) * screenShape.x\\n  );\\n\\n  vec2 offset = 0.5 * pixelRatio * lineWidth * vec2(\\n    sin(clipAngle),\\n    -cos(clipAngle)\\n  ) / screenShape;\\n\\n  gl_Position = vec4(startPoint.xy + startPoint.w * offset, startPoint.zw);\\n\\n  worldPosition = position;\\n  pixelArcLength = arcLength;\\n  fragColor = color;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3      clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float     dashScale;\\nuniform float     opacity;\\n\\nvarying vec3    worldPosition;\\nvarying float   pixelArcLength;\\nvarying vec4    fragColor;\\n\\nvoid main() {\\n  if (\\n    outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n    fragColor.a * opacity == 0.\\n  ) discard;\\n\\n  float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n  if(dashWeight < 0.5) {\\n    discard;\\n  }\\n  gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX  1.70141184e38\\n#define FLOAT_MIN  1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n  float av = abs(v);\\n\\n  //Handle special cases\\n  if(av < FLOAT_MIN) {\\n    return vec4(0.0, 0.0, 0.0, 0.0);\\n  } else if(v > FLOAT_MAX) {\\n    return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n  } else if(v < -FLOAT_MAX) {\\n    return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n  }\\n\\n  vec4 c = vec4(0,0,0,0);\\n\\n  //Compute exponent and mantissa\\n  float e = floor(log2(av));\\n  float m = av * pow(2.0, -e) - 1.0;\\n\\n  //Unpack mantissa\\n  c[1] = floor(128.0 * m);\\n  m -= c[1] / 128.0;\\n  c[2] = floor(32768.0 * m);\\n  m -= c[2] / 32768.0;\\n  c[3] = floor(8388608.0 * m);\\n\\n  //Unpack exponent\\n  float ebias = e + 127.0;\\n  c[0] = floor(ebias / 2.0);\\n  ebias -= c[0] * 2.0;\\n  c[1] += floor(ebias) * 128.0;\\n\\n  //Unpack sign bit\\n  c[0] += 128.0 * step(0.0, -v);\\n\\n  //Scale back to range\\n  return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n  gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];r.createShader=function(t){return i(t,a,o,null,l)},r.createPickShader=function(t){return i(t,a,s,null,l)}},{\"gl-shader\":132,glslify:231}],91:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new v(e,r,o,s,l,d);return m.update(t),m};var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"gl-texture2d\"),o=new Uint8Array(4),s=new Float32Array(o.buffer);var l=t(\"binary-search-bounds\"),c=t(\"ndarray\"),u=t(\"./lib/shaders\"),f=u.createShader,h=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function v(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var y=v.prototype;y.isTransparent=function(){return this.hasAlpha},y.isOpaque=function(){return!this.hasAlpha},y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.drawTransparent=y.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},y.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e<h.length;++e){var v,y,x,b=h[e-1],_=h[e];for(a.push(s),o.push(b.slice()),r=0;r<3;++r){if(isNaN(b[r])||isNaN(_[r])||!isFinite(b[r])||!isFinite(_[r])){if(!n&&i.length>0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(v=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],y=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):v=y=p,3===v.length&&(v=[v[0],v[1],v[2],1]),3===y.length&&(y=[y[0],y[1],y[2],1]),!this.hasAlpha&&v[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3]);u+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,v[0],v[1],v[2],v[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,y[0],y[1],y[2],y[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e<k.length;++e)k[e]=k[e-1]+k[e];var A=c(new Array(1024),[256,1,4]);for(e=0;e<256;++e){for(r=0;r<4;++r)A.set(e,0,r,0);1&l.le(k,k[k.length-1]*e/255)?A.set(e,0,0,0):A.set(e,0,0,255)}this.texture.setPixels(A)}},y.dispose=function(){this.shader.dispose(),this.vao.dispose(),this.buffer.dispose()},y.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=function(t,e,r,n){return o[0]=n,o[1]=r,o[2]=e,o[3]=t,s[0]}(t.value[0],t.value[1],t.value[2],0),r=l.le(this.arcLength,e);if(r<0)return null;if(r===this.arcLength.length-1)return new g(this.arcLength[this.arcLength.length-1],this.points[this.points.length-1].slice(),r);for(var n=this.points[r],i=this.points[Math.min(r+1,this.points.length-1)],a=(e-this.arcLength[r])/(this.arcLength[r+1]-this.arcLength[r]),c=1-a,u=[0,0,0],f=0;f<3;++f)u[f]=c*n[f]+a*i[f];var h=Math.min(a<.5?r:r+1,this.points.length-1);return new g(e,u,h,this.points[h])}},{\"./lib/shaders\":90,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259}],92:[function(t,e,r){e.exports=function(t){var e=new Float32Array(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e}},{}],93:[function(t,e,r){e.exports=function(){var t=new Float32Array(16);return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],94:[function(t,e,r){e.exports=function(t){var e=t[0],r=t[1],n=t[2],i=t[3],a=t[4],o=t[5],s=t[6],l=t[7],c=t[8],u=t[9],f=t[10],h=t[11],p=t[12],d=t[13],m=t[14],g=t[15];return(e*o-r*a)*(f*g-h*m)-(e*s-n*a)*(u*g-h*d)+(e*l-i*a)*(u*m-f*d)+(r*s-n*o)*(c*g-h*p)-(r*l-i*o)*(c*m-f*p)+(n*l-i*s)*(c*d-u*p)}},{}],95:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=r+r,s=n+n,l=i+i,c=r*o,u=n*o,f=n*s,h=i*o,p=i*s,d=i*l,m=a*o,g=a*s,v=a*l;return t[0]=1-f-d,t[1]=u+v,t[2]=h-g,t[3]=0,t[4]=u-v,t[5]=1-c-d,t[6]=p+m,t[7]=0,t[8]=h+g,t[9]=p-m,t[10]=1-c-f,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],96:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=n+n,l=i+i,c=a+a,u=n*s,f=n*l,h=n*c,p=i*l,d=i*c,m=a*c,g=o*s,v=o*l,y=o*c;return t[0]=1-(p+m),t[1]=f+y,t[2]=h-v,t[3]=0,t[4]=f-y,t[5]=1-(u+m),t[6]=d+g,t[7]=0,t[8]=h+v,t[9]=d-g,t[10]=1-(u+p),t[11]=0,t[12]=r[0],t[13]=r[1],t[14]=r[2],t[15]=1,t}},{}],97:[function(t,e,r){e.exports=function(t){return t[0]=1,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=1,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=1,t[11]=0,t[12]=0,t[13]=0,t[14]=0,t[15]=1,t}},{}],98:[function(t,e,r){e.exports=function(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=e[4],s=e[5],l=e[6],c=e[7],u=e[8],f=e[9],h=e[10],p=e[11],d=e[12],m=e[13],g=e[14],v=e[15],y=r*s-n*o,x=r*l-i*o,b=r*c-a*o,_=n*l-i*s,w=n*c-a*s,T=i*c-a*l,k=u*m-f*d,A=u*g-h*d,M=u*v-p*d,S=f*g-h*m,E=f*v-p*m,L=h*v-p*g,C=y*L-x*E+b*S+_*M-w*A+T*k;if(!C)return null;return C=1/C,t[0]=(s*L-l*E+c*S)*C,t[1]=(i*E-n*L-a*S)*C,t[2]=(m*T-g*w+v*_)*C,t[3]=(h*w-f*T-p*_)*C,t[4]=(l*M-o*L-c*A)*C,t[5]=(r*L-i*M+a*A)*C,t[6]=(g*b-d*T-v*x)*C,t[7]=(u*T-h*b+p*x)*C,t[8]=(o*E-s*M+c*k)*C,t[9]=(n*M-r*E-a*k)*C,t[10]=(d*w-m*b+v*y)*C,t[11]=(f*b-u*w-p*y)*C,t[12]=(s*A-o*S-l*k)*C,t[13]=(r*S-n*A+i*k)*C,t[14]=(m*x-d*_-g*y)*C,t[15]=(u*_-f*x+h*y)*C,t}},{}],99:[function(t,e,r){var n=t(\"./identity\");e.exports=function(t,e,r,i){var a,o,s,l,c,u,f,h,p,d,m=e[0],g=e[1],v=e[2],y=i[0],x=i[1],b=i[2],_=r[0],w=r[1],T=r[2];if(Math.abs(m-_)<1e-6&&Math.abs(g-w)<1e-6&&Math.abs(v-T)<1e-6)return n(t);f=m-_,h=g-w,p=v-T,d=1/Math.sqrt(f*f+h*h+p*p),a=x*(p*=d)-b*(h*=d),o=b*(f*=d)-y*p,s=y*h-x*f,(d=Math.sqrt(a*a+o*o+s*s))?(a*=d=1/d,o*=d,s*=d):(a=0,o=0,s=0);l=h*s-p*o,c=p*a-f*s,u=f*o-h*a,(d=Math.sqrt(l*l+c*c+u*u))?(l*=d=1/d,c*=d,u*=d):(l=0,c=0,u=0);return t[0]=a,t[1]=l,t[2]=f,t[3]=0,t[4]=o,t[5]=c,t[6]=h,t[7]=0,t[8]=s,t[9]=u,t[10]=p,t[11]=0,t[12]=-(a*m+o*g+s*v),t[13]=-(l*m+c*g+u*v),t[14]=-(f*m+h*g+p*v),t[15]=1,t}},{\"./identity\":97}],100:[function(t,e,r){e.exports=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],m=e[12],g=e[13],v=e[14],y=e[15],x=r[0],b=r[1],_=r[2],w=r[3];return t[0]=x*n+b*s+_*f+w*m,t[1]=x*i+b*l+_*h+w*g,t[2]=x*a+b*c+_*p+w*v,t[3]=x*o+b*u+_*d+w*y,x=r[4],b=r[5],_=r[6],w=r[7],t[4]=x*n+b*s+_*f+w*m,t[5]=x*i+b*l+_*h+w*g,t[6]=x*a+b*c+_*p+w*v,t[7]=x*o+b*u+_*d+w*y,x=r[8],b=r[9],_=r[10],w=r[11],t[8]=x*n+b*s+_*f+w*m,t[9]=x*i+b*l+_*h+w*g,t[10]=x*a+b*c+_*p+w*v,t[11]=x*o+b*u+_*d+w*y,x=r[12],b=r[13],_=r[14],w=r[15],t[12]=x*n+b*s+_*f+w*m,t[13]=x*i+b*l+_*h+w*g,t[14]=x*a+b*c+_*p+w*v,t[15]=x*o+b*u+_*d+w*y,t}},{}],101:[function(t,e,r){e.exports=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t}},{}],102:[function(t,e,r){e.exports=function(t,e,r,n,i){var a=1/Math.tan(e/2),o=1/(n-i);return t[0]=a/r,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=a,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=(i+n)*o,t[11]=-1,t[12]=0,t[13]=0,t[14]=2*i*n*o,t[15]=0,t}},{}],103:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,w,T,k,A,M,S,E=n[0],L=n[1],C=n[2],P=Math.sqrt(E*E+L*L+C*C);if(Math.abs(P)<1e-6)return null;E*=P=1/P,L*=P,C*=P,i=Math.sin(r),a=Math.cos(r),o=1-a,s=e[0],l=e[1],c=e[2],u=e[3],f=e[4],h=e[5],p=e[6],d=e[7],m=e[8],g=e[9],v=e[10],y=e[11],x=E*E*o+a,b=L*E*o+C*i,_=C*E*o-L*i,w=E*L*o-C*i,T=L*L*o+a,k=C*L*o+E*i,A=E*C*o+L*i,M=L*C*o-E*i,S=C*C*o+a,t[0]=s*x+f*b+m*_,t[1]=l*x+h*b+g*_,t[2]=c*x+p*b+v*_,t[3]=u*x+d*b+y*_,t[4]=s*w+f*T+m*k,t[5]=l*w+h*T+g*k,t[6]=c*w+p*T+v*k,t[7]=u*w+d*T+y*k,t[8]=s*A+f*M+m*S,t[9]=l*A+h*M+g*S,t[10]=c*A+p*M+v*S,t[11]=u*A+d*M+y*S,e!==t&&(t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t}},{}],104:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[4],o=e[5],s=e[6],l=e[7],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=e[3],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[4]=a*i+c*n,t[5]=o*i+u*n,t[6]=s*i+f*n,t[7]=l*i+h*n,t[8]=c*i-a*n,t[9]=u*i-o*n,t[10]=f*i-s*n,t[11]=h*i-l*n,t}},{}],105:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[8],u=e[9],f=e[10],h=e[11];e!==t&&(t[4]=e[4],t[5]=e[5],t[6]=e[6],t[7]=e[7],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i-c*n,t[1]=o*i-u*n,t[2]=s*i-f*n,t[3]=l*i-h*n,t[8]=a*n+c*i,t[9]=o*n+u*i,t[10]=s*n+f*i,t[11]=l*n+h*i,t}},{}],106:[function(t,e,r){e.exports=function(t,e,r){var n=Math.sin(r),i=Math.cos(r),a=e[0],o=e[1],s=e[2],l=e[3],c=e[4],u=e[5],f=e[6],h=e[7];e!==t&&(t[8]=e[8],t[9]=e[9],t[10]=e[10],t[11]=e[11],t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15]);return t[0]=a*i+c*n,t[1]=o*i+u*n,t[2]=s*i+f*n,t[3]=l*i+h*n,t[4]=c*i-a*n,t[5]=u*i-o*n,t[6]=f*i-s*n,t[7]=h*i-l*n,t}},{}],107:[function(t,e,r){e.exports=function(t,e,r){var n=r[0],i=r[1],a=r[2];return t[0]=e[0]*n,t[1]=e[1]*n,t[2]=e[2]*n,t[3]=e[3]*n,t[4]=e[4]*i,t[5]=e[5]*i,t[6]=e[6]*i,t[7]=e[7]*i,t[8]=e[8]*a,t[9]=e[9]*a,t[10]=e[10]*a,t[11]=e[11]*a,t[12]=e[12],t[13]=e[13],t[14]=e[14],t[15]=e[15],t}},{}],108:[function(t,e,r){e.exports=function(t,e,r){var n,i,a,o,s,l,c,u,f,h,p,d,m=r[0],g=r[1],v=r[2];e===t?(t[12]=e[0]*m+e[4]*g+e[8]*v+e[12],t[13]=e[1]*m+e[5]*g+e[9]*v+e[13],t[14]=e[2]*m+e[6]*g+e[10]*v+e[14],t[15]=e[3]*m+e[7]*g+e[11]*v+e[15]):(n=e[0],i=e[1],a=e[2],o=e[3],s=e[4],l=e[5],c=e[6],u=e[7],f=e[8],h=e[9],p=e[10],d=e[11],t[0]=n,t[1]=i,t[2]=a,t[3]=o,t[4]=s,t[5]=l,t[6]=c,t[7]=u,t[8]=f,t[9]=h,t[10]=p,t[11]=d,t[12]=n*m+s*g+f*v+e[12],t[13]=i*m+l*g+h*v+e[13],t[14]=a*m+c*g+p*v+e[14],t[15]=o*m+u*g+d*v+e[15]);return t}},{}],109:[function(t,e,r){e.exports=function(t,e){if(t===e){var r=e[1],n=e[2],i=e[3],a=e[6],o=e[7],s=e[11];t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=r,t[6]=e[9],t[7]=e[13],t[8]=n,t[9]=a,t[11]=e[14],t[12]=i,t[13]=o,t[14]=s}else t[0]=e[0],t[1]=e[4],t[2]=e[8],t[3]=e[12],t[4]=e[1],t[5]=e[5],t[6]=e[9],t[7]=e[13],t[8]=e[2],t[9]=e[6],t[10]=e[10],t[11]=e[14],t[12]=e[3],t[13]=e[7],t[14]=e[11],t[15]=e[15];return t}},{}],110:[function(t,e,r){\"use strict\";var n=t(\"barycentric\"),i=t(\"polytope-closest-point/lib/closest_point_2d.js\");function a(t,e){for(var r=[0,0,0,0],n=0;n<4;++n)for(var i=0;i<4;++i)r[i]+=t[4*n+i]*e[n];return r}function o(t,e,r,n,i){for(var o=a(n,a(r,a(e,[t[0],t[1],t[2],1]))),s=0;s<3;++s)o[s]/=o[3];return[.5*i[0]*(1+o[0]),.5*i[1]*(1-o[1])]}function s(t,e){for(var r=[0,0,0],n=0;n<t.length;++n)for(var i=t[n],a=e[n],o=0;o<3;++o)r[o]+=a*i[o];return r}e.exports=function(t,e,r,a,l,c){if(1===t.length)return[0,t[0].slice()];for(var u=new Array(t.length),f=0;f<t.length;++f)u[f]=o(t[f],r,a,l,c);var h=0,p=1/0;for(f=0;f<u.length;++f){for(var d=0,m=0;m<2;++m)d+=Math.pow(u[f][m]-e[m],2);d<p&&(p=d,h=f)}var g=function(t,e){if(2===t.length){for(var r=0,a=0,o=0;o<2;++o)r+=Math.pow(e[o]-t[0][o],2),a+=Math.pow(e[o]-t[1][o],2);return r=Math.sqrt(r),a=Math.sqrt(a),r+a<1e-6?[1,0]:[a/(r+a),r/(a+r)]}if(3===t.length){var s=[0,0];return i(t[0],t[1],t[2],e,s),n(t,s)}return[]}(u,e),v=0;for(f=0;f<3;++f){if(g[f]<-.001||g[f]>1.0001)return null;v+=g[f]}if(Math.abs(v-1)>.001)return null;return[h,s(t,g),g]}},{barycentric:14,\"polytope-closest-point/lib/closest_point_2d.js\":270}],111:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n           , view\\n           , projection\\n           , inverseModel;\\nuniform vec3 eyePosition\\n           , lightPosition;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n  return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n  gl_Position      = project(position);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * vec4(position , 1.0);\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal  = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  f_color          = color;\\n  f_data           = position;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n            , fresnel\\n            , kambient\\n            , kdiffuse\\n            , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n           , f_lightDirection\\n           , f_eyeDirection\\n           , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (f_color.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], f_data)\\n  ) discard;\\n\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_color = color;\\n  f_data  = position;\\n  f_uv    = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n  } else {\\n    gl_Position = projection * view * model * vec4(position, 1.0);\\n  }\\n  gl_PointSize = pointSize;\\n  f_color = color;\\n  f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n  if(dot(pointR, pointR) > 0.25) {\\n    discard;\\n  }\\n  gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n  f_id        = id;\\n  f_position  = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3  position;\\nattribute float pointSize;\\nattribute vec4  id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n  } else {\\n    gl_Position  = projection * view * model * vec4(position, 1.0);\\n    gl_PointSize = pointSize;\\n  }\\n  f_id         = id;\\n  f_position   = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n  gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n  gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},r.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},r.pickShader={vertex:u,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},r.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},r.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},{glslify:231}],112:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"normals\"),l=t(\"gl-mat4/multiply\"),c=t(\"gl-mat4/invert\"),u=t(\"ndarray\"),f=t(\"colormap\"),h=t(\"simplicial-complex-contour\"),p=t(\"typedarray-pool\"),d=t(\"./lib/shaders\"),m=t(\"./lib/closest-point\"),g=d.meshShader,v=d.wireShader,y=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,f,h,p,d,m,g,v,y,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=v,this.edgeIds=m,this.edgeVAO=y,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l<a;++l)for(var c=r[l],u=0;u<2;++u){var f=c[0];2===c.length&&(f=c[u]);for(var d=n[f][0],m=n[f][1],g=i[f],v=1-g,y=this.positions[d],x=this.positions[m],b=0;b<3;++b)o[s++]=g*y[b]+v*x[b]}this.contourCount=s/3|0,this.contourPositions.update(o.subarray(0,s)),p.free(o)}else this.contourCount=0},k.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"contourEnable\"in t&&(this.contourEnable=t.contourEnable),\"contourColor\"in t&&(this.contourColor=t.contourColor),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=t.opacity,this.opacity<1&&(this.hasAlpha=!0)),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale,this.hasAlpha=!0),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),t.texture?(this.texture.dispose(),this.texture=o(e,t.texture)):t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t,e){for(var r=f({colormap:t,nshades:256,format:\"rgba\"}),n=new Uint8Array(1024),i=0;i<256;++i){for(var a=r[i],o=0;o<3;++o)n[4*i+o]=a[o];n[4*i+3]=e?255*A(i/255,e):255*a[3]}return u(n,[256,256,4],[4,0,1])}(t.colormap,this.opacityscale)),this.texture.generateMipmap());var r=t.cells,n=t.positions;if(n&&r){var i=[],a=[],l=[],c=[],h=[],p=[],d=[],m=[],g=[],v=[],y=[],x=[],b=[],_=[];this.cells=r,this.positions=n;var w=t.vertexNormals,T=t.cellNormals,k=void 0===t.vertexNormalsEpsilon?1e-6:t.vertexNormalsEpsilon,M=void 0===t.faceNormalsEpsilon?1e-6:t.faceNormalsEpsilon;t.useFacetNormals&&!T&&(T=s.faceNormals(r,n,M)),T||w||(w=s.vertexNormals(r,n,k));var S=t.vertexColors,E=t.cellColors,L=t.meshColor||[1,1,1,1],C=t.vertexUVs,P=t.vertexIntensity,I=t.cellUVs,O=t.cellIntensity,z=1/0,D=-1/0;if(!C&&!I)if(P)if(t.vertexIntensityBounds)z=+t.vertexIntensityBounds[0],D=+t.vertexIntensityBounds[1];else for(var R=0;R<P.length;++R){var F=P[R];z=Math.min(z,F),D=Math.max(D,F)}else if(O)if(t.cellIntensityBounds)z=+t.cellIntensityBounds[0],D=+t.cellIntensityBounds[1];else for(R=0;R<O.length;++R){F=O[R];z=Math.min(z,F),D=Math.max(D,F)}else for(R=0;R<n.length;++R){F=n[R][2];z=Math.min(z,F),D=Math.max(D,F)}this.intensity=P||(O||function(t){for(var e=t.length,r=new Array(e),n=0;n<e;++n)r[n]=t[n][2];return r}(n)),this.pickVertex=!(O||E);var B=t.pointSizes,N=t.pointSize||1;this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]];for(R=0;R<n.length;++R)for(var j=n[R],U=0;U<3;++U)!isNaN(j[U])&&isFinite(j[U])&&(this.bounds[0][U]=Math.min(this.bounds[0][U],j[U]),this.bounds[1][U]=Math.max(this.bounds[1][U],j[U]));var V=0,H=0,q=0;t:for(R=0;R<r.length;++R){var G=r[R];switch(G.length){case 1:for(j=n[W=G[0]],U=0;U<3;++U)if(isNaN(j[U])||!isFinite(j[U]))continue t;v.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?y.push(X[0],X[1],X[2],this.opacity):(y.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],x.push(Z[0],Z[1]),B?b.push(B[W]):b.push(N),_.push(R),q+=1;break;case 2:for(U=0;U<2;++U){j=n[W=G[U]];for(var Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t}for(U=0;U<2;++U){j=n[W=G[U]];p.push(j[0],j[1],j[2]),X=S?S[W]:E?E[R]:L,this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?d.push(X[0],X[1],X[2],this.opacity):(d.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],m.push(Z[0],Z[1]),g.push(R)}H+=1;break;case 3:for(U=0;U<3;++U)for(j=n[W=G[U]],Y=0;Y<3;++Y)if(isNaN(j[Y])||!isFinite(j[Y]))continue t;for(U=0;U<3;++U){var W,X,Z,J;j=n[W=G[2-U]];i.push(j[0],j[1],j[2]),(X=S?S[W]:E?E[R]:L)?this.opacityscale&&P?a.push(X[0],X[1],X[2],this.opacity*A((P[W]-z)/(D-z),this.opacityscale)):3===X.length?a.push(X[0],X[1],X[2],this.opacity):(a.push(X[0],X[1],X[2],X[3]*this.opacity),X[3]<1&&(this.hasAlpha=!0)):a.push(.5,.5,.5,1),Z=C?C[W]:P?[(P[W]-z)/(D-z),0]:I?I[R]:O?[(O[R]-z)/(D-z),0]:[(j[2]-z)/(D-z),0],c.push(Z[0],Z[1]),J=w?w[W]:T[R],l.push(J[0],J[1],J[2]),h.push(R)}V+=1}}this.pointCount=q,this.edgeCount=H,this.triangleCount=V,this.pointPositions.update(v),this.pointColors.update(y),this.pointUVs.update(x),this.pointSizes.update(b),this.pointIds.update(new Uint32Array(_)),this.edgePositions.update(p),this.edgeColors.update(d),this.edgeUVs.update(m),this.edgeIds.update(new Uint32Array(g)),this.trianglePositions.update(i),this.triangleColors.update(a),this.triangleUVs.update(c),this.triangleNormals.update(l),this.triangleIds.update(new Uint32Array(h))}},k.drawTransparent=k.draw=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);var s={model:r,view:n,projection:i,inverseModel:w.slice(),clipBounds:a,kambient:this.ambientLight,kdiffuse:this.diffuseLight,kspecular:this.specularLight,roughness:this.roughness,fresnel:this.fresnel,eyePosition:[0,0,0],lightPosition:[0,0,0],contourColor:this.contourColor,texture:0};s.inverseModel=c(s.inverseModel,s.model),e.disable(e.CULL_FACE),this.texture.bind(0);var u=new Array(16);l(u,s.view,s.model),l(u,s.projection,u),c(u,u);for(o=0;o<3;++o)s.eyePosition[o]=u[12+o]/u[15];var f,h=u[15];for(o=0;o<3;++o)h+=this.lightPosition[o]*u[4*o+3];for(o=0;o<3;++o){for(var p=u[12+o],d=0;d<3;++d)p+=u[4*d+o]*this.lightPosition[d];s.lightPosition[o]=p/h}this.triangleCount>0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind());this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind());this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind());this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};((s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0)&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;a<r.length;++a)i[a]=n[r[a]];var o=t.coord[0],s=t.coord[1];if(!this.pickVertex){var l=this.positions[r[0]],c=this.positions[r[1]],u=this.positions[r[2]],f=[(l[0]+c[0]+u[0])/3,(l[1]+c[1]+u[1])/3,(l[2]+c[2]+u[2])/3];return{_cellCenter:!0,position:[o,s],index:e,cell:r,cellId:e,intensity:this.intensity[e],dataCoordinate:f}}var h=m(i,[o*this.pixelRatio,this._resolution[1]-s*this.pixelRatio],this._model,this._view,this._projection,this._resolution);if(!h)return null;var p=h[2],d=0;for(a=0;a<r.length;++a)d+=p[a]*this.intensity[r[a]];return{position:h[1],index:r[h[0]],cell:r,cellId:e,intensity:d,dataCoordinate:this.positions[r[h[0]]]}},k.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.lineShader.dispose(),this.pointShader.dispose(),this.pickShader.dispose(),this.pointPickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleNormals.dispose(),this.triangleIds.dispose(),this.edgeVAO.dispose(),this.edgePositions.dispose(),this.edgeColors.dispose(),this.edgeUVs.dispose(),this.edgeIds.dispose(),this.pointVAO.dispose(),this.pointPositions.dispose(),this.pointColors.dispose(),this.pointUVs.dispose(),this.pointSizes.dispose(),this.pointIds.dispose(),this.contourVAO.dispose(),this.contourPositions.dispose(),this.contourShader.dispose()},e.exports=function(t,e){1===arguments.length&&(t=(e=t).gl);var r=t.getExtension(\"OES_standard_derivatives\")||t.getExtension(\"MOZ_OES_standard_derivatives\")||t.getExtension(\"WEBKIT_OES_standard_derivatives\");if(!r)throw new Error(\"derivatives not supported\");var n=M(t),s=S(t),l=E(t),c=L(t),f=C(t),h=P(t),p=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));p.generateMipmap(),p.minFilter=t.LINEAR_MIPMAP_LINEAR,p.magFilter=t.LINEAR;var d=i(t),m=i(t),g=i(t),v=i(t),y=i(t),x=a(t,[{buffer:d,type:t.FLOAT,size:3},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:v,type:t.FLOAT,size:3}]),b=i(t),_=i(t),w=i(t),k=i(t),A=a(t,[{buffer:b,type:t.FLOAT,size:3},{buffer:k,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:_,type:t.FLOAT,size:4},{buffer:w,type:t.FLOAT,size:2}]),I=i(t),O=i(t),z=i(t),D=i(t),R=i(t),F=a(t,[{buffer:I,type:t.FLOAT,size:3},{buffer:R,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:O,type:t.FLOAT,size:4},{buffer:z,type:t.FLOAT,size:2},{buffer:D,type:t.FLOAT,size:1}]),B=i(t),N=a(t,[{buffer:B,type:t.FLOAT,size:3}]),j=new T(t,p,n,s,l,c,f,h,d,y,m,g,v,x,b,k,_,w,A,I,R,O,z,D,F,B,N);return j.update(e),j}},{\"./lib/closest-point\":110,\"./lib/shaders\":111,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-shader\":132,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,normals:261,\"simplicial-complex-contour\":291,\"typedarray-pool\":308}],113:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[0,0,0,1,1,0,1,1]),s=i(e,a.boxVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawBox=(s=[0,0],l=[0,0],function(t,e,r,n,i){var a=this.plot,o=this.shader,c=a.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,o.uniforms.lo=s,o.uniforms.hi=l,o.uniforms.color=i,c.drawArrays(c.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],114:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,o.gridVert,o.gridFrag),l=i(e,o.tickVert,o.gridFrag);return new s(t,r,a,l)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"binary-search-bounds\"),o=t(\"./shaders\");function s(t,e,r,n){this.plot=t,this.vbo=e,this.shader=r,this.tickShader=n,this.ticks=[[],[]]}function l(t,e){return t-e}var c,u,f,h,p,d=s.prototype;d.draw=(c=[0,0],u=[0,0],f=[0,0],function(){for(var t=this.plot,e=this.vbo,r=this.shader,n=this.ticks,i=t.gl,a=t._tickBounds,o=t.dataBox,s=t.viewBox,l=t.gridLineWidth,h=t.gridLineColor,p=t.gridLineEnable,d=t.pixelRatio,m=0;m<2;++m){var g=a[m],v=a[m+2]-g,y=.5*(o[m+2]+o[m]),x=o[m+2]-o[m];u[m]=2*v/x,c[m]=2*(g-y)/x}r.bind(),e.bind(),r.attributes.dataCoord.pointer(),r.uniforms.dataShift=c,r.uniforms.dataScale=u;var b=0;for(m=0;m<2;++m){f[0]=f[1]=0,f[m]=1,r.uniforms.dataAxis=f,r.uniforms.lineWidth=l[m]/(s[m+2]-s[m])*d,r.uniforms.color=h[m];var _=6*n[m].length;p[m]&&_&&i.drawArrays(i.TRIANGLES,b,_),b+=_}}),d.drawTickMarks=function(){var t=[0,0],e=[0,0],r=[1,0],n=[0,1],i=[0,0],o=[0,0];return function(){for(var s=this.plot,c=this.vbo,u=this.tickShader,f=this.ticks,h=s.gl,p=s._tickBounds,d=s.dataBox,m=s.viewBox,g=s.pixelRatio,v=s.screenBox,y=v[2]-v[0],x=v[3]-v[1],b=m[2]-m[0],_=m[3]-m[1],w=0;w<2;++w){var T=p[w],k=p[w+2]-T,A=.5*(d[w+2]+d[w]),M=d[w+2]-d[w];e[w]=2*k/M,t[w]=2*(T-A)/M}e[0]*=b/y,t[0]*=b/y,e[1]*=_/x,t[1]*=_/x,u.bind(),c.bind(),u.attributes.dataCoord.pointer();var S=u.uniforms;S.dataShift=t,S.dataScale=e;var E=s.tickMarkLength,L=s.tickMarkWidth,C=s.tickMarkColor,P=6*f[0].length,I=Math.min(a.ge(f[0],(d[0]-p[0])/(p[2]-p[0]),l),f[0].length),O=Math.min(a.gt(f[0],(d[2]-p[0])/(p[2]-p[0]),l),f[0].length),z=0+6*I,D=6*Math.max(0,O-I),R=Math.min(a.ge(f[1],(d[1]-p[1])/(p[3]-p[1]),l),f[1].length),F=Math.min(a.gt(f[1],(d[3]-p[1])/(p[3]-p[1]),l),f[1].length),B=P+6*R,N=6*Math.max(0,F-R);i[0]=2*(m[0]-E[1])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[1]*g/y,o[1]=L[1]*g/x,N&&(S.color=C[1],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[1]-E[0])/x-1,o[0]=L[0]*g/y,o[1]=E[0]*g/x,D&&(S.color=C[0],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D)),i[0]=2*(m[2]+E[3])/y-1,i[1]=(m[3]+m[1])/x-1,o[0]=E[3]*g/y,o[1]=L[3]*g/x,N&&(S.color=C[3],S.tickScale=o,S.dataAxis=n,S.screenOffset=i,h.drawArrays(h.TRIANGLES,B,N)),i[0]=(m[2]+m[0])/y-1,i[1]=2*(m[3]+E[2])/x-1,o[0]=L[2]*g/y,o[1]=E[2]*g/x,D&&(S.color=C[2],S.tickScale=o,S.dataAxis=r,S.screenOffset=i,h.drawArrays(h.TRIANGLES,z,D))}}(),d.update=(h=[1,1,-1,-1,1,-1],p=[1,-1,1,1,-1,-1],function(t){for(var e=t.ticks,r=t.bounds,n=new Float32Array(18*(e[0].length+e[1].length)),i=(this.plot.zeroLineEnable,0),a=[[],[]],o=0;o<2;++o)for(var s=a[o],l=e[o],c=r[o],u=r[o+2],f=0;f<l.length;++f){var d=(l[f].x-c)/(u-c);s.push(d);for(var m=0;m<6;++m)n[i++]=d,n[i++]=h[m],n[i++]=p[m]}this.ticks=a,this.vbo.update(n)}),d.dispose=function(){this.vbo.dispose(),this.shader.dispose(),this.tickShader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132}],115:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[-1,-1,-1,1,1,-1,1,1]),s=i(e,a.lineVert,a.lineFrag);return new o(t,r,s)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"./shaders\");function o(t,e,r){this.plot=t,this.vbo=e,this.shader=r}var s,l,c=o.prototype;c.bind=function(){var t=this.shader;this.vbo.bind(),this.shader.bind(),t.attributes.coord.pointer(),t.uniforms.screenBox=this.plot.screenBox},c.drawLine=(s=[0,0],l=[0,0],function(t,e,r,n,i,a){var o=this.plot,c=this.shader,u=o.gl;s[0]=t,s[1]=e,l[0]=r,l[1]=n,c.uniforms.start=s,c.uniforms.end=l,c.uniforms.width=i*o.pixelRatio,c.uniforms.color=a,u.drawArrays(u.TRIANGLE_STRIP,0,4)}),c.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"gl-buffer\":78,\"gl-shader\":132}],116:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=n([\"precision lowp float;\\n#define GLSLIFY 1\\nuniform vec4 color;\\nvoid main() {\\n  gl_FragColor = vec4(color.xyz * color.w, color.w);\\n}\\n\"]);e.exports={lineVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 start, end;\\nuniform float width;\\n\\nvec2 perp(vec2 v) {\\n  return vec2(v.y, -v.x);\\n}\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  vec2 delta = normalize(perp(start - end));\\n  vec2 offset = mix(start, end, 0.5 * (coord.y+1.0));\\n  gl_Position = vec4(screen(offset + 0.5 * width * delta * coord.x), 0, 1);\\n}\\n\"]),lineFrag:i,textVert:n([\"#define GLSLIFY 1\\nattribute vec3 textCoordinate;\\n\\nuniform vec2 dataScale, dataShift, dataAxis, screenOffset, textScale;\\nuniform float angle;\\n\\nvoid main() {\\n  float dataOffset  = textCoordinate.z;\\n  vec2 glyphOffset  = textCoordinate.xy;\\n  mat2 glyphMatrix = mat2(cos(angle), sin(angle), -sin(angle), cos(angle));\\n  vec2 screenCoordinate = dataAxis * (dataScale * dataOffset + dataShift) +\\n    glyphMatrix * glyphOffset * textScale + screenOffset;\\n  gl_Position = vec4(screenCoordinate, 0, 1);\\n}\\n\"]),textFrag:i,gridVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale;\\nuniform float lineWidth;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  pos += 10.0 * dataCoord.y * vec2(dataAxis.y, -dataAxis.x) + dataCoord.z * lineWidth;\\n  gl_Position = vec4(pos, 0, 1);\\n}\\n\"]),gridFrag:i,boxVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 coord;\\n\\nuniform vec4 screenBox;\\nuniform vec2 lo, hi;\\n\\nvec2 screen(vec2 v) {\\n  return 2.0 * (v - screenBox.xy) / (screenBox.zw - screenBox.xy) - 1.0;\\n}\\n\\nvoid main() {\\n  gl_Position = vec4(screen(mix(lo, hi, coord)), 0, 1);\\n}\\n\"]),tickVert:n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 dataCoord;\\n\\nuniform vec2 dataAxis, dataShift, dataScale, screenOffset, tickScale;\\n\\nvoid main() {\\n  vec2 pos = dataAxis * (dataScale * dataCoord.x + dataShift);\\n  gl_Position = vec4(pos + tickScale*dataCoord.yz + screenOffset, 0, 1);\\n}\\n\"])}},{glslify:231}],117:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e),a=i(e,s.textVert,s.textFrag);return new l(t,r,a)};var n=t(\"gl-buffer\"),i=t(\"gl-shader\"),a=t(\"text-cache\"),o=t(\"binary-search-bounds\"),s=t(\"./shaders\");function l(t,e,r){this.plot=t,this.vbo=e,this.shader=r,this.tickOffset=[[],[]],this.tickX=[[],[]],this.labelOffset=[0,0],this.labelCount=[0,0]}var c,u,f,h,p,d,m=l.prototype;m.drawTicks=(c=[0,0],u=[0,0],f=[0,0],function(t){var e=this.plot,r=this.shader,n=this.tickX[t],i=this.tickOffset[t],a=e.gl,s=e.viewBox,l=e.dataBox,h=e.screenBox,p=e.pixelRatio,d=e.tickEnable,m=e.tickPad,g=e.tickColor,v=e.tickAngle,y=e.labelEnable,x=e.labelPad,b=e.labelColor,_=e.labelAngle,w=this.labelOffset[t],T=this.labelCount[t],k=o.lt(n,l[t]),A=o.le(n,l[t+2]);c[0]=c[1]=0,c[t]=1,u[t]=(s[2+t]+s[t])/(h[2+t]-h[t])-1;var M=2/h[2+(1^t)]-h[1^t];u[1^t]=M*s[1^t]-1,d[t]&&(u[1^t]-=M*p*m[t],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=v[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],k<A&&i[A]>i[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=v[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),y[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=u,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],f=a[o],m=a[o+2]-f,g=i[o],v=i[o+2]-g;p[o]=2*l/u*m/v,h[o]=2*(s-c)/u*m/v}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e<h.length;++e){var p=h[e],d=p.x,m=p.text,g=p.font||\"sans-serif\";i=p.fontSize||12;for(var v=1/(c[o+2]-c[o]),y=c[o],x=m.split(\"\\n\"),b=0;b<x.length;b++)for(n=a(g,x[b]).data,r=0;r<n.length;r+=2)s.push(n[r]*i,-n[r+1]*i-b*i*1.2,(d-y)*v);u.push(Math.floor(s.length/3)),f.push(d)}this.tickOffset[o]=u,this.tickX[o]=f}for(o=0;o<2;++o){for(this.labelOffset[o]=Math.floor(s.length/3),n=a(t.labelFont[o],t.labels[o],{textAlign:\"center\"}).data,i=t.labelSize[o],e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.labelCount[o]=Math.floor(s.length/3)-this.labelOffset[o]}for(this.titleOffset=Math.floor(s.length/3),n=a(t.titleFont,t.title).data,i=t.titleSize,e=0;e<n.length;e+=2)s.push(n[e]*i,-n[e+1]*i,0);this.titleCount=Math.floor(s.length/3)-this.titleOffset,this.vbo.update(s)},m.dispose=function(){this.vbo.dispose(),this.shader.dispose()}},{\"./shaders\":116,\"binary-search-bounds\":31,\"gl-buffer\":78,\"gl-shader\":132,\"text-cache\":303}],118:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=n(e,[e.drawingBufferWidth,e.drawingBufferHeight]),c=new l(e,r);return c.grid=i(c),c.text=a(c),c.line=o(c),c.box=s(c),c.update(t),c};var n=t(\"gl-select-static\"),i=t(\"./lib/grid\"),a=t(\"./lib/text\"),o=t(\"./lib/line\"),s=t(\"./lib/box\");function l(t,e){this.gl=t,this.pickBuffer=e,this.screenBox=[0,0,t.drawingBufferWidth,t.drawingBufferHeight],this.viewBox=[0,0,0,0],this.dataBox=[-10,-10,10,10],this.gridLineEnable=[!0,!0],this.gridLineWidth=[1,1],this.gridLineColor=[[0,0,0,1],[0,0,0,1]],this.pixelRatio=1,this.tickMarkLength=[0,0,0,0],this.tickMarkWidth=[0,0,0,0],this.tickMarkColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[15,15,15,15],this.tickAngle=[0,0,0,0],this.tickEnable=[!0,!0,!0,!0],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[15,15,15,15],this.labelAngle=[0,Math.PI/2,0,3*Math.PI/2],this.labelEnable=[!0,!0,!0,!0],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.titleCenter=[0,0],this.titleEnable=!0,this.titleAngle=0,this.titleColor=[0,0,0,1],this.borderColor=[0,0,0,0],this.backgroundColor=[0,0,0,0],this.zeroLineEnable=[!0,!0],this.zeroLineWidth=[4,4],this.zeroLineColor=[[0,0,0,1],[0,0,0,1]],this.borderLineEnable=[!0,!0,!0,!0],this.borderLineWidth=[2,2,2,2],this.borderLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.grid=null,this.text=null,this.line=null,this.box=null,this.objects=[],this.overlays=[],this._tickBounds=[1/0,1/0,-1/0,-1/0],this.static=!1,this.dirty=!1,this.pickDirty=!1,this.pickDelay=120,this.pickRadius=10,this._pickTimeout=null,this._drawPick=this.drawPick.bind(this),this._depthCounter=0}var c=l.prototype;function u(t){for(var e=t.slice(),r=0;r<e.length;++r)e[r]=e[r].slice();return e}function f(t,e){return t.x-e.x}c.setDirty=function(){this.dirty=this.pickDirty=!0},c.setOverlayDirty=function(){this.dirty=!0},c.nextDepthValue=function(){return this._depthCounter++/65536},c.draw=function(){var t=this.gl,e=this.screenBox,r=this.viewBox,n=this.dataBox,i=this.pixelRatio,a=this.grid,o=this.line,s=this.text,l=this.objects;if(this._depthCounter=0,this.pickDirty&&(this._pickTimeout&&clearTimeout(this._pickTimeout),this.pickDirty=!1,this._pickTimeout=setTimeout(this._drawPick,this.pickDelay)),this.dirty){if(this.dirty=!1,t.bindFramebuffer(t.FRAMEBUFFER,null),t.enable(t.SCISSOR_TEST),t.disable(t.DEPTH_TEST),t.depthFunc(t.LESS),t.depthMask(!1),t.enable(t.BLEND),t.blendEquation(t.FUNC_ADD,t.FUNC_ADD),t.blendFunc(t.ONE,t.ONE_MINUS_SRC_ALPHA),this.borderColor){t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]);var c=this.borderColor;t.clearColor(c[0]*c[3],c[1]*c[3],c[2]*c[3],c[3]),t.clear(t.COLOR_BUFFER_BIT|t.DEPTH_BUFFER_BIT)}t.scissor(r[0],r[1],r[2]-r[0],r[3]-r[1]),t.viewport(r[0],r[1],r[2]-r[0],r[3]-r[1]);var u=this.backgroundColor;t.clearColor(u[0]*u[3],u[1]*u[3],u[2]*u[3],u[3]),t.clear(t.COLOR_BUFFER_BIT),a.draw();var f=this.zeroLineEnable,h=this.zeroLineColor,p=this.zeroLineWidth;if(f[0]||f[1]){o.bind();for(var d=0;d<2;++d)if(f[d]&&n[d]<=0&&n[d+2]>=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],h[d]):o.drawLine(e[0],m,e[2],m,p[d],h[d])}}for(d=0;d<l.length;++d)l[d].draw();t.viewport(e[0],e[1],e[2]-e[0],e[3]-e[1]),t.scissor(e[0],e[1],e[2]-e[0],e[3]-e[1]),this.grid.drawTickMarks(),o.bind();var g=this.borderLineEnable,v=this.borderLineWidth,y=this.borderLineColor;for(g[1]&&o.drawLine(r[0],r[1]-.5*v[1]*i,r[0],r[3]+.5*v[3]*i,v[1],y[1]),g[0]&&o.drawLine(r[0]-.5*v[0]*i,r[1],r[2]+.5*v[2]*i,r[1],v[0],y[0]),g[3]&&o.drawLine(r[2],r[1]-.5*v[1]*i,r[2],r[3]+.5*v[3]*i,v[3],y[3]),g[2]&&o.drawLine(r[0]-.5*v[0]*i,r[3],r[2]+.5*v[2]*i,r[3],v[2],y[2]),s.bind(),d=0;d<2;++d)s.drawTicks(d);this.titleEnable&&s.drawTitle();var x=this.overlays;for(d=0;d<x.length;++d)x[d].draw();t.disable(t.SCISSOR_TEST),t.disable(t.BLEND),t.depthMask(!0)}},c.drawPick=function(){if(!this.static){var t=this.pickBuffer;this.gl,this._pickTimeout=null,t.begin();for(var e=1,r=this.objects,n=0;n<r.length;++n)e=r[n].drawPick(e);t.end()}},c.pick=function(t,e){if(!this.static){var r=this.pixelRatio,n=this.pickPixelRatio,i=this.viewBox,a=0|Math.round((t-i[0]/r)*n),o=0|Math.round((e-i[1]/r)*n),s=this.pickBuffer.query(a,o,this.pickRadius);if(!s)return null;for(var l=s.id+(s.value[0]<<8)+(s.value[1]<<16)+(s.value[2]<<24),c=this.objects,u=0;u<c.length;++u){var f=c[u].pick(a,o,l);if(f)return f}return null}},c.setScreenBox=function(t){var e=this.screenBox,r=this.pixelRatio;e[0]=0|Math.round(t[0]*r),e[1]=0|Math.round(t[1]*r),e[2]=0|Math.round(t[2]*r),e[3]=0|Math.round(t[3]*r),this.setDirty()},c.setDataBox=function(t){var e=this.dataBox;(e[0]!==t[0]||e[1]!==t[1]||e[2]!==t[2]||e[3]!==t[3])&&(e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],this.setDirty())},c.setViewBox=function(t){var e=this.pixelRatio,r=this.viewBox;r[0]=0|Math.round(t[0]*e),r[1]=0|Math.round(t[1]*e),r[2]=0|Math.round(t[2]*e),r[3]=0|Math.round(t[3]*e);var n=this.pickPixelRatio;this.pickBuffer.shape=[0|Math.round((t[2]-t[0])*n),0|Math.round((t[3]-t[1])*n)],this.setDirty()},c.update=function(t){t=t||{};var e=this.gl;this.pixelRatio=t.pixelRatio||1;var r=this.pixelRatio;this.pickPixelRatio=Math.max(r,1),this.setScreenBox(t.screenBox||[0,0,e.drawingBufferWidth/r,e.drawingBufferHeight/r]);this.screenBox;this.setViewBox(t.viewBox||[.125*(this.screenBox[2]-this.screenBox[0])/r,.125*(this.screenBox[3]-this.screenBox[1])/r,.875*(this.screenBox[2]-this.screenBox[0])/r,.875*(this.screenBox[3]-this.screenBox[1])/r]);var n=this.viewBox,i=(n[2]-n[0])/(n[3]-n[1]);this.setDataBox(t.dataBox||[-10,-10/i,10,10/i]),this.borderColor=!1!==t.borderColor&&(t.borderColor||[0,0,0,0]).slice(),this.backgroundColor=(t.backgroundColor||[0,0,0,0]).slice(),this.gridLineEnable=(t.gridLineEnable||[!0,!0]).slice(),this.gridLineWidth=(t.gridLineWidth||[1,1]).slice(),this.gridLineColor=u(t.gridLineColor||[[.5,.5,.5,1],[.5,.5,.5,1]]),this.zeroLineEnable=(t.zeroLineEnable||[!0,!0]).slice(),this.zeroLineWidth=(t.zeroLineWidth||[4,4]).slice(),this.zeroLineColor=u(t.zeroLineColor||[[0,0,0,1],[0,0,0,1]]),this.tickMarkLength=(t.tickMarkLength||[0,0,0,0]).slice(),this.tickMarkWidth=(t.tickMarkWidth||[0,0,0,0]).slice(),this.tickMarkColor=u(t.tickMarkColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.titleCenter=(t.titleCenter||[.5*(n[0]+n[2])/r,(n[3]+120)/r]).slice(),this.titleEnable=!(\"titleEnable\"in t)||!!t.titleEnable,this.titleAngle=t.titleAngle||0,this.titleColor=(t.titleColor||[0,0,0,1]).slice(),this.labelPad=(t.labelPad||[15,15,15,15]).slice(),this.labelAngle=(t.labelAngle||[0,Math.PI/2,0,3*Math.PI/2]).slice(),this.labelEnable=(t.labelEnable||[!0,!0,!0,!0]).slice(),this.labelColor=u(t.labelColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.tickPad=(t.tickPad||[15,15,15,15]).slice(),this.tickAngle=(t.tickAngle||[0,0,0,0]).slice(),this.tickEnable=(t.tickEnable||[!0,!0,!0,!0]).slice(),this.tickColor=u(t.tickColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]),this.borderLineEnable=(t.borderLineEnable||[!0,!0,!0,!0]).slice(),this.borderLineWidth=(t.borderLineWidth||[2,2,2,2]).slice(),this.borderLineColor=u(t.borderLineColor||[[0,0,0,1],[0,0,0,1],[0,0,0,1],[0,0,0,1]]);var a=t.ticks||[[],[]],o=this._tickBounds;o[0]=o[1]=1/0,o[2]=o[3]=-1/0;for(var s=0;s<2;++s){var l=a[s].slice(0);0!==l.length&&(l.sort(f),o[s]=Math.min(o[s],l[0].x),o[s+2]=Math.max(o[s+2],l[l.length-1].x))}this.grid.update({bounds:o,ticks:a}),this.text.update({bounds:o,ticks:a,labels:t.labels||[\"x\",\"y\"],labelSize:t.labelSize||[12,12],labelFont:t.labelFont||[\"sans-serif\",\"sans-serif\"],title:t.title||\"\",titleSize:t.titleSize||18,titleFont:t.titleFont||\"sans-serif\"}),this.static=!!t.static,this.setDirty()},c.dispose=function(){this.box.dispose(),this.grid.dispose(),this.text.dispose(),this.line.dispose();for(var t=this.objects.length-1;t>=0;--t)this.objects[t].dispose();this.objects.length=0;for(t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setDirty();break}},c.addOverlay=function(t){this.overlays.indexOf(t)<0&&(this.overlays.push(t),this.setOverlayDirty())},c.removeOverlay=function(t){for(var e=this.overlays,r=0;r<e.length;++r)if(e[r]===t){e.splice(r,1),this.setOverlayDirty();break}}},{\"./lib/box\":113,\"./lib/grid\":114,\"./lib/line\":115,\"./lib/text\":117,\"gl-select-static\":131}],119:[function(t,e,r){\"use strict\";e.exports=function(t,e){t=t||document.body,e=e||{};var r=[.01,1/0];\"distanceLimits\"in e&&(r[0]=e.distanceLimits[0],r[1]=e.distanceLimits[1]);\"zoomMin\"in e&&(r[0]=e.zoomMin);\"zoomMax\"in e&&(r[1]=e.zoomMax);var c=i({center:e.center||[0,0,0],up:e.up||[0,1,0],eye:e.eye||[0,0,10],mode:e.mode||\"orbit\",distanceLimits:r}),u=[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],f=0,h=t.clientWidth,p=t.clientHeight,d={keyBindingMode:\"rotate\",enableWheel:!0,view:c,element:t,delay:e.delay||16,rotateSpeed:e.rotateSpeed||1,zoomSpeed:e.zoomSpeed||1,translateSpeed:e.translateSpeed||1,flipX:!!e.flipX,flipY:!!e.flipY,modes:c.modes,_ortho:e._ortho||e.projection&&\"orthographic\"===e.projection.type||!1,tick:function(){var e=n(),r=this.delay,i=e-2*r;c.idle(e-r),c.recalcMatrix(i),c.flush(e-(100+2*r));for(var a=!0,o=c.computedMatrix,s=0;s<16;++s)a=a&&u[s]===o[s],u[s]=o[s];var l=t.clientWidth===h&&t.clientHeight===p;return h=t.clientWidth,p=t.clientHeight,a?!l:(f=Math.exp(c.computedRadius[0]),!0)},lookAt:function(t,e,r){c.lookAt(c.lastT(),t,e,r)},rotate:function(t,e,r){c.rotate(c.lastT(),t,e,r)},pan:function(t,e,r){c.pan(c.lastT(),t,e,r)},translate:function(t,e,r){c.translate(c.lastT(),t,e,r)}};return Object.defineProperties(d,{matrix:{get:function(){return c.computedMatrix},set:function(t){return c.setMatrix(c.lastT(),t),c.computedMatrix},enumerable:!0},mode:{get:function(){return c.getMode()},set:function(t){var e=c.computedUp.slice(),r=c.computedEye.slice(),i=c.computedCenter.slice();if(c.setMode(t),\"turntable\"===t){var a=n();c._active.lookAt(a,r,i,e),c._active.lookAt(a+500,r,i,[0,0,1]),c._active.flush(a)}return c.getMode()},enumerable:!0},center:{get:function(){return c.computedCenter},set:function(t){return c.lookAt(c.lastT(),null,t),c.computedCenter},enumerable:!0},eye:{get:function(){return c.computedEye},set:function(t){return c.lookAt(c.lastT(),t),c.computedEye},enumerable:!0},up:{get:function(){return c.computedUp},set:function(t){return c.lookAt(c.lastT(),null,null,t),c.computedUp},enumerable:!0},distance:{get:function(){return f},set:function(t){return c.setDistance(c.lastT(),t),t},enumerable:!0},distanceLimits:{get:function(){return c.getDistanceLimits(r)},set:function(t){return c.setDistanceLimits(t),t},enumerable:!0}}),t.addEventListener(\"contextmenu\",(function(t){return t.preventDefault(),!1})),d._lastX=-1,d._lastY=-1,d._lastMods={shift:!1,control:!1,alt:!1,meta:!1},d.enableMouseListeners=function(){function e(e,r,i,a){var o=d.keyBindingMode;if(!1!==o){var s=\"rotate\"===o,l=\"pan\"===o,u=\"zoom\"===o,h=!!a.control,p=!!a.alt,m=!!a.shift,g=!!(1&e),v=!!(2&e),y=!!(4&e),x=1/t.clientHeight,b=x*(r-d._lastX),_=x*(i-d._lastY),w=d.flipX?1:-1,T=d.flipY?1:-1,k=Math.PI*d.rotateSpeed,A=n();if(-1!==d._lastX&&-1!==d._lastY&&((s&&g&&!h&&!p&&!m||g&&!h&&!p&&m)&&c.rotate(A,w*k*b,-T*k*_,0),(l&&g&&!h&&!p&&!m||v||g&&h&&!p&&!m)&&c.pan(A,-d.translateSpeed*b*f,d.translateSpeed*_*f,0),u&&g&&!h&&!p&&!m||y||g&&!h&&p&&!m)){var M=-d.zoomSpeed*_/window.innerHeight*(A-c.lastT())*100;c.pan(A,0,0,f*(Math.exp(M)-1))}return d._lastX=r,d._lastY=i,d._lastMods=a,!0}}d.mouseListener=a(t,e),t.addEventListener(\"touchstart\",(function(r){var n=s(r.changedTouches[0],t);e(0,n[0],n[1],d._lastMods),e(1,n[0],n[1],d._lastMods)}),!!l&&{passive:!0}),t.addEventListener(\"touchmove\",(function(r){var n=s(r.changedTouches[0],t);e(1,n[0],n[1],d._lastMods),r.preventDefault()}),!!l&&{passive:!1}),t.addEventListener(\"touchend\",(function(t){e(0,d._lastX,d._lastY,d._lastMods)}),!!l&&{passive:!0}),d.wheelListener=o(t,(function(t,e){if(!1!==d.keyBindingMode&&d.enableWheel){var r=d.flipX?1:-1,i=d.flipY?1:-1,a=n();if(Math.abs(t)>Math.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=t(\"right-now\"),i=t(\"3d-view\"),a=t(\"mouse-change\"),o=t(\"mouse-wheel\"),s=t(\"mouse-event-offset\"),l=t(\"has-passive-events\")},{\"3d-view\":7,\"has-passive-events\":232,\"mouse-change\":247,\"mouse-event-offset\":248,\"mouse-wheel\":250,\"right-now\":278}],120:[function(t,e,r){var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n  uv = position;\\n  gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n  vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n  gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},{\"gl-shader\":132,glslify:231}],121:[function(t,e,r){\"use strict\";var n=t(\"./camera.js\"),i=t(\"gl-axes3d\"),a=t(\"gl-axes3d/properties\"),o=t(\"gl-spikes3d\"),s=t(\"gl-select-static\"),l=t(\"gl-fbo\"),c=t(\"a-big-triangle\"),u=t(\"mouse-change\"),f=t(\"gl-mat4/perspective\"),h=t(\"gl-mat4/ortho\"),p=t(\"./lib/shader\"),d=t(\"is-mobile\")({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}if(e>0){r=Math.round(Math.pow(10,e));return Math.ceil(t/r)*r}return Math.ceil(t)}function v(t){return\"boolean\"!=typeof t||t}e.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;if(!e){if(e=document.createElement(\"canvas\"),t.container)t.container.appendChild(e);else document.body.appendChild(e)}var r=t.gl;r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d}));if(!r)throw new Error(\"webgl not supported\");var y=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],I=!0,O=!0,z=new Array(16),D=new Array(16),R={view:null,projection:z,model:D,_ortho:!1},F=(O=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),B=t.cameraObject||n(e,T),N={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:B,axes:A,axesPixels:null,spikes:S,bounds:y,objects:E,shape:F,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:v(t.autoResize),autoBounds:v(t.autoBounds),autoScale:!!t.autoScale,autoCenter:v(t.autoCenter),clipToBounds:v(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:R,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,O=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},j=[r.drawingBufferWidth/N.pixelRatio|0,r.drawingBufferHeight/N.pixelRatio|0];function U(){if(!N._stopped&&N.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*N.pixelRatio),a=0|Math.ceil(n*N.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",I=!0}}}N.autoResize&&U();function V(){for(var t=E.length,e=P.length,n=0;n<e;++n)C[n]=0;t:for(n=0;n<t;++n){var i=E[n],a=i.pickSlots;if(a){for(var o=0;o<e;++o)if(C[o]+a<255){L[n]=o,i.setPickBase(C[o]+1),C[o]+=a;continue t}var l=s(r,F);L[n]=e,P.push(l),C.push(a),i.setPickBase(1),e+=1}else L[n]=-1}for(;e>0&&0===C[e-1];)C.pop(),P.pop().dispose()}function H(){if(N.contextLost)return!0;r.isContextLost()&&(N.contextLost=!0,N.mouseListener.enabled=!1,N.selection.object=null,N.oncontextloss&&N.oncontextloss())}window.addEventListener(\"resize\",U),N.update=function(t){N._stopped||(t=t||{},I=!0,O=!0)},N.add=function(t){N._stopped||(t.axes=A,E.push(t),L.push(-1),I=!0,O=!0,V())},N.remove=function(t){if(!N._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),I=!0,O=!0,V())}},N.dispose=function(){if(!N._stopped&&(N._stopped=!0,window.removeEventListener(\"resize\",U),e.removeEventListener(\"webglcontextlost\",H),N.mouseListener.enabled=!1,!N.contextLost)){A.dispose(),S.dispose();for(var t=0;t<E.length;++t)E[t].dispose();b.dispose();for(t=0;t<P.length;++t)P[t].dispose();_.dispose(),r=null,A=null,S=null,E=[]}},N._mouseRotating=!1,N._prevButtons=0,N.enableMouseListeners=function(){N.mouseListener=u(e,(function(t,e,r){if(!N._stopped){var n=P.length,i=E.length,a=x.object;x.distance=1/0,x.mouse[0]=e,x.mouse[1]=r,x.object=null,x.screen=null,x.dataCoordinate=x.dataPosition=null;var o=!1;if(t&&N._prevButtons)N._mouseRotating=!0;else{N._mouseRotating&&(O=!0),N._mouseRotating=!1;for(var s=0;s<n;++s){var l=P[s].query(e,j[1]-r-1,N.pickRadius);if(l){if(l.distance>x.distance)continue;for(var c=0;c<i;++c){var u=E[c];if(L[c]===s){var f=u.pick(l);f&&(x.buttons=t,x.screen=l.coord,x.distance=l.distance,x.object=u,x.index=f.distance,x.dataPosition=f.position,x.dataCoordinate=f.dataCoordinate,x.data=f,o=!0)}}}}}a&&a!==x.object&&(a.highlight&&a.highlight(null),I=!0),x.object&&(x.object.highlight&&x.object.highlight(x.data),I=!0),(o=o||x.object!==a)&&N.onselect&&N.onselect(x),1&t&&!(1&N._prevButtons)&&N.onclick&&N.onclick(x),N._prevButtons=t}}))},e.addEventListener(\"webglcontextlost\",H);var q=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],G=[q[0].slice(),q[1].slice()];function Y(){if(!H()){U();var t=N.camera.tick();R.view=N.camera.matrix,I=I||t,O=O||t,A.pixelRatio=N.pixelRatio,S.pixelRatio=N.pixelRatio;var e=E.length,n=q[0],i=q[1];n[0]=n[1]=n[2]=1/0,i[0]=i[1]=i[2]=-1/0;for(var o=0;o<e;++o){(C=E[o]).pixelRatio=N.pixelRatio,C.axes=N.axes,I=I||!!C.dirty,O=O||!!C.dirty;var s=C.bounds;if(s)for(var l=s[0],u=s[1],p=0;p<3;++p)n[p]=Math.min(n[p],l[p]),i[p]=Math.max(i[p],u[p])}var d=N.bounds;if(N.autoBounds)for(p=0;p<3;++p){if(i[p]<n[p])n[p]=-1,i[p]=1;else{n[p]===i[p]&&(n[p]-=1,i[p]+=1);var m=.05*(i[p]-n[p]);n[p]=n[p]-m,i[p]=i[p]+m}d[0][p]=n[p],d[1][p]=i[p]}var v=!1;for(p=0;p<3;++p)v=v||G[0][p]!==d[0][p]||G[1][p]!==d[1][p],G[0][p]=d[0][p],G[1][p]=d[1][p];if(O=O||v,I=I||v){if(v){var y=[0,0,0];for(o=0;o<3;++o)y[o]=g((d[1][o]-d[0][o])/10);A.autoTicks?A.update({bounds:d,tickSpacing:y}):A.update({bounds:d})}var T=r.drawingBufferWidth,k=r.drawingBufferHeight;F[0]=T,F[1]=k,j[0]=0|Math.max(T/N.pixelRatio,1),j[1]=0|Math.max(k/N.pixelRatio,1),function(t,e){var r=t.bounds,n=t.cameraParams,i=n.projection,a=n.model,o=t.gl.drawingBufferWidth,s=t.gl.drawingBufferHeight,l=t.zNear,c=t.zFar,u=t.fovy,p=o/s;e?(h(i,-p,p,-1,1,l,c),n._ortho=!0):(f(i,u,p,l,c),n._ortho=!1);for(var d=0;d<16;++d)a[d]=0;a[15]=1;var m=0;for(d=0;d<3;++d)m=Math.max(m,r[1][d]-r[0][d]);for(d=0;d<3;++d)t.autoScale?a[5*d]=t.aspect[d]/(r[1][d]-r[0][d]):a[5*d]=1/m,t.autoCenter&&(a[12+d]=.5*-a[5*d]*(r[0][d]+r[1][d]))}(N,w);for(o=0;o<e;++o){(C=E[o]).axesBounds=d,N.clipToBounds&&(C.clipBounds=d)}x.object&&(N.snapToData?S.position=x.dataCoordinate:S.position=x.dataPosition,S.bounds=d),O&&(O=!1,function(){if(!H()){r.colorMask(!0,!0,!0,!0),r.depthMask(!0),r.disable(r.BLEND),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL);for(var t=E.length,e=P.length,n=0;n<e;++n){var i=P[n];i.shape=j,i.begin();for(var a=0;a<t;++a)if(L[a]===n){var o=E[a];o.drawPick&&(o.pixelRatio=1,o.drawPick(R))}i.end()}}}()),N.axesPixels=a(N.axes,R,T,k),N.onrender&&N.onrender(),r.bindFramebuffer(r.FRAMEBUFFER,null),r.viewport(0,0,T,k),N.clearRGBA(),r.depthMask(!0),r.colorMask(!0,!0,!0,!0),r.enable(r.DEPTH_TEST),r.depthFunc(r.LEQUAL),r.disable(r.BLEND),r.disable(r.CULL_FACE);var M=!1;A.enable&&(M=M||A.isTransparent(),A.draw(R)),S.axes=A,x.object&&S.draw(R),r.disable(r.CULL_FACE);for(o=0;o<e;++o){(C=E[o]).axes=A,C.pixelRatio=N.pixelRatio,C.isOpaque&&C.isOpaque()&&C.draw(R),C.isTransparent&&C.isTransparent()&&(M=!0)}if(M){b.shape=F,b.bind(),r.clear(r.DEPTH_BUFFER_BIT),r.colorMask(!1,!1,!1,!1),r.depthMask(!0),r.depthFunc(r.LESS),A.enable&&A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){(C=E[o]).isOpaque&&C.isOpaque()&&C.draw(R)}r.enable(r.BLEND),r.blendEquation(r.FUNC_ADD),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.colorMask(!0,!0,!0,!0),r.depthMask(!1),r.clearColor(0,0,0,0),r.clear(r.COLOR_BUFFER_BIT),A.isTransparent()&&A.drawTransparent(R);for(o=0;o<e;++o){var C;(C=E[o]).isTransparent&&C.isTransparent()&&C.drawTransparent(R)}r.bindFramebuffer(r.FRAMEBUFFER,null),r.blendFunc(r.ONE,r.ONE_MINUS_SRC_ALPHA),r.disable(r.DEPTH_TEST),_.bind(),b.color[0].bind(0),_.uniforms.accumBuffer=0,c(r),r.disable(r.BLEND)}I=!1;for(o=0;o<e;++o)E[o].dirty=!1}}}return N.enableMouseListeners(),function t(){if(N._stopped||N.contextLost)return;Y(),requestAnimationFrame(t)}(),N.redraw=function(){N._stopped||(I=!0,Y())},N},createCamera:n}},{\"./camera.js\":119,\"./lib/shader\":120,\"a-big-triangle\":8,\"gl-axes3d\":70,\"gl-axes3d/properties\":77,\"gl-fbo\":86,\"gl-mat4/ortho\":101,\"gl-mat4/perspective\":102,\"gl-select-static\":131,\"gl-spikes3d\":141,\"is-mobile\":238,\"mouse-change\":247}],122:[function(t,e,r){var n=t(\"glslify\");r.pointVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform float pointCloud;\\n\\nhighp float rand(vec2 co) {\\n  highp float a = 12.9898;\\n  highp float b = 78.233;\\n  highp float c = 43758.5453;\\n  highp float d = dot(co.xy, vec2(a, b));\\n  highp float e = mod(d, 3.14);\\n  return fract(sin(e) * c);\\n}\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n    // if we don't jitter the point size a bit, overall point cloud\\n    // saturation 'jumps' on zooming, which is disturbing and confusing\\n  gl_PointSize = pointSize * ((19.5 + rand(position)) / 20.0);\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    // get the same square surface as circle would be\\n    gl_PointSize *= 0.886;\\n  }\\n}\"]),r.pointFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color, borderColor;\\nuniform float centerFraction;\\nuniform float pointCloud;\\n\\nvoid main() {\\n  float radius;\\n  vec4 baseColor;\\n  if(pointCloud != 0.0) { // pointCloud is truthy\\n    if(centerFraction == 1.0) {\\n      gl_FragColor = color;\\n    } else {\\n      gl_FragColor = mix(borderColor, color, centerFraction);\\n    }\\n  } else {\\n    radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n    if(radius > 1.0) {\\n      discard;\\n    }\\n    baseColor = mix(borderColor, color, step(radius, centerFraction));\\n    gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n  }\\n}\\n\"]),r.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  vec3 hgPosition = matrix * vec3(position, 1);\\n  gl_Position  = vec4(hgPosition.xy, 0, hgPosition.z);\\n  gl_PointSize = pointSize;\\n\\n  vec4 id = pickId + pickOffset;\\n  id.y += floor(id.x / 256.0);\\n  id.x -= floor(id.x / 256.0) * 256.0;\\n\\n  id.z += floor(id.y / 256.0);\\n  id.y -= floor(id.y / 256.0) * 256.0;\\n\\n  id.w += floor(id.z / 256.0);\\n  id.z -= floor(id.z / 256.0) * 256.0;\\n\\n  fragId = id;\\n}\\n\"]),r.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n  float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n  if(radius > 1.0) {\\n    discard;\\n  }\\n  gl_FragColor = fragId / 255.0;\\n}\\n\"])},{glslify:231}],123:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"typedarray-pool\"),o=t(\"./lib/shader\");function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}e.exports=function(t,e){var r=t.gl,a=i(r),l=i(r),c=n(r,o.pointVertex,o.pointFragment),u=n(r,o.pickVertex,o.pickFragment),f=new s(t,a,l,c,u);return f.update(e),t.addObject(f),f};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e<n;e++)c[e]=e;this.points=s,this.offsetBuffer.update(l),this.pickBuffer.update(c),i||a.free(l),o||a.free(c),this.pointCount=n,this.pickOffset=0},u.unifiedDraw=(l=[1,0,0,0,1,0,0,0,1],c=[0,0,0,0],function(t){var e=void 0!==t,r=e?this.pickShader:this.shader,n=this.plot.gl,i=this.plot.dataBox;if(0===this.pointCount)return t;var a=i[2]-i[0],o=i[3]-i[1],s=function(t,e){var r,n=0,i=t.length>>>1;for(r=0;r<i;r++){var a=t[2*r],o=t[2*r+1];a>=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r<n||r>=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},{\"./lib/shader\":122,\"gl-buffer\":78,\"gl-shader\":132,\"typedarray-pool\":308}],124:[function(t,e,r){e.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],f=e[2],h=e[3],p=r[0],d=r[1],m=r[2],g=r[3];(a=c*p+u*d+f*m+h*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g);1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n);return t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*f+l*m,t[3]=s*h+l*g,t}},{}],125:[function(t,e,r){\"use strict\";e.exports=function(t){return t||0===t?t.toString():\"\"}},{}],126:[function(t,e,r){\"use strict\";var n=t(\"vectorize-text\");e.exports=function(t,e,r){var a=i[e];a||(a=i[e]={});if(t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,c,u=n(t,o);if(r&&1!==r){for(l=0;l<s.positions.length;++l)for(c=0;c<s.positions[l].length;++c)s.positions[l][c]/=r;for(l=0;l<u.positions.length;++l)for(c=0;c<u.positions[l].length;++c)u.positions[l][c]/=r}var f=[[1/0,1/0],[-1/0,-1/0]],h=u.positions.length;for(l=0;l<h;++l){var p=u.positions[l];for(c=0;c<2;++c)f[0][c]=Math.min(f[0][c],p[c]),f[1][c]=Math.max(f[1][c],p[c])}return a[t]=[s,u,f]};var i={}},{\"vectorize-text\":311}],127:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = 1.0;\\n    if(distance(highlightId, id) < 0.0001) {\\n      scale = highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1);\\n    vec4 viewPosition = view * worldPosition;\\n    viewPosition = viewPosition / viewPosition.w;\\n    vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float scale = pixelRatio;\\n    if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n      scale *= highlightScale;\\n    }\\n\\n    vec4 worldPosition = model * vec4(position, 1.0);\\n    vec4 viewPosition = view * worldPosition;\\n    vec4 clipPosition = projection * viewPosition;\\n    clipPosition /= clipPosition.w;\\n\\n    gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = position;\\n  }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n    gl_Position = vec4(0,0,0,0);\\n  } else {\\n    float lscale = pixelRatio * scale;\\n    if(distance(highlightId, id) < 0.0001) {\\n      lscale *= highlightScale;\\n    }\\n\\n    vec4 clipCenter   = projection * view * model * vec4(position, 1);\\n    vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n    vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n    gl_Position = clipPosition;\\n    interpColor = color;\\n    pickId = id;\\n    dataCoordinate = dataPosition;\\n  }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (\\n    outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n    interpColor.a * opacity == 0.\\n  ) discard;\\n  gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n  if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n  gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:u},h={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function v(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}r.createPerspective=function(t){return v(t,f)},r.createOrtho=function(t){return v(t,h)},r.createProject=function(t){return v(t,p)},r.createPickPerspective=function(t){return v(t,d)},r.createPickOrtho=function(t){return v(t,m)},r.createPickProject=function(t){return v(t,g)}},{\"gl-shader\":132,glslify:231}],128:[function(t,e,r){\"use strict\";var n=t(\"is-string-blank\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"typedarray-pool\"),s=t(\"gl-mat4/multiply\"),l=t(\"./lib/shaders\"),c=t(\"./lib/glyphs\"),u=t(\"./lib/get-simple-string\"),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function m(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,c,u,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}e.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),m=a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),v=new g(e,r,n,o,f,h,p,d,m,s,c,u);return v.update(t),v};var v=g.prototype;v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},v.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var y=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}function L(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||f,u=r.view||f,h=r.projection||f,d=e.axesBounds,m=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],y[0]=2/o.drawingBufferWidth,y[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=h,l.screenSize=y,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=m,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var v=T,L=0;L<16;++L)v[L]=0;for(L=0;L<4;++L)v[5*L]=1;v[5*g]=0,i[g]<0?v[12+g]=d[0][g]:v[12+g]=d[1][g],s(v,c,v),l.model=v;var C=(g+1)%3,P=(g+2)%3,I=M(x),O=M(b);I[C]=1,O[P]=1;var z=p(0,0,0,S(_,I)),D=p(0,0,0,S(w,O));if(Math.abs(z[1])>Math.abs(D[1])){var R=z;z=D,D=R,R=I,I=O,O=R;var F=C;C=P,P=F}z[0]<0&&(I[C]=-1),D[1]>0&&(O[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(c[4*C+L],2),N+=Math.pow(c[4*P+L],2);I[C]/=Math.sqrt(B),O[P]/=Math.sqrt(N),l.axes[0]=I,l.axes[1]=O,l.fragClipBounds[0]=E(k,m[0],g,-1e8),l.fragClipBounds[1]=E(k,m[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}var C=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function P(t,e,r,n,i,a,o){var s=r.gl;if((a===r.projectHasAlpha||o)&&L(e,r,n,i),a===r.hasAlpha||o){t.bind();var l=t.uniforms;l.model=n.model||f,l.view=n.view||f,l.projection=n.projection||f,y[0]=2/s.drawingBufferWidth,y[1]=2/s.drawingBufferHeight,l.screenSize=y,l.highlightId=r.highlightId,l.highlightScale=r.highlightScale,l.fragClipBounds=C,l.clipBounds=r.axes.bounds,l.opacity=r.opacity,l.pickGroup=r.pickId/255,l.pixelRatio=i,r.vao.bind(),r.vao.draw(s.TRIANGLES,r.vertexCount),r.lineWidth>0&&(s.lineWidth(r.lineWidth*i),r.vao.draw(s.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function I(t,e,r,i){var a;a=Array.isArray(t)?e<t.length?t[e]:void 0:t,a=u(a);var o=!0;n(a)&&(a=\"\\u25bc\",o=!1);var s=c(a,r,i);return{mesh:s[0],lines:s[1],bounds:s[2],visible:o}}v.draw=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!1,!1)},v.drawTransparent=function(t){P(this.useOrtho?this.orthoShader:this.shader,this.projectShader,this,t,this.pixelRatio,!0,!1)},v.drawPick=function(t){P(this.useOrtho?this.pickOrthoShader:this.pickPerspectiveShader,this.pickProjectShader,this,t,1,!0,!0)},v.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[2]+(t.value[1]<<8)+(t.value[0]<<16);if(e>=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},v.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},v.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){if(Array.isArray(t.projectOpacity))this.projectOpacity=t.projectOpacity.slice();else{r=+t.projectOpacity;this.projectOpacity=[r,r,r]}for(var n=0;n<3;++n)this.projectOpacity[n]=m(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=m(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else{i=[],a=[];for(n=0;n<c.length;++n)i[n]=c[n][0],a[n]=c[n][1]}var u=[1/0,1/0,1/0],f=[-1/0,-1/0,-1/0],h=t.glyph,p=t.color,d=t.size,g=t.angle,v=t.lineColor,y=-1,x=0,b=0,_=0;if(s.length){_=s.length;t:for(n=0;n<_;++n){for(var w=s[n],T=0;T<3;++T)if(isNaN(w[T])||!isFinite(w[T]))continue t;var k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;x+=3*k.cells.length,b+=2*A.edges.length}}var S=x+b,E=o.mallocFloat(3*S),L=o.mallocFloat(4*S),C=o.mallocFloat(2*S),P=o.mallocUint32(S);if(S>0){var O=0,z=x,D=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(v)&&Array.isArray(v[0]);t:for(n=0;n<_;++n){y+=1;for(w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T])}k=(N=I(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n<p.length?p[n]:[0,0,0,0]:p).length){for(T=0;T<3;++T)D[T]=U[T];D[3]=1}else if(4===U.length){for(T=0;T<4;++T)D[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else D[0]=D[1]=D[2]=0,D[3]=1;else D=[1,1,1,0];if(j)if(Array.isArray(v)){var U;if(3===(U=B?n<v.length?v[n]:[0,0,0,0]:v).length){for(T=0;T<3;++T)R[T]=U[T];R[T]=1}else if(4===U.length){for(T=0;T<4;++T)R[T]=U[T];!this.hasAlpha&&U[3]<1&&(this.hasAlpha=!0)}}else R[0]=R[1]=R[2]=0,R[3]=1;else R=[1,1,1,0];var V=.5;j?Array.isArray(d)?V=n<d.length?+d[n]:12:d?V=+d:this.useOrtho&&(V=12):V=0;var H=0;Array.isArray(g)?H=n<g.length?+g[n]:0:g&&(H=+g);var q=Math.cos(H),G=Math.sin(H);for(w=s[n],T=0;T<3;++T)f[T]=Math.max(f[T],w[T]),u[T]=Math.min(u[T],w[T]);var Y=i,W=a;Y=0;Array.isArray(i)?Y=n<i.length?i[n]:0:i&&(Y=i);W=0;Array.isArray(a)?W=n<a.length?a[n]:0:a&&(W=a);var X=[Y*=Y>0?1-M[0][0]:Y<0?1+M[1][0]:1,W*=W>0?1-M[0][1]:W<0?1+M[1][1]:1],Z=k.cells||[],J=k.positions||[];for(T=0;T<Z.length;++T)for(var K=Z[T],Q=0;Q<3;++Q){for(var $=0;$<3;++$)E[3*O+$]=w[$];for($=0;$<4;++$)L[4*O+$]=D[$];P[O]=y;var tt=J[K[Q]];C[2*O]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*O+1]=V*(G*tt[0]+q*tt[1]+X[1]),O+=1}for(Z=A.edges,J=A.positions,T=0;T<Z.length;++T)for(K=Z[T],Q=0;Q<2;++Q){for($=0;$<3;++$)E[3*z+$]=w[$];for($=0;$<4;++$)L[4*z+$]=R[$];P[z]=y;tt=J[K[Q]];C[2*z]=V*(q*tt[0]-G*tt[1]+X[0]),C[2*z+1]=V*(G*tt[0]+q*tt[1]+X[1]),z+=1}}}this.bounds=[u,f],this.points=s,this.pointCount=s.length,this.vertexCount=x,this.lineVertexCount=b,this.pointBuffer.update(E),this.colorBuffer.update(L),this.glyphBuffer.update(C),this.idBuffer.update(P),o.free(E),o.free(L),o.free(C),o.free(P)},v.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},{\"./lib/get-simple-string\":125,\"./lib/glyphs\":126,\"./lib/shaders\":127,\"gl-buffer\":78,\"gl-mat4/multiply\":100,\"gl-vao\":150,\"is-string-blank\":239,\"typedarray-pool\":308}],129:[function(t,e,r){\"use strict\";var n=t(\"glslify\");r.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n  gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),r.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n  gl_FragColor = color;\\n}\\n\"])},{glslify:231}],130:[function(t,e,r){\"use strict\";var n=t(\"gl-shader\"),i=t(\"gl-buffer\"),a=t(\"./lib/shaders\");function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}e.exports=function(t,e){var r=t.gl,s=i(r,[0,0,0,1,1,0,1,1]),l=n(r,a.boxVertex,a.boxFragment),c=new o(t,s,l);return c.update(e),t.addOverlay(c),c};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,f=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],h=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(f=Math.max(f,c[0]),h=Math.max(h,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p<f||d<h)){o.bind();var m=s[2]-s[0],g=s[3]-s[1];if(this.outerFill&&(o.drawBox(0,0,m,h,i),o.drawBox(0,h,f,d,i),o.drawBox(0,d,m,g,i),o.drawBox(p,h,m,d,i)),this.innerFill&&o.drawBox(f,h,p,d,n),r>0){var 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t.slice()})),this.center=(t.center||[1/0,1/0]).slice(),this.plot.setOverlayDirty()},i.draw=function(){var t=this.enable,e=this.width,r=this.color,n=this.center,i=this.plot,a=i.line,o=i.dataBox,s=i.viewBox;if(a.bind(),o[0]<=n[0]&&n[0]<=o[2]&&o[1]<=n[1]&&n[1]<=o[3]){var l=s[0]+(n[0]-o[0])/(o[2]-o[0])*(s[2]-s[0]),c=s[1]+(n[1]-o[1])/(o[3]-o[1])*(s[3]-s[1]);t[0]&&a.drawLine(l,c,s[0],c,e[0],r[0]),t[1]&&a.drawLine(l,c,l,s[1],e[1],r[1]),t[2]&&a.drawLine(l,c,s[2],c,e[2],r[2]),t[3]&&a.drawLine(l,c,l,s[3],e[3],r[3])}},i.dispose=function(){this.plot.removeOverlay(this)}},{}],140:[function(t,e,r){\"use strict\";var n=t(\"glslify\"),i=t(\"gl-shader\"),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, color;\\nattribute float weight;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 coordinates[3];\\nuniform vec4 colors[3];\\nuniform vec2 screenShape;\\nuniform float lineWidth;\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  vec3 vertexPosition = mix(coordinates[0],\\n    mix(coordinates[2], coordinates[1], 0.5 * (position + 1.0)), abs(position));\\n\\n  vec4 clipPos = projection * view * model * vec4(vertexPosition, 1.0);\\n  vec2 clipOffset = (projection * view * model * vec4(color, 0.0)).xy;\\n  vec2 delta = weight * clipOffset * screenShape;\\n  vec2 lineOffset = normalize(vec2(delta.y, -delta.x)) / screenShape;\\n\\n  gl_Position   = vec4(clipPos.xy + clipPos.w * 0.5 * lineWidth * lineOffset, clipPos.z, clipPos.w);\\n  fragColor     = color.x * colors[0] + color.y * colors[1] + color.z * colors[2];\\n}\\n\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n  gl_FragColor = fragColor;\\n}\"]);e.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec3\"},{name:\"weight\",type:\"float\"}])}},{\"gl-shader\":132,glslify:231}],141:[function(t,e,r){\"use strict\";var n=t(\"gl-buffer\"),i=t(\"gl-vao\"),a=t(\"./shaders/index\");e.exports=function(t,e){var r=[];function o(t,e,n,i,a,o){var s=[t,e,n,0,0,0,1];s[i+3]=1,s[i]=a,r.push.apply(r,s),s[6]=-1,r.push.apply(r,s),s[i]=o,r.push.apply(r,s),r.push.apply(r,s),s[6]=1,r.push.apply(r,s),s[i]=a,r.push.apply(r,s)}o(0,0,0,0,0,1),o(0,0,0,1,0,1),o(0,0,0,2,0,1),o(1,0,0,1,-1,1),o(1,0,0,2,-1,1),o(0,1,0,0,-1,1),o(0,1,0,2,-1,1),o(0,0,1,0,-1,1),o(0,0,1,1,-1,1);var l=n(t,r),c=i(t,[{type:t.FLOAT,buffer:l,size:3,offset:0,stride:28},{type:t.FLOAT,buffer:l,size:3,offset:12,stride:28},{type:t.FLOAT,buffer:l,size:1,offset:24,stride:28}]),u=a(t);u.attributes.position.location=0,u.attributes.color.location=1,u.attributes.weight.location=2;var f=new s(t,l,c,u);return f.update(e),f};var o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n,this.pixelRatio=1,this.bounds=[[-1e3,-1e3,-1e3],[1e3,1e3,1e3]],this.position=[0,0,0],this.lineWidth=[2,2,2],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.enabled=[!0,!0,!0],this.drawSides=[!0,!0,!0],this.axes=null}var l=s.prototype,c=[0,0,0],u=[0,0,0],f=[0,0];l.isTransparent=function(){return!1},l.drawTransparent=function(t){},l.draw=function(t){var e=this.gl,r=this.vao,n=this.shader;r.bind(),n.bind();var i,a=t.model||o,s=t.view||o,l=t.projection||o;this.axes&&(i=this.axes.lastCubeProps.axis);for(var h=c,p=u,d=0;d<3;++d)i&&i[d]<0?(h[d]=this.bounds[0][d],p[d]=this.bounds[1][d]):(h[d]=this.bounds[1][d],p[d]=this.bounds[0][d]);f[0]=e.drawingBufferWidth,f[1]=e.drawingBufferHeight,n.uniforms.model=a,n.uniforms.view=s,n.uniforms.projection=l,n.uniforms.coordinates=[this.position,h,p],n.uniforms.colors=this.colors,n.uniforms.screenShape=f;for(d=0;d<3;++d)n.uniforms.lineWidth=this.lineWidth[d]*this.pixelRatio,this.enabled[d]&&(r.draw(e.TRIANGLES,6,6*d),this.drawSides[d]&&r.draw(e.TRIANGLES,12,18+12*d));r.unbind()},l.update=function(t){t&&(\"bounds\"in t&&(this.bounds=t.bounds),\"position\"in t&&(this.position=t.position),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"colors\"in t&&(this.colors=t.colors),\"enabled\"in t&&(this.enabled=t.enabled),\"drawSides\"in t&&(this.drawSides=t.drawSides))},l.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},{\"./shaders/index\":140,\"gl-buffer\":78,\"gl-vao\":150}],142:[function(t,e,r){var n=t(\"glslify\"),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  // Scale the vector magnitude to stay constant with\\n  // model & view changes.\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * tubePosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n  f_eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n  // vec4 m_position  = model * vec4(tubePosition, 1.0);\\n  vec4 t_position  = view * tubePosition;\\n  gl_Position      = projection * t_position;\\n\\n  f_color          = color;\\n  f_data           = tubePosition.xyz;\\n  f_position       = position.xyz;\\n  f_uv             = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness,\\n  float fresnel) {\\n\\n  float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n  float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n  //Half angle vector\\n  vec3 H = normalize(lightDirection + viewDirection);\\n\\n  //Geometric term\\n  float NdotH = max(dot(surfaceNormal, H), 0.0);\\n  float VdotH = max(dot(viewDirection, H), 0.000001);\\n  float LdotH = max(dot(lightDirection, H), 0.000001);\\n  float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n  float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n  float G = min(1.0, min(G1, G2));\\n  \\n  //Distribution term\\n  float D = beckmannDistribution(NdotH, roughness);\\n\\n  //Fresnel term\\n  float F = pow(1.0 - VdotN, fresnel);\\n\\n  //Multiply terms and done\\n  return  G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n  vec3 N = normalize(f_normal);\\n  vec3 L = normalize(f_lightDirection);\\n  vec3 V = normalize(f_eyeDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n  // Return up-vector for only-z vector.\\n  // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n  // From the above if-statement we have ||a|| > 0  U  ||b|| > 0.\\n  // Assign z = 0, x = -b, y = a:\\n  // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n  if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n    return normalize(vec3(-v.y, v.x, 0.0));\\n  } else {\\n    return normalize(vec3(0.0, v.z, -v.y));\\n  }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n  float segmentCount = 8.0;\\n\\n  float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n  vec3 u = getOrthogonalVector(d);\\n  vec3 v = normalize(cross(u, d));\\n\\n  vec3 x = u * cos(angle) * length(d);\\n  vec3 y = v * sin(angle) * length(d);\\n  vec3 v3 = x + y;\\n\\n  normal = normalize(v3);\\n\\n  return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float tubeScale;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  vec3 normal;\\n  vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n  vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n  gl_Position = projection * view * tubePosition;\\n  f_id        = id;\\n  f_position  = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3  clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n  if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n  gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);r.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec4\"}]},r.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec4\"}]}},{glslify:231}],143:[function(t,e,r){\"use strict\";var n=t(\"gl-vec3\"),i=t(\"gl-vec4\"),a=[\"xyz\",\"xzy\",\"yxz\",\"yzx\",\"zxy\",\"zyx\"],o=function(t,e,r,a){for(var o=0,s=0;s<t.length;s++)for(var l=t[s].velocities,c=0;c<l.length;c++)o=Math.max(o,n.length(l[c]));var u=t.map((function(t){return function(t,e,r,a){for(var o=t.points,s=t.velocities,l=t.divergences,c=[],u=[],f=[],h=[],p=[],d=[],m=0,g=0,v=i.create(),y=i.create(),x=0;x<o.length;x++){var b=o[x],_=s[x],w=l[x];0===e&&(w=.05*r),g=n.length(_)/a,v=i.create(),n.copy(v,_),v[3]=w;for(var T=0;T<8;T++)p[T]=[b[0],b[1],b[2],T];if(h.length>0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(y,v,v,v,y,y),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=y;y=v,v=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s<u.length;s++){var m=u[s],g=f.length;f=f.concat(m.positions),p=p.concat(m.vectors),d=d.concat(m.vertexIntensity);for(c=0;c<m.cells.length;c++){var v=m.cells[c],y=[];h.push(y);for(var x=0;x<v.length;x++)y.push(v[x]+g)}}return{positions:f,cells:h,vectors:p,vertexIntensity:d,colormap:e}},s=function(t,e){var r,n=t.length;for(r=0;r<n;r++){var i=t[r];if(i===e)return r;if(i>e)return r-1}return r},l=function(t,e,r){return t<e?e:t>r?r:t},c=function(t){var e=1/0;t.sort((function(t,e){return t-e}));for(var r=t.length,n=1;n<r;n++){var i=Math.abs(t[n]-t[n-1]);i<e&&(e=i)}return e};e.exports=function(t,e){var r=t.startingPositions,i=t.maxLength||1e3,u=t.tubeSize||1,f=t.absoluteTubeSize,h=t.gridFill||\"+x+y+z\",p={};-1!==h.indexOf(\"-x\")&&(p.reversedX=!0),-1!==h.indexOf(\"-y\")&&(p.reversedY=!0),-1!==h.indexOf(\"-z\")&&(p.reversedZ=!0),p.filled=a.indexOf(h.replace(/-/g,\"\").replace(/\\+/g,\"\"));var d=t.getVelocity||function(e){return function(t,e,r){var i=e.vectors,a=e.meshgrid,o=t[0],c=t[1],u=t[2],f=a[0].length,h=a[1].length,p=a[2].length,d=s(a[0],o),m=s(a[1],c),g=s(a[2],u),v=d+1,y=m+1,x=g+1;if(d=l(d,0,f-1),v=l(v,0,f-1),m=l(m,0,h-1),y=l(y,0,h-1),g=l(g,0,p-1),x=l(x,0,p-1),d<0||m<0||g<0||v>f-1||y>h-1||x>p-1)return n.create();var b,_,w,T,k,A,M=a[0][d],S=a[0][v],E=a[1][m],L=a[1][y],C=a[2][g],P=(o-M)/(S-M),I=(c-E)/(L-E),O=(u-C)/(a[2][x]-C);switch(isFinite(P)||(P=.5),isFinite(I)||(I=.5),isFinite(O)||(O=.5),r.reversedX&&(d=f-1-d,v=f-1-v),r.reversedY&&(m=h-1-m,y=h-1-y),r.reversedZ&&(g=p-1-g,x=p-1-x),r.filled){case 5:k=g,A=x,w=m*p,T=y*p,b=d*p*h,_=v*p*h;break;case 4:k=g,A=x,b=d*p,_=v*p,w=m*p*f,T=y*p*f;break;case 3:w=m,T=y,k=g*h,A=x*h,b=d*h*p,_=v*h*p;break;case 2:w=m,T=y,b=d*h,_=v*h,k=g*h*f,A=x*h*f;break;case 1:b=d,_=v,k=g*f,A=x*f,w=m*f*p,T=y*f*p;break;default:b=d,_=v,w=m*f,T=y*f,k=g*f*h,A=x*f*h}var z=i[b+w+k],D=i[b+w+A],R=i[b+T+k],F=i[b+T+A],B=i[_+w+k],N=i[_+w+A],j=i[_+T+k],U=i[_+T+A],V=n.create(),H=n.create(),q=n.create(),G=n.create();n.lerp(V,z,B,P),n.lerp(H,D,N,P),n.lerp(q,R,j,P),n.lerp(G,F,U,P);var Y=n.create(),W=n.create();n.lerp(Y,V,q,I),n.lerp(W,H,G,I);var X=n.create();return n.lerp(X,Y,W,O),X}(e,t,p)},m=t.getDivergence||function(t,e){var r=n.create(),i=1e-4;n.add(r,t,[i,0,0]);var a=d(r);n.subtract(a,a,e),n.scale(a,a,1/i),n.add(r,t,[0,i,0]);var o=d(r);n.subtract(o,o,e),n.scale(o,o,1/i),n.add(r,t,[0,0,i]);var s=d(r);return n.subtract(s,s,e),n.scale(s,s,1/i),n.add(r,a,o),n.add(r,r,s),r},g=[],v=e[0][0],y=e[0][1],x=e[0][2],b=e[1][0],_=e[1][1],w=e[1][2],T=function(t){var e=t[0],r=t[1],n=t[2];return!(e<v||e>b||r<y||r>_||n<x||n>w)},k=10*n.distance(e[0],e[1])/i,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,l=0;l<s;l++){var u=t[l],f=u[0],h=u[1],p=u[2];i[f]||(e.push(f),i[f]=!0),a[h]||(r.push(h),a[h]=!0),o[p]||(n.push(p),o[p]=!0)}var d=c(e),m=c(r),g=c(n),v=Math.min(d,m,g);return isFinite(v)?v:1}(r));for(var L=0;L<E;L++){var C=n.create();n.copy(C,r[L]);var P=[C],I=[],O=d(C),z=C;I.push(O);var D=[],R=m(C,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F),g.push({points:P,velocities:I,divergences:D});for(var B=0;B<100*i&&P.length<i&&T(C);){B++;var N=n.clone(O),j=n.squaredLength(N);if(0===j)break;if(j>A&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),O=d(N),n.squaredDistance(z,N)-A>-1e-4*A){P.push(N),z=N,I.push(O);R=m(N,O),F=n.length(R);isFinite(F)&&F>S&&(S=F),D.push(F)}C=N}}var U=o(g,t.colormap,S,M);return f?U.tubeScale=f:(0===S&&(S=1),U.tubeScale=.5*u*M/S),U};var u=t(\"./lib/shaders\"),f=t(\"gl-cone3d\").createMesh;e.exports.createTubeMesh=function(t,e){return f(t,e,{shaders:u,traceType:\"streamtube\"})}},{\"./lib/shaders\":142,\"gl-cone3d\":79,\"gl-vec3\":169,\"gl-vec4\":205}],144:[function(t,e,r){var n=t(\"gl-shader\"),i=t(\"glslify\"),a=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute vec3 f;\\nattribute vec3 normal;\\n\\nuniform vec3 objectOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 lightPosition, eyePosition;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 localCoordinate = vec3(uv.zw, f.x);\\n  worldCoordinate = objectOffset + localCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n  vec4 clipPosition = projection * view * worldPosition;\\n  gl_Position = clipPosition;\\n  kill = f.y;\\n  value = f.z;\\n  planeCoordinate = uv.xy;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Lighting geometry parameters\\n  vec4 cameraCoordinate = view * worldPosition;\\n  cameraCoordinate.xyz /= cameraCoordinate.w;\\n  lightDirection = lightPosition - cameraCoordinate.xyz;\\n  eyeDirection   = eyePosition - cameraCoordinate.xyz;\\n  surfaceNormal  = normalize((vec4(normal,0) * inverseModel).xyz);\\n}\\n\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n  float NdotH = max(x, 0.0001);\\n  float cos2Alpha = NdotH * NdotH;\\n  float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n  float roughness2 = roughness * roughness;\\n  float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n  return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat beckmannSpecular(\\n  vec3 lightDirection,\\n  vec3 viewDirection,\\n  vec3 surfaceNormal,\\n  float roughness) {\\n  return beckmannDistribution(dot(surfaceNormal, normalize(lightDirection + viewDirection)), roughness);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  if (\\n    kill > 0.0 ||\\n    vColor.a == 0.0 ||\\n    outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n  ) discard;\\n\\n  vec3 N = normalize(surfaceNormal);\\n  vec3 V = normalize(eyeDirection);\\n  vec3 L = normalize(lightDirection);\\n\\n  if(gl_FrontFacing) {\\n    N = -N;\\n  }\\n\\n  float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n  float diffuse  = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n  //decide how to interpolate color \\u2014 in vertex or in fragment\\n  vec4 surfaceColor =\\n    step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n    step(.5, vertexColor) * vColor;\\n\\n  vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular,  1.0);\\n\\n  gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n  vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n  worldCoordinate = objectOffset + dataCoordinate;\\n  vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n  vec4 clipPosition = projection * view * worldPosition;\\n  clipPosition.z += zOffset;\\n\\n  gl_Position = clipPosition;\\n  value = f + objectOffset.z;\\n  kill = -1.0;\\n  planeCoordinate = uv.zw;\\n\\n  vColor = texture2D(colormap, vec2(value, value));\\n\\n  //Don't do lighting for contours\\n  surfaceNormal   = vec3(1,0,0);\\n  eyeDirection    = vec3(0,1,0);\\n  lightDirection  = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n  return ((p > max(a, b)) || \\n          (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n  return (outOfRange(a.x, b.x, p.x) ||\\n          outOfRange(a.y, b.y, p.y) ||\\n          outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n  return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n  float vh = 255.0 * v;\\n  float upper = floor(vh);\\n  float lower = fract(vh);\\n  return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n  if ((kill > 0.0) ||\\n      (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n  vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n  vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n  gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);r.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},r.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},r.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},{\"gl-shader\":132,glslify:231}],145:[function(t,e,r){\"use strict\";e.exports=function(t){var e=t.gl,r=y(e),n=b(e),s=x(e),l=_(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:40,offset:0},{buffer:c,size:3,stride:40,offset:16},{buffer:c,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,f,h,p,d,[0,0,0]),v={levels:[[],[],[]]};for(var w in t)v[w]=t[w];return v.colormap=v.colormap||\"jet\",g.update(v),g};var n=t(\"bit-twiddle\"),i=t(\"gl-buffer\"),a=t(\"gl-vao\"),o=t(\"gl-texture2d\"),s=t(\"typedarray-pool\"),l=t(\"colormap\"),c=t(\"ndarray-ops\"),u=t(\"ndarray-pack\"),f=t(\"ndarray\"),h=t(\"surface-nets\"),p=t(\"gl-mat4/multiply\"),d=t(\"gl-mat4/invert\"),m=t(\"binary-search-bounds\"),g=t(\"ndarray-gradient\"),v=t(\"./lib/shaders\"),y=v.createShader,x=v.createContourShader,b=v.createPickShader,_=v.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();function M(t,e,r,n,i,a,o,l,c,u,h,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;r<e.length;++r){if(e.length<2)return 1;if(e[r][0]===t)return e[r][1];if(e[r][0]>t&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},I=w.slice(),O=[1,0,0,0,1,0,0,0,1];function z(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=O,n.vertexColor=this.vertexColor;var s=I;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=C(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o<this.contourLevels[i].length;++o)o===this.highlightLevel[i]?(f.uniforms.contourColor=this.highlightColor[i],f.uniforms.contourTint=this.highlightTint[i]):0!==o&&o-1!==this.highlightLevel[i]||(f.uniforms.contourColor=this.contourColor[i],f.uniforms.contourTint=this.contourTint[i]),this._contourCounts[i][o]&&(f.uniforms.height=this.contourLevels[i][o],h.draw(r.LINES,this._contourCounts[i][o],this._contourOffsets[i][o]));for(i=0;i<3;++i)for(f.uniforms.model=u.projections[i],f.uniforms.clipBounds=u.clipBounds[i],o=0;o<3;++o)if(this.contourProject[i][o]){f.uniforms.permutation=k[o],r.lineWidth(this.contourWidth[o]*this.pixelRatio);for(var m=0;m<this.contourLevels[o].length;++m)m===this.highlightLevel[o]?(f.uniforms.contourColor=this.highlightColor[o],f.uniforms.contourTint=this.highlightTint[o]):0!==m&&m-1!==this.highlightLevel[o]||(f.uniforms.contourColor=this.contourColor[o],f.uniforms.contourTint=this.contourTint[o]),this._contourCounts[o][m]&&(f.uniforms.height=this.contourLevels[o][m],h.draw(r.LINES,this._contourCounts[o][m],this._contourOffsets[o][m]))}for(h.unbind(),(h=this._dynamicVAO).bind(),i=0;i<3;++i)if(0!==this._dynamicCounts[i])for(f.uniforms.model=n.model,f.uniforms.clipBounds=n.clipBounds,f.uniforms.permutation=k[i],r.lineWidth(this.dynamicWidth[i]*this.pixelRatio),f.uniforms.contourColor=this.dynamicColor[i],f.uniforms.contourTint=this.dynamicTint[i],f.uniforms.height=this.dynamicLevel[i],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]),o=0;o<3;++o)this.contourProject[o][i]&&(f.uniforms.model=u.projections[o],f.uniforms.clipBounds=u.clipBounds[o],h.draw(r.LINES,this._dynamicCounts[i],this._dynamicOffsets[i]));h.unbind()}}S.draw=function(t){return z.call(this,t,!1)},S.drawTransparent=function(t){return z.call(this,t,!0)};var D={model:w,view:w,projection:w,inverseModel:w,clipBounds:[[0,0,0],[0,0,0]],height:0,shape:[0,0],pickId:0,lowerBound:[0,0,0],upperBound:[0,0,0],zOffset:0,objectOffset:[0,0,0],permutation:[1,0,0,0,1,0,0,0,1],lightPosition:[0,0,0],eyePosition:[0,0,0]};function R(t,e){return Array.isArray(t)?[e(t[0]),e(t[1]),e(t[2])]:[e(t),e(t),e(t)]}function F(t){return Array.isArray(t)?3===t.length?[t[0],t[1],t[2],1]:[t[0],t[1],t[2],t[3]]:[0,0,0,1]}function B(t){if(Array.isArray(t)){if(Array.isArray(t))return[F(t[0]),F(t[1]),F(t[2])];var e=F(t);return[e.slice(),e.slice(),e.slice()]}}S.drawPick=function(t){t=t||{};var e=this.gl;e.disable(e.CULL_FACE);var r=D;r.model=t.model||w,r.view=t.view||w,r.projection=t.projection||w,r.shape=this._field[2].shape,r.pickId=this.pickId/255,r.lowerBound=this.bounds[0],r.upperBound=this.bounds[1],r.objectOffset=this.objectOffset,r.permutation=O;for(var n=0;n<2;++n)for(var i=r.clipBounds[n],a=0;a<3;++a)i[a]=Math.min(Math.max(this.clipBounds[n][a],-1e8),1e8);var o=C(r,this);if(o.showSurface){for(this._pickShader.bind(),this._pickShader.uniforms=r,this._vao.bind(),this._vao.draw(e.TRIANGLES,this._vertexCount),n=0;n<3;++n)this.surfaceProject[n]&&(this._pickShader.uniforms.model=o.projections[n],this._pickShader.uniforms.clipBounds=o.clipBounds[n],this._vao.draw(e.TRIANGLES,this._vertexCount));this._vao.unbind()}if(o.showContour){var s=this._contourPickShader;s.bind(),s.uniforms=r;var l=this._contourVAO;for(l.bind(),a=0;a<3;++a)for(e.lineWidth(this.contourWidth[a]*this.pixelRatio),s.uniforms.permutation=k[a],n=0;n<this.contourLevels[a].length;++n)this._contourCounts[a][n]&&(s.uniforms.height=this.contourLevels[a][n],l.draw(e.LINES,this._contourCounts[a][n],this._contourOffsets[a][n]));for(n=0;n<3;++n)for(s.uniforms.model=o.projections[n],s.uniforms.clipBounds=o.clipBounds[n],a=0;a<3;++a)if(this.contourProject[n][a]){s.uniforms.permutation=k[a],e.lineWidth(this.contourWidth[a]*this.pixelRatio);for(var c=0;c<this.contourLevels[a].length;++c)this._contourCounts[a][c]&&(s.uniforms.height=this.contourLevels[a][c],l.draw(e.LINES,this._contourCounts[a][c],this._contourOffsets[a][c]))}l.unbind()}},S.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=this._field[2].shape,r=this._pickResult,n=e[0]*(t.value[0]+(t.value[2]>>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var f=u?a:1-a,h=0;h<2;++h)for(var p=i+u,d=s+h,g=f*(h?l:1-l),v=0;v<3;++v)c[v]+=this._field[v].get(p,d)*g;for(var y=this._pickResult.level,x=0;x<3;++x)if(y[x]=m.le(this.contourLevels[x],c[x]),y[x]<0)this.contourLevels[x].length>0&&(y[x]=0);else if(y[x]<this.contourLevels[x].length-1){var b=this.contourLevels[x][y[x]],_=this.contourLevels[x][y[x]+1];Math.abs(b-c[x])>Math.abs(_-c[x])&&(y[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],v=0;v<3;++v)r.dataCoordinate[v]=this._field[v].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(v=0;v<2;++v)if(c.shape[v]!==a[v])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var v=0;v<a[0];++v)this._field[0].set(v+1,0,v);for(this._field[0].set(a[0]+1,0,a[0]-1),this._field[1].set(0,0,0),v=0;v<a[1];++v)this._field[1].set(0,v+1,v);this._field[1].set(0,a[1]+1,a[1]-1)}var y=this._field,x=f(s.mallocFloat(3*y[2].size*2),[3,a[0]+2,a[1]+2,2]);for(o=0;o<3;++o)g(x.pick(o),y[o],\"mirror\");var b=f(s.mallocFloat(3*y[2].size),[a[0]+2,a[1]+2,3]);for(o=0;o<a[0]+2;++o)for(v=0;v<a[1]+2;++v){var _=x.get(0,o,v,0),w=x.get(0,o,v,1),k=x.get(1,o,v,0),A=x.get(1,o,v,1),M=x.get(2,o,v,0),S=x.get(2,o,v,1),E=k*S-A*M,L=M*w-S*_,C=_*A-w*k,P=Math.sqrt(E*E+L*L+C*C);P<1e-8?(P=Math.max(Math.abs(E),Math.abs(L),Math.abs(C)))<1e-8?(C=1,L=E=0,P=1):P=1/P:P=1/Math.sqrt(P),b.set(o,v,0,E*P),b.set(o,v,1,L*P),b.set(o,v,2,C*P)}s.free(x.data);var I=[1/0,1/0,1/0],O=[-1/0,-1/0,-1/0],z=1/0,D=-1/0,F=(a[0]-1)*(a[1]-1)*6,N=s.mallocFloat(n.nextPow2(10*F)),j=0,U=0;for(o=0;o<a[0]-1;++o)t:for(v=0;v<a[1]-1;++v){for(var V=0;V<2;++V)for(var H=0;H<2;++H)for(var q=0;q<3;++q){var G=this._field[q].get(1+o+V,1+v+H);if(isNaN(G)||!isFinite(G))continue t}for(q=0;q<6;++q){var Y=o+T[q][0],W=v+T[q][1],X=this._field[0].get(Y+1,W+1),Z=this._field[1].get(Y+1,W+1);G=this._field[2].get(Y+1,W+1),E=b.get(Y+1,W+1,0),L=b.get(Y+1,W+1,1),C=b.get(Y+1,W+1,2),t.intensity&&(J=t.intensity.get(Y,W));var J=t.intensity?t.intensity.get(Y,W):G+this.objectOffset[2];N[j++]=Y,N[j++]=W,N[j++]=X,N[j++]=Z,N[j++]=G,N[j++]=0,N[j++]=J,N[j++]=E,N[j++]=L,N[j++]=C,I[0]=Math.min(I[0],X+this.objectOffset[0]),I[1]=Math.min(I[1],Z+this.objectOffset[1]),I[2]=Math.min(I[2],G+this.objectOffset[2]),z=Math.min(z,J),O[0]=Math.max(O[0],X+this.objectOffset[0]),O[1]=Math.max(O[1],Z+this.objectOffset[1]),O[2]=Math.max(O[2],G+this.objectOffset[2]),D=Math.max(D,J),U+=1}}for(t.intensityBounds&&(z=+t.intensityBounds[0],D=+t.intensityBounds[1]),o=6;o<j;o+=10)N[o]=(N[o]-z)/(D-z);this._vertexCount=U,this._coordinateBuffer.update(N.subarray(0,j)),s.freeFloat(N),s.free(b.data),this.bounds=[I,O],this.intensity=t.intensity||this._field[2],this.intensityBounds[0]===z&&this.intensityBounds[1]===D||(r=!0),this.intensityBounds=[z,D]}if(\"levels\"in t){var K=t.levels;for(K=Array.isArray(K[0])?K.slice():[[],[],K],o=0;o<3;++o)K[o]=K[o].slice(),K[o].sort((function(t,e){return t-e}));for(o=0;o<3;++o)for(v=0;v<K[o].length;++v)K[o][v]-=this.objectOffset[o];t:for(o=0;o<3;++o){if(K[o].length!==this.contourLevels[o].length){r=!0;break}for(v=0;v<K[o].length;++v)if(K[o][v]!==this.contourLevels[o][v]){r=!0;break t}}this.contourLevels=K}if(r){y=this._field,a=this.shape;for(var Q=[],$=0;$<3;++$){var tt=this.contourLevels[$],et=[],rt=[],nt=[0,0,0];for(o=0;o<tt.length;++o){var it=h(this._field[$],tt[o]);et.push(Q.length/5|0),U=0;t:for(v=0;v<it.cells.length;++v){var at=it.cells[v];for(q=0;q<2;++q){var ot=it.positions[at[q]],st=ot[0],lt=0|Math.floor(st),ct=st-lt,ut=ot[1],ft=0|Math.floor(ut),ht=ut-ft,pt=!1;e:for(var dt=0;dt<3;++dt){nt[dt]=0;var mt=($+dt+1)%3;for(V=0;V<2;++V){var gt=V?ct:1-ct;for(Y=0|Math.min(Math.max(lt+V,0),a[0]),H=0;H<2;++H){var vt=H?ht:1-ht;if(W=0|Math.min(Math.max(ft+H,0),a[1]),G=dt<2?this._field[mt].get(Y,W):(this.intensity.get(Y,W)-this.intensityBounds[0])/(this.intensityBounds[1]-this.intensityBounds[0]),!isFinite(G)||isNaN(G)){pt=!0;break e}var yt=gt*vt;nt[dt]+=yt*G}}}if(pt){if(q>0){for(var xt=0;xt<5;++xt)Q.pop();U-=1}continue t}Q.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[$]=et,this._contourCounts[$]=rt}var bt=s.mallocFloat(Q.length);for(o=0;o<Q.length;++o)bt[o]=Q[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,c=(o+2)%3,u=this._field[o],f=this._field[l],p=this._field[c],d=h(u,r[o]),m=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;e<m.length;++e)for(var v=m[e],y=0;y<2;++y){var x=g[v[y]],b=+x[0],_=0|b,w=0|Math.min(_+1,i[0]),T=b-_,k=1-T,A=+x[1],M=0|A,S=0|Math.min(M+1,i[1]),E=A-M,L=1-E,C=k*L,P=k*E,I=T*L,O=T*E,z=C*f.get(_,M)+P*f.get(_,S)+I*f.get(w,M)+O*f.get(w,S),D=C*p.get(_,M)+P*p.get(_,S)+I*p.get(w,M)+O*p.get(w,S);if(isNaN(z)||isNaN(D)){y&&(n-=1);break}a[2*n+0]=z,a[2*n+1]=D,n+=1}this._dynamicCounts[o]=n-this._dynamicOffsets[o]}else this.dynamicLevel[o]=NaN,this._dynamicCounts[o]=0;this._dynamicBuffer.update(a.subarray(0,2*n)),s.freeFloat(a)}}},{\"./lib/shaders\":144,\"binary-search-bounds\":31,\"bit-twiddle\":32,colormap:53,\"gl-buffer\":78,\"gl-mat4/invert\":98,\"gl-mat4/multiply\":100,\"gl-texture2d\":146,\"gl-vao\":150,ndarray:259,\"ndarray-gradient\":252,\"ndarray-ops\":254,\"ndarray-pack\":255,\"surface-nets\":302,\"typedarray-pool\":308}],146:[function(t,e,r){\"use strict\";var n=t(\"ndarray\"),i=t(\"ndarray-ops\"),a=t(\"typedarray-pool\");e.exports=function(t){if(arguments.length<=1)throw new Error(\"gl-texture2d: Missing arguments for texture2d constructor\");o||c(t);if(\"number\"==typeof arguments[1])return v(t,arguments[1],arguments[2],arguments[3]||t.RGBA,arguments[4]||t.UNSIGNED_BYTE);if(Array.isArray(arguments[1]))return v(t,0|arguments[1][0],0|arguments[1][1],arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(\"object\"==typeof arguments[1]){var e=arguments[1],r=u(e)?e:e.raw;if(r)return y(t,r,0|e.width,0|e.height,arguments[2]||t.RGBA,arguments[3]||t.UNSIGNED_BYTE);if(e.shape&&e.data&&e.stride)return x(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){o=[t.LINEAR,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_NEAREST],s=[t.NEAREST,t.LINEAR,t.NEAREST_MIPMAP_NEAREST,t.NEAREST_MIPMAP_LINEAR,t.LINEAR_MIPMAP_NEAREST,t.LINEAR_MIPMAP_LINEAR],l=[t.REPEAT,t.CLAMP_TO_EDGE,t.MIRRORED_REPEAT]}function u(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var f=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function m(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function v(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function y(t,e,r,n,i,a){var o=g(t);return 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n=t(\"./literals\");e.exports=n.slice().concat([\"layout\",\"centroid\",\"smooth\",\"case\",\"mat2x2\",\"mat2x3\",\"mat2x4\",\"mat3x2\",\"mat3x3\",\"mat3x4\",\"mat4x2\",\"mat4x3\",\"mat4x4\",\"uvec2\",\"uvec3\",\"uvec4\",\"samplerCubeShadow\",\"sampler2DArray\",\"sampler2DArrayShadow\",\"isampler2D\",\"isampler3D\",\"isamplerCube\",\"isampler2DArray\",\"usampler2D\",\"usampler3D\",\"usamplerCube\",\"usampler2DArray\",\"coherent\",\"restrict\",\"readonly\",\"writeonly\",\"resource\",\"atomic_uint\",\"noperspective\",\"patch\",\"sample\",\"subroutine\",\"common\",\"partition\",\"active\",\"filter\",\"image1D\",\"image2D\",\"image3D\",\"imageCube\",\"iimage1D\",\"iimage2D\",\"iimage3D\",\"iimageCube\",\"uimage1D\",\"uimage2D\",\"uimage3D\",\"uimageCube\",\"image1DArray\",\"image2DArray\",\"iimage1DArray\",\"iimage2DArray\",\"uimage1DArray\",\"uimage2DArray\",\"image1DShadow\",\"image2DShadow\",\"image1DArrayShadow\",\"image2DArrayShadow\",\"imageBuffer\",\"iimageBuffer\",\"uimageBuffer\",\"sampler1DArray\",\"sampler1DArrayShadow\",\"isampler1D\",\"isampler1DArray\",\"usampler1D\",\"usampler1DArray\",\"isampler2DRect\",\"usampler2DRect\",\"samplerBuffer\",\"isamplerBuffer\",\"usamplerBuffer\",\"sampler2DMS\",\"isampler2DMS\",\"usampler2DMS\",\"sampler2DMSArray\",\"isampler2DMSArray\",\"usampler2DMSArray\"])},{\"./literals\":228}],228:[function(t,e,r){e.exports=[\"precision\",\"highp\",\"mediump\",\"lowp\",\"attribute\",\"const\",\"uniform\",\"varying\",\"break\",\"continue\",\"do\",\"for\",\"while\",\"if\",\"else\",\"in\",\"out\",\"inout\",\"float\",\"int\",\"uint\",\"void\",\"bool\",\"true\",\"false\",\"discard\",\"return\",\"mat2\",\"mat3\",\"mat4\",\"vec2\",\"vec3\",\"vec4\",\"ivec2\",\"ivec3\",\"ivec4\",\"bvec2\",\"bvec3\",\"bvec4\",\"sampler1D\",\"sampler2D\",\"sampler3D\",\"samplerCube\",\"sampler1DShadow\",\"sampler2DShadow\",\"struct\",\"asm\",\"class\",\"union\",\"enum\",\"typedef\",\"template\",\"this\",\"packed\",\"goto\",\"switch\",\"default\",\"inline\",\"noinline\",\"volatile\",\"public\",\"static\",\"extern\",\"external\",\"interface\",\"long\",\"short\",\"double\",\"half\",\"fixed\",\"unsigned\",\"input\",\"output\",\"hvec2\",\"hvec3\",\"hvec4\",\"dvec2\",\"dvec3\",\"dvec4\",\"fvec2\",\"fvec3\",\"fvec4\",\"sampler2DRect\",\"sampler3DRect\",\"sampler2DRectShadow\",\"sizeof\",\"cast\",\"namespace\",\"using\"]},{}],229:[function(t,e,r){e.exports=[\"<<=\",\">>=\",\"++\",\"--\",\"<<\",\">>\",\"<=\",\">=\",\"==\",\"!=\",\"&&\",\"||\",\"+=\",\"-=\",\"*=\",\"/=\",\"%=\",\"&=\",\"^^\",\"^=\",\"|=\",\"(\",\")\",\"[\",\"]\",\".\",\"!\",\"~\",\"*\",\"/\",\"%\",\"+\",\"-\",\"<\",\">\",\"&\",\"^\",\"|\",\"?\",\":\",\"=\",\",\",\";\",\"{\",\"}\"]},{}],230:[function(t,e,r){var n=t(\"./index\");e.exports=function(t,e){var r=n(e),i=[];return i=(i=i.concat(r(t))).concat(r(null))}},{\"./index\":224}],231:[function(t,e,r){e.exports=function(t){\"string\"==typeof t&&(t=[t]);for(var e=[].slice.call(arguments,1),r=[],n=0;n<t.length-1;n++)r.push(t[n],e[n]||\"\");return r.push(t[n]),r.join(\"\")}},{}],232:[function(t,e,r){\"use strict\";var n=t(\"is-browser\");e.exports=n&&function(){var t=!1;try{var e=Object.defineProperty({},\"passive\",{get:function(){t=!0}});window.addEventListener(\"test\",null,e),window.removeEventListener(\"test\",null,e)}catch(e){t=!1}return t}()},{\"is-browser\":236}],233:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=t.length;if(0===r)throw new Error(\"Must have at least d+1 points\");var i=t[0].length;if(r<=i)throw new Error(\"Must input at least d+1 points\");var o=t.slice(0,i+1),s=n.apply(void 0,o);if(0===s)throw new Error(\"Input not in general position\");for(var l=new Array(i+1),u=0;u<=i;++u)l[u]=u;s<0&&(l[0]=1,l[1]=0);var f=new a(l,new Array(i+1),!1),h=f.adjacent,p=new Array(i+2);for(u=0;u<=i;++u){for(var d=l.slice(),m=0;m<=i;++m)m===u&&(d[m]=-1);var g=d[0];d[0]=d[1],d[1]=g;var v=new a(d,new Array(i+1),!0);h[u]=v,p[u]=v}p[i+1]=f;for(u=0;u<=i;++u){d=h[u].vertices;var y=h[u].adjacent;for(m=0;m<=i;++m){var x=d[m];if(x<0)y[m]=f;else for(var b=0;b<=i;++b)h[b].vertices.indexOf(x)<0&&(y[m]=h[b])}}var _=new c(i,o,p),w=!!e;for(u=i+1;u<r;++u)_.insert(t[u],w);return _.boundary()};var n=t(\"robust-orientation\"),i=t(\"simplicial-complex\").compareCells;function a(t,e,r){this.vertices=t,this.adjacent=e,this.boundary=r,this.lastVisited=-1}function o(t,e,r){this.vertices=t,this.cell=e,this.index=r}function s(t,e){return i(t.vertices,e.vertices)}a.prototype.flip=function(){var t=this.vertices[0];this.vertices[0]=this.vertices[1],this.vertices[1]=t;var e=this.adjacent[0];this.adjacent[0]=this.adjacent[1],this.adjacent[1]=e};var l=[];function c(t,e,r){this.dimension=t,this.vertices=e,this.simplices=r,this.interior=r.filter((function(t){return!t.boundary})),this.tuple=new Array(t+1);for(var i=0;i<=t;++i)this.tuple[i]=this.vertices[i];var a,o=l[t];o||(o=l[t]=((a=n[t+1])||(a=n),function(t){return function(){var e=this.tuple;return t.apply(this,e)}}(a))),this.orient=o}var u=c.prototype;u.handleBoundaryDegeneracy=function(t,e){var r=this.dimension,n=this.vertices.length-1,i=this.tuple,a=this.vertices,o=[t];for(t.lastVisited=-n;o.length>0;)for(var s=(t=o.pop()).adjacent,l=0;l<=r;++l){var c=s[l];if(c.boundary&&!(c.lastVisited<=-n)){for(var u=c.vertices,f=0;f<=r;++f){var h=u[f];i[f]=h<0?e:a[h]}var p=this.orient();if(p>0)return c;c.lastVisited=-n,0===p&&o.push(c)}}return null},u.walk=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,a=this.tuple,o=e?this.interior.length*Math.random()|0:this.interior.length-1,s=this.interior[o];t:for(;!s.boundary;){for(var l=s.vertices,c=s.adjacent,u=0;u<=n;++u)a[u]=i[l[u]];s.lastVisited=r;for(u=0;u<=n;++u){var f=c[u];if(!(f.lastVisited>=r)){var h=a[u];a[u]=t;var p=this.orient();if(a[u]=h,p<0){s=f;continue t}f.boundary?f.lastVisited=-r:f.lastVisited=r}}return}return s},u.addPeaks=function(t,e){var r=this.vertices.length-1,n=this.dimension,i=this.vertices,l=this.tuple,c=this.interior,u=this.simplices,f=[e];e.lastVisited=r,e.vertices[e.vertices.indexOf(-1)]=r,e.boundary=!1,c.push(e);for(var h=[];f.length>0;){var p=(e=f.pop()).vertices,d=e.adjacent,m=p.indexOf(r);if(!(m<0))for(var g=0;g<=n;++g)if(g!==m){var v=d[g];if(v.boundary&&!(v.lastVisited>=r)){var y=v.vertices;if(v.lastVisited!==-r){for(var x=0,b=0;b<=n;++b)y[b]<0?(x=b,l[b]=t):l[b]=i[y[b]];if(this.orient()>0){y[x]=r,v.boundary=!1,c.push(v),f.push(v),v.lastVisited=r;continue}v.lastVisited=-r}var _=v.adjacent,w=p.slice(),T=d.slice(),k=new a(w,T,!0);u.push(k);var A=_.indexOf(e);if(!(A<0)){_[A]=k,T[m]=v,w[g]=-1,T[g]=e,d[g]=k,k.flip();for(b=0;b<=n;++b){var M=w[b];if(!(M<0||M===r)){for(var S=new Array(n-1),E=0,L=0;L<=n;++L){var C=w[L];C<0||L===b||(S[E++]=C)}h.push(new o(S,k,b))}}}}}}h.sort(s);for(g=0;g+1<h.length;g+=2){var P=h[g],I=h[g+1],O=P.index,z=I.index;O<0||z<0||(P.cell.adjacent[P.index]=I.cell,I.cell.adjacent[I.index]=P.cell)}},u.insert=function(t,e){var r=this.vertices;r.push(t);var n=this.walk(t,e);if(n){for(var i=this.dimension,a=this.tuple,o=0;o<=i;++o){var s=n.vertices[o];a[o]=s<0?t:r[s]}var l=this.orient(a);l<0||(0!==l||(n=this.handleBoundaryDegeneracy(n,t)))&&this.addPeaks(t,n)}},u.boundary=function(){for(var t=this.dimension,e=[],r=this.simplices,n=r.length,i=0;i<n;++i){var a=r[i];if(a.boundary){for(var o=new Array(t),s=a.vertices,l=0,c=0,u=0;u<=t;++u)s[u]>=0?o[l++]=s[u]:c=1&u;if(c===(1&t)){var f=o[0];o[0]=o[1],o[1]=f}e.push(o)}}return e}},{\"robust-orientation\":284,\"simplicial-complex\":293}],234:[function(t,e,r){\"use strict\";var n=t(\"binary-search-bounds\");function i(t,e,r,n,i){this.mid=t,this.left=e,this.right=r,this.leftPoints=n,this.rightPoints=i,this.count=(e?e.count:0)+(r?r.count:0)+n.length}e.exports=function(t){if(!t||0===t.length)return new v(null);return new v(g(t))};var a=i.prototype;function o(t,e){t.mid=e.mid,t.left=e.left,t.right=e.right,t.leftPoints=e.leftPoints,t.rightPoints=e.rightPoints,t.count=e.count}function s(t,e){var r=g(e);t.mid=r.mid,t.left=r.left,t.right=r.right,t.leftPoints=r.leftPoints,t.rightPoints=r.rightPoints,t.count=r.count}function l(t,e){var r=t.intervals([]);r.push(e),s(t,r)}function c(t,e){var r=t.intervals([]),n=r.indexOf(e);return n<0?0:(r.splice(n,1),s(t,r),1)}function u(t,e,r){for(var n=0;n<t.length&&t[n][0]<=e;++n){var i=r(t[n]);if(i)return i}}function f(t,e,r){for(var n=t.length-1;n>=0&&t[n][1]>=e;--n){var i=r(t[n]);if(i)return i}}function h(t,e){for(var r=0;r<t.length;++r){var n=e(t[r]);if(n)return n}}function p(t,e){return t-e}function d(t,e){var r=t[0]-e[0];return r||t[1]-e[1]}function m(t,e){var r=t[1]-e[1];return r||t[0]-e[0]}function g(t){if(0===t.length)return null;for(var e=[],r=0;r<t.length;++r)e.push(t[r][0],t[r][1]);e.sort(p);var n=e[e.length>>1],a=[],o=[],s=[];for(r=0;r<t.length;++r){var l=t[r];l[1]<n?a.push(l):n<l[0]?o.push(l):s.push(l)}var c=s,u=s.slice();return c.sort(d),u.sort(m),new i(n,g(a),g(o),c,u)}function v(t){this.root=t}a.intervals=function(t){return t.push.apply(t,this.leftPoints),this.left&&this.left.intervals(t),this.right&&this.right.intervals(t),t},a.insert=function(t){var e=this.count-this.leftPoints.length;if(this.count+=1,t[1]<this.mid)this.left?4*(this.left.count+1)>3*(e+1)?l(this,t):this.left.insert(t):this.left=g([t]);else if(t[0]>this.mid)this.right?4*(this.right.count+1)>3*(e+1)?l(this,t):this.right.insert(t):this.right=g([t]);else{var r=n.ge(this.leftPoints,t,d),i=n.ge(this.rightPoints,t,m);this.leftPoints.splice(r,0,t),this.rightPoints.splice(i,0,t)}},a.remove=function(t){var e=this.count-this.leftPoints;if(t[1]<this.mid)return this.left?4*(this.right?this.right.count:0)>3*(e-1)?c(this,t):2===(s=this.left.remove(t))?(this.left=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(t[0]>this.mid)return this.right?4*(this.left?this.left.count:0)>3*(e-1)?c(this,t):2===(s=this.right.remove(t))?(this.right=null,this.count-=1,1):(1===s&&(this.count-=1),s):0;if(1===this.count)return this.leftPoints[0]===t?2:0;if(1===this.leftPoints.length&&this.leftPoints[0]===t){if(this.left&&this.right){for(var r=this,i=this.left;i.right;)r=i,i=i.right;if(r===this)i.right=this.right;else{var a=this.left,s=this.right;r.count-=i.count,r.right=i.left,i.left=a,i.right=s}o(this,i),this.count=(this.left?this.left.count:0)+(this.right?this.right.count:0)+this.leftPoints.length}else this.left?o(this,this.left):o(this,this.right);return 1}for(a=n.ge(this.leftPoints,t,d);a<this.leftPoints.length&&this.leftPoints[a][0]===t[0];++a)if(this.leftPoints[a]===t){this.count-=1,this.leftPoints.splice(a,1);for(s=n.ge(this.rightPoints,t,m);s<this.rightPoints.length&&this.rightPoints[s][1]===t[1];++s)if(this.rightPoints[s]===t)return this.rightPoints.splice(s,1),1}return 0},a.queryPoint=function(t,e){if(t<this.mid){if(this.left)if(r=this.left.queryPoint(t,e))return r;return u(this.leftPoints,t,e)}if(t>this.mid){var r;if(this.right)if(r=this.right.queryPoint(t,e))return r;return f(this.rightPoints,t,e)}return h(this.leftPoints,e)},a.queryInterval=function(t,e,r){var n;if(t<this.mid&&this.left&&(n=this.left.queryInterval(t,e,r)))return n;if(e>this.mid&&this.right&&(n=this.right.queryInterval(t,e,r)))return n;return e<this.mid?u(this.leftPoints,e,r):t>this.mid?f(this.rightPoints,t,r):h(this.leftPoints,r)};var y=v.prototype;y.insert=function(t){this.root?this.root.insert(t):this.root=new i(t[0],null,null,[t],[t])},y.remove=function(t){if(this.root){var e=this.root.remove(t);return 2===e&&(this.root=null),0!==e}return!1},y.queryPoint=function(t,e){if(this.root)return this.root.queryPoint(t,e)},y.queryInterval=function(t,e,r){if(t<=e&&this.root)return this.root.queryInterval(t,e,r)},Object.defineProperty(y,\"count\",{get:function(){return this.root?this.root.count:0}}),Object.defineProperty(y,\"intervals\",{get:function(){return this.root?this.root.intervals([]):[]}})},{\"binary-search-bounds\":31}],235:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=r;return e}},{}],236:[function(t,e,r){e.exports=!0},{}],237:[function(t,e,r){function n(t){return!!t.constructor&&\"function\"==typeof t.constructor.isBuffer&&t.constructor.isBuffer(t)}\n",
+       "/*!\n",
+       " * Determine if an object is a Buffer\n",
+       " *\n",
+       " * @author   Feross Aboukhadijeh <https://feross.org>\n",
+       " * @license  MIT\n",
+       " */\n",
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i}},{}],262:[function(t,e,r){\"use strict\";e.exports=function(t,e,r,n,i,a,o,s,l,c){var u=e+a+c;if(f>0){var f=Math.sqrt(u+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,c);f=Math.sqrt(2*h-u+1);e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},{}],263:[function(t,e,r){\"use strict\";e.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),u(r=[].slice.call(r,0,4),r);var i=new f(r,e,Math.log(n));i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up);return i};var n=t(\"filtered-vector\"),i=t(\"gl-mat4/lookAt\"),a=t(\"gl-mat4/fromQuat\"),o=t(\"gl-mat4/invert\"),s=t(\"./lib/quatFromFrame\");function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function c(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function u(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=c(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function f(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var h=f.prototype;h.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},h.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;u(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(var c=0,f=0;f<3;++f)c+=r[l+4*f]*i[f];r[12+l]=-c}},h.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},h.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},h.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},h.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var u=i[0],f=i[4],h=i[8],p=u*a+f*o+h*s,d=l(u-=a*p,f-=o*p,h-=s*p);u/=d,f/=d,h/=d;var m=i[2],g=i[6],v=i[10],y=m*a+g*o+v*s,x=m*u+g*f+v*h,b=l(m-=y*a+x*u,g-=y*o+x*f,v-=y*s+x*h);m/=b,g/=b,v/=b;var _=u*e+a*r,w=f*e+o*r,T=h*e+s*r;this.center.move(t,_,w,T);var k=Math.exp(this.computedRadius[0]);k=Math.max(1e-4,k+n),this.radius.set(t,Math.log(k))},h.rotate=function(t,e,r,n){this.recalcMatrix(t),e=e||0,r=r||0;var i=this.computedMatrix,a=i[0],o=i[4],s=i[8],u=i[1],f=i[5],h=i[9],p=i[2],d=i[6],m=i[10],g=e*a+r*u,v=e*o+r*f,y=e*s+r*h,x=-(d*y-m*v),b=-(m*g-p*y),_=-(p*v-d*g),w=Math.sqrt(Math.max(0,1-Math.pow(x,2)-Math.pow(b,2)-Math.pow(_,2))),T=c(x,b,_,w);T>1e-6?(x/=T,b/=T,_/=T,w/=T):(x=b=_=0,w=1);var k=this.computedRotation,A=k[0],M=k[1],S=k[2],E=k[3],L=A*w+E*x+M*_-S*b,C=M*w+E*b+S*x-A*_,P=S*w+E*_+A*b-M*x,I=E*w-A*x-M*b-S*_;if(n){x=p,b=d,_=m;var O=Math.sin(n)/l(x,b,_);x*=O,b*=O,_*=O,I=I*(w=Math.cos(e))-(L=L*w+I*x+C*_-P*b)*x-(C=C*w+I*b+P*x-L*_)*b-(P=P*w+I*_+L*b-C*x)*_}var z=c(L,C,P,I);z>1e-6?(L/=z,C/=z,P/=z,I/=z):(L=C=P=0,I=1),this.rotation.set(t,L,C,P,I)},h.lookAt=function(t,e,r,n){this.recalcMatrix(t),r=r||this.computedCenter,e=e||this.computedEye,n=n||this.computedUp;var a=this.computedMatrix;i(a,e,r,n);var o=this.computedRotation;s(o,a[0],a[1],a[2],a[4],a[5],a[6],a[8],a[9],a[10]),u(o,o),this.rotation.set(t,o[0],o[1],o[2],o[3]);for(var l=0,c=0;c<3;++c)l+=Math.pow(r[c]-e[c],2);this.radius.set(t,.5*Math.log(Math.max(l,1e-6))),this.center.set(t,r[0],r[1],r[2])},h.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},h.setMatrix=function(t,e){var r=this.computedRotation;s(r,e[0],e[1],e[2],e[4],e[5],e[6],e[8],e[9],e[10]),u(r,r),this.rotation.set(t,r[0],r[1],r[2],r[3]);var n=this.computedMatrix;o(n,e);var i=n[15];if(Math.abs(i)>1e-6){var a=n[12]/i,l=n[13]/i,c=n[14]/i;this.recalcMatrix(t);var f=Math.exp(this.computedRadius[0]);this.center.set(t,a-n[2]*f,l-n[6]*f,c-n[10]*f),this.radius.idle(t)}else this.center.idle(t),this.radius.idle(t)},h.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},h.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},h.getDistanceLimits=function(t){var e=this.radius.bounds;return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},h.toJSON=function(){return this.recalcMatrix(this.lastT()),{center:this.computedCenter.slice(),rotation:this.computedRotation.slice(),distance:Math.log(this.computedRadius[0]),zoomMin:this.radius.bounds[0][0],zoomMax:this.radius.bounds[1][0]}},h.fromJSON=function(t){var e=this.lastT(),r=t.center;r&&this.center.set(e,r[0],r[1],r[2]);var n=t.rotation;n&&this.rotation.set(e,n[0],n[1],n[2],n[3]);var i=t.distance;i&&i>0&&this.radius.set(e,Math.log(i)),this.setDistanceLimits(t.zoomMin,t.zoomMax)}},{\"./lib/quatFromFrame\":262,\"filtered-vector\":68,\"gl-mat4/fromQuat\":95,\"gl-mat4/invert\":98,\"gl-mat4/lookAt\":99}],264:[function(t,e,r){\n",
+       "/*!\n",
+       " * pad-left <https://github.com/jonschlinkert/pad-left>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT license.\n",
+       " */\n",
+       "\"use strict\";var n=t(\"repeat-string\");e.exports=function(t,e,r){return n(r=void 0!==r?r+\"\":\" \",e)+t}},{\"repeat-string\":277}],265:[function(t,e,r){e.exports=function(t,e){e||(e=[0,\"\"]),t=String(t);var r=parseFloat(t,10);return e[0]=r,e[1]=t.match(/[\\d.\\-\\+]*\\s*(.*)/)[1]||\"\",e}},{}],266:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=0|e.length,i=t.length,a=[new Array(r),new Array(r)],o=0;o<r;++o)a[0][o]=[],a[1][o]=[];for(o=0;o<i;++o){var s=t[o];a[0][s[0]].push(s),a[1][s[1]].push(s)}var l=[];for(o=0;o<r;++o)a[0][o].length+a[1][o].length===0&&l.push([o]);function c(t,e){var r=a[e][t[e]];r.splice(r.indexOf(t),1)}function u(t,r,i){for(var o,s,l,u=0;u<2;++u)if(a[u][r].length>0){o=a[u][r][0],l=u;break}s=o[1^l];for(var f=0;f<2;++f)for(var h=a[f][r],p=0;p<h.length;++p){var d=h[p],m=d[1^f];n(e[t],e[r],e[s],e[m])>0&&(o=d,s=m,l=f)}return i||o&&c(o,l),s}function f(t,r){var i=a[r][t][0],o=[t];c(i,r);for(var s=i[1^r];;){for(;s!==t;)o.push(s),s=u(o[o.length-2],s,!1);if(a[0][t].length+a[1][t].length===0)break;var l=o[o.length-1],f=t,h=o[1],p=u(l,f,!0);if(n(e[l],e[f],e[h],e[p])<0)break;o.push(t),s=u(l,f)}return o}function h(t,e){return e[1]===e[e.length-1]}for(o=0;o<r;++o)for(var p=0;p<2;++p){for(var d=[];a[p][o].length>0;){a[0][o].length;var m=f(o,p);h(0,m)?d.push.apply(d,m):(d.length>0&&l.push(d),d=m)}d.length>0&&l.push(d)}return l};var n=t(\"compare-angle\")},{\"compare-angle\":54}],267:[function(t,e,r){\"use strict\";e.exports=function(t,e){for(var r=n(t,e.length),i=new Array(e.length),a=new Array(e.length),o=[],s=0;s<e.length;++s){var l=r[s].length;a[s]=l,i[s]=!0,l<=1&&o.push(s)}for(;o.length>0;){var c=o.pop();i[c]=!1;var u=r[c];for(s=0;s<u.length;++s){var f=u[s];0==--a[f]&&o.push(f)}}var h=new Array(e.length),p=[];for(s=0;s<e.length;++s)if(i[s]){c=p.length;h[s]=c,p.push(e[s])}else h[s]=-1;var d=[];for(s=0;s<t.length;++s){var m=t[s];i[m[0]]&&i[m[1]]&&d.push([h[m[0]],h[m[1]]])}return[d,p]};var n=t(\"edges-to-adjacency-list\")},{\"edges-to-adjacency-list\":66}],268:[function(t,e,r){\"use strict\";e.exports=function(t,e){var r=c(t,e);t=r[0];for(var f=(e=r[1]).length,h=(t.length,n(t,e.length)),p=0;p<f;++p)if(h[p].length%2==1)throw new Error(\"planar-graph-to-polyline: graph must be manifold\");var d=i(t,e);var m=(d=d.filter((function(t){for(var r=t.length,n=[0],i=0;i<r;++i){var a=e[t[i]],l=e[t[(i+1)%r]],c=o(-a[0],a[1]),u=o(-a[0],l[1]),f=o(l[0],a[1]),h=o(l[0],l[1]);n=s(n,s(s(c,u),s(f,h)))}return n[n.length-1]>0}))).length,g=new Array(m),v=new Array(m);for(p=0;p<m;++p){g[p]=p;var y=new Array(m),x=d[p].map((function(t){return e[t]})),b=a([x]),_=0;t:for(var w=0;w<m;++w)if(y[w]=0,p!==w){for(var T=(H=d[w]).length,k=0;k<T;++k){var A=b(e[H[k]]);if(0!==A){A<0&&(y[w]=1,_+=1);continue t}}y[w]=1,_+=1}v[p]=[_,p,y]}v.sort((function(t,e){return e[0]-t[0]}));for(p=0;p<m;++p){var M=(y=v[p])[1],S=y[2];for(w=0;w<m;++w)S[w]&&(g[w]=M)}var E=function(t){for(var e=new Array(t),r=0;r<t;++r)e[r]=[];return e}(m);for(p=0;p<m;++p)E[p].push(g[p]),E[g[p]].push(p);var L={},C=u(f,!1);for(p=0;p<m;++p)for(T=(H=d[p]).length,w=0;w<T;++w){var P=H[w],I=H[(w+1)%T],O=Math.min(P,I)+\":\"+Math.max(P,I);if(O in L){var z=L[O];E[z].push(p),E[p].push(z),C[P]=C[I]=!0}else L[O]=p}function D(t){for(var e=t.length,r=0;r<e;++r)if(!C[t[r]])return!1;return!0}var R=[],F=u(m,-1);for(p=0;p<m;++p)g[p]!==p||D(d[p])?F[p]=-1:(R.push(p),F[p]=0);r=[];for(;R.length>0;){var B=R.pop(),N=E[B];l(N,(function(t,e){return t-e}));var j,U=N.length,V=F[B];if(0===V){var H=d[B];j=[H]}for(p=0;p<U;++p){var q=N[p];if(!(F[q]>=0))if(F[q]=1^V,R.push(q),0===V)D(H=d[q])||(H.reverse(),j.push(H))}0===V&&r.push(j)}return r};var n=t(\"edges-to-adjacency-list\"),i=t(\"planar-dual\"),a=t(\"point-in-big-polygon\"),o=t(\"two-product\"),s=t(\"robust-sum\"),l=t(\"uniq\"),c=t(\"./lib/trim-leaves\");function u(t,e){for(var r=new Array(t),n=0;n<t;++n)r[n]=e;return r}},{\"./lib/trim-leaves\":267,\"edges-to-adjacency-list\":66,\"planar-dual\":266,\"point-in-big-polygon\":269,\"robust-sum\":289,\"two-product\":306,uniq:310}],269:[function(t,e,r){e.exports=function(t){for(var e=t.length,r=[],a=[],s=0;s<e;++s)for(var u=t[s],f=u.length,h=f-1,p=0;p<f;h=p++){var d=u[h],m=u[p];d[0]===m[0]?a.push([d,m]):r.push([d,m])}if(0===r.length)return 0===a.length?c:(g=l(a),function(t){return g(t[0],t[1])?0:1});var g;var v=i(r),y=function(t,e){return function(r){var i=o.le(e,r[0]);if(i<0)return 1;var a=t[i];if(!a){if(!(i>0&&e[i]===r[0]))return 1;a=t[i-1]}for(var s=1;a;){var l=a.key,c=n(r,l[0],l[1]);if(l[0][0]<l[1][0])if(c<0)a=a.left;else{if(!(c>0))return 0;s=-1,a=a.right}else if(c>0)a=a.left;else{if(!(c<0))return 0;s=1,a=a.right}}return s}}(v.slabs,v.coordinates);return 0===a.length?y:function(t,e){return function(r){return t(r[0],r[1])?0:e(r)}}(l(a),y)};var n=t(\"robust-orientation\")[3],i=t(\"slab-decomposition\"),a=t(\"interval-tree-1d\"),o=t(\"binary-search-bounds\");function s(){return!0}function l(t){for(var e={},r=0;r<t.length;++r){var n=t[r],i=n[0][0],o=n[0][1],l=n[1][1],c=[Math.min(o,l),Math.max(o,l)];i in e?e[i].push(c):e[i]=[c]}var u={},f=Object.keys(e);for(r=0;r<f.length;++r){var h=e[f[r]];u[f[r]]=a(h)}return function(t){return function(e,r){var n=t[e];return!!n&&!!n.queryPoint(r,s)}}(u)}function c(t){return 1}},{\"binary-search-bounds\":31,\"interval-tree-1d\":234,\"robust-orientation\":284,\"slab-decomposition\":299}],270:[function(t,e,r){\"use strict\";var n=new Float64Array(4),i=new Float64Array(4),a=new Float64Array(4);e.exports=function(t,e,r,o,s){n.length<o.length&&(n=new Float64Array(o.length),i=new Float64Array(o.length),a=new Float64Array(o.length));for(var l=0;l<o.length;++l)n[l]=t[l]-o[l],i[l]=e[l]-t[l],a[l]=r[l]-t[l];var c=0,u=0,f=0,h=0,p=0,d=0;for(l=0;l<o.length;++l){var m=i[l],g=a[l],v=n[l];c+=m*m,u+=m*g,f+=g*g,h+=v*m,p+=v*g,d+=v*v}var y,x,b,_,w,T=Math.abs(c*f-u*u),k=u*p-f*h,A=u*h-c*p;if(k+A<=T)if(k<0)A<0&&h<0?(A=0,-h>=c?(k=1,y=c+2*h+d):y=h*(k=-h/c)+d):(k=0,p>=0?(A=0,y=d):-p>=f?(A=1,y=f+2*p+d):y=p*(A=-p/f)+d);else if(A<0)A=0,h>=0?(k=0,y=d):-h>=c?(k=1,y=c+2*h+d):y=h*(k=-h/c)+d;else{var M=1/T;y=(k*=M)*(c*k+u*(A*=M)+2*h)+A*(u*k+f*A+2*p)+d}else k<0?(b=f+p)>(x=u+h)?(_=b-x)>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d:(k=0,b<=0?(A=1,y=f+2*p+d):p>=0?(A=0,y=d):y=p*(A=-p/f)+d):A<0?(b=c+h)>(x=u+p)?(_=b-x)>=(w=c-2*u+f)?(A=1,k=0,y=f+2*p+d):y=(k=1-(A=_/w))*(c*k+u*A+2*h)+A*(u*k+f*A+2*p)+d:(A=0,b<=0?(k=1,y=c+2*h+d):h>=0?(k=0,y=d):y=h*(k=-h/c)+d):(_=f+p-u-h)<=0?(k=0,A=1,y=f+2*p+d):_>=(w=c-2*u+f)?(k=1,A=0,y=c+2*h+d):y=(k=_/w)*(c*k+u*(A=1-k)+2*h)+A*(u*k+f*A+2*p)+d;var S=1-k-A;for(l=0;l<o.length;++l)s[l]=S*t[l]+k*e[l]+A*r[l];return y<0?0:y}},{}],271:[function(t,e,r){e.exports=t(\"gl-quat/slerp\")},{\"gl-quat/slerp\":124}],272:[function(t,e,r){\"use strict\";var n=t(\"big-rat/add\");e.exports=function(t,e){for(var r=t.length,i=new Array(r),a=0;a<r;++a)i[a]=n(t[a],e[a]);return i}},{\"big-rat/add\":15}],273:[function(t,e,r){\"use strict\";e.exports=function(t){for(var e=new Array(t.length),r=0;r<t.length;++r)e[r]=n(t[r]);return e};var n=t(\"big-rat\")},{\"big-rat\":18}],274:[function(t,e,r){\"use strict\";var n=t(\"big-rat\"),i=t(\"big-rat/mul\");e.exports=function(t,e){for(var r=n(e),a=t.length,o=new Array(a),s=0;s<a;++s)o[s]=i(t[s],r);return o}},{\"big-rat\":18,\"big-rat/mul\":27}],275:[function(t,e,r){\"use strict\";var n=t(\"big-rat/sub\");e.exports=function(t,e){for(var r=t.length,i=new Array(r),a=0;a<r;++a)i[a]=n(t[a],e[a]);return i}},{\"big-rat/sub\":29}],276:[function(t,e,r){\"use strict\";var n=t(\"compare-cell\"),i=t(\"compare-oriented-cell\"),a=t(\"cell-orientation\");e.exports=function(t){t.sort(i);for(var e=t.length,r=0,o=0;o<e;++o){var s=t[o],l=a(s);if(0!==l){if(r>0){var c=t[r-1];if(0===n(s,c)&&a(c)!==l){r-=1;continue}}t[r++]=s}}return t.length=r,t}},{\"cell-orientation\":47,\"compare-cell\":56,\"compare-oriented-cell\":57}],277:[function(t,e,r){\n",
+       "/*!\n",
+       " * repeat-string <https://github.com/jonschlinkert/repeat-string>\n",
+       " *\n",
+       " * Copyright (c) 2014-2015, Jon Schlinkert.\n",
+       " * Licensed under the MIT License.\n",
+       " */\n",
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o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),m=Math.max(h,p,d);h===m?(s=s<0?-1:1,l=f=0):d===m?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=c(s,l,f);s/=g,l/=g,f/=g}var v,y,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=c(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Y),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,I=E*k+L*A+C*M;v=R<0?-Math.PI/2:Math.PI/2,y=Math.atan2(I,P)}else{var O=e[2],z=e[6],D=e[10],R=O*s+z*l+D*f,F=O*x+z*b+D*_,B=O*k+z*A+D*M;v=Math.asin(u(R)),y=Math.atan2(B,F)}this.angle.jump(t,y,v),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Y=V[14]/H,W=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*W,G-j*W,Y-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],f=e[1]-r[1],h=e[2]-r[2],p=c(l,f,h);if(!(p<1e-6)){l/=p,f/=p,h/=p;var d=this.computedRight,m=d[0],g=d[1],v=d[2],y=i*m+a*g+o*v,x=c(m-=y*i,g-=y*a,v-=y*o);if(!(x<.01&&(x=c(m=a*h-o*f,g=o*l-i*h,v=i*f-a*l))<1e-6)){m/=x,g/=x,v/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,v),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var b=a*v-o*g,_=o*m-i*v,w=i*g-a*m,T=c(b,_,w),k=i*l+a*f+o*h,A=m*l+g*f+v*h,M=(b/=T)*l+(_/=T)*f+(w/=T)*h,S=Math.asin(u(k)),E=Math.atan2(M,A),L=this.angle._state,C=L[L.length-1],P=L[L.length-2];C%=2*Math.PI;var I=Math.abs(C+2*Math.PI-E),O=Math.abs(C-E),z=Math.abs(C-2*Math.PI-E);I<O&&(C+=2*Math.PI),z<O&&(C-=2*Math.PI),this.angle.jump(this.angle.lastT(),C,P),this.angle.set(t,E,S)}}}}},{\"filtered-vector\":68,\"gl-mat4/invert\":98,\"gl-mat4/rotate\":103,\"gl-vec3/cross\":157,\"gl-vec3/dot\":162,\"gl-vec3/normalize\":179}],306:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var i=t*e,a=n*t,o=a-(a-t),s=t-o,l=n*e,c=l-(l-e),u=e-c,f=s*u-(i-o*c-s*c-o*u);if(r)return r[0]=f,r[1]=i,r;return[f,i]};var n=+(Math.pow(2,27)+1)},{}],307:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){var n=t+e,i=n-t,a=e-i,o=t-(n-i);if(r)return r[0]=o+a,r[1]=n,r;return[o+a,n]}},{}],308:[function(t,e,r){(function(e){(function(){\"use strict\";var 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null},r.mallocArrayBuffer=p,r.mallocUint8=d,r.mallocUint16=m,r.mallocUint32=g,r.mallocInt8=v,r.mallocInt16=y,r.mallocInt32=x,r.mallocFloat32=r.mallocFloat=b,r.mallocFloat64=r.mallocDouble=_,r.mallocUint8Clamped=w,r.mallocBigUint64=T,r.mallocBigInt64=k,r.mallocDataView=A,r.mallocBuffer=M,r.clearCache=function(){for(var t=0;t<32;++t)c.UINT8[t].length=0,c.UINT16[t].length=0,c.UINT32[t].length=0,c.INT8[t].length=0,c.INT16[t].length=0,c.INT32[t].length=0,c.FLOAT[t].length=0,c.DOUBLE[t].length=0,c.BIGUINT64[t].length=0,c.BIGINT64[t].length=0,c.UINT8C[t].length=0,u[t].length=0,f[t].length=0}}).call(this)}).call(this,void 0!==n?n:\"undefined\"!=typeof self?self:\"undefined\"!=typeof window?window:{})},{\"bit-twiddle\":32,buffer:3,dup:65}],309:[function(t,e,r){\"use strict\";function n(t){this.roots=new Array(t),this.ranks=new Array(t);for(var e=0;e<t;++e)this.roots[e]=e,this.ranks[e]=0}e.exports=n;var i=n.prototype;Object.defineProperty(i,\"length\",{get:function(){return this.roots.length}}),i.makeSet=function(){var t=this.roots.length;return this.roots.push(t),this.ranks.push(0),t},i.find=function(t){for(var e=t,r=this.roots;r[t]!==t;)t=r[t];for(;r[e]!==t;){var n=r[e];r[e]=t,e=n}return t},i.link=function(t,e){var r=this.find(t),n=this.find(e);if(r!==n){var i=this.ranks,a=this.roots,o=i[r],s=i[n];o<s?a[r]=n:s<o?a[n]=r:(a[n]=r,++i[r])}}},{}],310:[function(t,e,r){\"use strict\";e.exports=function(t,e,r){return 0===t.length?t:e?(r||t.sort(e),function(t,e){for(var r=1,n=t.length,i=t[0],a=t[0],o=1;o<n;++o)if(a=i,e(i=t[o],a)){if(o===r){r++;continue}t[r++]=i}return t.length=r,t}(t,e)):(r||t.sort(),function(t){for(var e=1,r=t.length,n=t[0],i=t[0],a=1;a<r;++a,i=n)if(i=n,(n=t[a])!==i){if(a===e){e++;continue}t[e++]=n}return t.length=e,t}(t))}},{}],311:[function(t,e,r){\"use strict\";e.exports=function(t,e){\"object\"==typeof e&&null!==e||(e={});return n(t,e.canvas||i,e.context||a,e)};var n=t(\"./lib/vtext\"),i=null,a=null;\"undefined\"!=typeof 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n=t(\"surface-nets\"),i=t(\"ndarray\"),a=t(\"simplify-planar-graph\"),o=t(\"clean-pslg\"),s=t(\"cdt2d\"),l=t(\"planar-graph-to-polyline\");function c(t,e,r,n){for(var i=\"<\"+t+\">\",a=\"</\"+t+\">\",o=i.length,s=a.length,l=\"+\"===e[0]||\"-\"===e[0],c=0,u=-s;c>-1&&-1!==(c=r.indexOf(i,c))&&-1!==(u=r.indexOf(a,c+o))&&!(u<=c);){for(var f=c;f<u+s;++f)if(f<c+o||f>=u)n[f]=null,r=r.substr(0,f)+\" \"+r.substr(f+1);else if(null!==n[f]){var h=n[f].indexOf(e[0]);-1===h?n[f]+=e:l&&(n[f]=n[f].substr(0,h+1)+(1+parseInt(n[f][h+1]))+n[f].substr(h+2))}var p=c+o,d=r.substr(p,u-p).indexOf(i);c=-1!==d?d:u+s}return n}function u(t,e){var r=n(t,128);return e?a(r.cells,r.positions,.25):{edges:r.cells,positions:r.positions}}function f(t,e,r,n){var i=u(t,n),a=function(t,e,r){for(var n=e.textAlign||\"start\",i=e.textBaseline||\"alphabetic\",a=[1<<30,1<<30],o=[0,0],s=t.length,l=0;l<s;++l)for(var c=t[l],u=0;u<2;++u)a[u]=0|Math.min(a[u],c[u]),o[u]=0|Math.max(o[u],c[u]);var 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+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = optuna.visualization.plot_contour(study, params=[\"max_depth\", \"min_samples_split\"])\n",
+    "fig.show()"
+   ]
+  },
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+   "cell_type": "code",
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+   "metadata": {},
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                       {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('baa0993e-7f6d-4830-8b9c-3d52a46db43f');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
+       "        var display = window.getComputedStyle(gd).display;\n",
+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
+       "    x.observe(notebookContainer, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "// Listen for the clearing of the current output cell\n",
+       "var outputEl = gd.closest('.output');\n",
+       "if (outputEl) {{\n",
+       "    x.observe(outputEl, {childList: true});\n",
+       "}}\n",
+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = optuna.visualization.plot_contour(study, params=[\"scaler\", \"n_estimators\"])\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "fa378193-667c-428c-8962-e0a0fb5fef06",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "g5.get_matrix(target=True).plot(kind='hist')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3589f00",
+   "metadata": {},
+   "source": [
+    "Fit a model without target. One dimensional targets are used by UMAP during fit. (n, k>1) targets are ignored during fit.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "f81a96d9-a45f-4bf4-a302-86af6d2aae15",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# let's removed `sex` as a feature, which splits the data strongly, and generate a new model,\n",
+    "feats = ['age', 'bmi', 'bp', 's1', 's2', 's3', 's4', 's5', 's6']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "363c5a6a-5183-4aae-9439-7756ccbb8bb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (221, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(train_diabetes)  # \n",
+    "# this time we scale the data \n",
+    "g6 = g.umap(X = feats, # y='target', # don't include target for fun (which helps supervise umap fit when 1-dimensional)\n",
+    "            use_scaler='standard', #None, #'robust', 'kbins', 'quantile', 'minmax'\n",
+    "            use_scaler_target='standard', \n",
+    "           )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "3509fac0-434f-4a08-bb54-256825b66af3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c18271bd-79cf-4d63-939f-551675d40341\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4705a7d98c40400bbe088753a420cd09&type=arrow&viztoken=923c3c7e-d306-44fd-922e-01d981e90761&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276817&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c18271bd-79cf-4d63-939f-551675d40341\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f7379c1b-65d8-4350-b38b-b57f54cd97b3",
+   "metadata": {},
+   "source": [
+    "# Digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "0eb0f820-7118-43bb-8419-52acb4a4f925",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data3 = load_digits()\n",
+    "digit_features = list(data3['feature_names'])\n",
+    "digits_df = pd.DataFrame(data3['data'], columns=digit_features)\n",
+    "digits_df['target'] = data3['target'].astype(int)\n",
+    "digits_df['names'] = digits_df.target.astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "e3aa6bdd-b0b6-46a7-93ae-041321bef461",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (898, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "a, b, c, d = train_test_split(digits_df, digits_df.target, train_size=0.5)\n",
+    "\n",
+    "g6=graphistry.nodes(a).umap(X=digit_features, \n",
+    "                              #y='target', this obviously works great to separate clusters during UMAP fit.\n",
+    "                              use_scaler=None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "a1156045-87df-443d-b75f-63d418941924",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"f7aef9c3-62b0-4ce7-a8c1-b477e999164e\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=daf370ca249e4b31a114ca5b2d8e5b87&type=arrow&viztoken=0cb1f1e1-ce6c-4ae6-9074-baf54fa41347&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276824&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#f7aef9c3-62b0-4ce7-a8c1-b477e999164e\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "afad90db-df22-4da6-abb9-cb161ef008c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 6.19 +/- 3.87\n",
+      " Max distance threshold; epsilon = 2.32\n",
+      " Finding 7 nearest neighbors\n",
+      " 145.83 neighbors per node within epsilon 2.32\n",
+      " 6293 total edges after dropping duplicates\n",
+      " ** Final graph has 957 nodes\n",
+      " - Batch has 899 nodes\n",
+      " - Brought in 58 nodes\n",
+      "--------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"3f756c06-83bc-4263-9635-a870c107731a\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=6ebd689772394c2fb369602b2cd55dfd&type=arrow&viztoken=b938c421-82be-4d22-8925-eaa61732ffe7&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276834&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#3f756c06-83bc-4263-9635-a870c107731a\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g7 = g6.transform_umap(b, min_dist='auto', verbose=True, merge_policy=True)\n",
+    "g7.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "be9b4506-960a-4204-b6c6-194d7fa9135d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------------------------------------\n",
+      "Infering edges over UMAP embedding\n",
+      "---------------------------------------------\n",
+      " Mean distance to existing nodes 6.24 +/- 3.87\n",
+      " Max distance threshold; epsilon = 2.38\n",
+      " Finding 7 nearest neighbors\n",
+      " 145.97 neighbors per node within epsilon 2.38\n",
+      " 6245 total edges after dropping duplicates\n",
+      " ** Final graph has 899 nodes\n",
+      "--------------------------------------------------\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"c123cbd1-808a-4f13-bd13-32a74e2cd525\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c9abaa5f21f346f38e4fa1a9c9864d6f&type=arrow&viztoken=0caca425-b248-4c48-8b11-792a0934671b&usertag=8a6d667e-pygraphistry-0.28.6+135.g2bf5581.dirty&splashAfter=1674276842&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#c123cbd1-808a-4f13-bd13-32a74e2cd525\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g7 = g6.transform_umap(b, min_dist='auto', verbose=True, merge_policy=False)\n",
+    "g7.bind(point_title='target').plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "987a2bce-ffed-4724-a8a7-d3dc07f48262",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([[ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.],\n",
+       "        [ 0.,  0., 13., 15., 10., 15.,  5.,  0.],\n",
+       "        [ 0.,  3., 15.,  2.,  0., 11.,  8.,  0.],\n",
+       "        [ 0.,  4., 12.,  0.,  0.,  8.,  8.,  0.],\n",
+       "        [ 0.,  5.,  8.,  0.,  0.,  9.,  8.,  0.],\n",
+       "        [ 0.,  4., 11.,  0.,  1., 12.,  7.,  0.],\n",
+       "        [ 0.,  2., 14.,  5., 10., 12.,  0.,  0.],\n",
+       "        [ 0.,  0.,  6., 13., 10.,  0.,  0.,  0.]]),\n",
+       " 0.0)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "digits_df[digit_features].iloc[0].values.reshape(8,8), df.iloc[0].target"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "050acfb4-301e-42bc-bc7b-961bc4793608",
+   "metadata": {},
+   "source": [
+    "# Build a GNN model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "d0d2c3c0-cfa4-4335-b2d4-fa77ec0eae94",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g6 = g2.build_gnn(y_nodes='target')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "7507c3d8-89d1-4d49-9534-9c2e50376934",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Graph(num_nodes=284, num_edges=4678,\n",
+       "      ndata_schemes={'feature': Scheme(shape=(30,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
+       "      edata_schemes={'feature': Scheme(shape=(286,), dtype=torch.float64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)})"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "G = g6.DGL_graph\n",
+    "G"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "e237ce99-191d-473d-80bb-49211978fd5f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# run a prediction task from https://docs.dgl.ai/tutorials/blitz/index.html"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.9.7 64-bit",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/demos/ai/OSINT/Chavismo.ipynb b/demos/ai/OSINT/Chavismo.ipynb
index 2457cfee06..95ad9f30b3 100644
--- a/demos/ai/OSINT/Chavismo.ipynb
+++ b/demos/ai/OSINT/Chavismo.ipynb
@@ -45,16 +45,6 @@
     "#! pip install --upgrade graphistry[ai]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "b6f55e41",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#cd .."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -63,6 +53,7 @@
    "outputs": [],
    "source": [
     "import graphistry\n",
+    "from graphistry.features import ModelDict, topic_model, search_model, qa_model\n",
     "\n",
     "import requests\n",
     "import pandas as pd\n",
@@ -317,9 +308,9 @@
     "        filename = f\"chavismo.xlsx\"\n",
     "        open(filename, \"wb\").write(r.content)\n",
     "        df = pd.read_excel(filename)\n",
-    "        df.to_csv(\"data/chavismo.csv\", header=True)\n",
+    "        df.to_csv(\"chavismo.csv\", header=True)\n",
     "    else:\n",
-    "        df = pd.read_csv('data/chavismo.csv', index_col=0)\n",
+    "        df = pd.read_csv('chavismo.csv', index_col=0)\n",
     "    return df\n",
     "\n",
     "df = download_chavismo_data(get_fresh=False)  # set to True to get latest data\n",
@@ -398,7 +389,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "RENDER = False # set to True to have plots generated inline, or paste the URLs into a tab to see the graphs"
+    "RENDER = True # set to True to have plots generated inline, or paste the URLs into a tab to see the graphs"
    ]
   },
   {
@@ -409,8 +400,25 @@
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"71de07c5-181b-4feb-9ff8-5a10283ff384\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=c209174e176d4261a58bd9dde23f946b&type=arrow&viztoken=c53dabd9-51ff-4208-86f3-216626fabf3d&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275170&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#71de07c5-181b-4feb-9ff8-5a10283ff384\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=e17a8abbcddc472c932cdb5b2c0fc2c2&type=arrow&viztoken=c8e856ba-5b7b-4592-b22e-6dfac7b411a9&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813426&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "execution_count": 11,
@@ -440,8 +448,25 @@
     },
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"df685266-d3a5-463f-9a5f-509071ff2566\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=4558ebb0113f4e828aae18a814e3d76f&type=arrow&viztoken=9d4da169-3ee5-49ef-be3d-b225dd772758&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275173&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#df685266-d3a5-463f-9a5f-509071ff2566\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=9b8312e901dd47999a91f39f66880983&type=arrow&viztoken=5fc71180-3c6e-4ff7-adc9-38938b6da275&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813431&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "execution_count": 12,
@@ -450,10 +475,10 @@
     }
    ],
    "source": [
-    "# one can also create a hypergraph with any number of columns of interest\n",
+    "# Create a hypergraph with any number of columns of interest\n",
     "hg = graphistry.hypergraph(df, ['Agent 1','Agent 2', 'Relationship'])\n",
     "gh = hg['graph']\n",
-    "gh.bind(point_title='Agent 1').plot(render=RENDER)"
+    "gh.bind(point_title='nodeID').plot(render=RENDER)"
    ]
   },
   {
@@ -477,39 +502,86 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "id": "9939d27f",
+   "id": "6699dc5c-16b2-4471-9a6f-9241c238d830",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "CPU times: user 2min 34s, sys: 11.3 s, total: 2min 46s\n",
-      "Wall time: 2min 31s\n"
+      "_____________________________________________________________\n",
+      "\n",
+      "sentence-transformers/msmarco-distilbert-base-v2 Search Model\n",
+      "_____________________________________________________________\n",
+      "\n",
+      "Updated: {'cardinality_threshold_target': 2, 'n_topics_target': 11}\n",
+      "_____________________________________________________________\n",
+      "\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'min_words': 0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'cardinality_threshold_target': 2, 'n_topics_target': 11}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "%%time\n",
-    "g = graphistry.nodes(df, 'Agent 1').edges(df, 'Agent 1', 'Agent 2')\n",
-    "# since we have edges, let's featurize (rather than umap, which would overwrite explicit edges)\n",
-    "# X = None will featurize ALL the columns and setting min_words=0 will treat them all as textual\n",
-    "g2 = g.featurize(y=['Relationship'], \n",
-    "                 model_name='msmarco-distilbert-base-v2', \n",
-    "                 min_words=0, # force textual encoding\n",
-    "                 cardinality_threshold_target=2, # force topic model (with low target cardinality)\n",
-    "                 n_topics_target=12)"
+    "search_model.update(dict(cardinality_threshold_target=2, n_topics_target=11))\n",
+    "search_model"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 15,
-   "id": "bcf9a948",
+   "id": "9939d27f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 35s, sys: 9.96 s, total: 2min 45s\n",
+      "Wall time: 2min 27s\n",
+      "____________________________________________________________\n",
+      "\n",
+      "Search model over features with `y=Relationship` topic model\n",
+      "____________________________________________________________\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'y': ['Relationship'], 'min_words': 0, 'model_name': 'sentence-transformers/msmarco-distilbert-base-v2', 'cardinality_threshold_target': 2, 'n_topics_target': 11}"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# save search instance after featurization \n",
-    "# g2.save_search_instance('data/chavismo.search')"
+    "%%time\n",
+    "\n",
+    "model = ModelDict('Search model over features with `y=Relationship` topic model', y=['Relationship'], \n",
+    "                 **search_model)\n",
+    "process = True\n",
+    "if process:\n",
+    "    g = graphistry.nodes(df.sample(len(df)), 'Agent 1').edges(df, 'Agent 1', 'Agent 2')\n",
+    "\n",
+    "    # since we have edges, let's featurize (rather than umap, which would overwrite explicit edges)\n",
+    "    # X = None will featurize ALL the columns and setting min_words=0 will treat them all as textual\n",
+    "    g2 = g.featurize(**model)\n",
+    "    g2.save_search_instance('chavismo.search')\n",
+    "else:\n",
+    "    g2 = graphistry.bind().load_search_instance('chavismo.search')\n",
+    "    \n",
+    "model"
    ]
   },
   {
@@ -564,124 +636,124 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-0.253210</td>\n",
-       "      <td>-0.092555</td>\n",
-       "      <td>-0.086723</td>\n",
-       "      <td>-0.157253</td>\n",
-       "      <td>-0.204900</td>\n",
-       "      <td>0.122437</td>\n",
-       "      <td>-0.529797</td>\n",
-       "      <td>-0.015322</td>\n",
-       "      <td>-0.564774</td>\n",
-       "      <td>0.769105</td>\n",
+       "      <th>1935</th>\n",
+       "      <td>-0.187781</td>\n",
+       "      <td>-0.387294</td>\n",
+       "      <td>-0.160111</td>\n",
+       "      <td>0.533505</td>\n",
+       "      <td>-0.012384</td>\n",
+       "      <td>-0.036983</td>\n",
+       "      <td>-0.227363</td>\n",
+       "      <td>0.305134</td>\n",
+       "      <td>-0.053135</td>\n",
+       "      <td>0.242913</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.170976</td>\n",
-       "      <td>-0.685452</td>\n",
-       "      <td>0.295997</td>\n",
-       "      <td>-0.593129</td>\n",
-       "      <td>0.390818</td>\n",
-       "      <td>0.249091</td>\n",
-       "      <td>-0.099062</td>\n",
-       "      <td>0.051298</td>\n",
-       "      <td>0.319292</td>\n",
-       "      <td>0.244462</td>\n",
+       "      <td>0.295774</td>\n",
+       "      <td>0.358959</td>\n",
+       "      <td>0.566532</td>\n",
+       "      <td>-0.852727</td>\n",
+       "      <td>0.118316</td>\n",
+       "      <td>-0.122535</td>\n",
+       "      <td>0.347825</td>\n",
+       "      <td>0.101922</td>\n",
+       "      <td>0.386412</td>\n",
+       "      <td>-0.644331</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.246941</td>\n",
-       "      <td>0.124175</td>\n",
-       "      <td>0.269999</td>\n",
-       "      <td>0.181342</td>\n",
-       "      <td>-0.505962</td>\n",
-       "      <td>0.210211</td>\n",
-       "      <td>0.140408</td>\n",
-       "      <td>0.468697</td>\n",
-       "      <td>0.321780</td>\n",
-       "      <td>-0.128226</td>\n",
+       "      <th>1583</th>\n",
+       "      <td>-0.389066</td>\n",
+       "      <td>-0.087771</td>\n",
+       "      <td>-0.775325</td>\n",
+       "      <td>0.130028</td>\n",
+       "      <td>0.172289</td>\n",
+       "      <td>-0.208622</td>\n",
+       "      <td>-0.874529</td>\n",
+       "      <td>0.382446</td>\n",
+       "      <td>0.483579</td>\n",
+       "      <td>-0.309687</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.896930</td>\n",
-       "      <td>-0.324542</td>\n",
-       "      <td>-0.291376</td>\n",
-       "      <td>-0.565900</td>\n",
-       "      <td>0.524987</td>\n",
-       "      <td>-0.196209</td>\n",
-       "      <td>-0.519527</td>\n",
-       "      <td>-0.216939</td>\n",
-       "      <td>0.862336</td>\n",
-       "      <td>0.193277</td>\n",
+       "      <td>0.152333</td>\n",
+       "      <td>-0.175365</td>\n",
+       "      <td>0.676532</td>\n",
+       "      <td>-0.393536</td>\n",
+       "      <td>0.014875</td>\n",
+       "      <td>-0.383976</td>\n",
+       "      <td>0.081081</td>\n",
+       "      <td>0.298928</td>\n",
+       "      <td>0.596412</td>\n",
+       "      <td>-0.605296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>-0.068820</td>\n",
-       "      <td>-0.323506</td>\n",
-       "      <td>-0.206149</td>\n",
-       "      <td>0.559626</td>\n",
-       "      <td>0.145691</td>\n",
-       "      <td>-0.100530</td>\n",
-       "      <td>-0.492642</td>\n",
-       "      <td>-0.207871</td>\n",
-       "      <td>-0.306911</td>\n",
-       "      <td>0.365237</td>\n",
+       "      <th>2351</th>\n",
+       "      <td>-0.423137</td>\n",
+       "      <td>0.182216</td>\n",
+       "      <td>-0.133435</td>\n",
+       "      <td>0.217782</td>\n",
+       "      <td>-0.094416</td>\n",
+       "      <td>-0.097829</td>\n",
+       "      <td>-0.876501</td>\n",
+       "      <td>0.797395</td>\n",
+       "      <td>-0.277968</td>\n",
+       "      <td>-0.045989</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.702710</td>\n",
-       "      <td>0.152173</td>\n",
-       "      <td>0.309572</td>\n",
-       "      <td>-0.461615</td>\n",
-       "      <td>0.666504</td>\n",
-       "      <td>-0.016534</td>\n",
-       "      <td>0.877414</td>\n",
-       "      <td>-0.203894</td>\n",
-       "      <td>0.450191</td>\n",
-       "      <td>-0.544430</td>\n",
+       "      <td>1.015161</td>\n",
+       "      <td>0.453668</td>\n",
+       "      <td>0.440656</td>\n",
+       "      <td>-0.040266</td>\n",
+       "      <td>0.693482</td>\n",
+       "      <td>0.169744</td>\n",
+       "      <td>0.060993</td>\n",
+       "      <td>-0.500294</td>\n",
+       "      <td>0.649334</td>\n",
+       "      <td>0.195239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>-0.193803</td>\n",
-       "      <td>-0.272143</td>\n",
-       "      <td>-0.208212</td>\n",
-       "      <td>0.341019</td>\n",
-       "      <td>-0.175586</td>\n",
-       "      <td>0.149454</td>\n",
-       "      <td>-0.245428</td>\n",
-       "      <td>-0.008559</td>\n",
-       "      <td>0.279371</td>\n",
-       "      <td>0.312212</td>\n",
+       "      <th>452</th>\n",
+       "      <td>-0.095392</td>\n",
+       "      <td>0.142223</td>\n",
+       "      <td>-0.686644</td>\n",
+       "      <td>0.674334</td>\n",
+       "      <td>-0.228214</td>\n",
+       "      <td>0.109001</td>\n",
+       "      <td>-0.440772</td>\n",
+       "      <td>0.124842</td>\n",
+       "      <td>-0.066046</td>\n",
+       "      <td>0.171612</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.451480</td>\n",
-       "      <td>0.413026</td>\n",
-       "      <td>-0.211110</td>\n",
-       "      <td>-0.550950</td>\n",
-       "      <td>0.556309</td>\n",
-       "      <td>-0.521065</td>\n",
-       "      <td>0.327162</td>\n",
-       "      <td>-0.200601</td>\n",
-       "      <td>0.148922</td>\n",
-       "      <td>-0.181214</td>\n",
+       "      <td>-0.009296</td>\n",
+       "      <td>0.564043</td>\n",
+       "      <td>0.178982</td>\n",
+       "      <td>-0.222556</td>\n",
+       "      <td>0.523386</td>\n",
+       "      <td>0.331242</td>\n",
+       "      <td>0.360143</td>\n",
+       "      <td>-0.011475</td>\n",
+       "      <td>0.105367</td>\n",
+       "      <td>0.146013</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.099981</td>\n",
-       "      <td>-0.147359</td>\n",
-       "      <td>-0.181383</td>\n",
-       "      <td>0.436541</td>\n",
-       "      <td>0.026173</td>\n",
-       "      <td>-0.218141</td>\n",
-       "      <td>-0.193041</td>\n",
-       "      <td>0.030840</td>\n",
-       "      <td>0.436779</td>\n",
-       "      <td>0.392634</td>\n",
+       "      <th>8</th>\n",
+       "      <td>-0.477632</td>\n",
+       "      <td>-0.784202</td>\n",
+       "      <td>-0.383131</td>\n",
+       "      <td>0.669990</td>\n",
+       "      <td>-0.100068</td>\n",
+       "      <td>-0.294655</td>\n",
+       "      <td>-0.627293</td>\n",
+       "      <td>0.058967</td>\n",
+       "      <td>-0.455835</td>\n",
+       "      <td>0.461948</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.505176</td>\n",
-       "      <td>0.505908</td>\n",
-       "      <td>-0.286257</td>\n",
-       "      <td>-0.614761</td>\n",
-       "      <td>0.811958</td>\n",
-       "      <td>-0.615687</td>\n",
-       "      <td>0.479651</td>\n",
-       "      <td>-0.345834</td>\n",
-       "      <td>0.185110</td>\n",
-       "      <td>-0.311448</td>\n",
+       "      <td>0.381159</td>\n",
+       "      <td>0.021592</td>\n",
+       "      <td>0.348291</td>\n",
+       "      <td>-0.903526</td>\n",
+       "      <td>0.694809</td>\n",
+       "      <td>0.018819</td>\n",
+       "      <td>-0.002837</td>\n",
+       "      <td>0.023364</td>\n",
+       "      <td>0.385524</td>\n",
+       "      <td>-0.437104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -708,124 +780,124 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2713</th>\n",
-       "      <td>0.149379</td>\n",
-       "      <td>-0.222494</td>\n",
-       "      <td>0.303324</td>\n",
-       "      <td>0.306326</td>\n",
-       "      <td>-0.254602</td>\n",
-       "      <td>-0.536148</td>\n",
-       "      <td>-0.470110</td>\n",
-       "      <td>0.565999</td>\n",
-       "      <td>0.020586</td>\n",
-       "      <td>0.609108</td>\n",
+       "      <th>334</th>\n",
+       "      <td>-0.593350</td>\n",
+       "      <td>0.112945</td>\n",
+       "      <td>0.123807</td>\n",
+       "      <td>0.948721</td>\n",
+       "      <td>-0.276408</td>\n",
+       "      <td>-0.603181</td>\n",
+       "      <td>-0.392917</td>\n",
+       "      <td>0.388957</td>\n",
+       "      <td>0.126814</td>\n",
+       "      <td>1.126764</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.090419</td>\n",
-       "      <td>0.125347</td>\n",
-       "      <td>0.540859</td>\n",
-       "      <td>-0.566799</td>\n",
-       "      <td>0.642269</td>\n",
-       "      <td>0.503141</td>\n",
-       "      <td>-0.070126</td>\n",
-       "      <td>-0.351428</td>\n",
-       "      <td>1.435632</td>\n",
-       "      <td>0.075295</td>\n",
+       "      <td>-0.089558</td>\n",
+       "      <td>-0.331218</td>\n",
+       "      <td>0.120006</td>\n",
+       "      <td>-0.712097</td>\n",
+       "      <td>-0.205538</td>\n",
+       "      <td>0.562405</td>\n",
+       "      <td>0.525867</td>\n",
+       "      <td>-0.204776</td>\n",
+       "      <td>-0.197538</td>\n",
+       "      <td>-0.018287</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2714</th>\n",
-       "      <td>0.202084</td>\n",
-       "      <td>-0.421907</td>\n",
-       "      <td>0.331125</td>\n",
-       "      <td>0.261079</td>\n",
-       "      <td>-0.179134</td>\n",
-       "      <td>-0.532431</td>\n",
-       "      <td>-0.164233</td>\n",
-       "      <td>0.277176</td>\n",
-       "      <td>-0.106925</td>\n",
-       "      <td>0.391598</td>\n",
+       "      <th>1264</th>\n",
+       "      <td>0.476763</td>\n",
+       "      <td>0.199641</td>\n",
+       "      <td>0.226375</td>\n",
+       "      <td>0.824276</td>\n",
+       "      <td>-0.438520</td>\n",
+       "      <td>-0.420465</td>\n",
+       "      <td>0.386475</td>\n",
+       "      <td>0.275413</td>\n",
+       "      <td>-0.350755</td>\n",
+       "      <td>0.047801</td>\n",
        "      <td>...</td>\n",
-       "      <td>-0.046802</td>\n",
-       "      <td>0.067547</td>\n",
-       "      <td>0.484514</td>\n",
-       "      <td>-0.824181</td>\n",
-       "      <td>0.475694</td>\n",
-       "      <td>0.329913</td>\n",
-       "      <td>-0.104037</td>\n",
-       "      <td>-0.155081</td>\n",
-       "      <td>0.928627</td>\n",
-       "      <td>-0.082177</td>\n",
+       "      <td>0.755453</td>\n",
+       "      <td>0.358454</td>\n",
+       "      <td>0.348341</td>\n",
+       "      <td>-1.133082</td>\n",
+       "      <td>-0.210484</td>\n",
+       "      <td>-0.764606</td>\n",
+       "      <td>0.916726</td>\n",
+       "      <td>-0.284010</td>\n",
+       "      <td>-0.056577</td>\n",
+       "      <td>0.407399</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2715</th>\n",
-       "      <td>-0.627377</td>\n",
-       "      <td>-0.391843</td>\n",
-       "      <td>0.218678</td>\n",
-       "      <td>-0.014267</td>\n",
-       "      <td>-0.460290</td>\n",
-       "      <td>-0.329174</td>\n",
-       "      <td>-0.482513</td>\n",
-       "      <td>0.604713</td>\n",
-       "      <td>0.011763</td>\n",
-       "      <td>0.018444</td>\n",
+       "      <th>1171</th>\n",
+       "      <td>-0.270009</td>\n",
+       "      <td>0.170292</td>\n",
+       "      <td>0.052011</td>\n",
+       "      <td>0.060369</td>\n",
+       "      <td>-0.265923</td>\n",
+       "      <td>0.717389</td>\n",
+       "      <td>-0.561747</td>\n",
+       "      <td>0.256904</td>\n",
+       "      <td>0.061957</td>\n",
+       "      <td>0.049001</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.275013</td>\n",
-       "      <td>0.164754</td>\n",
-       "      <td>0.408749</td>\n",
-       "      <td>-0.332821</td>\n",
-       "      <td>0.696610</td>\n",
-       "      <td>0.529032</td>\n",
-       "      <td>0.067081</td>\n",
-       "      <td>-0.382973</td>\n",
-       "      <td>1.094504</td>\n",
-       "      <td>0.503912</td>\n",
+       "      <td>-0.322604</td>\n",
+       "      <td>-0.409145</td>\n",
+       "      <td>0.270570</td>\n",
+       "      <td>-0.290132</td>\n",
+       "      <td>0.037762</td>\n",
+       "      <td>0.646035</td>\n",
+       "      <td>-0.226534</td>\n",
+       "      <td>0.178052</td>\n",
+       "      <td>0.890680</td>\n",
+       "      <td>-0.204422</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2716</th>\n",
-       "      <td>0.148124</td>\n",
-       "      <td>-0.041698</td>\n",
-       "      <td>0.083552</td>\n",
-       "      <td>0.132032</td>\n",
-       "      <td>-0.663597</td>\n",
-       "      <td>0.103373</td>\n",
-       "      <td>-0.261431</td>\n",
-       "      <td>-0.024310</td>\n",
-       "      <td>0.381935</td>\n",
-       "      <td>0.062252</td>\n",
+       "      <th>589</th>\n",
+       "      <td>0.217439</td>\n",
+       "      <td>0.188192</td>\n",
+       "      <td>-0.186874</td>\n",
+       "      <td>0.108394</td>\n",
+       "      <td>0.318829</td>\n",
+       "      <td>0.049164</td>\n",
+       "      <td>-0.460243</td>\n",
+       "      <td>0.502261</td>\n",
+       "      <td>-0.304636</td>\n",
+       "      <td>0.176173</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.362344</td>\n",
-       "      <td>0.360328</td>\n",
-       "      <td>-0.020871</td>\n",
-       "      <td>-0.640752</td>\n",
-       "      <td>0.638189</td>\n",
-       "      <td>-0.118096</td>\n",
-       "      <td>0.245733</td>\n",
-       "      <td>-0.404834</td>\n",
-       "      <td>0.754788</td>\n",
-       "      <td>-0.139678</td>\n",
+       "      <td>0.700581</td>\n",
+       "      <td>0.730317</td>\n",
+       "      <td>0.152944</td>\n",
+       "      <td>0.017145</td>\n",
+       "      <td>0.597685</td>\n",
+       "      <td>0.536023</td>\n",
+       "      <td>-0.020815</td>\n",
+       "      <td>-0.774539</td>\n",
+       "      <td>0.354638</td>\n",
+       "      <td>0.153618</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2717</th>\n",
-       "      <td>0.148124</td>\n",
-       "      <td>-0.041698</td>\n",
-       "      <td>0.083552</td>\n",
-       "      <td>0.132032</td>\n",
-       "      <td>-0.663597</td>\n",
-       "      <td>0.103373</td>\n",
-       "      <td>-0.261431</td>\n",
-       "      <td>-0.024310</td>\n",
-       "      <td>0.381935</td>\n",
-       "      <td>0.062252</td>\n",
+       "      <th>2342</th>\n",
+       "      <td>-0.477632</td>\n",
+       "      <td>-0.784202</td>\n",
+       "      <td>-0.383131</td>\n",
+       "      <td>0.669990</td>\n",
+       "      <td>-0.100068</td>\n",
+       "      <td>-0.294655</td>\n",
+       "      <td>-0.627293</td>\n",
+       "      <td>0.058967</td>\n",
+       "      <td>-0.455835</td>\n",
+       "      <td>0.461948</td>\n",
        "      <td>...</td>\n",
-       "      <td>0.362344</td>\n",
-       "      <td>0.360328</td>\n",
-       "      <td>-0.020871</td>\n",
-       "      <td>-0.640752</td>\n",
-       "      <td>0.638189</td>\n",
-       "      <td>-0.118096</td>\n",
-       "      <td>0.245733</td>\n",
-       "      <td>-0.404834</td>\n",
-       "      <td>0.754788</td>\n",
-       "      <td>-0.139678</td>\n",
+       "      <td>0.381159</td>\n",
+       "      <td>0.021592</td>\n",
+       "      <td>0.348291</td>\n",
+       "      <td>-0.903526</td>\n",
+       "      <td>0.694809</td>\n",
+       "      <td>0.018819</td>\n",
+       "      <td>-0.002837</td>\n",
+       "      <td>0.023364</td>\n",
+       "      <td>0.385524</td>\n",
+       "      <td>-0.437104</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -834,134 +906,134 @@
       ],
       "text/plain": [
        "      Agent 2_Source_Evidence_0  Agent 2_Source_Evidence_1  \\\n",
-       "0                     -0.253210                  -0.092555   \n",
-       "1                      0.246941                   0.124175   \n",
-       "2                     -0.068820                  -0.323506   \n",
-       "3                     -0.193803                  -0.272143   \n",
-       "4                      0.099981                  -0.147359   \n",
+       "1935                  -0.187781                  -0.387294   \n",
+       "1583                  -0.389066                  -0.087771   \n",
+       "2351                  -0.423137                   0.182216   \n",
+       "452                   -0.095392                   0.142223   \n",
+       "8                     -0.477632                  -0.784202   \n",
        "...                         ...                        ...   \n",
-       "2713                   0.149379                  -0.222494   \n",
-       "2714                   0.202084                  -0.421907   \n",
-       "2715                  -0.627377                  -0.391843   \n",
-       "2716                   0.148124                  -0.041698   \n",
-       "2717                   0.148124                  -0.041698   \n",
+       "334                   -0.593350                   0.112945   \n",
+       "1264                   0.476763                   0.199641   \n",
+       "1171                  -0.270009                   0.170292   \n",
+       "589                    0.217439                   0.188192   \n",
+       "2342                  -0.477632                  -0.784202   \n",
        "\n",
        "      Agent 2_Source_Evidence_2  Agent 2_Source_Evidence_3  \\\n",
-       "0                     -0.086723                  -0.157253   \n",
-       "1                      0.269999                   0.181342   \n",
-       "2                     -0.206149                   0.559626   \n",
-       "3                     -0.208212                   0.341019   \n",
-       "4                     -0.181383                   0.436541   \n",
+       "1935                  -0.160111                   0.533505   \n",
+       "1583                  -0.775325                   0.130028   \n",
+       "2351                  -0.133435                   0.217782   \n",
+       "452                   -0.686644                   0.674334   \n",
+       "8                     -0.383131                   0.669990   \n",
        "...                         ...                        ...   \n",
-       "2713                   0.303324                   0.306326   \n",
-       "2714                   0.331125                   0.261079   \n",
-       "2715                   0.218678                  -0.014267   \n",
-       "2716                   0.083552                   0.132032   \n",
-       "2717                   0.083552                   0.132032   \n",
+       "334                    0.123807                   0.948721   \n",
+       "1264                   0.226375                   0.824276   \n",
+       "1171                   0.052011                   0.060369   \n",
+       "589                   -0.186874                   0.108394   \n",
+       "2342                  -0.383131                   0.669990   \n",
        "\n",
        "      Agent 2_Source_Evidence_4  Agent 2_Source_Evidence_5  \\\n",
-       "0                     -0.204900                   0.122437   \n",
-       "1                     -0.505962                   0.210211   \n",
-       "2                      0.145691                  -0.100530   \n",
-       "3                     -0.175586                   0.149454   \n",
-       "4                      0.026173                  -0.218141   \n",
+       "1935                  -0.012384                  -0.036983   \n",
+       "1583                   0.172289                  -0.208622   \n",
+       "2351                  -0.094416                  -0.097829   \n",
+       "452                   -0.228214                   0.109001   \n",
+       "8                     -0.100068                  -0.294655   \n",
        "...                         ...                        ...   \n",
-       "2713                  -0.254602                  -0.536148   \n",
-       "2714                  -0.179134                  -0.532431   \n",
-       "2715                  -0.460290                  -0.329174   \n",
-       "2716                  -0.663597                   0.103373   \n",
-       "2717                  -0.663597                   0.103373   \n",
+       "334                   -0.276408                  -0.603181   \n",
+       "1264                  -0.438520                  -0.420465   \n",
+       "1171                  -0.265923                   0.717389   \n",
+       "589                    0.318829                   0.049164   \n",
+       "2342                  -0.100068                  -0.294655   \n",
        "\n",
        "      Agent 2_Source_Evidence_6  Agent 2_Source_Evidence_7  \\\n",
-       "0                     -0.529797                  -0.015322   \n",
-       "1                      0.140408                   0.468697   \n",
-       "2                     -0.492642                  -0.207871   \n",
-       "3                     -0.245428                  -0.008559   \n",
-       "4                     -0.193041                   0.030840   \n",
+       "1935                  -0.227363                   0.305134   \n",
+       "1583                  -0.874529                   0.382446   \n",
+       "2351                  -0.876501                   0.797395   \n",
+       "452                   -0.440772                   0.124842   \n",
+       "8                     -0.627293                   0.058967   \n",
        "...                         ...                        ...   \n",
-       "2713                  -0.470110                   0.565999   \n",
-       "2714                  -0.164233                   0.277176   \n",
-       "2715                  -0.482513                   0.604713   \n",
-       "2716                  -0.261431                  -0.024310   \n",
-       "2717                  -0.261431                  -0.024310   \n",
+       "334                   -0.392917                   0.388957   \n",
+       "1264                   0.386475                   0.275413   \n",
+       "1171                  -0.561747                   0.256904   \n",
+       "589                   -0.460243                   0.502261   \n",
+       "2342                  -0.627293                   0.058967   \n",
        "\n",
        "      Agent 2_Source_Evidence_8  Agent 2_Source_Evidence_9  ...  \\\n",
-       "0                     -0.564774                   0.769105  ...   \n",
-       "1                      0.321780                  -0.128226  ...   \n",
-       "2                     -0.306911                   0.365237  ...   \n",
-       "3                      0.279371                   0.312212  ...   \n",
-       "4                      0.436779                   0.392634  ...   \n",
+       "1935                  -0.053135                   0.242913  ...   \n",
+       "1583                   0.483579                  -0.309687  ...   \n",
+       "2351                  -0.277968                  -0.045989  ...   \n",
+       "452                   -0.066046                   0.171612  ...   \n",
+       "8                     -0.455835                   0.461948  ...   \n",
        "...                         ...                        ...  ...   \n",
-       "2713                   0.020586                   0.609108  ...   \n",
-       "2714                  -0.106925                   0.391598  ...   \n",
-       "2715                   0.011763                   0.018444  ...   \n",
-       "2716                   0.381935                   0.062252  ...   \n",
-       "2717                   0.381935                   0.062252  ...   \n",
+       "334                    0.126814                   1.126764  ...   \n",
+       "1264                  -0.350755                   0.047801  ...   \n",
+       "1171                   0.061957                   0.049001  ...   \n",
+       "589                   -0.304636                   0.176173  ...   \n",
+       "2342                  -0.455835                   0.461948  ...   \n",
        "\n",
        "      Agent 2_Source_Evidence_758  Agent 2_Source_Evidence_759  \\\n",
-       "0                        0.170976                    -0.685452   \n",
-       "1                        0.896930                    -0.324542   \n",
-       "2                        0.702710                     0.152173   \n",
-       "3                        0.451480                     0.413026   \n",
-       "4                        0.505176                     0.505908   \n",
+       "1935                     0.295774                     0.358959   \n",
+       "1583                     0.152333                    -0.175365   \n",
+       "2351                     1.015161                     0.453668   \n",
+       "452                     -0.009296                     0.564043   \n",
+       "8                        0.381159                     0.021592   \n",
        "...                           ...                          ...   \n",
-       "2713                    -0.090419                     0.125347   \n",
-       "2714                    -0.046802                     0.067547   \n",
-       "2715                     0.275013                     0.164754   \n",
-       "2716                     0.362344                     0.360328   \n",
-       "2717                     0.362344                     0.360328   \n",
+       "334                     -0.089558                    -0.331218   \n",
+       "1264                     0.755453                     0.358454   \n",
+       "1171                    -0.322604                    -0.409145   \n",
+       "589                      0.700581                     0.730317   \n",
+       "2342                     0.381159                     0.021592   \n",
        "\n",
        "      Agent 2_Source_Evidence_760  Agent 2_Source_Evidence_761  \\\n",
-       "0                        0.295997                    -0.593129   \n",
-       "1                       -0.291376                    -0.565900   \n",
-       "2                        0.309572                    -0.461615   \n",
-       "3                       -0.211110                    -0.550950   \n",
-       "4                       -0.286257                    -0.614761   \n",
+       "1935                     0.566532                    -0.852727   \n",
+       "1583                     0.676532                    -0.393536   \n",
+       "2351                     0.440656                    -0.040266   \n",
+       "452                      0.178982                    -0.222556   \n",
+       "8                        0.348291                    -0.903526   \n",
        "...                           ...                          ...   \n",
-       "2713                     0.540859                    -0.566799   \n",
-       "2714                     0.484514                    -0.824181   \n",
-       "2715                     0.408749                    -0.332821   \n",
-       "2716                    -0.020871                    -0.640752   \n",
-       "2717                    -0.020871                    -0.640752   \n",
+       "334                      0.120006                    -0.712097   \n",
+       "1264                     0.348341                    -1.133082   \n",
+       "1171                     0.270570                    -0.290132   \n",
+       "589                      0.152944                     0.017145   \n",
+       "2342                     0.348291                    -0.903526   \n",
        "\n",
        "      Agent 2_Source_Evidence_762  Agent 2_Source_Evidence_763  \\\n",
-       "0                        0.390818                     0.249091   \n",
-       "1                        0.524987                    -0.196209   \n",
-       "2                        0.666504                    -0.016534   \n",
-       "3                        0.556309                    -0.521065   \n",
-       "4                        0.811958                    -0.615687   \n",
+       "1935                     0.118316                    -0.122535   \n",
+       "1583                     0.014875                    -0.383976   \n",
+       "2351                     0.693482                     0.169744   \n",
+       "452                      0.523386                     0.331242   \n",
+       "8                        0.694809                     0.018819   \n",
        "...                           ...                          ...   \n",
-       "2713                     0.642269                     0.503141   \n",
-       "2714                     0.475694                     0.329913   \n",
-       "2715                     0.696610                     0.529032   \n",
-       "2716                     0.638189                    -0.118096   \n",
-       "2717                     0.638189                    -0.118096   \n",
+       "334                     -0.205538                     0.562405   \n",
+       "1264                    -0.210484                    -0.764606   \n",
+       "1171                     0.037762                     0.646035   \n",
+       "589                      0.597685                     0.536023   \n",
+       "2342                     0.694809                     0.018819   \n",
        "\n",
        "      Agent 2_Source_Evidence_764  Agent 2_Source_Evidence_765  \\\n",
-       "0                       -0.099062                     0.051298   \n",
-       "1                       -0.519527                    -0.216939   \n",
-       "2                        0.877414                    -0.203894   \n",
-       "3                        0.327162                    -0.200601   \n",
-       "4                        0.479651                    -0.345834   \n",
+       "1935                     0.347825                     0.101922   \n",
+       "1583                     0.081081                     0.298928   \n",
+       "2351                     0.060993                    -0.500294   \n",
+       "452                      0.360143                    -0.011475   \n",
+       "8                       -0.002837                     0.023364   \n",
        "...                           ...                          ...   \n",
-       "2713                    -0.070126                    -0.351428   \n",
-       "2714                    -0.104037                    -0.155081   \n",
-       "2715                     0.067081                    -0.382973   \n",
-       "2716                     0.245733                    -0.404834   \n",
-       "2717                     0.245733                    -0.404834   \n",
+       "334                      0.525867                    -0.204776   \n",
+       "1264                     0.916726                    -0.284010   \n",
+       "1171                    -0.226534                     0.178052   \n",
+       "589                     -0.020815                    -0.774539   \n",
+       "2342                    -0.002837                     0.023364   \n",
        "\n",
        "      Agent 2_Source_Evidence_766  Agent 2_Source_Evidence_767  \n",
-       "0                        0.319292                     0.244462  \n",
-       "1                        0.862336                     0.193277  \n",
-       "2                        0.450191                    -0.544430  \n",
-       "3                        0.148922                    -0.181214  \n",
-       "4                        0.185110                    -0.311448  \n",
+       "1935                     0.386412                    -0.644331  \n",
+       "1583                     0.596412                    -0.605296  \n",
+       "2351                     0.649334                     0.195239  \n",
+       "452                      0.105367                     0.146013  \n",
+       "8                        0.385524                    -0.437104  \n",
        "...                           ...                          ...  \n",
-       "2713                     1.435632                     0.075295  \n",
-       "2714                     0.928627                    -0.082177  \n",
-       "2715                     1.094504                     0.503912  \n",
-       "2716                     0.754788                    -0.139678  \n",
-       "2717                     0.754788                    -0.139678  \n",
+       "334                     -0.197538                    -0.018287  \n",
+       "1264                    -0.056577                     0.407399  \n",
+       "1171                     0.890680                    -0.204422  \n",
+       "589                      0.354638                     0.153618  \n",
+       "2342                     0.385524                    -0.437104  \n",
        "\n",
        "[2718 rows x 768 columns]"
       ]
@@ -972,8 +1044,8 @@
     }
    ],
    "source": [
-    "# the resulting X = features matrix\n",
-    "X = g2._get_feature('nodes')\n",
+    "# the resulting X = features matrix, sbert encoding\n",
+    "X = g2.get_matrix()\n",
     "X"
    ]
   },
@@ -1004,95 +1076,89 @@
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
-       "      <th>Relationship: sanctioned, sanction, violation</th>\n",
-       "      <th>Relationship: smuggling, bribery, in</th>\n",
+       "      <th>Relationship: occupied, functions, sanctions</th>\n",
+       "      <th>Relationship: laundering, overpricing, international</th>\n",
        "      <th>Relationship: integrates, company, in</th>\n",
+       "      <th>Relationship: complaints, corruption, traffic</th>\n",
+       "      <th>Relationship: sanctioned, sanction, evasion</th>\n",
+       "      <th>Relationship: facilitators, colleagues, student</th>\n",
+       "      <th>Relationship: designates, charge, rights</th>\n",
        "      <th>Relationship: connection, business, in</th>\n",
-       "      <th>Relationship: complaints, corruption, conspiracy</th>\n",
-       "      <th>Relationship: suscribed, traffic, contract</th>\n",
-       "      <th>Relationship: occupied, functions, sanctions</th>\n",
-       "      <th>Relationship: overpricing, currency, illegal</th>\n",
-       "      <th>Relationship: laundering, international, trials</th>\n",
        "      <th>Relationship: members, family, enemies</th>\n",
-       "      <th>Relationship: facilitators, extortion, friends</th>\n",
-       "      <th>Relationship: designates, colleagues, charge</th>\n",
+       "      <th>Relationship: smuggling, bribery, currency</th>\n",
+       "      <th>Relationship: conspiracy, suscribed, contract</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0.065003</td>\n",
-       "      <td>0.055670</td>\n",
-       "      <td>0.071783</td>\n",
-       "      <td>0.079073</td>\n",
-       "      <td>34.442679</td>\n",
-       "      <td>0.054766</td>\n",
-       "      <td>0.063643</td>\n",
-       "      <td>0.053862</td>\n",
-       "      <td>0.057605</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.052458</td>\n",
-       "      <td>0.053457</td>\n",
+       "      <th>1935</th>\n",
+       "      <td>0.072898</td>\n",
+       "      <td>0.553233</td>\n",
+       "      <td>0.089311</td>\n",
+       "      <td>0.068496</td>\n",
+       "      <td>0.075216</td>\n",
+       "      <td>0.058883</td>\n",
+       "      <td>0.059063</td>\n",
+       "      <td>0.091499</td>\n",
+       "      <td>0.067527</td>\n",
+       "      <td>42.147305</td>\n",
+       "      <td>42.766569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0.050119</td>\n",
-       "      <td>0.050523</td>\n",
-       "      <td>0.051003</td>\n",
+       "      <th>1583</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051689</td>\n",
-       "      <td>0.051122</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.050509</td>\n",
-       "      <td>0.050418</td>\n",
-       "      <td>0.054442</td>\n",
-       "      <td>15.039365</td>\n",
-       "      <td>0.050809</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0.082737</td>\n",
-       "      <td>0.052691</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.062643</td>\n",
-       "      <td>0.055349</td>\n",
-       "      <td>0.053101</td>\n",
-       "      <td>23.987324</td>\n",
-       "      <td>0.052330</td>\n",
-       "      <td>0.053825</td>\n",
+       "      <th>2351</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0.074388</td>\n",
-       "      <td>28.567443</td>\n",
-       "      <td>0.065518</td>\n",
-       "      <td>0.063843</td>\n",
-       "      <td>0.060323</td>\n",
-       "      <td>0.117900</td>\n",
-       "      <td>0.064166</td>\n",
-       "      <td>60.674413</td>\n",
-       "      <td>17.229793</td>\n",
-       "      <td>0.065414</td>\n",
-       "      <td>0.055578</td>\n",
-       "      <td>0.061222</td>\n",
+       "      <th>452</th>\n",
+       "      <td>23.982221</td>\n",
+       "      <td>0.053704</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.055351</td>\n",
+       "      <td>0.090587</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.062470</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.052716</td>\n",
+       "      <td>0.052951</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0.067568</td>\n",
-       "      <td>31.116316</td>\n",
-       "      <td>0.058412</td>\n",
-       "      <td>0.060998</td>\n",
-       "      <td>0.056463</td>\n",
-       "      <td>0.060142</td>\n",
-       "      <td>0.057811</td>\n",
-       "      <td>0.117512</td>\n",
-       "      <td>37.838020</td>\n",
-       "      <td>0.059851</td>\n",
-       "      <td>0.051573</td>\n",
-       "      <td>0.055335</td>\n",
+       "      <th>8</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -1107,246 +1173,227 @@
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
-       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2713</th>\n",
-       "      <td>0.051717</td>\n",
-       "      <td>0.053141</td>\n",
-       "      <td>23.986537</td>\n",
-       "      <td>0.073292</td>\n",
-       "      <td>0.059816</td>\n",
-       "      <td>0.053714</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.052808</td>\n",
+       "      <th>334</th>\n",
+       "      <td>0.072986</td>\n",
        "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051797</td>\n",
-       "      <td>0.063327</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2714</th>\n",
-       "      <td>0.072604</td>\n",
-       "      <td>0.052956</td>\n",
-       "      <td>0.092982</td>\n",
-       "      <td>23.941857</td>\n",
-       "      <td>0.063406</td>\n",
-       "      <td>0.054355</td>\n",
-       "      <td>0.061778</td>\n",
-       "      <td>0.052354</td>\n",
-       "      <td>0.054615</td>\n",
-       "      <td>0.052002</td>\n",
+       "      <th>1264</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
        "      <td>0.050000</td>\n",
-       "      <td>0.051090</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2715</th>\n",
-       "      <td>0.082737</td>\n",
-       "      <td>0.052691</td>\n",
-       "      <td>0.050000</td>\n",
-       "      <td>0.062643</td>\n",
-       "      <td>0.055349</td>\n",
-       "      <td>0.053101</td>\n",
-       "      <td>23.987324</td>\n",
-       "      <td>0.052330</td>\n",
-       "      <td>0.053825</td>\n",
+       "      <th>1171</th>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.051786</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050037</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.054220</td>\n",
        "      <td>0.050000</td>\n",
+       "      <td>0.050705</td>\n",
+       "      <td>18.039779</td>\n",
+       "      <td>0.052556</td>\n",
+       "      <td>0.050917</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2716</th>\n",
-       "      <td>0.071095</td>\n",
-       "      <td>0.299774</td>\n",
-       "      <td>0.070829</td>\n",
-       "      <td>0.064280</td>\n",
-       "      <td>0.060561</td>\n",
-       "      <td>0.064907</td>\n",
-       "      <td>0.062544</td>\n",
-       "      <td>25.575909</td>\n",
-       "      <td>20.649126</td>\n",
-       "      <td>0.051986</td>\n",
-       "      <td>0.070271</td>\n",
-       "      <td>0.058718</td>\n",
+       "      <th>589</th>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053772</td>\n",
+       "      <td>23.990746</td>\n",
+       "      <td>0.059230</td>\n",
+       "      <td>0.051608</td>\n",
+       "      <td>0.052662</td>\n",
+       "      <td>0.060380</td>\n",
+       "      <td>0.072165</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053006</td>\n",
+       "      <td>0.056433</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2717</th>\n",
-       "      <td>0.071095</td>\n",
-       "      <td>0.299774</td>\n",
-       "      <td>0.070829</td>\n",
-       "      <td>0.064280</td>\n",
-       "      <td>0.060561</td>\n",
-       "      <td>0.064907</td>\n",
-       "      <td>0.062544</td>\n",
-       "      <td>25.575909</td>\n",
-       "      <td>20.649126</td>\n",
-       "      <td>0.051986</td>\n",
-       "      <td>0.070271</td>\n",
-       "      <td>0.058718</td>\n",
+       "      <th>2342</th>\n",
+       "      <td>0.072986</td>\n",
+       "      <td>0.053850</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>16.497892</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.065366</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.053286</td>\n",
+       "      <td>0.052058</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>2718 rows × 12 columns</p>\n",
+       "<p>2718 rows × 11 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "      Relationship: sanctioned, sanction, violation  \\\n",
-       "0                                          0.065003   \n",
-       "1                                          0.050119   \n",
-       "2                                          0.082737   \n",
-       "3                                          0.074388   \n",
-       "4                                          0.067568   \n",
-       "...                                             ...   \n",
-       "2713                                       0.051717   \n",
-       "2714                                       0.072604   \n",
-       "2715                                       0.082737   \n",
-       "2716                                       0.071095   \n",
-       "2717                                       0.071095   \n",
+       "      Relationship: occupied, functions, sanctions  \\\n",
+       "1935                                      0.072898   \n",
+       "1583                                     23.982221   \n",
+       "2351                                     23.982221   \n",
+       "452                                      23.982221   \n",
+       "8                                         0.072986   \n",
+       "...                                            ...   \n",
+       "334                                       0.072986   \n",
+       "1264                                      0.072986   \n",
+       "1171                                      0.050000   \n",
+       "589                                       0.050000   \n",
+       "2342                                      0.072986   \n",
        "\n",
-       "      Relationship: smuggling, bribery, in  \\\n",
-       "0                                 0.055670   \n",
-       "1                                 0.050523   \n",
-       "2                                 0.052691   \n",
-       "3                                28.567443   \n",
-       "4                                31.116316   \n",
-       "...                                    ...   \n",
-       "2713                              0.053141   \n",
-       "2714                              0.052956   \n",
-       "2715                              0.052691   \n",
-       "2716                              0.299774   \n",
-       "2717                              0.299774   \n",
+       "      Relationship: laundering, overpricing, international  \\\n",
+       "1935                                              0.553233   \n",
+       "1583                                              0.053704   \n",
+       "2351                                              0.053704   \n",
+       "452                                               0.053704   \n",
+       "8                                                 0.053850   \n",
+       "...                                                    ...   \n",
+       "334                                               0.053850   \n",
+       "1264                                              0.053850   \n",
+       "1171                                              0.051786   \n",
+       "589                                               0.053772   \n",
+       "2342                                              0.053850   \n",
        "\n",
        "      Relationship: integrates, company, in  \\\n",
-       "0                                  0.071783   \n",
-       "1                                  0.051003   \n",
-       "2                                  0.050000   \n",
-       "3                                  0.065518   \n",
-       "4                                  0.058412   \n",
+       "1935                               0.089311   \n",
+       "1583                               0.050000   \n",
+       "2351                               0.050000   \n",
+       "452                                0.050000   \n",
+       "8                                  0.051025   \n",
        "...                                     ...   \n",
-       "2713                              23.986537   \n",
-       "2714                               0.092982   \n",
-       "2715                               0.050000   \n",
-       "2716                               0.070829   \n",
-       "2717                               0.070829   \n",
-       "\n",
-       "      Relationship: connection, business, in  \\\n",
-       "0                                   0.079073   \n",
-       "1                                   0.050000   \n",
-       "2                                   0.062643   \n",
-       "3                                   0.063843   \n",
-       "4                                   0.060998   \n",
-       "...                                      ...   \n",
-       "2713                                0.073292   \n",
-       "2714                               23.941857   \n",
-       "2715                                0.062643   \n",
-       "2716                                0.064280   \n",
-       "2717                                0.064280   \n",
+       "334                                0.051025   \n",
+       "1264                               0.051025   \n",
+       "1171                               0.050000   \n",
+       "589                               23.990746   \n",
+       "2342                               0.051025   \n",
        "\n",
-       "      Relationship: complaints, corruption, conspiracy  \\\n",
-       "0                                            34.442679   \n",
-       "1                                             0.051689   \n",
-       "2                                             0.055349   \n",
-       "3                                             0.060323   \n",
-       "4                                             0.056463   \n",
-       "...                                                ...   \n",
-       "2713                                          0.059816   \n",
-       "2714                                          0.063406   \n",
-       "2715                                          0.055349   \n",
-       "2716                                          0.060561   \n",
-       "2717                                          0.060561   \n",
+       "      Relationship: complaints, corruption, traffic  \\\n",
+       "1935                                       0.068496   \n",
+       "1583                                       0.055351   \n",
+       "2351                                       0.055351   \n",
+       "452                                        0.055351   \n",
+       "8                                          0.053534   \n",
+       "...                                             ...   \n",
+       "334                                        0.053534   \n",
+       "1264                                       0.053534   \n",
+       "1171                                       0.050037   \n",
+       "589                                        0.059230   \n",
+       "2342                                       0.053534   \n",
        "\n",
-       "      Relationship: suscribed, traffic, contract  \\\n",
-       "0                                       0.054766   \n",
-       "1                                       0.051122   \n",
-       "2                                       0.053101   \n",
-       "3                                       0.117900   \n",
-       "4                                       0.060142   \n",
-       "...                                          ...   \n",
-       "2713                                    0.053714   \n",
-       "2714                                    0.054355   \n",
-       "2715                                    0.053101   \n",
-       "2716                                    0.064907   \n",
-       "2717                                    0.064907   \n",
+       "      Relationship: sanctioned, sanction, evasion  \\\n",
+       "1935                                     0.075216   \n",
+       "1583                                     0.090587   \n",
+       "2351                                     0.090587   \n",
+       "452                                      0.090587   \n",
+       "8                                       16.497892   \n",
+       "...                                           ...   \n",
+       "334                                     16.497892   \n",
+       "1264                                    16.497892   \n",
+       "1171                                     0.050001   \n",
+       "589                                      0.051608   \n",
+       "2342                                    16.497892   \n",
        "\n",
-       "      Relationship: occupied, functions, sanctions  \\\n",
-       "0                                         0.063643   \n",
-       "1                                         0.050000   \n",
-       "2                                        23.987324   \n",
-       "3                                         0.064166   \n",
-       "4                                         0.057811   \n",
-       "...                                            ...   \n",
-       "2713                                      0.050000   \n",
-       "2714                                      0.061778   \n",
-       "2715                                     23.987324   \n",
-       "2716                                      0.062544   \n",
-       "2717                                      0.062544   \n",
+       "      Relationship: facilitators, colleagues, student  \\\n",
+       "1935                                         0.058883   \n",
+       "1583                                         0.050000   \n",
+       "2351                                         0.050000   \n",
+       "452                                          0.050000   \n",
+       "8                                            0.050000   \n",
+       "...                                               ...   \n",
+       "334                                          0.050000   \n",
+       "1264                                         0.050000   \n",
+       "1171                                         0.054220   \n",
+       "589                                          0.052662   \n",
+       "2342                                         0.050000   \n",
        "\n",
-       "      Relationship: overpricing, currency, illegal  \\\n",
-       "0                                         0.053862   \n",
-       "1                                         0.050509   \n",
-       "2                                         0.052330   \n",
-       "3                                        60.674413   \n",
-       "4                                         0.117512   \n",
-       "...                                            ...   \n",
-       "2713                                      0.052808   \n",
-       "2714                                      0.052354   \n",
-       "2715                                      0.052330   \n",
-       "2716                                     25.575909   \n",
-       "2717                                     25.575909   \n",
+       "      Relationship: designates, charge, rights  \\\n",
+       "1935                                  0.059063   \n",
+       "1583                                  0.050001   \n",
+       "2351                                  0.050001   \n",
+       "452                                   0.050001   \n",
+       "8                                     0.050001   \n",
+       "...                                        ...   \n",
+       "334                                   0.050001   \n",
+       "1264                                  0.050001   \n",
+       "1171                                  0.050000   \n",
+       "589                                   0.060380   \n",
+       "2342                                  0.050001   \n",
        "\n",
-       "      Relationship: laundering, international, trials  \\\n",
-       "0                                            0.057605   \n",
-       "1                                            0.050418   \n",
-       "2                                            0.053825   \n",
-       "3                                           17.229793   \n",
-       "4                                           37.838020   \n",
-       "...                                               ...   \n",
-       "2713                                         0.053850   \n",
-       "2714                                         0.054615   \n",
-       "2715                                         0.053825   \n",
-       "2716                                        20.649126   \n",
-       "2717                                        20.649126   \n",
+       "      Relationship: connection, business, in  \\\n",
+       "1935                                0.091499   \n",
+       "1583                                0.062470   \n",
+       "2351                                0.062470   \n",
+       "452                                 0.062470   \n",
+       "8                                   0.065366   \n",
+       "...                                      ...   \n",
+       "334                                 0.065366   \n",
+       "1264                                0.065366   \n",
+       "1171                                0.050705   \n",
+       "589                                 0.072165   \n",
+       "2342                                0.065366   \n",
        "\n",
        "      Relationship: members, family, enemies  \\\n",
-       "0                                   0.050000   \n",
-       "1                                   0.054442   \n",
-       "2                                   0.050000   \n",
-       "3                                   0.065414   \n",
-       "4                                   0.059851   \n",
+       "1935                                0.067527   \n",
+       "1583                                0.050000   \n",
+       "2351                                0.050000   \n",
+       "452                                 0.050000   \n",
+       "8                                   0.050000   \n",
        "...                                      ...   \n",
-       "2713                                0.050000   \n",
-       "2714                                0.052002   \n",
-       "2715                                0.050000   \n",
-       "2716                                0.051986   \n",
-       "2717                                0.051986   \n",
+       "334                                 0.050000   \n",
+       "1264                                0.050000   \n",
+       "1171                               18.039779   \n",
+       "589                                 0.050000   \n",
+       "2342                                0.050000   \n",
        "\n",
-       "      Relationship: facilitators, extortion, friends  \\\n",
-       "0                                           0.052458   \n",
-       "1                                          15.039365   \n",
-       "2                                           0.050000   \n",
-       "3                                           0.055578   \n",
-       "4                                           0.051573   \n",
-       "...                                              ...   \n",
-       "2713                                        0.051797   \n",
-       "2714                                        0.050000   \n",
-       "2715                                        0.050000   \n",
-       "2716                                        0.070271   \n",
-       "2717                                        0.070271   \n",
+       "      Relationship: smuggling, bribery, currency  \\\n",
+       "1935                                   42.147305   \n",
+       "1583                                    0.052716   \n",
+       "2351                                    0.052716   \n",
+       "452                                     0.052716   \n",
+       "8                                       0.053286   \n",
+       "...                                          ...   \n",
+       "334                                     0.053286   \n",
+       "1264                                    0.053286   \n",
+       "1171                                    0.052556   \n",
+       "589                                     0.053006   \n",
+       "2342                                    0.053286   \n",
        "\n",
-       "      Relationship: designates, colleagues, charge  \n",
-       "0                                         0.053457  \n",
-       "1                                         0.050809  \n",
-       "2                                         0.050000  \n",
-       "3                                         0.061222  \n",
-       "4                                         0.055335  \n",
-       "...                                            ...  \n",
-       "2713                                      0.063327  \n",
-       "2714                                      0.051090  \n",
-       "2715                                      0.050000  \n",
-       "2716                                      0.058718  \n",
-       "2717                                      0.058718  \n",
+       "      Relationship: conspiracy, suscribed, contract  \n",
+       "1935                                      42.766569  \n",
+       "1583                                       0.052951  \n",
+       "2351                                       0.052951  \n",
+       "452                                        0.052951  \n",
+       "8                                          0.052058  \n",
+       "...                                             ...  \n",
+       "334                                        0.052058  \n",
+       "1264                                       0.052058  \n",
+       "1171                                       0.050917  \n",
+       "589                                        0.056433  \n",
+       "2342                                       0.052058  \n",
        "\n",
-       "[2718 rows x 12 columns]"
+       "[2718 rows x 11 columns]"
       ]
      },
      "execution_count": 17,
@@ -1356,7 +1403,7 @@
    ],
    "source": [
     "# we've reorganized 68 relationships into N topics and we see it has understood the semantics correctly\n",
-    "y = g2._get_target('nodes')\n",
+    "y = g2.get_matrix(target=True)\n",
     "y"
    ]
   },
@@ -1378,7 +1425,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x360 with 1 Axes>"
       ]
@@ -1393,6 +1440,58 @@
     "y.plot(kind='hist', figsize=(10,5))  "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "46d25a40-deb2-4aac-96c6-d5b3e84a8d1d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2._nodes['rel_topic'] = [y.columns[k].replace('Relationship: ', '') for k in y.values.argmax(1)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "e81a96ce-407a-47ad-a31f-d6ac1798c4d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1935    conspiracy, suscribed, contract\n",
+       "1583     occupied, functions, sanctions\n",
+       "2351     occupied, functions, sanctions\n",
+       "452      occupied, functions, sanctions\n",
+       "8         sanctioned, sanction, evasion\n",
+       "                     ...               \n",
+       "334       sanctioned, sanction, evasion\n",
+       "1264      sanctioned, sanction, evasion\n",
+       "1171           members, family, enemies\n",
+       "589             integrates, company, in\n",
+       "2342      sanctioned, sanction, evasion\n",
+       "Name: rel_topic, Length: 2718, dtype: object"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g2._nodes['rel_topic']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "feba6d2b-12a9-4143-ba22-7a3fadd3d430",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "g2 = g2.nodes(g2._nodes, g2._node)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "53574e47",
@@ -1403,143 +1502,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "id": "ce53de1d",
-   "metadata": {
-    "scrolled": false
-   },
+   "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Agent 1</th>\n",
-       "      <th>Relationship</th>\n",
-       "      <th>Agent 2</th>\n",
-       "      <th>Source</th>\n",
-       "      <th>Evidence</th>\n",
-       "      <th>_distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2497</th>\n",
-       "      <td>Gustavo Adolfo Hernández Frieri (USA, Colombia)</td>\n",
-       "      <td>International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection</td>\n",
-       "      <td>Global Securities Trade Finance (Cayman Islands)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.</td>\n",
-       "      <td>12.718029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2498</th>\n",
-       "      <td>Abraham Edgardo Ortega (Venezuela)</td>\n",
-       "      <td>International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection</td>\n",
-       "      <td>Global Securities Trade Finance (Cayman Islands)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.</td>\n",
-       "      <td>12.718029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>109</th>\n",
-       "      <td>Alex Nain Saab Morán (Colombia)</td>\n",
-       "      <td>International trials. Money laundering. Fraud</td>\n",
-       "      <td>Devis José Mendoza (Colombia)</td>\n",
-       "      <td>Recognized communication media (trustable)</td>\n",
-       "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
-       "      <td>13.068510</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1744</th>\n",
-       "      <td>Nervis Gerardo Villalobos Cárdenas (Venezuela)</td>\n",
-       "      <td>International trials. Money laundering</td>\n",
-       "      <td>Grupo Swissinvest (No information available)</td>\n",
-       "      <td>Court document</td>\n",
-       "      <td>Judicial investigation in Spain points that through \"the structure of transnational character\" of Swissinvest group, \"money laundering operations were made\" inside and outside Spain to  “raise capital from crimes of corruption” commited through Pdvsa.</td>\n",
-       "      <td>13.110929</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>659</th>\n",
-       "      <td>Diosdado Cabello Rondón (Venezuela)</td>\n",
-       "      <td>Complaints for corruption</td>\n",
-       "      <td>Pedro Fritz Morejon Carrillo (Venezuela)</td>\n",
-       "      <td>Recognized communication media (trustable)</td>\n",
-       "      <td>Accused of laundering around USD $1.300 millions in Panama, Costa Rica, Madrid and USA through companies, using money from corruption, drug traffic and terrorism.</td>\n",
-       "      <td>13.302184</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
       "text/plain": [
-       "                                              Agent 1  \\\n",
-       "2497  Gustavo Adolfo Hernández Frieri (USA, Colombia)   \n",
-       "2498               Abraham Edgardo Ortega (Venezuela)   \n",
-       "109                   Alex Nain Saab Morán (Colombia)   \n",
-       "1744   Nervis Gerardo Villalobos Cárdenas (Venezuela)   \n",
-       "659               Diosdado Cabello Rondón (Venezuela)   \n",
-       "\n",
-       "                                                                                       Relationship  \\\n",
-       "2497  International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection   \n",
-       "2498  International trials. Money laundering. Bribery. Illegal Currency traffic. Company connection   \n",
-       "109                                                   International trials. Money laundering. Fraud   \n",
-       "1744                                                         International trials. Money laundering   \n",
-       "659                                                                       Complaints for corruption   \n",
-       "\n",
-       "                                               Agent 2  \\\n",
-       "2497  Global Securities Trade Finance (Cayman Islands)   \n",
-       "2498  Global Securities Trade Finance (Cayman Islands)   \n",
-       "109                      Devis José Mendoza (Colombia)   \n",
-       "1744      Grupo Swissinvest (No information available)   \n",
-       "659           Pedro Fritz Morejon Carrillo (Venezuela)   \n",
-       "\n",
-       "                                          Source  \\\n",
-       "2497                              Court document   \n",
-       "2498                              Court document   \n",
-       "109   Recognized communication media (trustable)   \n",
-       "1744                              Court document   \n",
-       "659   Recognized communication media (trustable)   \n",
-       "\n",
-       "                                                                                                                                                                                                                                                          Evidence  \\\n",
-       "2497                                                                                                                                  The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.    \n",
-       "2498                                                                                                                                  The Operation Money Flight case mentions that Frieri laundered money from Ortega through a false structure of mutual funds.    \n",
-       "109                                                                                        79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.   \n",
-       "1744  Judicial investigation in Spain points that through \"the structure of transnational character\" of Swissinvest group, \"money laundering operations were made\" inside and outside Spain to  “raise capital from crimes of corruption” commited through Pdvsa.    \n",
-       "659                                                                                           Accused of laundering around USD $1.300 millions in Panama, Costa Rica, Madrid and USA through companies, using money from corruption, drug traffic and terrorism.     \n",
-       "\n",
-       "      _distance  \n",
-       "2497  12.718029  \n",
-       "2498  12.718029  \n",
-       "109   13.068510  \n",
-       "1744  13.110929  \n",
-       "659   13.302184  "
+       "2498              smuggling, bribery, currency\n",
+       "2497              smuggling, bribery, currency\n",
+       "2482                  connection, business, in\n",
+       "1744    laundering, overpricing, international\n",
+       "2487                  connection, business, in\n",
+       "Name: rel_topic, dtype: object"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "res, query_vector = g2.search('money laundering', top_n=5)\n",
-    "res"
+    "res.rel_topic"
    ]
   },
   {
@@ -1547,7 +1532,7 @@
    "id": "d81d93d3",
    "metadata": {},
    "source": [
-    "# Search to Graph\n",
+    "## Search to Graph\n",
     "\n",
     "Pull in neighborhood data from a given search\n",
     "* the resulting graph will contain Agents connected to Agents that have been involved in Money Laundering (or whatever you wish to search for)\n",
@@ -1556,17 +1541,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "id": "01278b51",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"2e17769c-0082-4f83-be09-512b651e7804\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=d35eb79e8a4d4adb9c06802a85be56e9&type=arrow&viztoken=519f25ed-1907-4e9b-8e47-726ab151baa4&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275324&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#2e17769c-0082-4f83-be09-512b651e7804\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=5392c6cefae944b38d192740ece8db4a&type=arrow&viztoken=d8174842-dda1-4b4a-9ce9-973a97e2e136&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813586&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1578,7 +1580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "id": "69143805",
    "metadata": {},
    "outputs": [
@@ -1608,6 +1610,7 @@
        "      <th>Agent 2</th>\n",
        "      <th>Source</th>\n",
        "      <th>Evidence</th>\n",
+       "      <th>rel_topic</th>\n",
        "      <th>_distance</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1619,6 +1622,7 @@
        "      <td>Hermágoras González Polanco (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>Colombian druf trafficker, leader of Guajira Cartel, according to the Narcogram, he is linked to Tareck El Aissami.</td>\n",
+       "      <td>complaints, corruption, traffic</td>\n",
        "      <td>13.883661</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1628,6 +1632,7 @@
        "      <td>Robinson Ruíz Guerrero (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>13.998826</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1637,6 +1642,7 @@
        "      <td>Luis Alberto Saab Morán (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>14.068014</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1646,6 +1652,7 @@
        "      <td>Jaime Alberto Marín Zamora (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>Denounces links between drug lords, scam groups, money laundering and a network of SAIME offices. Ditter José Marcano was pointed.</td>\n",
+       "      <td>complaints, corruption, traffic</td>\n",
        "      <td>14.180918</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1655,6 +1662,7 @@
        "      <td>Amir Luis Saab Morán (Colombia)</td>\n",
        "      <td>Recognized communication media (trustable)</td>\n",
        "      <td>79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.</td>\n",
+       "      <td>laundering, overpricing, international</td>\n",
        "      <td>14.181194</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1697,15 +1705,15 @@
        "668                                        Denounces links between drug lords, scam groups, money laundering and a network of SAIME offices. Ditter José Marcano was pointed.   \n",
        "110   79th Court of Guarantee control of Colombia investigates money laundering, conspiracy to commit a crime, illicit enrichment, fake export or import and aggravated scam.   \n",
        "\n",
-       "      _distance  \n",
-       "2233  13.883661  \n",
-       "108   13.998826  \n",
-       "111   14.068014  \n",
-       "668   14.180918  \n",
-       "110   14.181194  "
+       "                                   rel_topic  _distance  \n",
+       "2233         complaints, corruption, traffic  13.883661  \n",
+       "108   laundering, overpricing, international  13.998826  \n",
+       "111   laundering, overpricing, international  14.068014  \n",
+       "668          complaints, corruption, traffic  14.180918  \n",
+       "110   laundering, overpricing, international  14.181194  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1717,17 +1725,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "id": "09685c42",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"572e7b0f-4a24-4e4d-a646-3e5a6dd6544f\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=79ce3fd4f80b4480bbb3cc90e3fdbc30&type=arrow&viztoken=a6331db5-c36e-46f5-b3cc-30a634144316&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275327&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#572e7b0f-4a24-4e4d-a646-3e5a6dd6544f\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=710e32ad2c574cf1a4ae0e11e13909ab&type=arrow&viztoken=cc06093d-e095-4474-b5d6-db43f7b33759&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813588&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1738,7 +1763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 26,
    "id": "bcfa273d",
    "metadata": {},
    "outputs": [
@@ -1782,8 +1807,8 @@
        "      <td>Included him on the list of sanctioned officials for being \"responsibles or accomplices of serious violations\" to Human Rights, \"important acts of corruption or both\".</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1191</th>\n",
-       "      <td>Jesús Rafael Suárez Chourio (Venezuela)</td>\n",
+       "      <th>2363</th>\n",
+       "      <td>Xavier Antonio Moreno Reandes (Venezuela)</td>\n",
        "      <td>Sanctioned by</td>\n",
        "      <td>On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned</td>\n",
        "    </tr>\n",
@@ -1795,15 +1820,15 @@
        "                                               Agent 1   Relationship  \\\n",
        "1384  Katherine Nayartih Haringhton Padrón (Venezuela)  Sanctioned by   \n",
        "1265       José Miguel Montoanda Rodríguez (Venezuela)  Sanctioned by   \n",
-       "1191           Jesús Rafael Suárez Chourio (Venezuela)  Sanctioned by   \n",
+       "2363         Xavier Antonio Moreno Reandes (Venezuela)  Sanctioned by   \n",
        "\n",
        "                                                                                                                                                                                    Evidence  \n",
        "1384  The only non military officer included on the decree 03/09/2015, in which President Barack Obama suspended visas and froze assets of government officials, for Human Rights violation.  \n",
        "1265               Included him on the list of sanctioned officials for being \"responsibles or accomplices of serious violations\" to Human Rights, \"important acts of corruption or both\".    \n",
-       "1191                                                                                              On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned  "
+       "2363                                                                                              On June 25th, 2018 the European Union included him on the list of  11 officials sanctioned  "
       ]
      },
-     "execution_count": 23,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1815,7 +1840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 27,
    "id": "653acdfc",
    "metadata": {},
    "outputs": [
@@ -1871,8 +1896,8 @@
        "      <td>National Audience and the Anti - Corruption Prosecutor of Spain investigate alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1126</th>\n",
-       "      <td>Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)</td>\n",
+       "      <th>2252</th>\n",
+       "      <td>Técnicas Reunidas Terca Ca (Venezuela)</td>\n",
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1881,8 +1906,8 @@
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2252</th>\n",
-       "      <td>Técnicas Reunidas Terca Ca (Venezuela)</td>\n",
+       "      <th>1126</th>\n",
+       "      <td>Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)</td>\n",
        "      <td>National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1906,9 +1931,9 @@
        "1165                                                Javier Andrés Alvarado Ochoa (Venezuela)   \n",
        "1330                                                       Juan Carlos Torres Inclán (Spain)   \n",
        "676                                                                    Duro Felguera (Spain)   \n",
-       "1126  Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)   \n",
-       "1468                                          Luís Barrios Melean (No information available)   \n",
        "2252                                                  Técnicas Reunidas Terca Ca (Venezuela)   \n",
+       "1468                                          Luís Barrios Melean (No information available)   \n",
+       "1126  Ingeniería Gestión de Proyectos de Energía, C.A. (Ingespre) (No information available)   \n",
        "2283                                                 Víctor Eduardo Aular Blanco (Venezuela)   \n",
        "360                                                  Carlos Eduardo Borges Polar (Venezuela)   \n",
        "\n",
@@ -1918,14 +1943,14 @@
        "1165                      National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
        "1330                      National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
        "676                    National Audience and the Anti - Corruption Prosecutor of Spain investigate alleged bribery and money laundering. Involved: Ministry of Energy and Mining , CORPOELEC.  \n",
-       "1126                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
-       "1468                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
        "2252                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
+       "1468                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
+       "1126                       National Audience and Anti - corruption Prosecutor of Spain investigates alleged bribery and money laundering. Involved Ministry of Energy and Mining , CORPOELEC.  \n",
        "2283                                                                             Member of the Strategic Execution Committee of the Financial area. Official Gazzette 39.182, May 20th, 2009.  \n",
        "360                      Director of the Internal Operations Office (in charge), of the Sectoral Vice - Presidency of Public Works and Services. Official Gazzette 41.182 of June 28th, 2017.  "
       ]
      },
-     "execution_count": 24,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1937,17 +1962,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 28,
    "id": "f103792c",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"fe00684e-59c9-47c2-aedf-be8a43b9ade8\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=36be92cecd6e44b5a938409ea468222a&type=arrow&viztoken=5cbc4642-597c-4eea-97ed-4dd9e4324d86&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275329&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
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+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
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+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=a936df2d05dd4196addee7b8527d6a46&type=arrow&viztoken=d4ed2c99-1df4-4a35-a032-f398f4580209&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813591&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 28,
      "metadata": {},
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@@ -1958,38 +2000,72 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "id": "423315b6",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"600b4be7-d557-4064-8ed7-da99f26b09df\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=3d99ac0015b8429b9d9e6b889813a3c9&type=arrow&viztoken=d2bc9d5a-51d3-4d74-8be8-a6fdf9798cf2&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275332&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#600b4be7-d557-4064-8ed7-da99f26b09df\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=b810f623437f48b4aa82a3b005810eed&type=arrow&viztoken=bccdca8f-215c-41b7-a1c5-28f14f95acb5&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813594&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "g2.search_graph('drug trafficking').plot(render=RENDER)"
+    "g2.search_graph('drug trafficking').dbscan().plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 32,
    "id": "e2604442",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"4f8886ea-30a2-440f-b55b-c08ea4e2615b\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=345f62d275524a62811aafc86801aebd&type=arrow&viztoken=4da3512b-4f7d-488f-ab63-30e6133d3fc0&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275334&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#4f8886ea-30a2-440f-b55b-c08ea4e2615b\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=fc826e3132da4cd391f0b4c5aeea939e&type=arrow&viztoken=07862a3c-a473-4e11-b8d1-750a6d4dba93&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813596&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2000,23 +2076,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 33,
    "id": "b4f5ebcf",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"0418b4ec-53d0-4e1c-890f-3e9dbdfd7e21\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=741fca805dda4482993aec4027a2bb85&type=arrow&viztoken=8f73cbe5-21da-419e-a92c-493fb9fc492d&usertag=8a6d667e-pygraphistry-0.28.6+133.g649ac53.dirty&splashAfter=1674275337&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#0418b4ec-53d0-4e1c-890f-3e9dbdfd7e21\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
       "text/plain": [
-       "'https://hub.graphistry.com/graph/graph.html?dataset=3ea11287101d47d7ac6748a04a669e98&type=arrow&viztoken=be9ab400-53d0-4b08-9a1e-c0cafc7fc5b7&usertag=8a6d667e-pygraphistry-0.28.4+72.g2a02e2b.dirty&splashAfter=1668813599&info=true'"
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# paste in url to see in new tab \n",
     "g2.search_graph('oil and energy companies').plot(render=RENDER)"
    ]
   },
@@ -2034,14 +2126,6 @@
     "\n",
     "Join the Graphistry-Community Slack! \n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "91380fec",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/demos/ai/OSINT/jack-donations.ipynb b/demos/ai/OSINT/jack-donations.ipynb
index 7abddb9f20..1f02d64a2d 100644
--- a/demos/ai/OSINT/jack-donations.ipynb
+++ b/demos/ai/OSINT/jack-donations.ipynb
@@ -6,30 +6,39 @@
    "metadata": {},
    "source": [
     "________________\n",
-    "# Jack's money went here. \n",
+    "# Jack's Money Went Here \n",
     "\n",
-    "## Where is twitter likely to lean more and less now that  he's leaving? Where will there be matching donations?\n",
+    "Where is twitter likely to lean more and less now that he's leaving? Where will there be matching donations?\n",
     "\n",
-    "Jack Dorsey is pledging over 466 million dollars and wants matching donations. His rational is simple -- billionaires can spare a tithe to help communities and people, and compounded over a few hundred of his closest friends, have a tremendous impact. \n",
+    "Jack Dorsey is pledging over 466 million dollars and wants matching donations. His rational is simple -- billionaires can spare a tithe to help communities and people, and compounded over a few hundred of his closest friends, have a tremendous impact. What edifice could be built with donations to these entities? What do their service offerings look like when seen as a whole? What are their moving parts?\n",
     "\n",
     "This dataset is based off of the tweet https://twitter.com/jack/status/1247616214769086465 which lists pledged organizations and their donation. \n",
-    "__________________________\n",
-    "### We will learn how to quickly data science this dataset. We will select feature representations and visualize the resulting graph using UMAP.\n",
+    "__________________________________________________________________\n",
+    "\n",
+    "We will learn how to quickly data science this dataset. We will select feature representations and visualize the resulting graph using UMAP.\n",
     "\n",
     "Featurization is the foundation of datascience. Likewise, Graph Thinking requires edges between nodes. Many times the data we have from databases/dataframes is tabular and row like -- with no incling of an edge table. This does *not* have to be an impediment for *Graph Thinking and materialization of datascience workflows*. \n",
     "\n",
     "UMAP is a powerful tool that projects complex, heterogeneous data coming from potentially many different distributions, down to lower dimensional embeddings and projections. The embedding estimates similarity between the rows, or nodes of the data, and thus forms a graph. \n",
     "\n",
     "Standardizing a feature set across the databases used in every modern company and then sending it to UMAP serves as a powerful graph generation tool.  \n",
-    "____________________________\n",
-    "Here we demonstrate how to Featurize and use UMAP to generate implicit graphs. The features may then be used in subsequent modeling using your favorite libraries -- sklearn, tensorflow, pytorch[, geometric, lightening, ...], cuGraph, DGL, etc. We demonstrate 4 featurization methods -- (latent embeddings, transformer embeddings, ngrams embeddings, one-hot encodings) that may be mixed and used to make different features for different columns, automatically. \n",
+    "__________________________________________________________________\n",
+    "\n",
+    "Here we demonstrate how to Featurize and use UMAP to generate implicit graphs. The features may then be used in subsequent modeling using your favorite libraries -- sklearn, tensorflow, pytorch[, geometric, lightening, ...], cuGraph, DGL, etc. We demonstrate 4 featurization methods -- \n",
+    "\n",
+    "* latent embeddings, \n",
+    "* transformer embeddings, \n",
+    "* ngrams embeddings, \n",
+    "* one-hot encodings\n",
     "\n",
-    "Furthermore, when we `g.plot()` the results, it is layed out according to the 2-dimensional UMAP projection of the data -- nearness in that projection represents nearness in the resulting features. We will test this empiracally using the different featurization methods for textual, numeric and categorical data. "
+    "that may be mixed and used to make different features for different columns, automatically. \n",
+    "\n",
+    "Furthermore, when we `g.plot()` the results, it is layed out according to the 2-dimensional UMAP projection of the data -- nearness in that projection represents nearness in the resulting features. We will test this empirically using the different featurization methods for textual, numeric and categorical data. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "a069ef73",
    "metadata": {},
    "outputs": [],
@@ -39,17 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "97443b1c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# cd .."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "b7de987a",
    "metadata": {},
    "outputs": [],
@@ -66,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "461a22ec",
    "metadata": {},
    "outputs": [],
@@ -76,7 +75,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "id": "950f6310",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RENDER=False  # set to True for inline Graphistry Plots"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
    "id": "90875a39",
    "metadata": {},
    "outputs": [],
@@ -89,32 +98,177 @@
    "id": "9acb2823",
    "metadata": {},
    "source": [
-    "## Data cleaning\n",
+    "## Data loading & cleaning\n",
     "We already added the dataset from the twitter link, downloading a copy (as of May 2022) from the google drive. We need to remove the first few rows to make a valid dataframe. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "id": "0ffe9b64",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Date</th>\n",
+       "      <th>Amount</th>\n",
+       "      <th>Category</th>\n",
+       "      <th>Grantee</th>\n",
+       "      <th>Twitter</th>\n",
+       "      <th>Link</th>\n",
+       "      <th>Why?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>3/21/2022</td>\n",
+       "      <td>$2,000,000</td>\n",
+       "      <td>Social Justice</td>\n",
+       "      <td>REFORM Alliance</td>\n",
+       "      <td>@REFORM</td>\n",
+       "      <td>https://reformalliance.com</td>\n",
+       "      <td>REFORM Alliance is committed to transforming t...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>World Central Kitchen</td>\n",
+       "      <td>@WCKitchen</td>\n",
+       "      <td>https://wck.org/</td>\n",
+       "      <td>World Central Kitchen is serving thousands of ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Sunflower of Peace</td>\n",
+       "      <td>@SunflowerFund</td>\n",
+       "      <td>https://www.sunflowerofpeace.com</td>\n",
+       "      <td>Sunflower of Peace is providing medical and hu...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Razom, Inc.</td>\n",
+       "      <td>@razomforukraine</td>\n",
+       "      <td>https://razomforukraine.org</td>\n",
+       "      <td>Razom is supporting Ukrainian people in their ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3/10/2022</td>\n",
+       "      <td>$1,000,000</td>\n",
+       "      <td>Crisis Relief</td>\n",
+       "      <td>Nova Ukraine</td>\n",
+       "      <td>@novaukraine</td>\n",
+       "      <td>https://novaukraine.org</td>\n",
+       "      <td>Nova Ukraine, a Bay Area-based humanitarian no...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Date     Amount         Category                Grantee  \\\n",
+       "0  3/21/2022  $2,000,000  Social Justice        REFORM Alliance   \n",
+       "1  3/10/2022  $1,000,000   Crisis Relief  World Central Kitchen   \n",
+       "2  3/10/2022  $1,000,000   Crisis Relief     Sunflower of Peace   \n",
+       "3  3/10/2022  $1,000,000   Crisis Relief            Razom, Inc.   \n",
+       "4  3/10/2022  $1,000,000   Crisis Relief           Nova Ukraine   \n",
+       "\n",
+       "            Twitter                              Link  \\\n",
+       "0           @REFORM        https://reformalliance.com   \n",
+       "1        @WCKitchen                  https://wck.org/   \n",
+       "2    @SunflowerFund  https://www.sunflowerofpeace.com   \n",
+       "3  @razomforukraine       https://razomforukraine.org   \n",
+       "4      @novaukraine           https://novaukraine.org   \n",
+       "\n",
+       "                                                Why?  \n",
+       "0  REFORM Alliance is committed to transforming t...  \n",
+       "1  World Central Kitchen is serving thousands of ...  \n",
+       "2  Sunflower of Peace is providing medical and hu...  \n",
+       "3  Razom is supporting Ukrainian people in their ...  \n",
+       "4  Nova Ukraine, a Bay Area-based humanitarian no...  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/f8d7b8f279a5ffbd710c301fc402ec43/raw/95a722f5c65812322eaf085c1123b58d3ec3da3a/jack_donations.csv')\n",
     "df = df.fillna('')\n",
     "columns = df.iloc[3].values  \n",
     "ndf = pd.DataFrame(df[4:].values, columns=columns)\n",
-    "ndf"
+    "ndf.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "50f59d83",
+   "metadata": {},
+   "source": [
+    "Notice that the Category labels are mixed and interwoven. \n",
+    "We will show how to standardize it without having to do data cleaning or mapping"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "e52b4e5d",
+   "execution_count": 7,
+   "id": "ac1b493e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Social Justice', 'Crisis Relief',\n",
+       "       'COVID-19, Girls Health & Education',\n",
+       "       'Social Justice, Girls Health & Education', 'COVID-19',\n",
+       "       'Social Justice, COVID-19', 'Girls Health & Education',\n",
+       "       'UBI, Social Justice', 'Girls Health & Education, COVID-19',\n",
+       "       'COVID-19, Social Justice', 'UBI',\n",
+       "       'COVID-19, Social Justice, Girls Health & Education',\n",
+       "       'Girls Health & Education; COVID-19', 'COVID-19; Social Justice',\n",
+       "       'Girls Health & Education; Social Justice',\n",
+       "       'COVID-19; Girls Health & Education', 'UBI; COVID-19',\n",
+       "       'COVID-19 & Social Justice',\n",
+       "       'Social Justice, UBI, Girls Health & Education', 'COVID-19, UBI',\n",
+       "       \"Where it's needed most\", 'COVID-19 '], dtype=object)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "ndf.Category.unique()"
+    "ndf.Category.unique()  # seems like there are 4-6 topics here"
    ]
   },
   {
@@ -122,24 +276,40 @@
    "id": "b454348e",
    "metadata": {},
    "source": [
-    "# Create the Graph\n",
+    "# Featurize\n",
     "\n",
-    "We will use `g.umap` to featurize and create edges. The details of how UMAP is able to create edges between rows in the data is beyond the scope of this tutorial, however, suffic it to say, it is automatically inferring a network of related entities based off of their column features. \n",
+    "We will use `g.umap` to featurize and create edges. The details of how UMAP is able to create edges between rows in the data is beyond the scope of this tutorial, however, suffic it to say, it is automatically inferring a network of related entities based off their column features. \n",
     "\n",
     "Here is the dataset as graph, \n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "c986ff93",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "g = graphistry.nodes(ndf).bind(point_title='Category').umap()\n",
-    "g.plot()  # fly around the clusters and click on nodes and edges. "
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 0) in UMAP fit, as it is not one dimensionalOMP: Info #273: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=ae47f85d8eaa4edfa6a3bc0c1124e313&type=arrow&viztoken=41ef5acc-41e3-49e9-99ac-2a57158b31c8&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009074&info=true&play=0'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g = graphistry.nodes(ndf).umap()\n",
+    "g.bind(point_title='Grantee').plot(render=RENDER)  # fly around the clusters and click on nodes and edges. "
    ]
   },
   {
@@ -147,7 +317,7 @@
    "id": "255c8496",
    "metadata": {},
    "source": [
-    "## The above featurized every column over the entire datase. Exploring the nodes and their nearest neighbors indeed clusters similar rows -- all in two lines of code!"
+    "The above featurized every column over the entire datase. Exploring the nodes and their nearest neighbors indeed clusters similar rows -- all in two lines of code!"
    ]
   },
   {
@@ -155,17 +325,39 @@
    "id": "d76e628d",
    "metadata": {},
    "source": [
-    "# Some light analysis and enrichment \n",
+    "## Light analysis and enrichment \n",
     "\n",
     "Lets convert Amount column into numeric, and then see who is getting what by category and grantee."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "a8ced06c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      $2,000,000\n",
+       "1      $1,000,000\n",
+       "2      $1,000,000\n",
+       "3      $1,000,000\n",
+       "4      $1,000,000\n",
+       "          ...    \n",
+       "280       $13,333\n",
+       "281    $2,000,000\n",
+       "282    $1,000,000\n",
+       "283    $2,100,000\n",
+       "284      $100,000\n",
+       "Name:  Amount , Length: 285, dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "#ndf.columns\n",
     "ndf[' Amount ']"
@@ -173,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "8077b2d0",
    "metadata": {},
    "outputs": [],
@@ -193,10 +385,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "b0e0c683",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0      2000000.0\n",
+       "1      1000000.0\n",
+       "2      1000000.0\n",
+       "3      1000000.0\n",
+       "4      1000000.0\n",
+       "         ...    \n",
+       "280      13333.0\n",
+       "281    2000000.0\n",
+       "282    1000000.0\n",
+       "283    2100000.0\n",
+       "284     100000.0\n",
+       "Name: $ amount, Length: 285, dtype: float64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "ndf['$ amount']"
    ]
@@ -206,17 +420,51 @@
    "id": "ac95f782",
    "metadata": {},
    "source": [
-    "## Many of these categories are not distinct. But due to data coming in with different notation, it seems distinct. \n",
+    "Many of these categories are not distinct. But due to data coming in with different notation, it seems distinct. \n",
     "\n",
     "We will show in the next section how to deal with this by using the graphistry pipeline to convert the `Category` into a latent target that organizes the labels.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "6e4fcbaf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Category\n",
+       "COVID-19                                              $153,882,590.0\n",
+       "COVID-19                                               $85,019,328.0\n",
+       "COVID-19 & Social Justice                                 $505,468.0\n",
+       "COVID-19, Girls Health & Education                      $4,265,000.0\n",
+       "COVID-19, Social Justice                                $1,800,000.0\n",
+       "COVID-19, Social Justice, Girls Health & Education        $250,000.0\n",
+       "COVID-19, UBI                                           $8,000,000.0\n",
+       "COVID-19; Girls Health & Education                      $9,920,000.0\n",
+       "COVID-19; Social Justice                                $5,090,080.0\n",
+       "Crisis Relief                                           $7,500,000.0\n",
+       "Girls Health & Education                               $30,300,000.0\n",
+       "Girls Health & Education, COVID-19                      $1,250,000.0\n",
+       "Girls Health & Education; COVID-19                     $12,000,000.0\n",
+       "Girls Health & Education; Social Justice                $2,500,000.0\n",
+       "Social Justice                                         $84,119,845.0\n",
+       "Social Justice, COVID-19                                  $300,000.0\n",
+       "Social Justice, Girls Health & Education                $9,934,000.0\n",
+       "Social Justice, UBI, Girls Health & Education           $1,100,000.0\n",
+       "UBI                                                    $10,210,000.0\n",
+       "UBI, Social Justice                                     $1,000,000.0\n",
+       "UBI; COVID-19                                          $35,000,000.0\n",
+       "Where it's needed most                                  $3,000,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "current_funding_by_category = ndf.groupby('Category')['$ amount'].sum()\n",
     "current_funding_by_category.map(lambda x: '${:3,}'.format(x))"
@@ -224,21 +472,66 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "59b71456",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(15,7))\n",
-    "current_funding_by_category.plot(kind='bar', rot=52)"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Category'>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plt.figure(figsize=(10,5))\n",
+    "current_funding_by_category.plot(kind='bar', rot=82)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "382780f5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Grantee\n",
+       "Vital Strategies: Resolve To Save Lives    $38,000,000.0\n",
+       "CORE: Community Organized Relief Effort    $30,000,000.0\n",
+       "Clara Lionel Foundation                    $28,877,000.0\n",
+       "Reinvent Stockton Foundation               $18,000,000.0\n",
+       "CARE                                       $16,000,000.0\n",
+       "Give2SF                                    $15,000,000.0\n",
+       "Open Research Lab Income Project           $15,000,000.0\n",
+       "REFORM Alliance                            $12,000,000.0\n",
+       "World Central Kitchen                      $11,585,500.0\n",
+       "Indiana University Foundation              $10,025,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "grantees = ndf.groupby('Grantee')['$ amount'].sum()\n",
     "grants_sorted = grantees.sort_values()\n",
@@ -248,78 +541,123 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "d7d0ff87",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Grantee'>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# largest grants\n",
-    "fig = plt.figure(figsize=(15,7))\n",
+    "fig = plt.figure(figsize=(10,5))\n",
     "ax= plt.subplot()\n",
-    "# ax.set_xticks(range(len(label_list)))\n",
-    "# ax.set_xticklabels(label_list, rotation=19)\n",
     "res = grants_sorted[-10:]\n",
     "\n",
-    "res.plot(kind='bar', rot=52)"
+    "res.plot(kind='bar', rot=49)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "14330bfc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Grantee'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x360 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# smallest grants\n",
-    "fig = plt.figure(figsize=(15,7))\n",
+    "fig = plt.figure(figsize=(10,5))\n",
     "ax= plt.subplot()\n",
-    "# ax.set_xticks(range(len(label_list)))\n",
-    "# ax.set_xticklabels(label_list, rotation=19)\n",
     "res = grants_sorted[:10]\n",
     "\n",
-    "res.plot(kind='bar', rot = 52)"
+    "res.plot(kind='bar', rot = 29)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "2ad231a8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Total Pledged $466,946,311.0'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "'Total Pledged ${:3,}'.format(current_funding_by_category.sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "217026ee",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Total Pledged $466,946,311.0'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# and this should be the same too\n",
     "'Total Pledged ${:3,}'.format(grantees.sum())"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "50f59d83",
-   "metadata": {},
-   "source": [
-    "## Notice that the Category labels are mixed and interwoven \n",
-    "We will show how judicious choice of parameters can standardize it without having to do data cleaning or mapping"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ac1b493e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ndf.Category.unique()  # seems like there are 4-6 topics here"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "b0565e61",
@@ -339,31 +677,12 @@
     "____________________________"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "22c6b5c7",
-   "metadata": {},
-   "source": [
-    "In the following, we concentrate on the textual `Why?` column as it describes the row/entity in question. Further, we select `y='Category'` as a target variable, and will encode it using a Topic Model as well as standard One-Hot-Encoding.\n",
-    "\n",
-    "\n",
-    "In the following we will show how to encode textual and categorical data using \n",
-    "\n",
-    "1) Topic Models\n",
-    "\n",
-    "2) Sentence Transformers\n",
-    "\n",
-    "3) Ngrams \n",
-    "\n",
-    "And see the resulting graphs. We will use the Topic label generated by `y='Category'` to color the graphs, as well as `$ amount` \n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "2255d688",
    "metadata": {},
    "source": [
-    "# Topic Model (latent-) features"
+    "## Topic Model"
    ]
   },
   {
@@ -374,208 +693,609 @@
     "We encode the data using Topic Models. This turns the textual features into latent vectors. Likewise, we can do the same for the target data. \n",
     "\n",
     "\n",
-    "Notice that we set `cardinality_threshold_target` very low and `min_words` very high to force featurization as topic models rather than one-hot or topic encoded;\n",
+    "Notice that we set `cardinality_threshold_target` very low and `min_words` very high to force featurization as topic models rather than one-hot or sbert embeddings;\n",
+    "\n",
     "1) encode target using a topic model, and set `n_topics_target` as the dimension of the latent target factorization. This choice is based on the fact that there are really only 4-6 or so distinct categories across the labels, but they are mixed together. The labels are in fact Hierarchical categories. We can use the topic model to find the lowest moments of this Hierarchical classification in the distributional sense. \n",
     "\n",
-    "2) and like\n",
-    "wise for the features `Why?`, and set `n_topics` as the dimension of the latent feature factorization."
+    "2) Encode the `Why?` column as a `n_topics` -dimensional factorization."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "71ad1fe5",
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 4) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
    "source": [
     "g = graphistry.nodes(ndf).bind(point_title='Category')\n",
     "\n",
     "g2 = g.umap(X=['Why?'], y = ['Category'], \n",
-    "            min_words=50000, # encode as topic model by setting min_words high\n",
+    "            min_words=1e9, # encode as topic model by setting min_words high\n",
+    "            n_topics=42, # latent embedding size of `Why`\n",
     "            n_topics_target=4, # turn categories into a 4dim vector of regressive targets\n",
-    "            n_topics=21, # latent embedding size \n",
-    "            cardinality_threshold_target=2, # make sure that we throw targets into topic model over targets\n",
+    "            cardinality_threshold_target=2, # force topic model over target `Category`\n",
+    "            use_scaler=None,\n",
+    "            use_scaler_target=None\n",
     "            ) "
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "8cb9e6cd",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.label_encoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b650ef59",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pretend you have a minibatch of new data -- transform under the fit from the above\n",
-    "new_df, new_y = ndf.sample(5), ndf.sample(5) # pd.DataFrame({'Category': ndf['Category'].sample(5)})\n",
-    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "dc99ac85",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5076e613",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure()\n",
-    "plt.imshow(g2._node_target, aspect='auto', cmap='hot')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d95f4e1b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.label_encoder"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "577b32ab",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2._node_encoder.y.plot(kind='bar', figsize=(15,7)) # easier to see than before"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cd1e7ffc",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# likewise you can play with how many edges to include using,\n",
-    "g2 = g2.filter_weighted_edges(scale=0.25)  # lower positive values of scale mean closer similarity \n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "93e6ae81",
    "metadata": {},
    "source": [
-    "## We have featurized the data and also run UMAP, which projects the features into a 2-dimensional space while generating edges.\n",
-    "\n",
     "Plotting the result shows the similarity between entities. It does a good job overall at clustering by topic. Click in and check out some nearby nodes. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "0cdf2370",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=039654b935e6476dbe4a232e28609ae1&type=arrow&viztoken=c8fd440d-2b3a-4fbf-868c-f6c93e141154&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009084&info=true&play=0'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot()"
+    "g2.bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "7a970c06",
+   "execution_count": 21,
+   "id": "b650ef59",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "X = g2._node_features \n",
-    "X"
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in''SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?: relationships, relationship, trevortext</th>\n",
+       "      <th>Why?: thousands, kitchen, kitchens</th>\n",
+       "      <th>Why?: multiracial, language, analysis</th>\n",
+       "      <th>Why?: foundation, partnership, barbados</th>\n",
+       "      <th>Why?: humanitarian, distributing, distributed</th>\n",
+       "      <th>Why?: vulnerable, coordinated, outbreak</th>\n",
+       "      <th>Why?: marginalized, globalgiving, emergency</th>\n",
+       "      <th>Why?: sustainable, livelihoods, livelihood</th>\n",
+       "      <th>Why?: healthcare, results, children</th>\n",
+       "      <th>Why?: movementhub, strengthening, snapshots</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Why?: washingtonians, incarceration, restoration</th>\n",
+       "      <th>Why?: leadership, confidence, confidently</th>\n",
+       "      <th>Why?: entrepreneurs, entrepreneurship, tomorrow</th>\n",
+       "      <th>Why?: coronavirus, families, primarily</th>\n",
+       "      <th>Why?: california, disproportionately, lgbtiq</th>\n",
+       "      <th>Why?: individuals, undiagnosed, disabilities</th>\n",
+       "      <th>Why?: engineering, criminal, complete</th>\n",
+       "      <th>Why?: disparities, nonprofit, socioeconomic</th>\n",
+       "      <th>Why?: simultaneously, immediate, richmond</th>\n",
+       "      <th>Why?: constitution, employers, nationwide</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.140277</td>\n",
+       "      <td>0.160064</td>\n",
+       "      <td>0.193184</td>\n",
+       "      <td>0.154880</td>\n",
+       "      <td>6.899020</td>\n",
+       "      <td>0.088743</td>\n",
+       "      <td>0.161605</td>\n",
+       "      <td>0.126051</td>\n",
+       "      <td>0.084490</td>\n",
+       "      <td>143.538295</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.157501</td>\n",
+       "      <td>0.123325</td>\n",
+       "      <td>0.107698</td>\n",
+       "      <td>0.179143</td>\n",
+       "      <td>19.065436</td>\n",
+       "      <td>0.121403</td>\n",
+       "      <td>0.159580</td>\n",
+       "      <td>0.132481</td>\n",
+       "      <td>0.247654</td>\n",
+       "      <td>0.115000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.213641</td>\n",
+       "      <td>0.383104</td>\n",
+       "      <td>15.883280</td>\n",
+       "      <td>0.155844</td>\n",
+       "      <td>0.233854</td>\n",
+       "      <td>0.197296</td>\n",
+       "      <td>0.221445</td>\n",
+       "      <td>0.269628</td>\n",
+       "      <td>0.203888</td>\n",
+       "      <td>0.218177</td>\n",
+       "      <td>...</td>\n",
+       "      <td>68.431846</td>\n",
+       "      <td>19.142002</td>\n",
+       "      <td>0.439962</td>\n",
+       "      <td>0.354134</td>\n",
+       "      <td>0.326861</td>\n",
+       "      <td>0.530609</td>\n",
+       "      <td>3.105993</td>\n",
+       "      <td>0.318049</td>\n",
+       "      <td>0.378531</td>\n",
+       "      <td>234.320552</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.156088</td>\n",
+       "      <td>0.212351</td>\n",
+       "      <td>0.241647</td>\n",
+       "      <td>0.163209</td>\n",
+       "      <td>0.152243</td>\n",
+       "      <td>0.192223</td>\n",
+       "      <td>0.230188</td>\n",
+       "      <td>0.214602</td>\n",
+       "      <td>4.308181</td>\n",
+       "      <td>0.157184</td>\n",
+       "      <td>...</td>\n",
+       "      <td>276.339497</td>\n",
+       "      <td>0.621190</td>\n",
+       "      <td>0.170634</td>\n",
+       "      <td>0.133303</td>\n",
+       "      <td>0.176993</td>\n",
+       "      <td>0.187097</td>\n",
+       "      <td>0.265365</td>\n",
+       "      <td>0.113609</td>\n",
+       "      <td>0.249689</td>\n",
+       "      <td>26.072265</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.193366</td>\n",
+       "      <td>0.153392</td>\n",
+       "      <td>0.122779</td>\n",
+       "      <td>0.126448</td>\n",
+       "      <td>0.113965</td>\n",
+       "      <td>0.210225</td>\n",
+       "      <td>0.201695</td>\n",
+       "      <td>0.160518</td>\n",
+       "      <td>0.161135</td>\n",
+       "      <td>0.175002</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.129757</td>\n",
+       "      <td>0.146117</td>\n",
+       "      <td>0.159293</td>\n",
+       "      <td>30.262957</td>\n",
+       "      <td>0.106157</td>\n",
+       "      <td>30.781927</td>\n",
+       "      <td>0.122815</td>\n",
+       "      <td>0.119214</td>\n",
+       "      <td>67.870143</td>\n",
+       "      <td>0.139393</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.162594</td>\n",
+       "      <td>0.130315</td>\n",
+       "      <td>0.157815</td>\n",
+       "      <td>0.258591</td>\n",
+       "      <td>1.791601</td>\n",
+       "      <td>6.279698</td>\n",
+       "      <td>0.541006</td>\n",
+       "      <td>45.667235</td>\n",
+       "      <td>0.228501</td>\n",
+       "      <td>50.486164</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.160795</td>\n",
+       "      <td>0.230575</td>\n",
+       "      <td>0.167335</td>\n",
+       "      <td>0.603552</td>\n",
+       "      <td>24.346636</td>\n",
+       "      <td>32.922770</td>\n",
+       "      <td>0.299201</td>\n",
+       "      <td>0.405175</td>\n",
+       "      <td>0.236851</td>\n",
+       "      <td>41.980704</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 42 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Why?: relationships, relationship, trevortext  \\\n",
+       "103                                       0.140277   \n",
+       "62                                        0.213641   \n",
+       "11                                        0.156088   \n",
+       "244                                       0.193366   \n",
+       "24                                        0.162594   \n",
+       "\n",
+       "     Why?: thousands, kitchen, kitchens  \\\n",
+       "103                            0.160064   \n",
+       "62                             0.383104   \n",
+       "11                             0.212351   \n",
+       "244                            0.153392   \n",
+       "24                             0.130315   \n",
+       "\n",
+       "     Why?: multiracial, language, analysis  \\\n",
+       "103                               0.193184   \n",
+       "62                               15.883280   \n",
+       "11                                0.241647   \n",
+       "244                               0.122779   \n",
+       "24                                0.157815   \n",
+       "\n",
+       "     Why?: foundation, partnership, barbados  \\\n",
+       "103                                 0.154880   \n",
+       "62                                  0.155844   \n",
+       "11                                  0.163209   \n",
+       "244                                 0.126448   \n",
+       "24                                  0.258591   \n",
+       "\n",
+       "     Why?: humanitarian, distributing, distributed  \\\n",
+       "103                                       6.899020   \n",
+       "62                                        0.233854   \n",
+       "11                                        0.152243   \n",
+       "244                                       0.113965   \n",
+       "24                                        1.791601   \n",
+       "\n",
+       "     Why?: vulnerable, coordinated, outbreak  \\\n",
+       "103                                 0.088743   \n",
+       "62                                  0.197296   \n",
+       "11                                  0.192223   \n",
+       "244                                 0.210225   \n",
+       "24                                  6.279698   \n",
+       "\n",
+       "     Why?: marginalized, globalgiving, emergency  \\\n",
+       "103                                     0.161605   \n",
+       "62                                      0.221445   \n",
+       "11                                      0.230188   \n",
+       "244                                     0.201695   \n",
+       "24                                      0.541006   \n",
+       "\n",
+       "     Why?: sustainable, livelihoods, livelihood  \\\n",
+       "103                                    0.126051   \n",
+       "62                                     0.269628   \n",
+       "11                                     0.214602   \n",
+       "244                                    0.160518   \n",
+       "24                                    45.667235   \n",
+       "\n",
+       "     Why?: healthcare, results, children  \\\n",
+       "103                             0.084490   \n",
+       "62                              0.203888   \n",
+       "11                              4.308181   \n",
+       "244                             0.161135   \n",
+       "24                              0.228501   \n",
+       "\n",
+       "     Why?: movementhub, strengthening, snapshots  ...  \\\n",
+       "103                                   143.538295  ...   \n",
+       "62                                      0.218177  ...   \n",
+       "11                                      0.157184  ...   \n",
+       "244                                     0.175002  ...   \n",
+       "24                                     50.486164  ...   \n",
+       "\n",
+       "     Why?: washingtonians, incarceration, restoration  \\\n",
+       "103                                          0.157501   \n",
+       "62                                          68.431846   \n",
+       "11                                         276.339497   \n",
+       "244                                          0.129757   \n",
+       "24                                           0.160795   \n",
+       "\n",
+       "     Why?: leadership, confidence, confidently  \\\n",
+       "103                                   0.123325   \n",
+       "62                                   19.142002   \n",
+       "11                                    0.621190   \n",
+       "244                                   0.146117   \n",
+       "24                                    0.230575   \n",
+       "\n",
+       "     Why?: entrepreneurs, entrepreneurship, tomorrow  \\\n",
+       "103                                         0.107698   \n",
+       "62                                          0.439962   \n",
+       "11                                          0.170634   \n",
+       "244                                         0.159293   \n",
+       "24                                          0.167335   \n",
+       "\n",
+       "     Why?: coronavirus, families, primarily  \\\n",
+       "103                                0.179143   \n",
+       "62                                 0.354134   \n",
+       "11                                 0.133303   \n",
+       "244                               30.262957   \n",
+       "24                                 0.603552   \n",
+       "\n",
+       "     Why?: california, disproportionately, lgbtiq  \\\n",
+       "103                                     19.065436   \n",
+       "62                                       0.326861   \n",
+       "11                                       0.176993   \n",
+       "244                                      0.106157   \n",
+       "24                                      24.346636   \n",
+       "\n",
+       "     Why?: individuals, undiagnosed, disabilities  \\\n",
+       "103                                      0.121403   \n",
+       "62                                       0.530609   \n",
+       "11                                       0.187097   \n",
+       "244                                     30.781927   \n",
+       "24                                      32.922770   \n",
+       "\n",
+       "     Why?: engineering, criminal, complete  \\\n",
+       "103                               0.159580   \n",
+       "62                                3.105993   \n",
+       "11                                0.265365   \n",
+       "244                               0.122815   \n",
+       "24                                0.299201   \n",
+       "\n",
+       "     Why?: disparities, nonprofit, socioeconomic  \\\n",
+       "103                                     0.132481   \n",
+       "62                                      0.318049   \n",
+       "11                                      0.113609   \n",
+       "244                                     0.119214   \n",
+       "24                                      0.405175   \n",
+       "\n",
+       "     Why?: simultaneously, immediate, richmond  \\\n",
+       "103                                   0.247654   \n",
+       "62                                    0.378531   \n",
+       "11                                    0.249689   \n",
+       "244                                  67.870143   \n",
+       "24                                    0.236851   \n",
+       "\n",
+       "     Why?: constitution, employers, nationwide  \n",
+       "103                                   0.115000  \n",
+       "62                                  234.320552  \n",
+       "11                                   26.072265  \n",
+       "244                                   0.139393  \n",
+       "24                                   41.980704  \n",
+       "\n",
+       "[5 rows x 42 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# suppose we have a minibatch of new data -- transform under the fit from the above\n",
+    "new_df = new_y = ndf.sample(5) # pd.DataFrame({'Category': ndf['Category'].sample(5)})\n",
+    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
+    "a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "789b09d8",
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 22,
+   "id": "00fc2685",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category: justice, social, 19</th>\n",
+       "      <th>Category: crisis, relief, covid</th>\n",
+       "      <th>Category: needed, where, most</th>\n",
+       "      <th>Category: education, health, girls</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.050010</td>\n",
+       "      <td>10.548916</td>\n",
+       "      <td>0.051025</td>\n",
+       "      <td>0.050049</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>18.041799</td>\n",
+       "      <td>0.050008</td>\n",
+       "      <td>0.056675</td>\n",
+       "      <td>0.051518</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Category: justice, social, 19  Category: crisis, relief, covid  \\\n",
+       "103                      18.041799                         0.050008   \n",
+       "62                       18.041799                         0.050008   \n",
+       "11                       18.041799                         0.050008   \n",
+       "244                       0.050010                        10.548916   \n",
+       "24                       18.041799                         0.050008   \n",
+       "\n",
+       "     Category: needed, where, most  Category: education, health, girls  \n",
+       "103                       0.056675                            0.051518  \n",
+       "62                        0.056675                            0.051518  \n",
+       "11                        0.056675                            0.051518  \n",
+       "244                       0.051025                            0.050049  \n",
+       "24                        0.056675                            0.051518  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "y = g2._node_target  # we've reduced 22 columns into 5\n",
-    "y"
+    "b"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "126d5473",
+   "execution_count": 23,
+   "id": "cd1e7ffc",
    "metadata": {},
    "outputs": [],
    "source": [
-    "## we can inspect the topics from the column headers\n",
-    "label_list = y.columns\n",
-    "label_list"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "7396c76b",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "## and see them across rows of the data\n",
-    "fig = plt.figure(figsize=(17,10))\n",
-    "ax = plt.subplot()\n",
-    "plt.imshow(y, aspect='auto', cmap='hot')\n",
-    "plt.colorbar()\n",
-    "plt.ylabel('row number of data')\n",
-    "ax.set_xticks(range(len(label_list)))\n",
-    "ax.set_xticklabels(label_list, rotation=39)\n",
-    "print(f'See the abundance of the data in the latent vector of the corresponding targets')"
+    "# likewise you can play with how many edges to include using,\n",
+    "g2 = g2.filter_weighted_edges(scale=0.5)  # lower positive values of scale mean closer similarity "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "b9dd69ea",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:ylabel='support across data'>"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# find the marginal in the category topic distribution\n",
-    "y.sum(0).plot(kind='bar', ylabel='support across data', rot=79)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "bcf88b65",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "## Looking at the above bar chart we may read off the most "
+    "y = g2._node_target\n",
+    "y.sum(0).plot(kind='bar', ylabel='support across data', rot=19)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "63b817ae",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 1: \t\t\t\t Evidence\n",
+      "Category: justice, social, 19\n",
+      "-----------------------------------\n",
+      "-- Social Justice,    92\n",
+      "-- COVID-19; Social Justice,    8\n",
+      "-- COVID-19, Social Justice,    2\n",
+      "-- Social Justice, COVID-19,    1\n",
+      "-- UBI, Social Justice,    1\n",
+      "-- COVID-19 & Social Justice,    1\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 2: \t\t\t\t Evidence\n",
+      "Category: crisis, relief, covid\n",
+      "-----------------------------------\n",
+      "-- COVID-19,    70\n",
+      "-- COVID-19 ,    55\n",
+      "-- Crisis Relief,    8\n",
+      "-- UBI; COVID-19,    3\n",
+      "-- COVID-19, UBI,    2\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 3: \t\t\t\t Evidence\n",
+      "Category: needed, where, most\n",
+      "-----------------------------------\n",
+      "-- UBI,    4\n",
+      "-- Where it's needed most,    1\n",
+      "\n",
+      "--------------------------------------------------\n",
+      "Topic 4: \t\t\t\t Evidence\n",
+      "Category: education, health, girls\n",
+      "-----------------------------------\n",
+      "-- Girls Health & Education,    16\n",
+      "-- Social Justice, Girls Health & Education,    6\n",
+      "-- COVID-19, Girls Health & Education,    4\n",
+      "-- Girls Health & Education; COVID-19,    3\n",
+      "-- COVID-19; Girls Health & Education,    3\n",
+      "-- Girls Health & Education, COVID-19,    2\n",
+      "-- COVID-19, Social Justice, Girls Health & Education,    1\n",
+      "-- Girls Health & Education; Social Justice,    1\n",
+      "-- Social Justice, UBI, Girls Health & Education,    1\n"
+     ]
+    }
+   ],
    "source": [
     "# Let's see how the category columns are supported by the data\n",
     "from collections import Counter\n",
@@ -585,7 +1305,7 @@
     "    top_category = Counter(ndf.loc[indices].Category)\n",
     "    print()\n",
     "    print('-'*50)\n",
-    "    print(f'Topic {topic_number}: \\t\\t\\t\\t Evidence')\n",
+    "    print(f'Topic {topic_number+1}: \\t\\t\\t\\t Evidence')\n",
     "    print(f'{y.columns[topic_number]}')\n",
     "    print('-'*35)\n",
     "    for t, c in top_category.most_common():\n",
@@ -597,7 +1317,7 @@
    "id": "efe62b1e",
    "metadata": {},
    "source": [
-    "### We see that different spellings, spaces, etc or use of ;, , etc map to the same topic. This is a useful way to disambiguate when there are many similar categories without having to do a lot of data cleaning and prep.\n",
+    "We see that different spellings, spaces, etc or use of ;, , etc map to the same topic. This is a useful way to disambiguate when there are many similar categories without having to do a lot of data cleaning and prep.\n",
     "\n",
     "The choice of `n_topics_target` sets the prior on the Dirty_Cat GapEncoder used under the hood"
    ]
@@ -607,24 +1327,15 @@
    "id": "530cde56",
    "metadata": {},
    "source": [
-    "## Let's add the Category Topic Number as a feature to help us visualize using the Histogram Feature of the Graphistry UI\n",
+    "_________________________________________________________________________________________\n",
+    "Let's add the Category Topic Number as a feature to help us visualize using the Histogram Feature of the Graphistry UI\n",
     "\n",
-    "This reduces the naive one-hot-encoding of 22 columns down the the number set by the `n_topics_target=5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db1b9ea4",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tops"
+    "This reduces the naive one-hot-encoding of 22 columns down the the number set by the `n_topics_target`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "5724c75f",
    "metadata": {},
    "outputs": [],
@@ -635,10 +1346,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "ad387dc0",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        Category: justice, social, 19\n",
+       "1      Category: crisis, relief, covid\n",
+       "2      Category: crisis, relief, covid\n",
+       "3      Category: crisis, relief, covid\n",
+       "4      Category: crisis, relief, covid\n",
+       "                    ...               \n",
+       "280    Category: crisis, relief, covid\n",
+       "281    Category: crisis, relief, covid\n",
+       "282    Category: crisis, relief, covid\n",
+       "283    Category: crisis, relief, covid\n",
+       "284    Category: crisis, relief, covid\n",
+       "Name: topic, Length: 285, dtype: object"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "g2._nodes.topic"
    ]
@@ -654,20 +1387,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "5d46b0ab",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=e306d4cb9d9c46e2b451e1739b42fd1f&type=arrow&viztoken=194b1f39-29fa-40e5-b80e-790bf099ecaa&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009087&info=true&play=0'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3 = g2.bind(point_title='topic')\n",
-    "g3.plot()"
+    "g2.bind(point_title='Grantee').plot(render=RENDER)  # color by `topic` in histogram"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "id": "6c17e7c4",
    "metadata": {},
    "outputs": [],
@@ -677,10 +1418,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "c3ad0af4",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "topic\n",
+       "Category: crisis, relief, covid       $289,401,918.0\n",
+       "Category: justice, social, 19          $92,815,393.0\n",
+       "Category: education, health, girls     $71,519,000.0\n",
+       "Category: needed, where, most          $13,210,000.0\n",
+       "Name: $ amount, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "topic_sums = ndf.groupby('topic')['$ amount'].sum()\n",
     "topic_sums.sort_values()[::-1].apply(lambda x : '${:3,}'.format(x))"
@@ -691,7 +1448,7 @@
    "id": "058f5eef",
    "metadata": {},
    "source": [
-    "## hence we have Crisis Relief, Social Justice, Health Education Girls, and UBI occupying the main topics across the target"
+    "Hence we have Crisis Relief, Social Justice, Health Education Girls, and UBI occupying the main topics across the target"
    ]
   },
   {
@@ -700,8 +1457,8 @@
    "metadata": {},
    "source": [
     "------------------------------------------------------------------------------------------\n",
-    "# Let's move on to point 2) \n",
-    "# Sentence Transformer Encodings\n",
+    "Let's move on to point 2) \n",
+    "## Sentence Transformer Model\n",
     "\n",
     "To trigger the sentence encoder, just lower the `min_words` count (which previously we had set to higher than the number of words across the `Why?` column) to some small value or zero to force encoding any X=[..] columns, since it sets the minimum number of words to consider passing on to the (sentence, ngram) embedding pipelines.  \n",
     "\n",
@@ -710,97 +1467,690 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "id": "0c4ceacb",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 7) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
    "source": [
-    "g2 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
+    "g3 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
     "            min_words=0, \n",
     "            model_name ='paraphrase-MiniLM-L6-v2', \n",
     "            cardinality_threshold_target=2,\n",
-    "            scale=0.6)"
+    "            use_scaler=None,\n",
+    "            use_scaler_target=None,\n",
+    "            scale=0.5)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 32,
    "id": "e137b52c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2.search('carbon neutral')[0][['Why?']]"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>Climate Justice Alliance (CJA) formed in 2013 ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>The Climate and Clean Energy Equity Fund (Equi...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>138</th>\n",
+       "      <td>The Richmond Rapid Response Fund (R3F) is a wr...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>The Deep South Center for Environment Justice,...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113</th>\n",
+       "      <td>For 40 years, Futures Without Violence has pio...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>DC or Nothing, Inc. is a nonprofit organizatio...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>134</th>\n",
+       "      <td>To support Oxfam’s response to the ongoing imp...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>With the support of #StartSmall ALIMA is opera...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>161</th>\n",
+       "      <td>To support the \"AEGIS Study\" Fund to address S...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>39</th>\n",
+       "      <td>The Caribbean Climate Justice Project seeks to...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  Why?\n",
+       "28   Climate Justice Alliance (CJA) formed in 2013 ...\n",
+       "27   The Climate and Clean Energy Equity Fund (Equi...\n",
+       "138  The Richmond Rapid Response Fund (R3F) is a wr...\n",
+       "29   The Deep South Center for Environment Justice,...\n",
+       "113  For 40 years, Futures Without Violence has pio...\n",
+       "46   DC or Nothing, Inc. is a nonprofit organizatio...\n",
+       "134  To support Oxfam’s response to the ongoing imp...\n",
+       "54   With the support of #StartSmall ALIMA is opera...\n",
+       "161  To support the \"AEGIS Study\" Fund to address S...\n",
+       "39   The Caribbean Climate Justice Project seeks to..."
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.search('carbon neutral')[0][['Why?']]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "a222ef95",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'$13,776,250.0'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "'${:3,}'.format(g2.search('carbon neutral')[0]['$ amount'].sum())"
+    "# make quick semantic estimates\n",
+    "'${:3,}'.format(g3.search('carbon neutral')[0]['$ amount'].sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "7bbcec3a",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2.search('sustainable homes and communities')[0][['Why?','$ amount']]#.sum()"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?</th>\n",
+       "      <th>$ amount</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>237</th>\n",
+       "      <td>For the #FirstOfTheMonth rent relief project a...</td>\n",
+       "      <td>530000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>184</th>\n",
+       "      <td>Funds will be used towards their mission of pr...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>To support in its efforts to empower people wi...</td>\n",
+       "      <td>4720000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>225</th>\n",
+       "      <td>Supports the 30-day Rent Relief program and fu...</td>\n",
+       "      <td>200000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>Funds support the Navajo Water Project - conne...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>177</th>\n",
+       "      <td>To support the COVID-19 Resilience Fund, servi...</td>\n",
+       "      <td>2000000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>For over 60 years, Public Health Solutions (PH...</td>\n",
+       "      <td>200000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>226</th>\n",
+       "      <td>Funding to be used as leverage in negotiations...</td>\n",
+       "      <td>300000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>The Deep South Center for Environment Justice,...</td>\n",
+       "      <td>300000.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>110</th>\n",
+       "      <td>Mission Neighborhood Centers (MNC), founded in...</td>\n",
+       "      <td>1000000.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                  Why?   $ amount\n",
+       "237  For the #FirstOfTheMonth rent relief project a...   530000.0\n",
+       "184  Funds will be used towards their mission of pr...  1000000.0\n",
+       "142  To support in its efforts to empower people wi...  4720000.0\n",
+       "225  Supports the 30-day Rent Relief program and fu...   200000.0\n",
+       "253  Funds support the Navajo Water Project - conne...  1000000.0\n",
+       "177  To support the COVID-19 Resilience Fund, servi...  2000000.0\n",
+       "122  For over 60 years, Public Health Solutions (PH...   200000.0\n",
+       "226  Funding to be used as leverage in negotiations...   300000.0\n",
+       "29   The Deep South Center for Environment Justice,...   300000.0\n",
+       "110  Mission Neighborhood Centers (MNC), founded in...  1000000.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g3.search('sustainable homes and communities')[0][['Why?','$ amount']]#.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "id": "1cc5bd36",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'$11,250,000.0'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "'${:3,}'.format(g2.search('sustainable homes and communities')[0]['$ amount'].sum())"
+    "'${:3,}'.format(g3.search('sustainable homes and communities')[0]['$ amount'].sum())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "3cc28169",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=6c44a692de41484f9e403eea134435dc&type=arrow&viztoken=479610e9-66b9-483d-a242-5dd476faaa08&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009105&info=true&play=0'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# see the queries landscape  -- paste url with .plot(render=False)\n",
-    "g2.search_graph('sustainable homes and communities', scale=0.90, top_n=10).bind(point_title='Why?').plot(render=False)"
+    "# see the queries landscape  -- paste url to see graph if g.plot(render=False)\n",
+    "g3.search_graph('sustainable homes and communities', scale=0.90, top_n=10).bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 37,
    "id": "6bf9f793",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Why?_Grantee_0</th>\n",
+       "      <th>Why?_Grantee_1</th>\n",
+       "      <th>Why?_Grantee_2</th>\n",
+       "      <th>Why?_Grantee_3</th>\n",
+       "      <th>Why?_Grantee_4</th>\n",
+       "      <th>Why?_Grantee_5</th>\n",
+       "      <th>Why?_Grantee_6</th>\n",
+       "      <th>Why?_Grantee_7</th>\n",
+       "      <th>Why?_Grantee_8</th>\n",
+       "      <th>Why?_Grantee_9</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Why?_Grantee_374</th>\n",
+       "      <th>Why?_Grantee_375</th>\n",
+       "      <th>Why?_Grantee_376</th>\n",
+       "      <th>Why?_Grantee_377</th>\n",
+       "      <th>Why?_Grantee_378</th>\n",
+       "      <th>Why?_Grantee_379</th>\n",
+       "      <th>Why?_Grantee_380</th>\n",
+       "      <th>Why?_Grantee_381</th>\n",
+       "      <th>Why?_Grantee_382</th>\n",
+       "      <th>Why?_Grantee_383</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.443781</td>\n",
+       "      <td>0.228207</td>\n",
+       "      <td>-0.095960</td>\n",
+       "      <td>0.233459</td>\n",
+       "      <td>0.089919</td>\n",
+       "      <td>0.261963</td>\n",
+       "      <td>0.181132</td>\n",
+       "      <td>-0.332808</td>\n",
+       "      <td>-0.131169</td>\n",
+       "      <td>0.098321</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.112939</td>\n",
+       "      <td>-0.039801</td>\n",
+       "      <td>0.344712</td>\n",
+       "      <td>-0.148454</td>\n",
+       "      <td>0.092307</td>\n",
+       "      <td>0.554271</td>\n",
+       "      <td>0.729110</td>\n",
+       "      <td>0.292012</td>\n",
+       "      <td>-0.235918</td>\n",
+       "      <td>0.717677</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>-0.117390</td>\n",
+       "      <td>-0.122771</td>\n",
+       "      <td>-0.457182</td>\n",
+       "      <td>-0.289880</td>\n",
+       "      <td>0.091984</td>\n",
+       "      <td>0.300274</td>\n",
+       "      <td>-0.013144</td>\n",
+       "      <td>-0.218964</td>\n",
+       "      <td>0.034323</td>\n",
+       "      <td>0.037846</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.269020</td>\n",
+       "      <td>0.114675</td>\n",
+       "      <td>0.134028</td>\n",
+       "      <td>0.085543</td>\n",
+       "      <td>-0.306746</td>\n",
+       "      <td>0.037034</td>\n",
+       "      <td>0.167620</td>\n",
+       "      <td>0.081501</td>\n",
+       "      <td>0.109638</td>\n",
+       "      <td>-0.272466</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.066493</td>\n",
+       "      <td>0.095128</td>\n",
+       "      <td>-0.273875</td>\n",
+       "      <td>-0.108320</td>\n",
+       "      <td>0.208153</td>\n",
+       "      <td>0.393189</td>\n",
+       "      <td>0.207741</td>\n",
+       "      <td>0.060979</td>\n",
+       "      <td>-0.189427</td>\n",
+       "      <td>0.035519</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.117062</td>\n",
+       "      <td>-0.089552</td>\n",
+       "      <td>0.247655</td>\n",
+       "      <td>0.248977</td>\n",
+       "      <td>0.026250</td>\n",
+       "      <td>0.274958</td>\n",
+       "      <td>0.396113</td>\n",
+       "      <td>-0.109153</td>\n",
+       "      <td>0.155618</td>\n",
+       "      <td>-0.087428</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.230028</td>\n",
+       "      <td>0.167471</td>\n",
+       "      <td>0.209063</td>\n",
+       "      <td>0.097104</td>\n",
+       "      <td>0.082054</td>\n",
+       "      <td>0.183794</td>\n",
+       "      <td>-0.266914</td>\n",
+       "      <td>-0.023539</td>\n",
+       "      <td>-0.543348</td>\n",
+       "      <td>-0.263615</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063171</td>\n",
+       "      <td>0.154695</td>\n",
+       "      <td>-0.307326</td>\n",
+       "      <td>-0.197053</td>\n",
+       "      <td>0.221207</td>\n",
+       "      <td>-0.098222</td>\n",
+       "      <td>0.054051</td>\n",
+       "      <td>-0.118295</td>\n",
+       "      <td>-0.154315</td>\n",
+       "      <td>0.197405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>-0.169691</td>\n",
+       "      <td>0.559551</td>\n",
+       "      <td>-0.129676</td>\n",
+       "      <td>-0.366660</td>\n",
+       "      <td>0.267032</td>\n",
+       "      <td>-0.058975</td>\n",
+       "      <td>-0.005459</td>\n",
+       "      <td>0.062295</td>\n",
+       "      <td>0.050921</td>\n",
+       "      <td>0.362040</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.112664</td>\n",
+       "      <td>0.112096</td>\n",
+       "      <td>-0.116451</td>\n",
+       "      <td>0.187891</td>\n",
+       "      <td>-0.142040</td>\n",
+       "      <td>0.025833</td>\n",
+       "      <td>-0.695716</td>\n",
+       "      <td>0.017772</td>\n",
+       "      <td>0.010284</td>\n",
+       "      <td>0.043964</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 384 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Why?_Grantee_0  Why?_Grantee_1  Why?_Grantee_2  Why?_Grantee_3  \\\n",
+       "103        0.443781        0.228207       -0.095960        0.233459   \n",
+       "62        -0.117390       -0.122771       -0.457182       -0.289880   \n",
+       "11         0.066493        0.095128       -0.273875       -0.108320   \n",
+       "244        0.230028        0.167471        0.209063        0.097104   \n",
+       "24        -0.169691        0.559551       -0.129676       -0.366660   \n",
+       "\n",
+       "     Why?_Grantee_4  Why?_Grantee_5  Why?_Grantee_6  Why?_Grantee_7  \\\n",
+       "103        0.089919        0.261963        0.181132       -0.332808   \n",
+       "62         0.091984        0.300274       -0.013144       -0.218964   \n",
+       "11         0.208153        0.393189        0.207741        0.060979   \n",
+       "244        0.082054        0.183794       -0.266914       -0.023539   \n",
+       "24         0.267032       -0.058975       -0.005459        0.062295   \n",
+       "\n",
+       "     Why?_Grantee_8  Why?_Grantee_9  ...  Why?_Grantee_374  Why?_Grantee_375  \\\n",
+       "103       -0.131169        0.098321  ...         -0.112939         -0.039801   \n",
+       "62         0.034323        0.037846  ...          0.269020          0.114675   \n",
+       "11        -0.189427        0.035519  ...          0.117062         -0.089552   \n",
+       "244       -0.543348       -0.263615  ...          0.063171          0.154695   \n",
+       "24         0.050921        0.362040  ...          0.112664          0.112096   \n",
+       "\n",
+       "     Why?_Grantee_376  Why?_Grantee_377  Why?_Grantee_378  Why?_Grantee_379  \\\n",
+       "103          0.344712         -0.148454          0.092307          0.554271   \n",
+       "62           0.134028          0.085543         -0.306746          0.037034   \n",
+       "11           0.247655          0.248977          0.026250          0.274958   \n",
+       "244         -0.307326         -0.197053          0.221207         -0.098222   \n",
+       "24          -0.116451          0.187891         -0.142040          0.025833   \n",
+       "\n",
+       "     Why?_Grantee_380  Why?_Grantee_381  Why?_Grantee_382  Why?_Grantee_383  \n",
+       "103          0.729110          0.292012         -0.235918          0.717677  \n",
+       "62           0.167620          0.081501          0.109638         -0.272466  \n",
+       "11           0.396113         -0.109153          0.155618         -0.087428  \n",
+       "244          0.054051         -0.118295         -0.154315          0.197405  \n",
+       "24          -0.695716          0.017772          0.010284          0.043964  \n",
+       "\n",
+       "[5 rows x 384 columns]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# or transform on new data as before\n",
-    "a, b = g2.transform(new_df, new_y, kind='nodes')\n",
+    "a, b = g3.transform(new_df, new_y, kind='nodes')\n",
     "a"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "142b85db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Category: covid, ubi, 19</th>\n",
+       "      <th>Category: 19, it, ubi</th>\n",
+       "      <th>Category: justice, social, most</th>\n",
+       "      <th>Category: 19, it, ubi</th>\n",
+       "      <th>Category: education, health, girls</th>\n",
+       "      <th>Category: crisis, relief, needed</th>\n",
+       "      <th>Category: ubi, 19, it</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>10.517500</td>\n",
+       "      <td>0.068372</td>\n",
+       "      <td>0.050004</td>\n",
+       "      <td>0.064094</td>\n",
+       "      <td>0.050020</td>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.050007</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.050003</td>\n",
+       "      <td>0.098460</td>\n",
+       "      <td>17.976374</td>\n",
+       "      <td>0.050914</td>\n",
+       "      <td>0.051539</td>\n",
+       "      <td>0.050121</td>\n",
+       "      <td>0.072589</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Category: covid, ubi, 19  Category: 19, it, ubi  \\\n",
+       "103                  0.050003               0.098460   \n",
+       "62                   0.050003               0.098460   \n",
+       "11                   0.050003               0.098460   \n",
+       "244                 10.517500               0.068372   \n",
+       "24                   0.050003               0.098460   \n",
+       "\n",
+       "     Category: justice, social, most  Category: 19, it, ubi  \\\n",
+       "103                        17.976374               0.050914   \n",
+       "62                         17.976374               0.050914   \n",
+       "11                         17.976374               0.050914   \n",
+       "244                         0.050004               0.064094   \n",
+       "24                         17.976374               0.050914   \n",
+       "\n",
+       "     Category: education, health, girls  Category: crisis, relief, needed  \\\n",
+       "103                            0.051539                          0.050121   \n",
+       "62                             0.051539                          0.050121   \n",
+       "11                             0.051539                          0.050121   \n",
+       "244                            0.050020                          0.050003   \n",
+       "24                             0.051539                          0.050121   \n",
+       "\n",
+       "     Category: ubi, 19, it  \n",
+       "103               0.072589  \n",
+       "62                0.072589  \n",
+       "11                0.072589  \n",
+       "244               0.050007  \n",
+       "24                0.072589  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "5a3033a4",
    "metadata": {},
    "source": [
-    "## Clicking around to nearest neighbors demonstrates good semantic similarity, as seen by the Paraphrase Model `paraphrase-MiniLM-L6-v2`"
+    "Clicking around to nearest neighbors demonstrates good semantic similarity, as seen by the Paraphrase Model `paraphrase-MiniLM-L6-v2`"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "9d33ea95",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=51fe80f88c464b5eb049dd382cfb9b46&type=arrow&viztoken=ab98054f-1141-4657-b76d-9a98b4753917&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009108&info=true&play=0'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot()"
+    "g3.bind(point_title='Grantee').plot(render=RENDER)"
    ]
   },
   {
@@ -808,70 +2158,81 @@
    "id": "7cbb210c",
    "metadata": {},
    "source": [
-    "## Suppose we wanted to add the Grantee column as a feature: \n",
-    "To include it in the sentence transformer model, reduce the` min_words` threshold to include it. If we want the column `Grantee` to be encoded as a topic model, set `min_words` to between the average of `Why?` (higher) and `Grantee` (lower) and `$ amount` (which is just 1). This may seem a bit sloppy as an API, nevertheless useful across many datasets since if a column is truly categorical, its cardinality is usually well under that of a truly textual feature. Moreover, if you want all columns to be textually encoded, set `min_words=0`. "
+    "Suppose we wanted to add the `$ amount` column as a feature: \n",
+    "\n",
+    "To include it in the sentence transformer model, reduce the` min_words` threshold to include it. If we want the column `Grantee` to be encoded as a topic model, set `min_words` to between the average of `Why?` (higher) and `Grantee` (lower).  It nevertheless is useful across many datasets since if a column is truly categorical, its cardinality is usually well under that of a truly textual feature. Moreover, if you want all columns to be textually encoded, set `min_words=0`. \n",
+    "\n",
+    "The `$ amount` column will be passed in and scaled according to `use_scaler`, while `use_scaler_target` selects how to scale targets \n",
+    "\n",
+    "(exercise:  `use_scaler_target='kbins'` to see the difference in `g._node_target` \n",
+    "\n",
+    "or scale the dataframe directly (this transforms the batch dataframe)\n",
+    "\n",
+    "`a, b = g.scale(ndf, ydf=ndf, 'nodes', use_scaler_target='kbins', n_bins=9))` "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 40,
    "id": "4ef2870c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g2 = g.umap(X = ['Why?', 'Grantee', '$ amount'], y = 'Category',\n",
-    "            min_words=2,\n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 22) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g3 = g.umap(X = ['Why?', 'Grantee', '$ amount'], y = 'Category',\n",
+    "            min_words=2, # don't set to zero or it will stringify the `$ amount`\n",
     "            model_name ='paraphrase-MiniLM-L6-v2',\n",
     "            use_scaler=None,\n",
-    "           ) "
+    "           )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 41,
    "id": "97bdaa46",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Why?', 'Grantee']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2._node_encoder.text_cols"
+    "g3._node_encoder.text_cols"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "05b61370",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# just for fun, can we find outliers (which we know will be influenced by the numeric $ amount)\n",
-    "from graphistry.outliers import detect_outliers\n",
-    "\n",
-    "# organized by amount\n",
-    "embedding = g2._xy\n",
-    "clfs, ax, fig = detect_outliers(embedding.values, name='Donations', contamination=0.3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b608b9cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# the different models\n",
-    "clfs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "id": "33f3bc17",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=22d84e2ef2de4cb4830ea1c6c9dc2f70&type=arrow&viztoken=b5067b0e-4c9b-4341-8c41-ce05998aacd1&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009127&info=true&play=0'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g2.plot() # color/size the noded by `$ amount`"
+    "g3.plot(render=RENDER) # color/size the noded by `$ amount`, mimics graph above as they use the same embedding xys"
    ]
   },
   {
@@ -879,7 +2240,9 @@
    "id": "f014b4e0",
    "metadata": {},
    "source": [
-    "# Lastly, suppose we want a plain Ngrams model matrix, and for a change, one-hot-encode the target `Category`\n",
+    "## NGRAMS model\n",
+    "\n",
+    "Lastly, suppose we want a plain Ngrams model matrix, and for a change, one-hot-encode the target `Category`\n",
     "\n",
     "Set `use_ngrams = True`\n",
     "and set the `cardinality_threshold_target` > cardinality(`Category`).\n",
@@ -889,84 +2252,585 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 43,
    "id": "8c1a588c",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (285, 22) in UMAP fit, as it is not one dimensional"
+     ]
+    }
+   ],
+   "source": [
+    "g4 = g.umap(X = ['Why?', 'Grantee'], y = 'Category', \n",
     "            use_ngrams=True, \n",
     "            ngram_range=(1,3), \n",
     "            min_df=2, \n",
     "            max_df=0.3,\n",
-    "            cardinality_threshold_target=400\n",
-    "           )  # this will one-hot-encode the target, as we have less than 400 total `categories`"
+    "            use_scaler=None,\n",
+    "            cardinality_threshold_target=400 # this will one-hot-encode the target, \n",
+    "                                                # as we have less than 400 total `categories`\n",
+    "           )"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "id": "e1e2683c",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'https://hub.graphistry.com/graph/graph.html?dataset=aeb09ebc8a914fe8851b7bc4ea255c9c&type=arrow&viztoken=f768c875-ee7d-4cea-b514-a3c8ba0d59bc&usertag=f680a57a-pygraphistry-0.28.7&splashAfter=1672009131&info=true&play=0'"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3.bind(point_title='Category').plot()"
+    "g4.bind(point_title='Category').plot(render=RENDER)  # umap-ing ngrams is not as useful as sentence embeddings as you may visually see, however they can be useful graphs nonetheless. Press `play` in the UI."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 45,
    "id": "d570f001",
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "g3._node_features  # a standard tfidf ngrams matrix"
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>reform</th>\n",
+       "      <th>alliance</th>\n",
+       "      <th>committed</th>\n",
+       "      <th>criminal</th>\n",
+       "      <th>justice</th>\n",
+       "      <th>system</th>\n",
+       "      <th>throughout</th>\n",
+       "      <th>united</th>\n",
+       "      <th>states</th>\n",
+       "      <th>by</th>\n",
+       "      <th>...</th>\n",
+       "      <th>mayor office</th>\n",
+       "      <th>for homeless</th>\n",
+       "      <th>match</th>\n",
+       "      <th>hospital</th>\n",
+       "      <th>2m</th>\n",
+       "      <th>go towards</th>\n",
+       "      <th>grant 2m</th>\n",
+       "      <th>will go towards</th>\n",
+       "      <th>total grant 2m</th>\n",
+       "      <th>clinics</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
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+       "    <tr>\n",
+       "      <th>4</th>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>280</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>281</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>282</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>283</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>284</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>285 rows × 3889 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     reform  alliance  committed  criminal  justice  system  throughout  \\\n",
+       "0       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "1       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "2       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "3       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "4       0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "..      ...       ...        ...       ...      ...     ...         ...   \n",
+       "280     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "281     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "282     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "283     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "284     0.0       0.0        0.0       0.0      0.0     0.0         0.0   \n",
+       "\n",
+       "     united  states   by  ...  mayor office  for homeless  match  hospital  \\\n",
+       "0       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "1       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "2       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "3       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "4       0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "..      ...     ...  ...  ...           ...           ...    ...       ...   \n",
+       "280     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "281     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "282     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "283     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "284     0.0     0.0  0.0  ...           0.0           0.0    0.0       0.0   \n",
+       "\n",
+       "      2m  go towards  grant 2m  will go towards  total grant 2m  clinics  \n",
+       "0    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "1    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "2    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "3    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "4    0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "..   ...         ...       ...              ...             ...      ...  \n",
+       "280  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "281  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "282  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "283  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "284  0.0         0.0       0.0              0.0             0.0      0.0  \n",
+       "\n",
+       "[285 rows x 3889 columns]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g4._node_features  # a standard tfidf ngrams matrix"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "id": "338010f2",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Pipeline(steps=[('vect',\n",
+       "                 CountVectorizer(max_df=0.3, min_df=2, ngram_range=(1, 3))),\n",
+       "                ('tfidf', TfidfTransformer())])"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g3._node_encoder.text_model  #sklearn pipeline "
+    "g4._node_encoder.text_model  #sklearn pipeline "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 47,
    "id": "e7582131",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3889"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "## vocab size\n",
-    "len(g3._node_encoder.text_model[0].vocabulary_)"
+    "len(g4._node_encoder.text_model[0].vocabulary_)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "id": "9e691b4d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "'SuperVectorizer' object has no attribute 'get_feature_names_in'"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>6.100185</td>\n",
+       "      <td>-1.109381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>62</th>\n",
+       "      <td>6.937139</td>\n",
+       "      <td>-2.348813</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>6.991597</td>\n",
+       "      <td>-2.461275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>5.651282</td>\n",
+       "      <td>-1.004279</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>5.893190</td>\n",
+       "      <td>-3.356526</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            x         y\n",
+       "103  6.100185 -1.109381\n",
+       "62   6.937139 -2.348813\n",
+       "11   6.991597 -2.461275\n",
+       "244  5.651282 -1.004279\n",
+       "24   5.893190 -3.356526"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# or transform new data: \n",
-    "emb, a, b = g2.transform_umap(new_df, new_y, kind='nodes')\n",
+    "emb, a, b = g4.transform_umap(new_df, new_y, kind='nodes')\n",
     "emb"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "id": "5bc7b2c0",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Naive Indicator Variables\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1224x720 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# we include the naive indicator variable for completeness.\n",
-    "y = g3._node_target\n",
+    "y = g4._node_target\n",
     "label_list = b.columns\n",
     "\n",
     "fig = plt.figure(figsize=(17,10))\n",
@@ -981,22 +2845,22 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec83a920",
+   "id": "22596c6d",
    "metadata": {},
    "source": [
     "# Contributions\n",
     "\n",
-    "We've seen how we may pull in tabular data that exists in the wild and quickly make features and graphs that allow semantic and topological exploration and traversals. \n",
+    "Input tabular data that exists in the wild and quickly make features and graphs that allow semantic and topological exploration and traversals. \n",
     "\n",
-    "In this way one can quickly track a variety of datasets and (in this case) gauge growth, investment, and promise fullfillment and transparently using Graph Thinking and analysis.\n",
+    "Quickly track a variety of datasets and gauge growth, investment, and promise fullfillment and transparently using Graph Thinking and Analysis. In Jack's case, we see the possibility of a multibillion dollar edific erected around Covid-19, Girls Education and Social Justice. Further downstream modeling might tell us what such an edific is able to manufacture as a force for Good.\n",
     "\n",
-    "Encoding text, categorical, and numeric features while exploring the relationships can be time consuming tasks. We hope that Graphistry[ai] demonstrates an exciting and visually compelling way to explore Graph Data. \n",
+    "Encoding text, categorical, and numeric features while exploring the relationships can be time consuming tasks. \n",
     "\n",
-    "Now you can mix and match features, augment it with more columns via enrichment, and pivot large amounts of data using natural language search, all using a few lines of code. The features produced may then be used in downstream models, whose outputs could be added and the entire process repeated.\n",
+    "PyGraphistry[ai] demonstrates an exciting and visually accelerated way to explore Graph Data. \n",
     "\n",
-    "Let us know what you think!\n",
+    "It allows quick Mix and Match featurization models and types, while pivoting on large amounts of data using natural language search, in just a few lines of code. The resulting features may then be used in downstream models.\n",
     "\n",
-    "Join our Slack: Graphistry-Community\n"
+    "Join our Slack: Graphistry-Community"
    ]
   },
   {
diff --git a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
index 9ed84b308b..b07a7403f8 100644
--- a/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+++ b/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "f9de6fd3-b87b-4dc4-8d1c-b8f3feceb5e6",
    "metadata": {},
    "outputs": [],
@@ -31,20 +31,39 @@
     "import pandas as pd\n",
     "import graphistry\n",
     "\n",
+    "from graphistry.features import topic_model, search_model, ModelDict\n",
+    "\n",
     "import os\n",
     "from joblib import load, dump\n",
     "from collections import Counter\n",
     "\n",
     "import numpy as np\n",
-    "import matplotlib.pylab as plt\n",
-    "\n",
-    "from sklearn.cluster import DBSCAN\n",
-    "from sknetwork.ranking import PageRank\n"
+    "import matplotlib.pylab as plt\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
+   "id": "8e1747b9-c903-4398-9aa0-b52b69fce021",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(137)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "6d2669fd-6164-4376-81bd-79c6c6f4112f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "RENDER = True # set to True to render Graphistry UI inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
    "id": "59e1cc0b",
    "metadata": {},
    "outputs": [],
@@ -74,12 +93,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "id": "fe6e61b0",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# cite data source\n",
+    "# data source citation\n",
     "# \"\"\"A. D. Kent, \"Cybersecurity Data Sources for Dynamic Network Research,\"\n",
     "# in Dynamic Networks in Cybersecurity, 2015.\n",
     "\n",
@@ -103,10 +122,153 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "efe68cf8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>time</th>\n",
+       "      <th>src_domain</th>\n",
+       "      <th>dst_domain</th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>auth_type</th>\n",
+       "      <th>logontype</th>\n",
+       "      <th>authentication_orientation</th>\n",
+       "      <th>success_or_failure</th>\n",
+       "      <th>RED</th>\n",
+       "      <th>feats</th>\n",
+       "      <th>feats2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>30526246</th>\n",
+       "      <td>155805</td>\n",
+       "      <td>C7048$@DOM1</td>\n",
+       "      <td>C7048$@DOM1</td>\n",
+       "      <td>C7048</td>\n",
+       "      <td>TGT</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C7048 TGT ? ?</td>\n",
+       "      <td>C7048 TGT</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5928201</th>\n",
+       "      <td>37690</td>\n",
+       "      <td>C15034$@DOM1</td>\n",
+       "      <td>C15034$@DOM1</td>\n",
+       "      <td>C15034</td>\n",
+       "      <td>C467</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C15034 C467 ? ?</td>\n",
+       "      <td>C15034 C467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21160461</th>\n",
+       "      <td>116992</td>\n",
+       "      <td>U2075@DOM1</td>\n",
+       "      <td>U2075@DOM1</td>\n",
+       "      <td>C529</td>\n",
+       "      <td>C529</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOff</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C529 C529 ? Network</td>\n",
+       "      <td>C529 C529</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2182328</th>\n",
+       "      <td>22019</td>\n",
+       "      <td>C3547$@DOM1</td>\n",
+       "      <td>C3547$@DOM1</td>\n",
+       "      <td>C457</td>\n",
+       "      <td>C457</td>\n",
+       "      <td>?</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOff</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C457 C457 ? Network</td>\n",
+       "      <td>C457 C457</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28495743</th>\n",
+       "      <td>145572</td>\n",
+       "      <td>C567$@DOM1</td>\n",
+       "      <td>C567$@DOM1</td>\n",
+       "      <td>C574</td>\n",
+       "      <td>C523</td>\n",
+       "      <td>Kerberos</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C574 C523 Kerberos Network</td>\n",
+       "      <td>C574 C523</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            time    src_domain    dst_domain src_computer dst_computer  \\\n",
+       "30526246  155805   C7048$@DOM1   C7048$@DOM1        C7048          TGT   \n",
+       "5928201    37690  C15034$@DOM1  C15034$@DOM1       C15034         C467   \n",
+       "21160461  116992    U2075@DOM1    U2075@DOM1         C529         C529   \n",
+       "2182328    22019   C3547$@DOM1   C3547$@DOM1         C457         C457   \n",
+       "28495743  145572    C567$@DOM1    C567$@DOM1         C574         C523   \n",
+       "\n",
+       "         auth_type logontype authentication_orientation success_or_failure  \\\n",
+       "30526246         ?         ?                        TGS            Success   \n",
+       "5928201          ?         ?                        TGS            Success   \n",
+       "21160461         ?   Network                     LogOff            Success   \n",
+       "2182328          ?   Network                     LogOff            Success   \n",
+       "28495743  Kerberos   Network                      LogOn            Success   \n",
+       "\n",
+       "          RED                       feats       feats2  \n",
+       "30526246  0.0               C7048 TGT ? ?    C7048 TGT  \n",
+       "5928201   0.0             C15034 C467 ? ?  C15034 C467  \n",
+       "21160461  0.0         C529 C529 ? Network    C529 C529  \n",
+       "2182328   0.0         C457 C457 ? Network    C457 C457  \n",
+       "28495743  0.0  C574 C523 Kerberos Network    C574 C523  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# small sample (get almost equivalent results without overheating computer over the 1.6B events in the full dataset)\n",
     "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/c7b50d0c03dc59f63c48d68d696958ff/raw/31d918267f86f8252d42d2e9597ba6fc03fcdac2/redteam_50k.csv', index_col=0)\n",
@@ -115,23 +277,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "03610297",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(50000, 12)\n"
+     ]
+    }
+   ],
    "source": [
     "print(df.shape) # -> 50k"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "66c5126e",
    "metadata": {},
    "outputs": [],
    "source": [
     "# here are the post-facto red team events\n",
-    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)"
+    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)\n",
+    "red_team['feats2'] = red_team.feats"
    ]
   },
   {
@@ -146,10 +317,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "3641d3b5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(19013, 12)\n"
+     ]
+    }
+   ],
    "source": [
     "process = True  \n",
     "# makes a combined feature we can use for topic modeling!\n",
@@ -159,9 +338,9 @@
     "    # and one of just computer to computer \n",
     "    df['feats2'] = df.src_computer + ' ' + df.dst_computer\n",
     "    ndf = df.drop_duplicates(subset=['feats'])\n",
-    "    ndf.to_parquet('../data/auth-50k-feats-one-column.parquet')\n",
+    "    ndf.to_parquet('auth-feats-one-column.parquet')\n",
     "else:\n",
-    "    ndf = pd.read_parquet('../data/auth-50k-feats-one-column.parquet')\n",
+    "    ndf = pd.read_parquet('auth-feats-one-column.parquet')\n",
     "    \n",
     "print(ndf.shape)"
    ]
@@ -177,10 +356,289 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "d67c86b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>index</th>\n",
+       "      <th>time</th>\n",
+       "      <th>src_domain</th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>feats</th>\n",
+       "      <th>RED</th>\n",
+       "      <th>feats2</th>\n",
+       "      <th>dst_domain</th>\n",
+       "      <th>auth_type</th>\n",
+       "      <th>logontype</th>\n",
+       "      <th>authentication_orientation</th>\n",
+       "      <th>success_or_failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>150885</td>\n",
+       "      <td>U620@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C1003</td>\n",
+       "      <td>C17693 C1003</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C1003</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>151036</td>\n",
+       "      <td>U748@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C305</td>\n",
+       "      <td>C17693 C305</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C305</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>151648</td>\n",
+       "      <td>U748@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C728</td>\n",
+       "      <td>C17693 C728</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C728</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>151993</td>\n",
+       "      <td>U6115@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C1173</td>\n",
+       "      <td>C17693 C1173</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C1173</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>153792</td>\n",
+       "      <td>U636@DOM1</td>\n",
+       "      <td>C17693</td>\n",
+       "      <td>C294</td>\n",
+       "      <td>C17693 C294</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>C17693 C294</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>8463107</td>\n",
+       "      <td>48263</td>\n",
+       "      <td>C11843$@DOM1</td>\n",
+       "      <td>C11843</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>C11843 C528 Kerberos Network</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C11843 C528</td>\n",
+       "      <td>C11843$@DOM1</td>\n",
+       "      <td>Kerberos</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>14394630</td>\n",
+       "      <td>77937</td>\n",
+       "      <td>C8470$@DOM1</td>\n",
+       "      <td>C8470</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>C8470 C528 NTLM Network</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C8470 C528</td>\n",
+       "      <td>C8470$@DOM1</td>\n",
+       "      <td>NTLM</td>\n",
+       "      <td>Network</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>33398153</td>\n",
+       "      <td>173300</td>\n",
+       "      <td>C716$@DOM1</td>\n",
+       "      <td>C716</td>\n",
+       "      <td>C716</td>\n",
+       "      <td>C716 C716 ? ?</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C716 C716</td>\n",
+       "      <td>C716$@DOM1</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>AuthMap</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>18353851</td>\n",
+       "      <td>102472</td>\n",
+       "      <td>U7365@DOM1</td>\n",
+       "      <td>C16126</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>C16126 C586 ? ?</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C16126 C586</td>\n",
+       "      <td>U7365@DOM1</td>\n",
+       "      <td>?</td>\n",
+       "      <td>?</td>\n",
+       "      <td>TGS</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>27372458</td>\n",
+       "      <td>141156</td>\n",
+       "      <td>NETWORK SERVICE@C6215</td>\n",
+       "      <td>C6215</td>\n",
+       "      <td>C6215</td>\n",
+       "      <td>C6215 C6215 Negotiate Service</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>C6215 C6215</td>\n",
+       "      <td>NETWORK SERVICE@C6215</td>\n",
+       "      <td>Negotiate</td>\n",
+       "      <td>Service</td>\n",
+       "      <td>LogOn</td>\n",
+       "      <td>Success</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 13 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          index    time             src_domain src_computer dst_computer  \\\n",
+       "0             0  150885              U620@DOM1       C17693        C1003   \n",
+       "1             1  151036              U748@DOM1       C17693         C305   \n",
+       "2             2  151648              U748@DOM1       C17693         C728   \n",
+       "3             3  151993             U6115@DOM1       C17693        C1173   \n",
+       "4             4  153792              U636@DOM1       C17693         C294   \n",
+       "...         ...     ...                    ...          ...          ...   \n",
+       "19008   8463107   48263           C11843$@DOM1       C11843         C528   \n",
+       "19009  14394630   77937            C8470$@DOM1        C8470         C528   \n",
+       "19010  33398153  173300             C716$@DOM1         C716         C716   \n",
+       "19011  18353851  102472             U7365@DOM1       C16126         C586   \n",
+       "19012  27372458  141156  NETWORK SERVICE@C6215        C6215        C6215   \n",
+       "\n",
+       "                               feats  RED        feats2  \\\n",
+       "0                       C17693 C1003  1.0  C17693 C1003   \n",
+       "1                        C17693 C305  1.0   C17693 C305   \n",
+       "2                        C17693 C728  1.0   C17693 C728   \n",
+       "3                       C17693 C1173  1.0  C17693 C1173   \n",
+       "4                        C17693 C294  1.0   C17693 C294   \n",
+       "...                              ...  ...           ...   \n",
+       "19008   C11843 C528 Kerberos Network  0.0   C11843 C528   \n",
+       "19009        C8470 C528 NTLM Network  0.0    C8470 C528   \n",
+       "19010                  C716 C716 ? ?  0.0     C716 C716   \n",
+       "19011                C16126 C586 ? ?  0.0   C16126 C586   \n",
+       "19012  C6215 C6215 Negotiate Service  0.0   C6215 C6215   \n",
+       "\n",
+       "                  dst_domain  auth_type logontype authentication_orientation  \\\n",
+       "0                        NaN        NaN       NaN                        NaN   \n",
+       "1                        NaN        NaN       NaN                        NaN   \n",
+       "2                        NaN        NaN       NaN                        NaN   \n",
+       "3                        NaN        NaN       NaN                        NaN   \n",
+       "4                        NaN        NaN       NaN                        NaN   \n",
+       "...                      ...        ...       ...                        ...   \n",
+       "19008           C11843$@DOM1   Kerberos   Network                      LogOn   \n",
+       "19009            C8470$@DOM1       NTLM   Network                      LogOn   \n",
+       "19010             C716$@DOM1          ?         ?                    AuthMap   \n",
+       "19011             U7365@DOM1          ?         ?                        TGS   \n",
+       "19012  NETWORK SERVICE@C6215  Negotiate   Service                      LogOn   \n",
+       "\n",
+       "      success_or_failure  \n",
+       "0                    NaN  \n",
+       "1                    NaN  \n",
+       "2                    NaN  \n",
+       "3                    NaN  \n",
+       "4                    NaN  \n",
+       "...                  ...  \n",
+       "19008            Success  \n",
+       "19009            Success  \n",
+       "19010            Success  \n",
+       "19011            Success  \n",
+       "19012            Success  \n",
+       "\n",
+       "[19762 rows x 13 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# make a subsampled dataframe with the anom red-team data at top...so we can keep track.\n",
     "# we don't need the full `df`, only the unique entries of 'feats' in `ndf` for \n",
@@ -192,7 +650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "5f62b7b5",
    "metadata": {},
    "outputs": [],
@@ -203,10 +661,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "5ffd6aac",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "749.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# total number of red team events\n",
     "tdf.RED.sum()"
@@ -222,59 +691,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "72c53f98",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# some enrichments\n",
-    "def pagerank(g):\n",
-    "    from sknetwork.ranking import PageRank\n",
-    "    adj = g._weighted_adjacency\n",
-    "    pagerank = PageRank()\n",
-    "    ranks = pagerank.fit_transform(adj)\n",
-    "    g._nodes['pagerank'] = ranks\n",
-    "    return g\n",
-    "\n",
-    "def cluster(g):\n",
-    "    \"\"\"\n",
-    "        Fits clustering on UMAP embeddings\n",
-    "    \"\"\"\n",
-    "    dbscan = DBSCAN()\n",
-    "    labels = dbscan.fit_predict(g._node_embedding)\n",
-    "    g._nodes['cluster'] = labels\n",
-    "    cnt = Counter(labels)\n",
-    "    return g, dbscan, cnt\n",
-    "\n",
-    "def get_confidences_per_cluster(g, cnt):\n",
+    "def get_confidences_per_cluster(g, col='RED', verbose=False):\n",
     "    \"\"\"\n",
     "        From DBSCAN clusters, will assess how many Red Team events exist,\n",
     "        assessing confidence.\n",
+    "        \n",
     "    \"\"\"\n",
     "    resses = []\n",
     "    df = g._nodes\n",
+    "    labels = df._dbscan\n",
+    "    cnt = Counter(labels)\n",
     "    for clust, count in cnt.most_common():\n",
-    "        res = df[df.cluster==clust]\n",
+    "        res = df[df._dbscan==clust]\n",
     "        n = res.shape[0]\n",
-    "        n_reds = res.RED.sum()\n",
+    "        n_reds = res[col].sum()\n",
     "        resses.append([clust, n_reds/n, n_reds, n])\n",
-    "        if n_reds>0:\n",
+    "        if n_reds>0 and verbose:\n",
     "            print('-'*20)\n",
     "            print(f'cluster: {clust}\\n   red {100*n_reds/n:.2f}% or {n_reds} out of {count}')\n",
-    "    conf_dict = {k[0]:k[1] for k in resses}\n",
-    "    confidence = [conf_dict[k] for k in df.cluster.values]\n",
+    "    conf_dict = {k[0]: k[1] for k in resses}\n",
+    "    confidence = [conf_dict[k] for k in df._dbscan.values]\n",
     "    g._nodes['confidence'] = confidence\n",
-    "    return g, pd.DataFrame(resses, columns=['cluster', 'confidence', 'n_red', 'total_in_cluster'])\n",
-    "\n",
-    "\n",
-    "def enrich(g):\n",
-    "    \"\"\"\n",
-    "        Full Pipeline \n",
-    "    \"\"\"\n",
-    "    g = pagerank(g)\n",
-    "    g, dbscan, cnt = cluster(g)\n",
-    "    g, cluster_confidences = get_confidences_per_cluster(g, cnt)\n",
-    "    return g, dbscan, cluster_confidences\n",
+    "    conf_df = pd.DataFrame(resses, columns=['_dbscan', 'confidence', 'n_red', 'total_in_cluster'])\n",
+    "    conf_df = conf_df.sort_values(by='confidence', ascending=False)\n",
+    "    return g, conf_df\n",
     "    "
    ]
   },
@@ -289,31 +734,201 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
+   "id": "504781dc-9fbe-467c-9b4d-2e907133cfb7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "\n",
+      "A topic model for computer to computer + metadata cyber auth logs\n",
+      "_________________________________________________________________\n",
+      "\n",
+      "Updated: {'n_topics': 32, 'X': ['feats']}\n",
+      "_________________________________________________________________\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'cardinality_threshold': 2, 'cardinality_threshold_target': 2, 'n_topics': 32, 'n_topics_target': 10, 'min_words': 1000000000.0, 'X': ['feats']}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# this is a convienence method for setting parameters in `g.featurize()/umap()` -- just a verbose dictionary\n",
+    "cyber_model = ModelDict('A topic model for computer to computer + metadata cyber auth logs', **topic_model)\n",
+    "\n",
+    "cyber_model.update(dict(n_topics=32, X=['feats']))  # name the column to featurize, which we lumped into `feats`\n",
+    "\n",
+    "cyber_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "id": "6909cc90",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensionalOMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--------------------\n",
+      "cluster: 3\n",
+      "   red 0.66% or 22.0 out of 3331\n",
+      "--------------------\n",
+      "cluster: 39\n",
+      "   red 0.41% or 3.0 out of 724\n",
+      "--------------------\n",
+      "cluster: 9\n",
+      "   red 1.15% or 3.0 out of 260\n",
+      "--------------------\n",
+      "cluster: 38\n",
+      "   red 0.38% or 1.0 out of 260\n",
+      "--------------------\n",
+      "cluster: 13\n",
+      "   red 0.43% or 1.0 out of 234\n",
+      "--------------------\n",
+      "cluster: 8\n",
+      "   red 95.06% or 77.0 out of 81\n",
+      "--------------------\n",
+      "cluster: 1\n",
+      "   red 100.00% or 53.0 out of 53\n",
+      "--------------------\n",
+      "cluster: 10\n",
+      "   red 91.84% or 45.0 out of 49\n",
+      "--------------------\n",
+      "cluster: 12\n",
+      "   red 82.61% or 38.0 out of 46\n",
+      "--------------------\n",
+      "cluster: 22\n",
+      "   red 95.65% or 44.0 out of 46\n",
+      "--------------------\n",
+      "cluster: 18\n",
+      "   red 92.11% or 35.0 out of 38\n",
+      "--------------------\n",
+      "cluster: 19\n",
+      "   red 82.86% or 29.0 out of 35\n",
+      "--------------------\n",
+      "cluster: 15\n",
+      "   red 86.67% or 26.0 out of 30\n",
+      "--------------------\n",
+      "cluster: 27\n",
+      "   red 92.59% or 25.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 32\n",
+      "   red 100.00% or 27.0 out of 27\n",
+      "--------------------\n",
+      "cluster: 28\n",
+      "   red 100.00% or 26.0 out of 26\n",
+      "--------------------\n",
+      "cluster: 6\n",
+      "   red 84.00% or 21.0 out of 25\n",
+      "--------------------\n",
+      "cluster: 2\n",
+      "   red 87.50% or 21.0 out of 24\n",
+      "--------------------\n",
+      "cluster: 35\n",
+      "   red 100.00% or 24.0 out of 24\n",
+      "--------------------\n",
+      "cluster: 0\n",
+      "   red 100.00% or 23.0 out of 23\n",
+      "--------------------\n",
+      "cluster: 11\n",
+      "   red 100.00% or 23.0 out of 23\n",
+      "--------------------\n",
+      "cluster: 30\n",
+      "   red 81.25% or 13.0 out of 16\n",
+      "--------------------\n",
+      "cluster: 17\n",
+      "   red 93.33% or 14.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 21\n",
+      "   red 100.00% or 15.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 23\n",
+      "   red 100.00% or 15.0 out of 15\n",
+      "--------------------\n",
+      "cluster: 4\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 29\n",
+      "   red 100.00% or 14.0 out of 14\n",
+      "--------------------\n",
+      "cluster: 7\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 37\n",
+      "   red 100.00% or 13.0 out of 13\n",
+      "--------------------\n",
+      "cluster: 14\n",
+      "   red 100.00% or 11.0 out of 11\n",
+      "--------------------\n",
+      "cluster: 5\n",
+      "   red 100.00% or 10.0 out of 10\n",
+      "--------------------\n",
+      "cluster: 25\n",
+      "   red 100.00% or 9.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 33\n",
+      "   red 88.89% or 8.0 out of 9\n",
+      "--------------------\n",
+      "cluster: 20\n",
+      "   red 100.00% or 6.0 out of 6\n",
+      "--------------------\n",
+      "cluster: 36\n",
+      "   red 100.00% or 6.0 out of 6\n",
+      "--------------------\n",
+      "cluster: 16\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 24\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 26\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 31\n",
+      "   red 100.00% or 5.0 out of 5\n",
+      "--------------------\n",
+      "cluster: 34\n",
+      "   red 100.00% or 1.0 out of 1\n",
+      "CPU times: user 3min 40s, sys: 39.2 s, total: 4min 19s\n",
+      "Wall time: 2min 7s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True  # set to false after it's run for ease of speed\n",
     "if process:\n",
-    "    g = graphistry.nodes(tdf, 'node')\n",
-    "    g5 = g.umap(X=['feats'], \n",
-    "                min_words=1000000, # force high so that we don't use Sentence Transformers\n",
-    "                cardinality_threshold=4, # set low so we force topic model\n",
-    "                n_topics=32, # number of topics\n",
-    "                use_scaler=None,\n",
-    "                use_scaler_target=None\n",
-    "               )\n",
+    "    # ##################################\n",
+    "    g = graphistry.nodes(tdf, 'node')  # two lines does the heavy lifting\n",
+    "    g5 = g.umap(**cyber_model).dbscan(min_dist=0.2)\n",
+    "    # #########################\n",
     "    \n",
-    "    g5, dbscan, cluster_confidences = enrich(g5)\n",
-    "\n",
-    "    g5.build_index()\n",
-    "    g5.save_search_instance('../data/auth-feat-topic.search')\n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5, verbose=True)\n",
+    "    g5.save_search_instance('auth-feat-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
-    "    g5 = g.load_search_instance('../data/auth-feat-topic.search')\n",
-    "    g5, dbscan, cluster_confidences = enrich(g5)\n"
+    "    g5 = g.load_search_instance('auth-feat-topic.search')\n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5)"
    ]
   },
   {
@@ -321,40 +936,581 @@
    "id": "54c13cba-bc36-4d49-8e7a-7dc05b27610a",
    "metadata": {},
    "source": [
-    "## Plot it\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "## Plot Graph\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "279fef41",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"4c5a28f9-d525-490b-9d7a-216ba63590ed\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=d4ac3b5b9916432ca0b62973d587cfef&type=arrow&viztoken=9394d462-ab46-47c7-b5d6-458755f64975&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267572&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#4c5a28f9-d525-490b-9d7a-216ba63590ed\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g5.name('auth 50k topic feats no target').plot(render=False)"
+    "g5.name('auth topic feats no target').plot(render=RENDER)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "79ece955",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feats: c9990, c9994, c9997</th>\n",
+       "      <th>feats: kerberos, u1, u7</th>\n",
+       "      <th>feats: c528, c5252, c5281</th>\n",
+       "      <th>feats: c612, c6121, c6125</th>\n",
+       "      <th>feats: c2446, c2444, c24464</th>\n",
+       "      <th>feats: c13713, c13130, c13134</th>\n",
+       "      <th>feats: c586, c5866, c5864</th>\n",
+       "      <th>feats: c467, c4674, c4667</th>\n",
+       "      <th>feats: unlock, c1111, c11114</th>\n",
+       "      <th>feats: c625, c6257, c6255</th>\n",
+       "      <th>...</th>\n",
+       "      <th>feats: c5299, c529, c5294</th>\n",
+       "      <th>feats: c16616, c16168, c16663</th>\n",
+       "      <th>feats: microsoft_authentication_package_v1_0, microsoft_authentication_package_v1_, microsoft_authentication_package_v1</th>\n",
+       "      <th>feats: c1065, c10658, c10652</th>\n",
+       "      <th>feats: cachedinteractive, remoteinteractive, interactive</th>\n",
+       "      <th>feats: c3888, c3884, u608</th>\n",
+       "      <th>feats: c2327, c2323, c2727</th>\n",
+       "      <th>feats: negotiate, service, c14514</th>\n",
+       "      <th>feats: c8282, c8280, c8289</th>\n",
+       "      <th>feats: c1964, c1968, c25685</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.052992</td>\n",
+       "      <td>0.050029</td>\n",
+       "      <td>0.051413</td>\n",
+       "      <td>0.051403</td>\n",
+       "      <td>0.050019</td>\n",
+       "      <td>0.061212</td>\n",
+       "      <td>0.051419</td>\n",
+       "      <td>0.050463</td>\n",
+       "      <td>0.057304</td>\n",
+       "      <td>0.051460</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051392</td>\n",
+       "      <td>0.063923</td>\n",
+       "      <td>0.050267</td>\n",
+       "      <td>0.125901</td>\n",
+       "      <td>1.258763</td>\n",
+       "      <td>0.052462</td>\n",
+       "      <td>0.051519</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.051962</td>\n",
+       "      <td>0.054784</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.609871</td>\n",
+       "      <td>0.050030</td>\n",
+       "      <td>0.051420</td>\n",
+       "      <td>0.051410</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.091486</td>\n",
+       "      <td>0.051426</td>\n",
+       "      <td>0.050465</td>\n",
+       "      <td>0.061034</td>\n",
+       "      <td>0.051467</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051399</td>\n",
+       "      <td>0.069057</td>\n",
+       "      <td>0.050285</td>\n",
+       "      <td>0.062053</td>\n",
+       "      <td>1.321739</td>\n",
+       "      <td>0.053182</td>\n",
+       "      <td>0.051527</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.051972</td>\n",
+       "      <td>0.057155</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.051975</td>\n",
+       "      <td>0.050030</td>\n",
+       "      <td>0.557747</td>\n",
+       "      <td>0.054309</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.070115</td>\n",
+       "      <td>0.051457</td>\n",
+       "      <td>0.050475</td>\n",
+       "      <td>0.060911</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051429</td>\n",
+       "      <td>0.068827</td>\n",
+       "      <td>0.050290</td>\n",
+       "      <td>0.061931</td>\n",
+       "      <td>1.385945</td>\n",
+       "      <td>0.053210</td>\n",
+       "      <td>0.053094</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.059695</td>\n",
+       "      <td>0.057138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.052089</td>\n",
+       "      <td>0.050032</td>\n",
+       "      <td>0.051534</td>\n",
+       "      <td>0.051523</td>\n",
+       "      <td>0.050023</td>\n",
+       "      <td>0.069080</td>\n",
+       "      <td>0.051541</td>\n",
+       "      <td>0.050501</td>\n",
+       "      <td>3.781985</td>\n",
+       "      <td>0.051586</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051511</td>\n",
+       "      <td>0.074502</td>\n",
+       "      <td>0.050296</td>\n",
+       "      <td>0.089917</td>\n",
+       "      <td>2.585125</td>\n",
+       "      <td>0.052992</td>\n",
+       "      <td>0.051650</td>\n",
+       "      <td>0.051786</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.056803</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.612539</td>\n",
+       "      <td>0.050031</td>\n",
+       "      <td>0.051482</td>\n",
+       "      <td>0.051472</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.070362</td>\n",
+       "      <td>0.051488</td>\n",
+       "      <td>0.050485</td>\n",
+       "      <td>0.061027</td>\n",
+       "      <td>0.051532</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.566014</td>\n",
+       "      <td>0.069057</td>\n",
+       "      <td>0.050295</td>\n",
+       "      <td>0.062066</td>\n",
+       "      <td>1.322035</td>\n",
+       "      <td>0.053263</td>\n",
+       "      <td>0.073381</td>\n",
+       "      <td>0.050077</td>\n",
+       "      <td>0.052059</td>\n",
+       "      <td>0.057233</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>0.051856</td>\n",
+       "      <td>22.477729</td>\n",
+       "      <td>7.183005</td>\n",
+       "      <td>0.051355</td>\n",
+       "      <td>0.050015</td>\n",
+       "      <td>0.061363</td>\n",
+       "      <td>0.065023</td>\n",
+       "      <td>0.050447</td>\n",
+       "      <td>2.851467</td>\n",
+       "      <td>0.051411</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.080029</td>\n",
+       "      <td>1.255318</td>\n",
+       "      <td>0.056317</td>\n",
+       "      <td>0.056565</td>\n",
+       "      <td>0.057033</td>\n",
+       "      <td>0.066903</td>\n",
+       "      <td>0.051468</td>\n",
+       "      <td>0.054699</td>\n",
+       "      <td>1.611046</td>\n",
+       "      <td>0.054593</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>0.051961</td>\n",
+       "      <td>0.077069</td>\n",
+       "      <td>5.711064</td>\n",
+       "      <td>0.051431</td>\n",
+       "      <td>0.050016</td>\n",
+       "      <td>0.051497</td>\n",
+       "      <td>0.069693</td>\n",
+       "      <td>0.050472</td>\n",
+       "      <td>0.050832</td>\n",
+       "      <td>0.051490</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.093134</td>\n",
+       "      <td>0.051501</td>\n",
+       "      <td>0.055322</td>\n",
+       "      <td>0.051199</td>\n",
+       "      <td>1.592440</td>\n",
+       "      <td>0.057984</td>\n",
+       "      <td>0.051550</td>\n",
+       "      <td>0.057041</td>\n",
+       "      <td>3.204279</td>\n",
+       "      <td>0.051984</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>0.052328</td>\n",
+       "      <td>0.050035</td>\n",
+       "      <td>0.051708</td>\n",
+       "      <td>0.051696</td>\n",
+       "      <td>4.051562</td>\n",
+       "      <td>0.051775</td>\n",
+       "      <td>0.987526</td>\n",
+       "      <td>0.050557</td>\n",
+       "      <td>0.050983</td>\n",
+       "      <td>0.051765</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051682</td>\n",
+       "      <td>6.652512</td>\n",
+       "      <td>0.050303</td>\n",
+       "      <td>0.051419</td>\n",
+       "      <td>0.083564</td>\n",
+       "      <td>0.052265</td>\n",
+       "      <td>0.051837</td>\n",
+       "      <td>0.050006</td>\n",
+       "      <td>0.052377</td>\n",
+       "      <td>0.054756</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>0.052075</td>\n",
+       "      <td>0.050032</td>\n",
+       "      <td>0.058136</td>\n",
+       "      <td>0.798598</td>\n",
+       "      <td>4.051370</td>\n",
+       "      <td>0.054901</td>\n",
+       "      <td>7.388775</td>\n",
+       "      <td>0.050498</td>\n",
+       "      <td>0.052861</td>\n",
+       "      <td>0.051575</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.057975</td>\n",
+       "      <td>2.655457</td>\n",
+       "      <td>0.050278</td>\n",
+       "      <td>0.053364</td>\n",
+       "      <td>0.053534</td>\n",
+       "      <td>0.052306</td>\n",
+       "      <td>0.051639</td>\n",
+       "      <td>0.050021</td>\n",
+       "      <td>0.052118</td>\n",
+       "      <td>0.054182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>0.052223</td>\n",
+       "      <td>0.055972</td>\n",
+       "      <td>0.051631</td>\n",
+       "      <td>0.064040</td>\n",
+       "      <td>0.050018</td>\n",
+       "      <td>0.051695</td>\n",
+       "      <td>0.051638</td>\n",
+       "      <td>0.050532</td>\n",
+       "      <td>1.069696</td>\n",
+       "      <td>7.039859</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051607</td>\n",
+       "      <td>0.051698</td>\n",
+       "      <td>0.051944</td>\n",
+       "      <td>0.092037</td>\n",
+       "      <td>0.051386</td>\n",
+       "      <td>0.052162</td>\n",
+       "      <td>0.051755</td>\n",
+       "      <td>26.060354</td>\n",
+       "      <td>0.052269</td>\n",
+       "      <td>0.052257</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feats: c9990, c9994, c9997  feats: kerberos, u1, u7  \\\n",
+       "0                        0.052992                 0.050029   \n",
+       "1                        1.609871                 0.050030   \n",
+       "2                        0.051975                 0.050030   \n",
+       "3                        0.052089                 0.050032   \n",
+       "4                        1.612539                 0.050031   \n",
+       "...                           ...                      ...   \n",
+       "19008                    0.051856                22.477729   \n",
+       "19009                    0.051961                 0.077069   \n",
+       "19010                    0.052328                 0.050035   \n",
+       "19011                    0.052075                 0.050032   \n",
+       "19012                    0.052223                 0.055972   \n",
+       "\n",
+       "       feats: c528, c5252, c5281  feats: c612, c6121, c6125  \\\n",
+       "0                       0.051413                   0.051403   \n",
+       "1                       0.051420                   0.051410   \n",
+       "2                       0.557747                   0.054309   \n",
+       "3                       0.051534                   0.051523   \n",
+       "4                       0.051482                   0.051472   \n",
+       "...                          ...                        ...   \n",
+       "19008                   7.183005                   0.051355   \n",
+       "19009                   5.711064                   0.051431   \n",
+       "19010                   0.051708                   0.051696   \n",
+       "19011                   0.058136                   0.798598   \n",
+       "19012                   0.051631                   0.064040   \n",
+       "\n",
+       "       feats: c2446, c2444, c24464  feats: c13713, c13130, c13134  \\\n",
+       "0                         0.050019                       0.061212   \n",
+       "1                         0.050016                       0.091486   \n",
+       "2                         0.050016                       0.070115   \n",
+       "3                         0.050023                       0.069080   \n",
+       "4                         0.050016                       0.070362   \n",
+       "...                            ...                            ...   \n",
+       "19008                     0.050015                       0.061363   \n",
+       "19009                     0.050016                       0.051497   \n",
+       "19010                     4.051562                       0.051775   \n",
+       "19011                     4.051370                       0.054901   \n",
+       "19012                     0.050018                       0.051695   \n",
+       "\n",
+       "       feats: c586, c5866, c5864  feats: c467, c4674, c4667  \\\n",
+       "0                       0.051419                   0.050463   \n",
+       "1                       0.051426                   0.050465   \n",
+       "2                       0.051457                   0.050475   \n",
+       "3                       0.051541                   0.050501   \n",
+       "4                       0.051488                   0.050485   \n",
+       "...                          ...                        ...   \n",
+       "19008                   0.065023                   0.050447   \n",
+       "19009                   0.069693                   0.050472   \n",
+       "19010                   0.987526                   0.050557   \n",
+       "19011                   7.388775                   0.050498   \n",
+       "19012                   0.051638                   0.050532   \n",
+       "\n",
+       "       feats: unlock, c1111, c11114  feats: c625, c6257, c6255  ...  \\\n",
+       "0                          0.057304                   0.051460  ...   \n",
+       "1                          0.061034                   0.051467  ...   \n",
+       "2                          0.060911                   0.051500  ...   \n",
+       "3                          3.781985                   0.051586  ...   \n",
+       "4                          0.061027                   0.051532  ...   \n",
+       "...                             ...                        ...  ...   \n",
+       "19008                      2.851467                   0.051411  ...   \n",
+       "19009                      0.050832                   0.051490  ...   \n",
+       "19010                      0.050983                   0.051765  ...   \n",
+       "19011                      0.052861                   0.051575  ...   \n",
+       "19012                      1.069696                   7.039859  ...   \n",
+       "\n",
+       "       feats: c5299, c529, c5294  feats: c16616, c16168, c16663  \\\n",
+       "0                       0.051392                       0.063923   \n",
+       "1                       0.051399                       0.069057   \n",
+       "2                       0.051429                       0.068827   \n",
+       "3                       0.051511                       0.074502   \n",
+       "4                       0.566014                       0.069057   \n",
+       "...                          ...                            ...   \n",
+       "19008                   0.080029                       1.255318   \n",
+       "19009                   0.093134                       0.051501   \n",
+       "19010                   0.051682                       6.652512   \n",
+       "19011                   0.057975                       2.655457   \n",
+       "19012                   0.051607                       0.051698   \n",
+       "\n",
+       "       feats: microsoft_authentication_package_v1_0, microsoft_authentication_package_v1_, microsoft_authentication_package_v1  \\\n",
+       "0                                               0.050267                                                                         \n",
+       "1                                               0.050285                                                                         \n",
+       "2                                               0.050290                                                                         \n",
+       "3                                               0.050296                                                                         \n",
+       "4                                               0.050295                                                                         \n",
+       "...                                                  ...                                                                         \n",
+       "19008                                           0.056317                                                                         \n",
+       "19009                                           0.055322                                                                         \n",
+       "19010                                           0.050303                                                                         \n",
+       "19011                                           0.050278                                                                         \n",
+       "19012                                           0.051944                                                                         \n",
+       "\n",
+       "       feats: c1065, c10658, c10652  \\\n",
+       "0                          0.125901   \n",
+       "1                          0.062053   \n",
+       "2                          0.061931   \n",
+       "3                          0.089917   \n",
+       "4                          0.062066   \n",
+       "...                             ...   \n",
+       "19008                      0.056565   \n",
+       "19009                      0.051199   \n",
+       "19010                      0.051419   \n",
+       "19011                      0.053364   \n",
+       "19012                      0.092037   \n",
+       "\n",
+       "       feats: cachedinteractive, remoteinteractive, interactive  \\\n",
+       "0                                               1.258763          \n",
+       "1                                               1.321739          \n",
+       "2                                               1.385945          \n",
+       "3                                               2.585125          \n",
+       "4                                               1.322035          \n",
+       "...                                                  ...          \n",
+       "19008                                           0.057033          \n",
+       "19009                                           1.592440          \n",
+       "19010                                           0.083564          \n",
+       "19011                                           0.053534          \n",
+       "19012                                           0.051386          \n",
+       "\n",
+       "       feats: c3888, c3884, u608  feats: c2327, c2323, c2727  \\\n",
+       "0                       0.052462                    0.051519   \n",
+       "1                       0.053182                    0.051527   \n",
+       "2                       0.053210                    0.053094   \n",
+       "3                       0.052992                    0.051650   \n",
+       "4                       0.053263                    0.073381   \n",
+       "...                          ...                         ...   \n",
+       "19008                   0.066903                    0.051468   \n",
+       "19009                   0.057984                    0.051550   \n",
+       "19010                   0.052265                    0.051837   \n",
+       "19011                   0.052306                    0.051639   \n",
+       "19012                   0.052162                    0.051755   \n",
+       "\n",
+       "       feats: negotiate, service, c14514  feats: c8282, c8280, c8289  \\\n",
+       "0                               0.051145                    0.051962   \n",
+       "1                               0.050077                    0.051972   \n",
+       "2                               0.050077                    0.059695   \n",
+       "3                               0.051786                    0.052132   \n",
+       "4                               0.050077                    0.052059   \n",
+       "...                                  ...                         ...   \n",
+       "19008                           0.054699                    1.611046   \n",
+       "19009                           0.057041                    3.204279   \n",
+       "19010                           0.050006                    0.052377   \n",
+       "19011                           0.050021                    0.052118   \n",
+       "19012                          26.060354                    0.052269   \n",
+       "\n",
+       "       feats: c1964, c1968, c25685  \n",
+       "0                         0.054784  \n",
+       "1                         0.057155  \n",
+       "2                         0.057138  \n",
+       "3                         0.056803  \n",
+       "4                         0.057233  \n",
+       "...                            ...  \n",
+       "19008                     0.054593  \n",
+       "19009                     0.051984  \n",
+       "19010                     0.054756  \n",
+       "19011                     0.054182  \n",
+       "19012                     0.052257  \n",
+       "\n",
+       "[19762 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# see how the model has organized features\n",
     "X = g5._node_features\n",
     "X"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "87b32e09-3ca4-49de-b8c3-2b40ffa2b01d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = g5.get_matrix(['interactive', 'c17'])\n",
+    "x.plot()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "632d6d0f-8212-4f4a-a920-7600d7456351",
    "metadata": {},
    "source": [
-    "## Put model into Predict Mode\n",
+    "## Predict | Online Mode\n",
     "\n",
-    "Once a model is fit, can predict on new batches as we demonstrate here\n",
+    "Once a model is fit, predict on new batches as we demonstrate here\n",
     "\n",
     "There are two main methods\n",
     "\n",
@@ -367,23 +1523,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "7b44d418",
    "metadata": {},
    "outputs": [],
    "source": [
     "# first sample a batch from the normal data (auth=df)\n",
-    "emb_normal, xp_normal, _ = g5.transform_umap(df.sample(200), None, kind='nodes')\n",
+    "emb_normal, xp_normal, _ = g5.transform_umap(df.sample(200), None, kind='nodes', return_graph=False)\n",
     "# then transform all the red team data\n",
-    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes')"
+    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes', return_graph=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "d0aebbbc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>x</th>\n",
+       "      <th>y</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>9.232593</td>\n",
+       "      <td>0.724252</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5.324008</td>\n",
+       "      <td>-8.997888</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.624950</td>\n",
+       "      <td>-0.399632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>9.591936</td>\n",
+       "      <td>-0.037859</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13.842589</td>\n",
+       "      <td>-3.487622</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>10.193441</td>\n",
+       "      <td>12.514707</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>4.766062</td>\n",
+       "      <td>-1.102680</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>9.568494</td>\n",
+       "      <td>-1.873951</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>11.638880</td>\n",
+       "      <td>-0.451751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>3.685098</td>\n",
+       "      <td>-6.050752</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               x          y\n",
+       "0       9.232593   0.724252\n",
+       "1       5.324008  -8.997888\n",
+       "2      10.624950  -0.399632\n",
+       "3       9.591936  -0.037859\n",
+       "4      13.842589  -3.487622\n",
+       "...          ...        ...\n",
+       "19008  10.193441  12.514707\n",
+       "19009   4.766062  -1.102680\n",
+       "19010   9.568494  -1.873951\n",
+       "19011  11.638880  -0.451751\n",
+       "19012   3.685098  -6.050752\n",
+       "\n",
+       "[19762 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# all emb's have this form\n",
     "g5._node_embedding"
@@ -391,16 +1655,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "8a8d5aa9",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.PathCollection at 0x1819f25e0>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# scatter to see how well it does.\n",
     "plt.figure(figsize=(10,7))\n",
-    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y , c='b')  # the totality of the fit data\n",
+    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y , c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
     "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data\n",
-    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation"
+    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data, to see if they occlude "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "f9f98708-f18f-4248-96fb-498a4becad89",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#g5.transform_umap(df.sample(200).append(red_team)).plot()"
    ]
   },
   {
@@ -410,17 +1708,25 @@
    "source": [
     "## 96% Reduction in Alerts\n",
     "\n",
-    "This indicates a huge reduction in the search space needed \n",
+    "This indicates a huge reduction in the search space needed.\n",
     "\n",
-    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (via Kafka, Splunk, etc)"
+    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (gotten via Kafka, Splunk, etc)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "id": "14d207db-9a58-45a3-9876-058632389f17",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "94.11%\n"
+     ]
+    }
+   ],
    "source": [
     "# percent of RED team labels we get with 10% confidence or above\n",
     "p = cluster_confidences[cluster_confidences.confidence>0.1].n_red.sum()/cluster_confidences[cluster_confidences.confidence>0.1].total_in_cluster.sum()\n",
@@ -429,21 +1735,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "755a3f27-935d-4ba8-96cb-cbff11fdf00e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18998"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# number of data points not to consider (and it's more if we look at df proper!)\n",
+    "# number of data points *not* to consider (and it's more if we look at df proper!)\n",
     "cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "id": "5fd1cc50-0900-4694-8400-c426e314ec2e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Alert Reduction 96.13%\n"
+     ]
+    }
+   ],
    "source": [
     "p = cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()/cluster_confidences.total_in_cluster.sum()\n",
     "print(f'Alert Reduction {100*p:.2f}%')"
@@ -451,10 +1776,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "id": "0ee508a5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.figure(figsize=(10,7))\n",
     "plt.plot(np.cumsum([k[2] for k in cluster_confidences.values]))\n",
@@ -477,27 +1822,146 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "id": "e0c6a16d-a899-43b6-a7ba-75b45f855a78",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 14s, sys: 38.6 s, total: 3min 52s\n",
+      "Wall time: 1min 34s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True\n",
     "if process:\n",
+    "    # ##################################  # an example of setting features explicitly, could use ModelDict \n",
     "    g = graphistry.nodes(tdf, 'node')\n",
     "    g6 = g.umap(X=['feats'], y =['RED'], \n",
-    "                min_words=100000, \n",
-    "                cardinality_threshold=2, \n",
+    "                min_words=100000, # set high to bypass sbert encoding\n",
+    "                cardinality_threshold=2, # set low to force topic modeling\n",
     "                n_topics=32,\n",
-    "                use_scaler_target=None)\n",
-    "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n",
-    "    g6.build_index()\n",
-    "    g6.save_search_instance('../data/auth-feat-supervised-topic.search')\n",
+    "                use_scaler_target=None,  # keep labels unscaled\n",
+    "                dbscan=True)  # add dbscan here\n",
+    "    # ##################################\n",
+    "    \n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
+    "    g6.save_search_instance('auth-feat-supervised-topic.search')\n",
     "else:\n",
     "    g = graphistry.bind()\n",
-    "    g6 = g.load_search_instance('../data/auth-feat-supervised-topic.search')\n",
-    "    g6, dbscan6, cluster_confidences6  = enrich(g6)\n"
+    "    g6 = g.load_search_instance('auth-feat-supervised-topic.search')\n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a98ef657-5307-41d9-ae31-79c1794b3728",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>RED</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 1 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       RED\n",
+       "0        1\n",
+       "1        1\n",
+       "2        1\n",
+       "3        1\n",
+       "4        1\n",
+       "...    ...\n",
+       "19008    0\n",
+       "19009    0\n",
+       "19010    0\n",
+       "19011    0\n",
+       "19012    0\n",
+       "\n",
+       "[19762 rows x 1 columns]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "g6.get_matrix(target=True).astype(int)"
    ]
   },
   {
@@ -508,17 +1972,45 @@
    },
    "source": [
     "### Plot\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `_dbscan` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "id": "16e09a7d",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"49d22432-d076-41b8-849e-57e30a21bd11\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=f5595a60c43e453bae8acee0787236f8&type=arrow&viztoken=52e094f5-e046-4fb7-a1fc-1c654915be20&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267679&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#49d22432-d076-41b8-849e-57e30a21bd11\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g6.name('auth 50k topic with supervised umap').plot(render=False)"
+    "g6.name('auth topic with supervised umap').plot(render=RENDER)"
    ]
   },
   {
@@ -532,55 +2024,590 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
+   "id": "1731ae44-57e0-4c3e-bad0-ac486bba589c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0        C17693 C1003\n",
+       "1         C17693 C305\n",
+       "2         C17693 C728\n",
+       "3        C17693 C1173\n",
+       "4         C17693 C294\n",
+       "             ...     \n",
+       "19008     C11843 C528\n",
+       "19009      C8470 C528\n",
+       "19010       C716 C716\n",
+       "19011     C16126 C586\n",
+       "19012     C6215 C6215\n",
+       "Name: feats2, Length: 19762, dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tdf['feats2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
    "id": "099b9d38",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (19762, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 3min 5s, sys: 38.7 s, total: 3min 44s\n",
+      "Wall time: 1min 35s\n"
+     ]
+    }
+   ],
    "source": [
     "%%time\n",
     "process = True\n",
     "if process:\n",
+    "    # #####################################\n",
     "    g = graphistry.nodes(tdf, 'node')\n",
     "    g7 = g.umap(X=['feats2'], #y =['RED'], \n",
     "                min_words=100000, \n",
     "                cardinality_threshold=2, \n",
     "                n_topics=32,\n",
-    "                use_scaler_target=None)\n",
-    "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n",
-    "    g7.build_index()\n",
-    "    g7.save_search_instance('../data/auth-feat-just-ip-topic.search')\n",
+    "                use_scaler=None,\n",
+    "                use_scaler_target=None, \n",
+    "                dbscan=True)  # add dbscan here\n",
+    "    # ###################################\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n",
+    "    g7.save_search_instance('auth-just-ip-topic.search')\n",
     "else:\n",
-    "    g7 = graphistry.bind().load_search_instance('../data/auth-feat-just-ip-topic.search')\n",
-    "    g7, dbscan7, cluster_confidences7  = enrich(g7)\n"
+    "    g7 = graphistry.bind().load_search_instance('auth-just-ip-topic.search')\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "id": "836883cb-bc66-4a40-9ca8-f01fd38b6f2a",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "source": [
     "### Plot\n",
-    "Color by `confidence` and hover over `red` team histogram to see where events occur"
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 33,
    "id": "c1e586a3",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"706035a0-ea0b-46ca-9c7e-e9dac5cc57f3\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=7befa6dc341c47b3a546be171ad384a9&type=arrow&viztoken=deb6ce2d-6a97-4538-9b96-c90272b9f0e9&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267779&info=true&play=0\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#706035a0-ea0b-46ca-9c7e-e9dac5cc57f3\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "g7.name('auth 50k topic only ips no supervision').plot(render=False)"
+    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)\n",
+    "# very similar to graph with metadata included, showing that ip-ip is strong indicator of phenomenon"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "id": "5f93d747",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>feats2: c586, c585, c5864</th>\n",
+       "      <th>feats2: c1961, c10196, c1901</th>\n",
+       "      <th>feats2: c16916, c16169, c1616</th>\n",
+       "      <th>feats2: c3636, c6363, c6365</th>\n",
+       "      <th>feats2: c4944, c4444, c8444</th>\n",
+       "      <th>feats2: c1065, c10652, c10585</th>\n",
+       "      <th>feats2: c15556, c15550, c1555</th>\n",
+       "      <th>feats2: c5999, c10999, c599</th>\n",
+       "      <th>feats2: c17693, c6937, c3937</th>\n",
+       "      <th>feats2: c8882, c8880, c8889</th>\n",
+       "      <th>...</th>\n",
+       "      <th>feats2: c2890, c280, tgt</th>\n",
+       "      <th>feats2: c3333, c3303, c3033</th>\n",
+       "      <th>feats2: c11187, c1118, c1111</th>\n",
+       "      <th>feats2: c1798, c1772, c1778</th>\n",
+       "      <th>feats2: c3435, c3434, c3597</th>\n",
+       "      <th>feats2: c2106, c210, c10000</th>\n",
+       "      <th>feats2: c1085, c1080, c1081</th>\n",
+       "      <th>feats2: c457, c222, c452</th>\n",
+       "      <th>feats2: c1268, c1226, c12689</th>\n",
+       "      <th>feats2: c6604, c16604, c16048</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.051269</td>\n",
+       "      <td>0.052370</td>\n",
+       "      <td>0.059563</td>\n",
+       "      <td>0.053012</td>\n",
+       "      <td>0.051056</td>\n",
+       "      <td>0.051073</td>\n",
+       "      <td>0.059119</td>\n",
+       "      <td>0.052026</td>\n",
+       "      <td>7.893405</td>\n",
+       "      <td>0.051389</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.101370</td>\n",
+       "      <td>0.059551</td>\n",
+       "      <td>1.219262</td>\n",
+       "      <td>0.051273</td>\n",
+       "      <td>2.699579</td>\n",
+       "      <td>3.244251</td>\n",
+       "      <td>0.051143</td>\n",
+       "      <td>0.059730</td>\n",
+       "      <td>0.051639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.051271</td>\n",
+       "      <td>0.053484</td>\n",
+       "      <td>0.066529</td>\n",
+       "      <td>0.053060</td>\n",
+       "      <td>0.051057</td>\n",
+       "      <td>0.561800</td>\n",
+       "      <td>0.067701</td>\n",
+       "      <td>0.052046</td>\n",
+       "      <td>7.851341</td>\n",
+       "      <td>0.051392</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>2.741899</td>\n",
+       "      <td>0.068965</td>\n",
+       "      <td>1.368653</td>\n",
+       "      <td>1.024759</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.108473</td>\n",
+       "      <td>0.051145</td>\n",
+       "      <td>0.070319</td>\n",
+       "      <td>0.052000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.051264</td>\n",
+       "      <td>0.053257</td>\n",
+       "      <td>0.064578</td>\n",
+       "      <td>0.053130</td>\n",
+       "      <td>0.051051</td>\n",
+       "      <td>0.051067</td>\n",
+       "      <td>0.065562</td>\n",
+       "      <td>0.052079</td>\n",
+       "      <td>7.391063</td>\n",
+       "      <td>0.051386</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.063343</td>\n",
+       "      <td>0.051320</td>\n",
+       "      <td>0.066675</td>\n",
+       "      <td>1.671434</td>\n",
+       "      <td>0.051267</td>\n",
+       "      <td>0.051138</td>\n",
+       "      <td>0.097529</td>\n",
+       "      <td>0.051139</td>\n",
+       "      <td>0.067757</td>\n",
+       "      <td>0.051922</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.051251</td>\n",
+       "      <td>0.052477</td>\n",
+       "      <td>0.065590</td>\n",
+       "      <td>0.052994</td>\n",
+       "      <td>0.051041</td>\n",
+       "      <td>0.051057</td>\n",
+       "      <td>0.063403</td>\n",
+       "      <td>0.052009</td>\n",
+       "      <td>7.892231</td>\n",
+       "      <td>0.051369</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050000</td>\n",
+       "      <td>0.051307</td>\n",
+       "      <td>3.263992</td>\n",
+       "      <td>3.747229</td>\n",
+       "      <td>0.053271</td>\n",
+       "      <td>0.051127</td>\n",
+       "      <td>0.192025</td>\n",
+       "      <td>0.051127</td>\n",
+       "      <td>0.063663</td>\n",
+       "      <td>0.051661</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.051314</td>\n",
+       "      <td>0.053518</td>\n",
+       "      <td>0.066485</td>\n",
+       "      <td>0.053112</td>\n",
+       "      <td>1.583872</td>\n",
+       "      <td>0.051108</td>\n",
+       "      <td>0.067694</td>\n",
+       "      <td>0.052094</td>\n",
+       "      <td>7.662636</td>\n",
+       "      <td>0.051438</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.051372</td>\n",
+       "      <td>0.069020</td>\n",
+       "      <td>1.370369</td>\n",
+       "      <td>0.051317</td>\n",
+       "      <td>2.325817</td>\n",
+       "      <td>0.109128</td>\n",
+       "      <td>0.051183</td>\n",
+       "      <td>0.070308</td>\n",
+       "      <td>0.052039</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19008</th>\n",
+       "      <td>0.065538</td>\n",
+       "      <td>0.052886</td>\n",
+       "      <td>0.057171</td>\n",
+       "      <td>0.051741</td>\n",
+       "      <td>0.566411</td>\n",
+       "      <td>0.051513</td>\n",
+       "      <td>0.057498</td>\n",
+       "      <td>0.051674</td>\n",
+       "      <td>0.064283</td>\n",
+       "      <td>0.051968</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.051546</td>\n",
+       "      <td>0.051878</td>\n",
+       "      <td>4.103773</td>\n",
+       "      <td>0.056866</td>\n",
+       "      <td>0.071395</td>\n",
+       "      <td>0.051617</td>\n",
+       "      <td>0.066317</td>\n",
+       "      <td>0.051617</td>\n",
+       "      <td>0.058158</td>\n",
+       "      <td>0.052165</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19009</th>\n",
+       "      <td>0.071523</td>\n",
+       "      <td>0.053326</td>\n",
+       "      <td>0.052552</td>\n",
+       "      <td>0.052867</td>\n",
+       "      <td>1.101434</td>\n",
+       "      <td>0.052483</td>\n",
+       "      <td>0.052288</td>\n",
+       "      <td>0.052754</td>\n",
+       "      <td>0.055194</td>\n",
+       "      <td>0.585756</td>\n",
+       "      <td>...</td>\n",
+       "      <td>4.655373</td>\n",
+       "      <td>0.053099</td>\n",
+       "      <td>0.052118</td>\n",
+       "      <td>0.052588</td>\n",
+       "      <td>0.052969</td>\n",
+       "      <td>0.052658</td>\n",
+       "      <td>0.051738</td>\n",
+       "      <td>0.052659</td>\n",
+       "      <td>0.052080</td>\n",
+       "      <td>0.053095</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19010</th>\n",
+       "      <td>0.052127</td>\n",
+       "      <td>0.052384</td>\n",
+       "      <td>3.672215</td>\n",
+       "      <td>1.093262</td>\n",
+       "      <td>0.051764</td>\n",
+       "      <td>0.051788</td>\n",
+       "      <td>0.051649</td>\n",
+       "      <td>0.051980</td>\n",
+       "      <td>0.053686</td>\n",
+       "      <td>0.052330</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.052224</td>\n",
+       "      <td>0.051528</td>\n",
+       "      <td>0.051865</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.051912</td>\n",
+       "      <td>0.070156</td>\n",
+       "      <td>0.051913</td>\n",
+       "      <td>0.051500</td>\n",
+       "      <td>0.052221</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19011</th>\n",
+       "      <td>4.188590</td>\n",
+       "      <td>0.052729</td>\n",
+       "      <td>2.703301</td>\n",
+       "      <td>1.608644</td>\n",
+       "      <td>0.051619</td>\n",
+       "      <td>0.051642</td>\n",
+       "      <td>0.055151</td>\n",
+       "      <td>0.051817</td>\n",
+       "      <td>0.053484</td>\n",
+       "      <td>0.052138</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.052040</td>\n",
+       "      <td>0.055276</td>\n",
+       "      <td>0.054890</td>\n",
+       "      <td>0.051957</td>\n",
+       "      <td>0.051755</td>\n",
+       "      <td>0.059697</td>\n",
+       "      <td>0.051756</td>\n",
+       "      <td>4.386074</td>\n",
+       "      <td>0.052217</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19012</th>\n",
+       "      <td>0.051894</td>\n",
+       "      <td>0.052122</td>\n",
+       "      <td>0.051637</td>\n",
+       "      <td>0.051835</td>\n",
+       "      <td>0.051572</td>\n",
+       "      <td>2.638263</td>\n",
+       "      <td>2.734615</td>\n",
+       "      <td>0.051763</td>\n",
+       "      <td>0.053282</td>\n",
+       "      <td>0.052075</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.050001</td>\n",
+       "      <td>0.051980</td>\n",
+       "      <td>0.051362</td>\n",
+       "      <td>0.051660</td>\n",
+       "      <td>0.051899</td>\n",
+       "      <td>2.212243</td>\n",
+       "      <td>0.051121</td>\n",
+       "      <td>0.051704</td>\n",
+       "      <td>0.051338</td>\n",
+       "      <td>0.051977</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>19762 rows × 32 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       feats2: c586, c585, c5864  feats2: c1961, c10196, c1901  \\\n",
+       "0                       0.051269                      0.052370   \n",
+       "1                       0.051271                      0.053484   \n",
+       "2                       0.051264                      0.053257   \n",
+       "3                       0.051251                      0.052477   \n",
+       "4                       0.051314                      0.053518   \n",
+       "...                          ...                           ...   \n",
+       "19008                   0.065538                      0.052886   \n",
+       "19009                   0.071523                      0.053326   \n",
+       "19010                   0.052127                      0.052384   \n",
+       "19011                   4.188590                      0.052729   \n",
+       "19012                   0.051894                      0.052122   \n",
+       "\n",
+       "       feats2: c16916, c16169, c1616  feats2: c3636, c6363, c6365  \\\n",
+       "0                           0.059563                     0.053012   \n",
+       "1                           0.066529                     0.053060   \n",
+       "2                           0.064578                     0.053130   \n",
+       "3                           0.065590                     0.052994   \n",
+       "4                           0.066485                     0.053112   \n",
+       "...                              ...                          ...   \n",
+       "19008                       0.057171                     0.051741   \n",
+       "19009                       0.052552                     0.052867   \n",
+       "19010                       3.672215                     1.093262   \n",
+       "19011                       2.703301                     1.608644   \n",
+       "19012                       0.051637                     0.051835   \n",
+       "\n",
+       "       feats2: c4944, c4444, c8444  feats2: c1065, c10652, c10585  \\\n",
+       "0                         0.051056                       0.051073   \n",
+       "1                         0.051057                       0.561800   \n",
+       "2                         0.051051                       0.051067   \n",
+       "3                         0.051041                       0.051057   \n",
+       "4                         1.583872                       0.051108   \n",
+       "...                            ...                            ...   \n",
+       "19008                     0.566411                       0.051513   \n",
+       "19009                     1.101434                       0.052483   \n",
+       "19010                     0.051764                       0.051788   \n",
+       "19011                     0.051619                       0.051642   \n",
+       "19012                     0.051572                       2.638263   \n",
+       "\n",
+       "       feats2: c15556, c15550, c1555  feats2: c5999, c10999, c599  \\\n",
+       "0                           0.059119                     0.052026   \n",
+       "1                           0.067701                     0.052046   \n",
+       "2                           0.065562                     0.052079   \n",
+       "3                           0.063403                     0.052009   \n",
+       "4                           0.067694                     0.052094   \n",
+       "...                              ...                          ...   \n",
+       "19008                       0.057498                     0.051674   \n",
+       "19009                       0.052288                     0.052754   \n",
+       "19010                       0.051649                     0.051980   \n",
+       "19011                       0.055151                     0.051817   \n",
+       "19012                       2.734615                     0.051763   \n",
+       "\n",
+       "       feats2: c17693, c6937, c3937  feats2: c8882, c8880, c8889  ...  \\\n",
+       "0                          7.893405                     0.051389  ...   \n",
+       "1                          7.851341                     0.051392  ...   \n",
+       "2                          7.391063                     0.051386  ...   \n",
+       "3                          7.892231                     0.051369  ...   \n",
+       "4                          7.662636                     0.051438  ...   \n",
+       "...                             ...                          ...  ...   \n",
+       "19008                      0.064283                     0.051968  ...   \n",
+       "19009                      0.055194                     0.585756  ...   \n",
+       "19010                      0.053686                     0.052330  ...   \n",
+       "19011                      0.053484                     0.052138  ...   \n",
+       "19012                      0.053282                     0.052075  ...   \n",
+       "\n",
+       "       feats2: c2890, c280, tgt  feats2: c3333, c3303, c3033  \\\n",
+       "0                      0.050000                     0.101370   \n",
+       "1                      0.050000                     2.741899   \n",
+       "2                      0.063343                     0.051320   \n",
+       "3                      0.050000                     0.051307   \n",
+       "4                      0.050001                     0.051372   \n",
+       "...                         ...                          ...   \n",
+       "19008                  0.051546                     0.051878   \n",
+       "19009                  4.655373                     0.053099   \n",
+       "19010                  0.050001                     0.052224   \n",
+       "19011                  0.050001                     0.052040   \n",
+       "19012                  0.050001                     0.051980   \n",
+       "\n",
+       "       feats2: c11187, c1118, c1111  feats2: c1798, c1772, c1778  \\\n",
+       "0                          0.059551                     1.219262   \n",
+       "1                          0.068965                     1.368653   \n",
+       "2                          0.066675                     1.671434   \n",
+       "3                          3.263992                     3.747229   \n",
+       "4                          0.069020                     1.370369   \n",
+       "...                             ...                          ...   \n",
+       "19008                      4.103773                     0.056866   \n",
+       "19009                      0.052118                     0.052588   \n",
+       "19010                      0.051528                     0.051865   \n",
+       "19011                      0.055276                     0.054890   \n",
+       "19012                      0.051362                     0.051660   \n",
+       "\n",
+       "       feats2: c3435, c3434, c3597  feats2: c2106, c210, c10000  \\\n",
+       "0                         0.051273                     2.699579   \n",
+       "1                         1.024759                     0.051145   \n",
+       "2                         0.051267                     0.051138   \n",
+       "3                         0.053271                     0.051127   \n",
+       "4                         0.051317                     2.325817   \n",
+       "...                            ...                          ...   \n",
+       "19008                     0.071395                     0.051617   \n",
+       "19009                     0.052969                     0.052658   \n",
+       "19010                     0.052132                     0.051912   \n",
+       "19011                     0.051957                     0.051755   \n",
+       "19012                     0.051899                     2.212243   \n",
+       "\n",
+       "       feats2: c1085, c1080, c1081  feats2: c457, c222, c452  \\\n",
+       "0                         3.244251                  0.051143   \n",
+       "1                         0.108473                  0.051145   \n",
+       "2                         0.097529                  0.051139   \n",
+       "3                         0.192025                  0.051127   \n",
+       "4                         0.109128                  0.051183   \n",
+       "...                            ...                       ...   \n",
+       "19008                     0.066317                  0.051617   \n",
+       "19009                     0.051738                  0.052659   \n",
+       "19010                     0.070156                  0.051913   \n",
+       "19011                     0.059697                  0.051756   \n",
+       "19012                     0.051121                  0.051704   \n",
+       "\n",
+       "       feats2: c1268, c1226, c12689  feats2: c6604, c16604, c16048  \n",
+       "0                          0.059730                       0.051639  \n",
+       "1                          0.070319                       0.052000  \n",
+       "2                          0.067757                       0.051922  \n",
+       "3                          0.063663                       0.051661  \n",
+       "4                          0.070308                       0.052039  \n",
+       "...                             ...                            ...  \n",
+       "19008                      0.058158                       0.052165  \n",
+       "19009                      0.052080                       0.053095  \n",
+       "19010                      0.051500                       0.052221  \n",
+       "19011                      4.386074                       0.052217  \n",
+       "19012                      0.051338                       0.051977  \n",
+       "\n",
+       "[19762 rows x 32 columns]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "X = g7._get_feature('nodes')\n",
+    "X = g7.get_matrix()\n",
     "X"
    ]
   },
@@ -590,14 +2617,18 @@
    "metadata": {},
    "source": [
     "# Conditional Probability\n",
-    "Let's see if  can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins."
+    "Let's see if conditiona probability of computer to computer connections can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins. \n",
+    "\n",
+    "The conditional graph is however useful to see aggregate behavior, and coloring by 'red' team shows topology of Infection"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "id": "2d6f58dd",
-   "metadata": {},
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
     "g = graphistry.edges(tdf, \"src_computer\", \"dst_computer\")"
@@ -605,49 +2636,159 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "54b83f83",
-   "metadata": {},
+   "execution_count": 37,
+   "id": "f3b44db2-b34e-4398-8c5a-7a10bbe5d681",
+   "metadata": {
+    "tags": []
+   },
    "outputs": [],
    "source": [
-    "def conditional_probability(x, given, df):\n",
-    "    \"\"\"conditional probability function over categorical variables\n",
-    "       p(x|given) = p(x,given)/p(given)\n",
-    "        \n",
-    "    Args:\n",
-    "        x: the column variable of interest given the column 'given'\n",
-    "        given: the variabe to fix constant\n",
-    "        df: dataframe with columns [given, x]\n",
-    "    Returns:\n",
-    "        pd.DataFrame: the conditional probability of x given the column 'given'\n",
-    "    \"\"\"\n",
-    "    return df.groupby([given])[x].apply(lambda g: g.value_counts()/len(g))\n"
+    "x='dst_computer'\n",
+    "given='src_computer'\n",
+    "cg = g.conditional_graph(x, given, kind='edges')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "fd738336",
+   "execution_count": 38,
+   "id": "3b2af6a2-4f10-4707-beb8-4f3447d3e3b8",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>_probs</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C1</td>\n",
+       "      <td>C612</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C2997</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10718</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C100</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17831</th>\n",
+       "      <td>C9990</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17832</th>\n",
+       "      <td>C9992</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17833</th>\n",
+       "      <td>C9994</td>\n",
+       "      <td>C9994</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17834</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17835</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C625</td>\n",
+       "      <td>0.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17836 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      src_computer dst_computer    _probs\n",
+       "0               C1         C612  1.000000\n",
+       "1              C10          C10  0.333333\n",
+       "2              C10        C2997  0.333333\n",
+       "3              C10       C10718  0.333333\n",
+       "4             C100         C528  0.500000\n",
+       "...            ...          ...       ...\n",
+       "17831        C9990         C528  0.250000\n",
+       "17832        C9992         C586  1.000000\n",
+       "17833        C9994        C9994  1.000000\n",
+       "17834        C9997         C586  0.500000\n",
+       "17835        C9997         C625  0.500000\n",
+       "\n",
+       "[17836 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "x='dst_computer'\n",
-    "given='src_computer'\n",
-    "condprobs = conditional_probability(x, given, df=tdf)\n",
-    "\n",
-    "cprob = pd.DataFrame(list(condprobs.index), columns=[given, x])\n",
-    "cprob['_probs'] = condprobs.values"
+    "# the new edge dataframe assess conditiona prob of computer-to-computer connection\n",
+    "cprob = cg._edges\n",
+    "cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 39,
    "id": "5258aee1",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# now enrich the edges dataframe with the redteam data\n",
-    "# since cprobs lost those labels during the function cal\n",
+    "# enrich the edges dataframe with the redteam data\n",
+    "# since cprobs lost those labels during the function call\n",
     "indx = cprob.src_computer.isin(red_team.src_computer) & cprob.dst_computer.isin(red_team.dst_computer)\n",
     "cprob.loc[indx, 'red'] = 1\n",
     "cprob.loc[~indx, 'red'] = 0"
@@ -655,114 +2796,218 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# full condprob graph \n",
-    "cg = graphistry.edges(cprob, x, given).bind(edge_weight='_probs')\n",
-    "cg.plot(render=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "42fb3dff",
-   "metadata": {},
-   "source": [
-    "## Learning\n",
-    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d2cd536",
-   "metadata": {},
-   "source": [
-    "Likewise the transpose conditional is even worse \n",
-    "with prob_detection ~ 6e-5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "18eafcff",
+   "execution_count": 40,
+   "id": "7ff921fc-3ecd-4404-acd7-8db943a4ebcc",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>src_computer</th>\n",
+       "      <th>dst_computer</th>\n",
+       "      <th>_probs</th>\n",
+       "      <th>red</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C1</td>\n",
+       "      <td>C612</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C2997</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C10</td>\n",
+       "      <td>C10718</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C100</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17831</th>\n",
+       "      <td>C9990</td>\n",
+       "      <td>C528</td>\n",
+       "      <td>0.250000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17832</th>\n",
+       "      <td>C9992</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17833</th>\n",
+       "      <td>C9994</td>\n",
+       "      <td>C9994</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17834</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C586</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17835</th>\n",
+       "      <td>C9997</td>\n",
+       "      <td>C625</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>17836 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      src_computer dst_computer    _probs  red\n",
+       "0               C1         C612  1.000000  0.0\n",
+       "1              C10          C10  0.333333  0.0\n",
+       "2              C10        C2997  0.333333  0.0\n",
+       "3              C10       C10718  0.333333  0.0\n",
+       "4             C100         C528  0.500000  0.0\n",
+       "...            ...          ...       ...  ...\n",
+       "17831        C9990         C528  0.250000  0.0\n",
+       "17832        C9992         C586  1.000000  0.0\n",
+       "17833        C9994        C9994  1.000000  0.0\n",
+       "17834        C9997         C586  0.500000  0.0\n",
+       "17835        C9997         C625  0.500000  0.0\n",
+       "\n",
+       "[17836 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# let's repeat but with reverse conditional\n",
-    "x='src_computer'\n",
-    "given='dst_computer'\n",
-    "condprobs2 = conditional_probability(x, given, df=tdf)\n",
-    "\n",
-    "cprob2 = pd.DataFrame(list(condprobs2.index), columns=[given, x])\n",
-    "cprob2['_probs'] = condprobs2.values"
+    "cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "74913e34",
+   "execution_count": 41,
+   "id": "b4b10152-cac9-4497-b016-dd67b54cdcf2",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# now enrich the edges dataframe with the redteam data\n",
-    "indx = cprob2.src_computer.isin(red_team.src_computer) & cprob2.dst_computer.isin(red_team.dst_computer)\n",
-    "cprob2.loc[indx, 'red'] = 1\n",
-    "cprob2.loc[~indx, 'red'] = 0"
+    "# add edges back to graphistry instance\n",
+    "cg._edges = cprob"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "22f4ac54",
+   "execution_count": 42,
+   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "            <iframe id=\"261a3679-9fa9-476f-be9f-c387259d95fa\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=8925a5854a68463a9b416463265bdd28&type=arrow&viztoken=5154942a-080e-47ea-8934-55fe43aa0055&usertag=8a6d667e-pygraphistry-0.28.6+132.gf997752.dirty&splashAfter=1674267791&info=true\"\n",
+       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
+       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
+       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
+       "                    \n",
+       "            >\n",
+       "            </iframe>\n",
+       "        \n",
+       "            <script>\n",
+       "                try {\n",
+       "                  $(\"#261a3679-9fa9-476f-be9f-c387259d95fa\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
+       "                } catch (e) { console.error('exn catching scroll', e); }\n",
+       "            </script>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "cg2 = graphistry.edges(cprob2, x, given).bind(edge_weight='_probs')\n",
-    "cg2.plot(render=False)\n",
-    "# same conclusion as above..."
+    "# full condprob graph\n",
+    "cg.plot(render=RENDER)"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "db832e1c",
+   "cell_type": "markdown",
+   "id": "42fb3dff",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# # let's see the probs better:\n",
-    "# for src in red_team.src_computer.unique():\n",
-    "#     for dst in red_team.dst_computer.unique():\n",
-    "#         if dst in condprobs[src]:\n",
-    "#             print('-'*30)\n",
-    "#             print(f'given src {src} -> dst {dst}')\n",
-    "#             print('-'*10)\n",
-    "#             print(f'   {condprobs[src][dst]*100:.2f}%')\n",
-    "#             print()"
+    "## Learning\n",
+    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most of the events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "21f51de6",
+   "cell_type": "markdown",
+   "id": "9d2cd536",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "# for dst in red_team.dst_computer.unique():\n",
-    "#     for src in red_team.src_computer.unique():\n",
-    "#         if src in condprobs2[dst]:\n",
-    "#             print('-'*20)\n",
-    "#             print(f'given dst {dst} -> src {src}')\n",
-    "#             print('-'*10)\n",
-    "#             print(f'  {condprobs2[dst][src]*100:.2f}%')\n",
-    "#             print()"
+    "Likewise the transpose conditional is even worse \n",
+    "with prob_detection ~ 6e-5"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "c3a008f6-75ed-4045-b13c-494cb015d185",
+   "id": "e0cbef82-421d-489e-8666-84d412cae5a9",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
new file mode 100644
index 0000000000..5b8db6ae70
--- /dev/null
+++ b/demos/ai/cyber/redteam-umap-gtc-gpu.ipynb
@@ -0,0 +1,1034 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "beb5e3e3-f8cd-40ed-bc63-8a862000f192",
+   "metadata": {},
+   "source": [
+    "# Analyzing Network Identity Data and Red Team Response with Graphistry AutoML + UMAP\n",
+    "\n",
+    "We find a simple model that when clustered in a 2d plane via UMAP allows fast identification of anomalous \n",
+    "computer to computer connections"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f9de6fd3-b87b-4dc4-8d1c-b8f3feceb5e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# ! pip install graphistry[ai] \n",
+    "! pip install --user --no-deps git+https://github.com/graphistry/pygraphistry.git@cudf-alex3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0215906c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "from joblib import load, dump\n",
+    "from collections import Counter\n",
+    "\n",
+    "import numpy as np\n",
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "import graphistry\n",
+    "from graphistry.features import topic_model, search_model, ModelDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b34bebd-c91d-49fe-82c9-ec1c83a4a6c1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graphistry.__version__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8e1747b9-c903-4398-9aa0-b52b69fce021",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "np.random.seed(137)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6d2669fd-6164-4376-81bd-79c6c6f4112f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "RENDER = True # set to True to render Graphistry UI inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "59e1cc0b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "graphistry.register(api=3, protocol=\"https\", server=\"hub.graphistry.com\", username = '..',\n",
+    "                    #os.environ['USERNAME'], \n",
+    "                    password='..'\n",
+    "                    #os.environ['GRAPHISTRY_PASSWORD']\n",
+    "                   )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "877b4e50-8fa8-4663-bba0-91b661fc735f",
+   "metadata": {},
+   "source": [
+    "Alert on & visualize anomalous identity events\n",
+    "\n",
+    "Demo dataset: 1.6B windows events over 58 days => logins by 12K user over 14K systems\n",
+    "adapt to any identity system with logins. Here we subsample down to a small set of 50k events to prove out the pipeline. \n",
+    "\n",
+    "* => Can we identify accounts & computers acting anomalously? Resources being oddly accessed?\n",
+    "* => Can we spot the red team?\n",
+    "* => Operations: Identity incident alerting + identity data investigations\n",
+    "\n",
+    "Community/contact for help handling bigger-than-memory & additional features\n",
+    "\n",
+    "Runs on both CPU + multi-GPU\n",
+    "Tools: PyGraphistry[AI], DGL + PyTorch, and NVIDIA RAPIDS / umap-learn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe6e61b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# data source citation\n",
+    "# \"\"\"A. D. Kent, \"Cybersecurity Data Sources for Dynamic Network Research,\"\n",
+    "# in Dynamic Networks in Cybersecurity, 2015.\n",
+    "\n",
+    "# @InProceedings{akent-2015-enterprise-data,\n",
+    "#    author = {Alexander D. Kent},\n",
+    "#    title = {{Cybersecurity Data Sources for Dynamic Network Research}},\n",
+    "#    year = 2015,\n",
+    "#    booktitle = {Dynamic Networks in Cybersecurity},\n",
+    "#    month =        jun,\n",
+    "#    publisher = {Imperial College Press}\n",
+    "# }\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "554c0d85-1c8a-47f0-87ec-1629d7f7ba3b",
+   "metadata": {},
+   "source": [
+    "# Get the Data\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "efe68cf8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# small sample (get almost equivalent results without overheating computer over the 1.6B events in the full dataset)\n",
+    "df = pd.read_csv('https://gist.githubusercontent.com/silkspace/c7b50d0c03dc59f63c48d68d696958ff/raw/31d918267f86f8252d42d2e9597ba6fc03fcdac2/redteam_50k.csv', index_col=0)\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "93bab916-a6c1-4a63-95de-2e8d2a72d8a6",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2023-03-20T17:41:26.708147Z",
+     "iopub.status.busy": "2023-03-20T17:41:26.707740Z",
+     "iopub.status.idle": "2023-03-20T17:41:26.711459Z",
+     "shell.execute_reply": "2023-03-20T17:41:26.710695Z",
+     "shell.execute_reply.started": "2023-03-20T17:41:26.708118Z"
+    }
+   },
+   "source": [
+    "# Graphistry UMAP in a single line of code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "29e99375-5b24-4760-b5ed-909f51949f7f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# umap pipeline in one line\n",
+    "g = graphistry.nodes(df.sample(1000)).umap(engine='umap_learn')\n",
+    "g.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03610297",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(df.shape) # -> 50+k"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66c5126e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# here are the post-facto red team events\n",
+    "red_team = pd.read_csv('https://gist.githubusercontent.com/silkspace/5cf5a94b9ac4b4ffe38904f20d93edb1/raw/888dabd86f88ea747cf9ff5f6c44725e21536465/redteam_labels.csv', index_col=0)\n",
+    "red_team['feats2'] = red_team.feats  # since red team data didn't include metadata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c6615aa",
+   "metadata": {},
+   "source": [
+    "# Modeling\n",
+    "\n",
+    "Make sure you `mkdir(data)` or change path below\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3641d3b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "process = True  \n",
+    "# makes a combined feature we can use for topic modeling!\n",
+    "if process:\n",
+    "    # we create two types of models\n",
+    "    df['feats'] = df.src_computer + ' ' + df.dst_computer + ' ' + df.auth_type + ' ' + df.logontype\n",
+    "    # and one of just computer to computer \n",
+    "    df['feats2'] = df.src_computer + ' ' + df.dst_computer\n",
+    "    ndf = df.drop_duplicates(subset=['feats'])\n",
+    "    ndf.to_parquet('auth-feats-one-column.parquet')\n",
+    "else:\n",
+    "    ndf = pd.read_parquet('auth-feats-one-column.parquet')\n",
+    "    \n",
+    "print(ndf.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32d1755d",
+   "metadata": {},
+   "source": [
+    "## Red Team Data \n",
+    "Add it to the front of the DataFrame so we can keep track of it"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d67c86b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# make a subsampled dataframe with the anom red-team data at top...so we can keep track.\n",
+    "# we don't need the full `df`, only the unique entries of 'feats' in `ndf` for \n",
+    "# fitting a model (in a few cells below)\n",
+    "\n",
+    "tdf = pd.concat([red_team.reset_index(), ndf.reset_index()])\n",
+    "tdf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f62b7b5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add a fidicial index used later\n",
+    "tdf['node'] = range(len(tdf))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5ffd6aac",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# total number of red team events\n",
+    "tdf.RED.sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4264d547-b4a9-49d1-bc68-894f1e839c38",
+   "metadata": {},
+   "source": [
+    "## Enrichment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72c53f98",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_confidences_per_cluster(g, col='RED', verbose=False):\n",
+    "    \"\"\"\n",
+    "        From DBSCAN clusters, will assess how many Red Team events exist,\n",
+    "        assessing confidence.\n",
+    "        \n",
+    "    \"\"\"\n",
+    "    resses = []\n",
+    "    df = g._nodes\n",
+    "    labels = df._dbscan\n",
+    "    cnt = Counter(labels)\n",
+    "    for clust, count in cnt.most_common():\n",
+    "        res = df[df._dbscan==clust]\n",
+    "        n = res.shape[0]\n",
+    "        n_reds = res[col].sum()\n",
+    "        resses.append([clust, n_reds/n, n_reds, n])\n",
+    "        if n_reds>0 and verbose:\n",
+    "            print('-'*20)\n",
+    "            print(f'cluster: {clust}\\n   red {100*n_reds/n:.2f}% or {n_reds} out of {count}')\n",
+    "    conf_dict = {k[0]: k[1] for k in resses}\n",
+    "    confidence = [conf_dict[k] for k in df._dbscan.values]\n",
+    "    # enrichment\n",
+    "    g._nodes['confidence'] = confidence\n",
+    "    conf_df = pd.DataFrame(resses, columns=['_dbscan', 'confidence', 'n_red', 'total_in_cluster'])\n",
+    "    conf_df = conf_df.sort_values(by='confidence', ascending=False)\n",
+    "    return g, conf_df\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9a3da6e3-b280-4c69-b0e0-4a92d9aac231",
+   "metadata": {},
+   "source": [
+    "# The Full UMAP Pipelines\n",
+    "Fit a model on 'feats' column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "504781dc-9fbe-467c-9b4d-2e907133cfb7",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# this is a convienence method for setting parameters in `g.featurize()/umap()` -- just a verbose dictionary\n",
+    "cyber_model = ModelDict('A topic model for computer to computer', **topic_model)\n",
+    "\n",
+    "# umap_params_gpu = {'n_components': 2, \n",
+    "#                    'n_neighbors': 20,\n",
+    "#                    'min_dist': 0.1, \n",
+    "#                    'spread': 1, \n",
+    "#                    'local_connectivity': 1, \n",
+    "#                    'repulsion_strength': 2, \n",
+    "#                    'negative_sample_rate': 5}\n",
+    "#cyber_model.update(umap_params_gpu)\n",
+    "\n",
+    "cyber_model.update(dict(n_topics=32, X=['feats2']))  # name the column to featurize, which we lumped into `feats2`\n",
+    "\n",
+    "cyber_model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5474ef79-b2dd-4299-bee7-e12d94c79613",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# if you stop processing during execution, sometimes calling this will unblock you on subsequent calls should it give an error.\n",
+    "#g.reset_caches()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6909cc90",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True  # set to false after it's run for ease of speed\n",
+    "if process:\n",
+    "    # ##################################\n",
+    "    g = graphistry.nodes(tdf, 'node')  # two lines does the heavy lifting\n",
+    "    # gpu version, will detect gpu and run\n",
+    "    #g5 = g.umap(engine='auto', **cyber_model, verbose=True).dbscan(min_dist=1, verbose=True)\n",
+    "    \n",
+    "    # cpu version\n",
+    "    g5 = g.umap(engine='umap_learn', **cyber_model, verbose=True).dbscan(min_dist=0.1, verbose=True)\n",
+    "    # #########################\n",
+    "    \n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5, verbose=True)\n",
+    "    g5.save_search_instance('auth-feat-topic.search')\n",
+    "else:\n",
+    "    g = graphistry.bind()\n",
+    "    g5 = g.load_search_instance('auth-feat-topic.search')\n",
+    "    g5, cluster_confidences = get_confidences_per_cluster(g5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "01632281-2ace-4917-9932-86b507b3d9e3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# nodes dataframe is now enriched with _dbscan label\n",
+    "g5._nodes._dbscan"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9c1ba011-2aaa-4663-a319-4478502b1b8e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the UMAP coordinates\n",
+    "g5._node_embedding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "54c13cba-bc36-4d49-8e7a-7dc05b27610a",
+   "metadata": {},
+   "source": [
+    "## Plot Graph\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "279fef41",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g5.name('auth test').plot(render=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79ece955",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# see how the model has organized features\n",
+    "X = g5._node_features\n",
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "87b32e09-3ca4-49de-b8c3-2b40ffa2b01d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x = g5.get_matrix(['interactive', 'c17', 'microsoft'])\n",
+    "x.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "632d6d0f-8212-4f4a-a920-7600d7456351",
+   "metadata": {},
+   "source": [
+    "## Predict | Online Mode\n",
+    "\n",
+    "Once a model is fit, predict on new batches as we demonstrate here\n",
+    "\n",
+    "There are three main methods\n",
+    "\n",
+    "`g.transform` and `g.transform_umap` and if dbscan has been run, `g.transform_dbscan` \n",
+    "\n",
+    "see help(*) on each to learn more\n",
+    "\n",
+    "One may save the model as above, load it, and wrap in a FastAPI endpoint, etc, to serve in production pipelines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7b44d418",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# first sample a batch from the normal data (auth=df)\n",
+    "sdf = df.sample(200)\n",
+    "emb_normal, xp_normal, _ = g5.transform_umap(sdf, None, kind='nodes', return_graph=False)\n",
+    "# then transform all the red team data\n",
+    "emb_red, xp_red, _ = g5.transform_umap(red_team, None, kind='nodes', return_graph=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe3e5058-6ac6-4d1a-a368-66ecd5dd703b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "emb_red"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f2b6c471-338a-40d6-92a8-03c2505c433f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# transform_dbscan will predict on new data (here just red_team to prove it works)\n",
+    "g7 = g5.transform_dbscan(red_team, None, kind='nodes', return_graph=True, verbose=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ad82c787-c246-440d-9ed6-97ddc2805491",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "_, ccdf = get_confidences_per_cluster(g7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5e0760fe-40c0-45b9-a787-d4f98d557c24",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "print(f'total confidence across clusters {ccdf.confidence.mean()*100}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2911840d-ffd7-4815-97fd-53bc43cbc522",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g7.plot()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ace3e171-2e33-435e-82d7-7158d7931d14",
+   "metadata": {},
+   "source": [
+    "# We can simulate how a batch of new data would behave"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03672813-db4e-4d0c-a5f5-598ab165986c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# cpu version\n",
+    "plt.figure(figsize=(10,7))\n",
+    "plt.scatter(g5._node_embedding.x, g5._node_embedding.y, c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data\n",
+    "plt.scatter(emb_red.x, emb_red.y, c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x, emb_normal.y, c='g') # batch of new data, to see if they occlude "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8a8d5aa9",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# gpu version\n",
+    "# scatter to see how well it does.\n",
+    "plt.figure(figsize=(10,7))\n",
+    "plt.scatter(g5._node_embedding.x.to_numpy(), g5._node_embedding.y.to_numpy() , c='b', s=60, alpha=0.5)  # the totality of the fit data\n",
+    "plt.scatter(emb_normal.x.to_numpy(), emb_normal.y.to_numpy(), c='g') # batch of new data\n",
+    "plt.scatter(emb_red.x.to_numpy(), emb_red.y.to_numpy(), c='r') # red labels to show good cluster seperation\n",
+    "plt.scatter(emb_normal.x.to_numpy(), emb_normal.y.to_numpy(), c='g') # batch of new data, to see if they occlude "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b53dd8ed-39b2-4000-9ec7-139d1e2a6a85",
+   "metadata": {},
+   "source": [
+    "## 96% Reduction in Alerts\n",
+    "\n",
+    "This indicates a huge reduction in the search space needed.\n",
+    "\n",
+    "Since we have clear cluster assignments along with (post facto) confidences of known anomalous activity, we can reduce the search space on new events (gotten via Kafka, Splunk, etc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14d207db-9a58-45a3-9876-058632389f17",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# percent of RED team labels we get with 10% confidence or above\n",
+    "p = cluster_confidences[cluster_confidences.confidence>0.1].n_red.sum()/cluster_confidences[cluster_confidences.confidence>0.1].total_in_cluster.sum()\n",
+    "print(f'{100*p:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "755a3f27-935d-4ba8-96cb-cbff11fdf00e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# number of data points *not* to consider (and it's more if we look at df proper!)\n",
+    "cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fd1cc50-0900-4694-8400-c426e314ec2e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "p = cluster_confidences[cluster_confidences.confidence<0.1].total_in_cluster.sum()/cluster_confidences.total_in_cluster.sum()\n",
+    "print(f'Alert Reduction {100*p:.2f}%')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0ee508a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "plt.figure(figsize=(10,7))\n",
+    "plt.plot(np.cumsum([k[2] for k in cluster_confidences.values]))\n",
+    "plt.xlabel('Anomolous Cluster Number')  # shows that we can ignore first clusters (containing most of the alerts)\n",
+    "plt.ylabel('Number of Identified Red Team Events')\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f168ac8-2324-4f47-b0d7-e4a0b041624f",
+   "metadata": {},
+   "source": [
+    "## Supervised UMAP\n",
+    "Here we use the RED team label to help supervise the UMAP fit. \n",
+    "This might be useful once teams have actually identified RED team events \n",
+    "and want to help separate clusters. \n",
+    "While separation is better, the unsupervised version does well without."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "34ad4768-58e5-493e-a5e8-6f4748168e05",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# g.reset_caches()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0c6a16d-a899-43b6-a7ba-75b45f855a78",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True\n",
+    "if process:\n",
+    "    # ##################################  # an example of setting features explicitly, could use ModelDict \n",
+    "    g = graphistry.nodes(tdf, 'node')\n",
+    "    g6 = g.umap(X=['feats'], y =['RED'], \n",
+    "                min_words=100000, # set high to bypass sbert encoding\n",
+    "                cardinality_threshold=2, # set low to force topic modeling\n",
+    "                n_topics=32,\n",
+    "                spread=1,\n",
+    "                use_scaler_target=None,  # keep labels unscaled\n",
+    "                dbscan=True, engine='umap_learn')  # add dbscan here\n",
+    "    # ##################################\n",
+    "    \n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6, verbose=True)\n",
+    "    g6.save_search_instance('auth-feat-supervised-topic.search')\n",
+    "else:\n",
+    "    g = graphistry.bind()\n",
+    "    g6 = g.load_search_instance('auth-feat-supervised-topic.search')\n",
+    "    g6, cluster_confidences6  = get_confidences_per_cluster(g6)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a98ef657-5307-41d9-ae31-79c1794b3728",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g6.get_matrix(target=True).astype(int)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cc72ab4-c0da-4541-b32b-aa771d6e510f",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Plot\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `_dbscan` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "16e09a7d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g6.name('auth topic with supervised umap').plot(render=RENDER)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "88169a53",
+   "metadata": {},
+   "source": [
+    "## A model of Computer-Computer and metadata features\n",
+    "Here we include `auth_type` and `logontype` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1731ae44-57e0-4c3e-bad0-ac486bba589c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "tdf['feats']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "35b03bc4-915b-431b-ada5-d8281a4ece6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "process = True\n",
+    "if process:\n",
+    "    # #####################################\n",
+    "    g = graphistry.nodes(tdf, 'node')\n",
+    "    g7 = g.umap(X=['feats'], #y =['RED'], \n",
+    "                min_words=100000, \n",
+    "                cardinality_threshold=2, \n",
+    "                n_topics=32,\n",
+    "                use_scaler=None,\n",
+    "                use_scaler_target=None, \n",
+    "                spread=1,\n",
+    "                dbscan=True, engine='auto')  # add dbscan here\n",
+    "    # ###################################\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n",
+    "    #g7.save_search_instance('auth-just-ip-topic.search')\n",
+    "else:\n",
+    "    g7 = graphistry.bind().load_search_instance('auth-just-ip-topic.search')\n",
+    "    g7, cluster_confidences7  = get_confidences_per_cluster(g7)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f291e227-ae14-4205-96dd-3c1de29d12e6",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cluster_confidences7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "836883cb-bc66-4a40-9ca8-f01fd38b6f2a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "### Plot\n",
+    "Color by `confidence` and hover over `red` team histogram to see where events occur. Alternatively, color by `cluster` assignment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1e586a3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g7.name('auth topic ips-ips only, no supervision').plot(render=RENDER)\n",
+    "# very similar to graph with metadata included, showing that ip-ip is strong indicator of phenomenon"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6cf68ed4",
+   "metadata": {},
+   "source": [
+    "# Conditional Probability\n",
+    "Let's see if conditiona probability of computer to computer connections can give us good histograms to tease out red team nodes? This is to baseline the above UMAP models, and we find in retrospect, UMAP wins. \n",
+    "\n",
+    "The conditional graph is however useful to see aggregate behavior, and coloring by 'red' team shows topology of Infection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d6f58dd",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "g = graphistry.edges(tdf, \"src_computer\", \"dst_computer\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f3b44db2-b34e-4398-8c5a-7a10bbe5d681",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "x='dst_computer'\n",
+    "given='src_computer'\n",
+    "cg = g.conditional_graph(x, given, kind='edges')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b2af6a2-4f10-4707-beb8-4f3447d3e3b8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# the new edge dataframe assess conditiona prob of computer-to-computer connection\n",
+    "cprob = cg._edges\n",
+    "cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5258aee1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# enrich the edges dataframe with the redteam data\n",
+    "# since cprobs lost those labels during the function call\n",
+    "indx = cprob.src_computer.isin(red_team.src_computer) & cprob.dst_computer.isin(red_team.dst_computer)\n",
+    "cprob.loc[indx, 'red'] = 1\n",
+    "cprob.loc[~indx, 'red'] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ff921fc-3ecd-4404-acd7-8db943a4ebcc",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b4b10152-cac9-4497-b016-dd67b54cdcf2",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# add edges back to graphistry instance\n",
+    "cg._edges = cprob"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9b3af1cd-6423-4484-8b99-81fad821f118",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# full condprob graph\n",
+    "cg.plot(render=RENDER)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "42fb3dff",
+   "metadata": {},
+   "source": [
+    "## Learning\n",
+    "The conditional graph shows that most of the edge probabilities are between 4e-7 and 0.03, whose bucket contains most of the events. Thus the chances of finding the red team edges are ~ 1e-4 -- slim indeed. UMAP wins."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d2cd536",
+   "metadata": {},
+   "source": [
+    "Likewise the transpose conditional is even worse \n",
+    "with prob_detection ~ 6e-5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e0cbef82-421d-489e-8666-84d412cae5a9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb b/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
index 14e67afb7f..849887d4b9 100644
--- a/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
+++ b/demos/demos_databases_apis/gpu_rapids/part_iv_gpu_cuml.ipynb
@@ -63,7 +63,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 2,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -101,43 +101,43 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>61</td>\n",
-       "      <td>26</td>\n",
-       "      <td>937</td>\n",
-       "      <td>2019-04-05</td>\n",
-       "      <td>113.47,20.34</td>\n",
+       "      <td>32</td>\n",
+       "      <td>185</td>\n",
+       "      <td>357</td>\n",
+       "      <td>2017-06-16</td>\n",
+       "      <td>117.81,22.87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>30</td>\n",
-       "      <td>19</td>\n",
-       "      <td>972</td>\n",
-       "      <td>2019-08-17</td>\n",
-       "      <td>117.61,20.24</td>\n",
+       "      <td>66</td>\n",
+       "      <td>86</td>\n",
+       "      <td>84</td>\n",
+       "      <td>2020-03-30</td>\n",
+       "      <td>110.07,20.52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>27</td>\n",
-       "      <td>134</td>\n",
-       "      <td>760</td>\n",
-       "      <td>2020-05-30</td>\n",
-       "      <td>115.11,23.5</td>\n",
+       "      <td>28</td>\n",
+       "      <td>26</td>\n",
+       "      <td>862</td>\n",
+       "      <td>2019-05-12</td>\n",
+       "      <td>116.16,23.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>55</td>\n",
-       "      <td>44</td>\n",
-       "      <td>864</td>\n",
-       "      <td>2016-08-17</td>\n",
-       "      <td>119.14,21.56</td>\n",
+       "      <td>69</td>\n",
+       "      <td>193</td>\n",
+       "      <td>607</td>\n",
+       "      <td>2019-03-11</td>\n",
+       "      <td>112.21,23.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>24</td>\n",
-       "      <td>184</td>\n",
-       "      <td>938</td>\n",
-       "      <td>2017-09-30</td>\n",
-       "      <td>113.64,23.54</td>\n",
+       "      <td>34</td>\n",
+       "      <td>27</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2019-08-06</td>\n",
+       "      <td>114.56,20.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -149,43 +149,43 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>995</th>\n",
-       "      <td>69</td>\n",
-       "      <td>72</td>\n",
-       "      <td>887</td>\n",
-       "      <td>2019-10-26</td>\n",
-       "      <td>115.18,23.8</td>\n",
+       "      <td>52</td>\n",
+       "      <td>128</td>\n",
+       "      <td>435</td>\n",
+       "      <td>2016-10-19</td>\n",
+       "      <td>115.3,23.67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>996</th>\n",
-       "      <td>33</td>\n",
-       "      <td>29</td>\n",
-       "      <td>651</td>\n",
-       "      <td>2020-06-15</td>\n",
-       "      <td>117.05,21.3</td>\n",
+       "      <td>67</td>\n",
+       "      <td>116</td>\n",
+       "      <td>97</td>\n",
+       "      <td>2016-04-24</td>\n",
+       "      <td>117.69,23.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>997</th>\n",
-       "      <td>18</td>\n",
-       "      <td>101</td>\n",
-       "      <td>517</td>\n",
-       "      <td>2019-04-14</td>\n",
-       "      <td>111.96,23.58</td>\n",
+       "      <td>32</td>\n",
+       "      <td>55</td>\n",
+       "      <td>915</td>\n",
+       "      <td>2018-11-07</td>\n",
+       "      <td>113.63,22.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>998</th>\n",
-       "      <td>65</td>\n",
-       "      <td>19</td>\n",
-       "      <td>974</td>\n",
-       "      <td>2019-05-22</td>\n",
-       "      <td>112.48,23.63</td>\n",
+       "      <td>72</td>\n",
+       "      <td>68</td>\n",
+       "      <td>148</td>\n",
+       "      <td>2020-05-23</td>\n",
+       "      <td>116.39,21.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>999</th>\n",
-       "      <td>23</td>\n",
-       "      <td>42</td>\n",
-       "      <td>156</td>\n",
-       "      <td>2020-12-10</td>\n",
-       "      <td>118.72,22.49</td>\n",
+       "      <td>56</td>\n",
+       "      <td>19</td>\n",
+       "      <td>932</td>\n",
+       "      <td>2016-04-23</td>\n",
+       "      <td>116.2,23.54</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -193,23 +193,23 @@
        "</div>"
       ],
       "text/plain": [
-       "     age  user_id  profile        date      location\n",
-       "0     61       26      937  2019-04-05  113.47,20.34\n",
-       "1     30       19      972  2019-08-17  117.61,20.24\n",
-       "2     27      134      760  2020-05-30   115.11,23.5\n",
-       "3     55       44      864  2016-08-17  119.14,21.56\n",
-       "4     24      184      938  2017-09-30  113.64,23.54\n",
-       "..   ...      ...      ...         ...           ...\n",
-       "995   69       72      887  2019-10-26   115.18,23.8\n",
-       "996   33       29      651  2020-06-15   117.05,21.3\n",
-       "997   18      101      517  2019-04-14  111.96,23.58\n",
-       "998   65       19      974  2019-05-22  112.48,23.63\n",
-       "999   23       42      156  2020-12-10  118.72,22.49\n",
+       "    age user_id profile        date      location\n",
+       "0    32     185     357  2017-06-16  117.81,22.87\n",
+       "1    66      86      84  2020-03-30  110.07,20.52\n",
+       "2    28      26     862  2019-05-12  116.16,23.02\n",
+       "3    69     193     607  2019-03-11  112.21,23.25\n",
+       "4    34      27       4  2019-08-06  114.56,20.99\n",
+       "..   ..     ...     ...         ...           ...\n",
+       "995  52     128     435  2016-10-19   115.3,23.67\n",
+       "996  67     116      97  2016-04-24  117.69,23.92\n",
+       "997  32      55     915  2018-11-07  113.63,22.74\n",
+       "998  72      68     148  2020-05-23  116.39,21.25\n",
+       "999  56      19     932  2016-04-23   116.2,23.54\n",
        "\n",
        "[1000 rows x 5 columns]"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -230,12 +230,13 @@
     "df['lat']=np.round(np.random.uniform(110, 120,size=(samples)), 2)\n",
     "df['location']=df['lat'].astype(str) +\",\"+ df[\"lon\"].astype(str) \n",
     "df.drop(columns=['lat','lon'],inplace=True)\n",
+    "df = df.applymap(str)\n",
     "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 3,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -243,38 +244,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.03180466492970784 line/min: 31441.928478420414']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"d9d175c0-26d5-4056-b8c7-b22828cd5b39\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=0cf4dfc3b18044199b33d1089f5b9c64&type=arrow&viztoken=c846ac21-e17d-4d22-87db-19ab202ecf0d&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820405&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#d9d175c0-26d5-4056-b8c7-b22828cd5b39\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.14064184427261353 line/min: 7110.259433612426']\n"
+     ]
     }
    ],
    "source": [
@@ -296,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 4,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -304,38 +285,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.02227895657221476 line/min: 44885.40550625031']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"95993363-1f8e-4866-ad87-0b7a9fb25d5b\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=b8031f5c47214275b8ae8d4c50b2bd7c&type=arrow&viztoken=15b29905-a47d-4bee-986e-7e9983506b1d&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820408&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#95993363-1f8e-4866-ad87-0b7a9fb25d5b\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0287002166112264 line/min: 34842.94260026035']\n"
+     ]
     }
    ],
    "source": [
@@ -350,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 5,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -358,38 +319,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.023025786876678465 line/min: 43429.56900260569']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"58624fc9-fcef-4650-8d27-8e8111b20f5d\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=bf0b491ec4264ec489d64edb8fdf8a72&type=arrow&viztoken=cb6b7978-1d15-4d8d-9f2f-8579be7f8f35&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820508&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#58624fc9-fcef-4650-8d27-8e8111b20f5d\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 115,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0024895787239074705 line/min: 401674.38386140653']\n"
+     ]
     }
    ],
    "source": [
@@ -411,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 6,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -419,61 +360,30 @@
    },
    "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "['time: 0.003930246829986573 line/min: 254436.94588602122']\n"
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 14) in UMAP fit, as it is not one dimensional"
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"e51a24bb-44f3-4fe8-990e-6fcef25e2948\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=77aaa2167df04db49aed214cfd548256&type=arrow&viztoken=26ab4e56-2798-4449-83ba-e116f07b36f8&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663820701&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#e51a24bb-44f3-4fe8-990e-6fcef25e2948\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 117,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['time: 0.0022179365158081056 line/min: 450869.5325013168']\n"
+     ]
     }
    ],
    "source": [
     "g = graphistry.nodes(df)\n",
     "t=time()\n",
-    "g2 = g.umap(X=['user_id'],y=['date','location'], feature_engine='torch', n_neighbors= 2,min_dist=.5, spread=.1, local_connectivity=2, n_components=5,metric='hellinger')\n",
+    "g2 = g.umap(X=['user_id'],y=['date','location'], feature_engine='torch', n_neighbors= 2,min_dist=.1, spread=.1, local_connectivity=2, n_components=5,metric='hellinger')\n",
     "min=(time()-t)/60\n",
     "lin=df.shape[0]/min\n",
     "print(['time: '+str(min)+' line/min: '+str(lin)])\n",
-    "g2.plot()\n"
+    "g2.plot(render=False)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -483,18 +393,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 7,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.004134837786356608 line/min: 241847.4560960093']\n"
+      "['time: 0.00446544885635376 line/min: 223941.65338544376']\n"
      ]
     }
    ],
@@ -509,18 +426,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 8,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "* Ignoring target column of shape (1000, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.06711641947428386 line/min: 14899.483730403068']\n"
+      "['time: 0.11818180878957113 line/min: 8461.539134001174']\n"
      ]
     }
    ],
@@ -542,18 +466,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 12,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "! Failed umap speedup attempt. Continuing without memoization speedups.* Ignoring target column of shape (220, 0) in UMAP fit, as it is not one dimensional"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['time: 0.0008151054382324219 line/min: 269903.7323037323']\n"
+      "['time: 0.008098324139912924 line/min: 27166.11439590581']\n"
      ]
     }
    ],
@@ -570,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 13,
    "metadata": {
     "vscode": {
      "languageId": "python"
@@ -582,15 +513,15 @@
      "output_type": "stream",
      "text": [
       "<class 'pandas.core.frame.DataFrame'>\n",
-      "Int64Index: 3728 entries, 0 to 3749\n",
+      "Int64Index: 2410 entries, 0 to 2821\n",
       "Data columns (total 3 columns):\n",
       " #   Column         Non-Null Count  Dtype  \n",
       "---  ------         --------------  -----  \n",
-      " 0   _src_implicit  3728 non-null   int32  \n",
-      " 1   _dst_implicit  3728 non-null   int32  \n",
-      " 2   _weight        3728 non-null   float32\n",
+      " 0   _src_implicit  2410 non-null   int32  \n",
+      " 1   _dst_implicit  2410 non-null   int32  \n",
+      " 2   _weight        2410 non-null   float32\n",
       "dtypes: float32(1), int32(2)\n",
-      "memory usage: 72.8 KB\n",
+      "memory usage: 47.1 KB\n",
       "None\n"
      ]
     },
@@ -622,34 +553,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>1046</th>\n",
-       "      <td>71</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0.205078</td>\n",
+       "      <th>671</th>\n",
+       "      <td>51</td>\n",
+       "      <td>123</td>\n",
+       "      <td>0.017956</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>642</th>\n",
-       "      <td>41</td>\n",
-       "      <td>74</td>\n",
-       "      <td>0.176112</td>\n",
+       "      <th>2123</th>\n",
+       "      <td>167</td>\n",
+       "      <td>194</td>\n",
+       "      <td>0.663975</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>811</th>\n",
-       "      <td>53</td>\n",
-       "      <td>152</td>\n",
-       "      <td>0.079932</td>\n",
+       "      <th>1761</th>\n",
+       "      <td>139</td>\n",
+       "      <td>78</td>\n",
+       "      <td>0.113361</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2699</th>\n",
-       "      <td>171</td>\n",
-       "      <td>70</td>\n",
-       "      <td>0.140091</td>\n",
+       "      <th>2444</th>\n",
+       "      <td>191</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.999991</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1466</th>\n",
-       "      <td>101</td>\n",
-       "      <td>144</td>\n",
-       "      <td>0.050159</td>\n",
+       "      <th>2441</th>\n",
+       "      <td>190</td>\n",
+       "      <td>152</td>\n",
+       "      <td>0.544303</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -657,14 +588,14 @@
       ],
       "text/plain": [
        "      _src_implicit  _dst_implicit   _weight\n",
-       "1046             71            144  0.205078\n",
-       "642              41             74  0.176112\n",
-       "811              53            152  0.079932\n",
-       "2699            171             70  0.140091\n",
-       "1466            101            144  0.050159"
+       "671              51            123  0.017956\n",
+       "2123            167            194  0.663975\n",
+       "1761            139             78  0.113361\n",
+       "2444            191              3  0.999991\n",
+       "2441            190            152  0.544303"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -676,55 +607,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 16,
    "metadata": {
     "vscode": {
      "languageId": "python"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "            <iframe id=\"385dd123-b816-4166-9341-2ce7155cc477\" src=\"https://hub.graphistry.com/graph/graph.html?dataset=b533c6cb4e11467abfe7121b1f6ca4ef&type=arrow&viztoken=bee2c0fc-ffa8-4fbc-8fee-b1af06f1f217&usertag=39269b0c-pygraphistry-0.27.2+4.ga674343.dirty&splashAfter=1663819187&info=true&play=0\"\n",
-       "                    allowfullscreen=\"true\" webkitallowfullscreen=\"true\" mozallowfullscreen=\"true\"\n",
-       "                    oallowfullscreen=\"true\" msallowfullscreen=\"true\"\n",
-       "                    style=\"width:100%; height:500px; border: 1px solid #DDD; overflow: hidden\"\n",
-       "                    \n",
-       "            >\n",
-       "            </iframe>\n",
-       "        \n",
-       "            <script>\n",
-       "                try {\n",
-       "                  $(\"#385dd123-b816-4166-9341-2ce7155cc477\").bind('mousewheel', function(e) { e.preventDefault(); });\n",
-       "                } catch (e) { console.error('exn catching scroll', e); }\n",
-       "            </script>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "g3.plot()"
+    "#g3.plot()"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "python"
-    }
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -733,7 +625,18 @@
    "language": "python",
    "name": "python3"
   },
-  "orig_nbformat": 4,
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.15"
+  },
   "vscode": {
    "interpreter": {
     "hash": "21c4dad877b49e935d0a60da22bc51e9bfc4901bc58e488dc71d08b8faef6557"
diff --git a/docker/test-gpu-local.sh b/docker/test-gpu-local.sh
index 12667b3a04..158584f9b6 100755
--- a/docker/test-gpu-local.sh
+++ b/docker/test-gpu-local.sh
@@ -45,5 +45,4 @@ docker run \
     graphistry/test-gpu:${TEST_CPU_VERSION} \
         --maxfail=1 \
         --ignore=graphistry/tests/test_feature_utils.py \
-        --ignore=graphistry/tests/test_umap_utils.py \
         $@
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 319295df56..1f47612c1a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -37,9 +37,10 @@
     #'sphinx.ext.autosummary',
     #'sphinx.ext.intersphinx',
     "sphinx.ext.ifconfig",
-    "sphinx_autodoc_typehints",
+    "sphinx_autodoc_typehints"
 ]
 
+
 #FIXME Why is sphinx/autodoc failing here?
 nitpick_ignore = [
     ('py:class', '1'),  # Ex: api : Optional[Literal[1, 3]]
@@ -53,13 +54,24 @@
     ('py:class', 'graphistry.layouts.LayoutsMixin'),
     ('py:class', 'graphistry.compute.ComputeMixin'),
     ('py:class', 'graphistry.compute.conditional.ConditionalMixin'),
+    ('py:class', 'graphistry.compute.cluster.ClusterMixin'),
     ('py:class', 'graphistry.Plottable.Plottable'),
+    ('py:class', 'graphistry.plugins.cugraph.compute_cugraph'),
+    ('py:class', 'graphistry.plugins.cugraph.from_cugraph'),
+    ('py:class', 'graphistry.plugins.igraph.compute_igraph'),
+    ('py:class', 'graphistry.plugins.igraph.from_igraph'),
+    ('py:class', 'graphistry.plugins.igraph.layout_igraph'),
     ('py:class', 'graphistry.feature_utils.FeatureMixin'),
     ('py:class', 'graphistry.dgl_utils.DGLGraphMixin'),
     ('py:class', 'graphistry.umap_utils.UMAPMixin'),
     ('py:class', 'graphistry.text_utils.SearchToGraphMixin'),
     ('py:class', 'graphistry.embed_utils.HeterographEmbedModuleMixin'),
     ('py:class', 'graphistry.PlotterBase.PlotterBase'),
+    ('py:class', 'graphistry.compute.ast.ASTObject'),
+    ('py:class', 'Plotter'),
+    ('py:class', 'Plottable'),
+    ('py:class', 'CuGraphKind'),
+    ('py:class', 'cugraph.Graph'),
     ('py:class', 'IGraph graph'),
     ('py:class', 'igraph'),
     ('py:class', 'dgl'),
@@ -84,6 +96,7 @@
     ('py:data', 'typing.List'),
     ('py:data', 'typing.Literal'),
     ('py:data', 'typing.Optional'),
+    ('py:data', 'typing.Callable'),
     ('py:data', 'typing.Tuple'),
     ('py:data', 'typing.Union'),
     ('py:class','pandas.core.frame.DataFrame')
diff --git a/docs/source/graphistry.compute.rst b/docs/source/graphistry.compute.rst
index 6ea4bdedbd..c610034aab 100644
--- a/docs/source/graphistry.compute.rst
+++ b/docs/source/graphistry.compute.rst
@@ -1,29 +1,58 @@
-graphistry.layout package
-=========================
-
-Subpackages
------------
+ComputeMixin module
+------------------------------------------------
 
-.. toctree::
-   :maxdepth: 4
+.. automodule:: graphistry.compute.ComputeMixin
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
 
 
-Submodules
-----------
+Chain
+---------------
 
-graphistry.compute.ComputeMixin module
-------------------------------------------------
+.. automodule:: graphistry.compute.chain
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
 
-.. automodule:: graphistry.compute.ComputeMixin
+Cluster
+---------------
+.. automodule:: graphistry.compute.cluster
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
 
+Collapse
+---------------
+.. automodule:: graphistry.compute.collapse
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
 
-Module contents
+Conditional
 ---------------
+.. automodule:: graphistry.compute.conditional
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+Filter by Dictionary
+--------------------
+.. automodule:: graphistry.compute.filter_by_dict
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
 
-.. automodule:: graphistry.compute
+Hop
+---------------
+.. automodule:: graphistry.compute.hop
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
diff --git a/docs/source/graphistry.layout.gib.rst b/docs/source/graphistry.layout.gib.rst
index 51352d8212..50b21ec335 100644
--- a/docs/source/graphistry.layout.gib.rst
+++ b/docs/source/graphistry.layout.gib.rst
@@ -1,3 +1,7 @@
+:orphan:
+
+.. ^ FIXME
+
 graphistry.layout.gib package
 ==================================
 
@@ -11,3 +15,5 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+
+   
diff --git a/docs/source/graphistry.layout.graph.rst b/docs/source/graphistry.layout.graph.rst
index 72d559ad11..283119ece6 100644
--- a/docs/source/graphistry.layout.graph.rst
+++ b/docs/source/graphistry.layout.graph.rst
@@ -59,3 +59,7 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+   
+graphistry.layout.gib
+  
+
diff --git a/docs/source/graphistry.layout.rst b/docs/source/graphistry.layout.rst
index 7675db6e35..b2c1f8c43e 100644
--- a/docs/source/graphistry.layout.rst
+++ b/docs/source/graphistry.layout.rst
@@ -1,16 +1,59 @@
-graphistry.layout package
-=========================
 
-Subpackages
------------
 
-.. toctree::
-   :maxdepth: 4
+edge Module
+-----------------------------------
+
+.. automodule:: graphistry.layout.graph.edge
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+edgeBase Module
+---------------------------------------
+
+.. automodule:: graphistry.layout.graph.edgeBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+graph Module
+------------------------------------
+
+.. automodule:: graphistry.layout.graph.graph
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+graphBase Module
+----------------------------------------
+
+.. automodule:: graphistry.layout.graph.graphBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+vertex Module
+-------------------------------------
+
+.. automodule:: graphistry.layout.graph.vertex
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+vertexBase Module
+-----------------------------------------
+
+.. automodule:: graphistry.layout.graph.vertexBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
 
-   graphistry.layout.gib
-   graphistry.layout.graph
-   graphistry.layout.sugiyama
-   graphistry.layout.utils
 
 Module contents
 ---------------
@@ -19,3 +62,4 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+   :noindex:
diff --git a/docs/source/graphistry.layout.sugiyama.rst b/docs/source/graphistry.layout.sugiyama.rst
index 41b83f7cb1..40ffaf7e83 100644
--- a/docs/source/graphistry.layout.sugiyama.rst
+++ b/docs/source/graphistry.layout.sugiyama.rst
@@ -1,3 +1,5 @@
+:orphan:
+
 graphistry.layout.sugiyama package
 ==================================
 
@@ -19,3 +21,6 @@ Module contents
    :members:
    :undoc-members:
    :show-inheritance:
+
+
+.. FIXME:orphan
\ No newline at end of file
diff --git a/docs/source/graphistry.layout.utils.rst b/docs/source/graphistry.layout.utils.rst
index de1d80140d..76b71fdc52 100644
--- a/docs/source/graphistry.layout.utils.rst
+++ b/docs/source/graphistry.layout.utils.rst
@@ -1,6 +1,11 @@
 graphistry.layout.utils package
 ===============================
 
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.layout.graph
+
 Submodules
 ----------
 
diff --git a/docs/source/graphistry.plotter.rst b/docs/source/graphistry.plotter.rst
new file mode 100644
index 0000000000..98079a1bc7
--- /dev/null
+++ b/docs/source/graphistry.plotter.rst
@@ -0,0 +1,17 @@
+Plotter Base
+----------------------
+.. automodule:: graphistry.PlotterBase
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
+
+Plotter Modules
+----------------------
+.. automodule:: graphistry.Plottable
+   :members:
+   :undoc-members:
+   :show-inheritance:
+   :noindex:
+
diff --git a/docs/source/graphistry.plugins.rst b/docs/source/graphistry.plugins.rst
index d3e64fc1ab..492e4ac3fb 100644
--- a/docs/source/graphistry.plugins.rst
+++ b/docs/source/graphistry.plugins.rst
@@ -1,17 +1,4 @@
-graphistry.plugins package
-==========================
-
-Subpackages
------------
-
-.. toctree::
-   :maxdepth: 4
-
-
-Submodules
-----------
-
-graphistry.plugins.igraph module
+iGraph
 ------------------------------------------------
 
 .. automodule:: graphistry.plugins.igraph
@@ -20,10 +7,10 @@ graphistry.plugins.igraph module
    :show-inheritance:
 
 
-Module contents
+CuGraph
 ---------------
 
-.. automodule:: graphistry.plugins
+.. automodule:: graphistry.plugins.cugraph
    :members:
    :undoc-members:
    :show-inheritance:
diff --git a/docs/source/graphistry.plugins_types.rst b/docs/source/graphistry.plugins_types.rst
index 2b9b21ee1c..1b07a10b54 100644
--- a/docs/source/graphistry.plugins_types.rst
+++ b/docs/source/graphistry.plugins_types.rst
@@ -1,6 +1,12 @@
 graphistry.plugins\_types package
 =================================
 
+
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.layout.utils
+
 Submodules
 ----------
 
diff --git a/docs/source/graphistry.rst b/docs/source/graphistry.rst
index c9fbcfa4dd..2fd55094a2 100644
--- a/docs/source/graphistry.rst
+++ b/docs/source/graphistry.rst
@@ -1,42 +1,88 @@
-graphistry package
+plotter
+=======
+.. toctree::
+   :maxdepth: 3
+
+   graphistry.plotter
+
+
+Plugins
+==================
+.. toctree::
+   :maxdepth: 3
+
+
+   graphistry.plugins
+ 
+
+
+Compute 
 ==================
 .. toctree::
    :maxdepth: 3
 
    graphistry.compute
+
+
+Layouts
+==================
+.. toctree::
+   :maxdepth: 3
+
+
    graphistry.layout
-   graphistry.plugins
-   graphistry.plugins_types
 
 
-graphistry.plotter module
--------------------------
+Featurize 
+==================
+.. automodule:: graphistry.feature_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 
-.. automodule:: graphistry.plotter
+UMAP 
+==================
+.. automodule:: graphistry.umap_utils
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.pygraphistry module
-------------------------------
 
-.. automodule:: graphistry.pygraphistry
+Semantic Search 
+==================
+.. automodule:: graphistry.text_utils
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.arrow_uploader module
---------------------------------
+DBScan
+==================
+.. automodule:: graphistry.compute.cluster
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+Arrow uploader Module
+============================
 
 .. automodule:: graphistry.arrow_uploader
     :members:
     :undoc-members:
     :show-inheritance:
 
-graphistry.ArrowFileUploader module
------------------------------------
+Arrow File Uploader Module
+============================
 
 .. automodule:: graphistry.ArrowFileUploader
     :members:
     :undoc-members:
     :show-inheritance:
+
+Versioneer
+==================
+
+.. automodule:: graphistry._version
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 1943a5cf72..9b10c2c91c 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,8 +1,65 @@
-PyGraphistry's documentation (|version|)
+PyGraphistry: Explore Relationships
 ========================================
+.. only:: html
 
-Quickstart:
-`Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
+   .. image:: https://readthedocs.org/projects/pygraphistry/badge/?version=latest
+      :target: https://pygraphistry.readthedocs.io/en/latest/?badge=latest
+      :alt: Documentation Status
+
+
+   .. image:: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+      :target: https://github.com/graphistry/pygraphistry/workflows/CI%20Tests/badge.svg
+      :alt: Build Status
+
+
+   .. image:: https://github.com/graphistry/pygraphistry/workflows/CodeQL/badge.svg
+      :target: https://github.com/graphistry/pygraphistry/actions?query=workflow%3ACodeQL
+      :alt: CodeQL Status
+
+   .. image:: https://img.shields.io/pypi/v/graphistry.svg
+      :target: https://pypi.python.org/pypi/graphistry
+      :alt: PyPi Status
+
+   .. image:: https://img.shields.io/pypi/dm/graphistry
+      :target: https://img.shields.io/pypi/dm/graphistry
+      :alt: PyPi Downloads
+
+
+   .. image:: https://img.shields.io/pypi/l/graphistry.svg
+      :target: https://pypi.python.org/pypi/graphistry
+      :alt: License
+
+   .. .. image:: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+   ..    :target: https://img.shields.io/uptimerobot/status/m787548531-e9c7b7508fc76fea927e2313?label=hub.graphistry.com
+   ..    :alt: License
+
+   .. .. image:: https://img.shields.io/badge/slack-Graphistry%20chat-orange.svg?logo=slack
+   ..       :target: https://join.slack.com/t/graphistry-community/shared_invite/zt-53ik36w2-fpP0Ibjbk7IJuVFIRSnr6g
+   ..       :alt: Slack
+
+   .. image:: https://img.shields.io/twitter/follow/graphistry
+         :target: https://twitter.com/graphistry
+         :alt: Twitter
+
+.. Quickstart:
+.. `Read our tutorial <https://github.com/graphistry/pygraphistry/blob/master/README.md>`_
+
+PyGraphistry is a Python visual graph AI library to extract, transform, analyze, model, and visualize big graphs, and especially alongside Graphistry end-to-end GPU server sessions. Installing optional graphistry[ai] dependencies adds graph autoML, including automatic feature engineering, UMAP, and graph neural net support. Combined, PyGraphistry reduces your time to graph for going from raw data to visualizations and AI models down to three lines of code.
+Here in our docstrings you can find useful packages, modules, and commands to maximize your graph AI experience with PyGraphistry. In the navbar you can find an overview of all the packages and modules we provided and a few useful highlighted ones as well. You can search for them on our Search page. For a full tutorial, refer to our `PyGraphistry <https://github.com/graphistry/pygraphistry/>`_ repo.
+
+.. .. image:: docs/static/docstring.png
+..       :width: 600
+..       :alt: PyGraphistry
+
+
+.. Click to open interactive version! (For server-backed interactive analytics, use an API key)
+
+
+.. .. raw:: html
+
+..        <iframe width="600" height="400" src="https://hub.graphistry.com/graph/graph.html?dataset=Facebook&splashAfter=true" frameborder="0" allowfullscreen></iframe>
+
+For self-hosting and access to a free API key, refer to our Graphistry `Hub <https://hub.graphistry.com/>`_.
 
 .. toctree::
    :maxdepth: 3
@@ -10,6 +67,13 @@ Quickstart:
    graphistry
    modules
 
+Articles
+==================
+* `Graphistry: Visual Graph AI Interactive demo <https://github.com/graphistry/pygraphistry/blob/master/README.md#demo-of-friendship-communities-on-facebook/>`_
+* `PyGraphistry + Databricks <https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/databricks_pyspark/graphistry-notebook-dashboard.ipynb>`_
+* `PyGraphistry + UMAP <https://github.com/graphistry/pygraphistry/blob/master/demos/demos_databases_apis/umap_learn/umap_learn.ipynb>`_
+
+
 Indices and tables
 ==================
 
diff --git a/docs/source/modules.rst b/docs/source/modules.rst
index 2d0d70fd92..ced1d0941f 100644
--- a/docs/source/modules.rst
+++ b/docs/source/modules.rst
@@ -1,5 +1,5 @@
-doc
-===
+Modules
+#####################
 
 .. toctree::
    :maxdepth: 4
diff --git a/docs/source/versioneer.rst b/docs/source/versioneer.rst
index 804c171da3..1f5d4bae40 100644
--- a/docs/source/versioneer.rst
+++ b/docs/source/versioneer.rst
@@ -1,2 +1,6 @@
-versioneer module
-=================
+.. versioneer module
+.. =================
+.. toctree:: 
+   :maxdepth: 2
+   
+   graphistry.plugins_types
diff --git a/docs/static/docstring.png b/docs/static/docstring.png
new file mode 100644
index 0000000000..a61e42c592
Binary files /dev/null and b/docs/static/docstring.png differ
diff --git a/graphistry/Plottable.py b/graphistry/Plottable.py
index ca0aaca31d..8a483ddade 100644
--- a/graphistry/Plottable.py
+++ b/graphistry/Plottable.py
@@ -84,7 +84,7 @@ class Plottable(object):
     # embed utils
     _relation : Optional[str]
     _use_feat: bool
-    triplets: Optional[List]  # actually torch.Tensor too
+    _triplets: Optional[List]  # actually torch.Tensor too
     _kg_embed_dim: int
     
 
diff --git a/graphistry/PlotterBase.py b/graphistry/PlotterBase.py
index 7d22469d8f..0b9dbe9235 100644
--- a/graphistry/PlotterBase.py
+++ b/graphistry/PlotterBase.py
@@ -165,23 +165,20 @@ def __init__(self, *args, **kwargs):
         self._bolt_driver : any = None
         self._tigergraph : any = None
 
+        # feature engineering
         self._node_embedding = None
         self._node_encoder = None
         self._node_features = None
-        self._node_ordinal_pipeline = None
-        self._node_ordinal_pipeline_target = None,
+        self._node_features_raw = None
         self._node_target = None
         self._node_target_encoder = None
-        self._node_text_model = None
 
         self._edge_embedding = None
         self._edge_encoder = None
         self._edge_features = None
-        self._edge_ordinal_pipeline = None
-        self._edge_ordinal_pipeline_target = None
+        self._edge_features_raw = None
         self._edge_target = None
         self._edge_target_encoder = None
-        self._edge_text_model = None
 
         self._weighted_adjacency_nodes = None
         self._weighted_adjacency_edges = None
@@ -190,6 +187,7 @@ def __init__(self, *args, **kwargs):
         self._weighted_edges_df_from_edges = None
         self._xy = None
 
+        # the fit umap instance
         self._umap = None
 
         self._adjacency = None
@@ -201,6 +199,13 @@ def __init__(self, *args, **kwargs):
         self._use_feat: bool = False
         self._triplets: Optional[List] = None 
         self._kg_embed_dim: int = 128
+        
+        # Dbscan
+        self._node_dbscan = None  # the fit dbscan instance
+        self._edge_dbscan = None
+        
+        # DGL
+        self.DGL_graph = None  # the DGL graph
 
 
     def __repr__(self):
@@ -295,7 +300,7 @@ def style(self, fg=None, bg=None, page=None, logo=None):
         :param fg: Dictionary {'blendMode': str} of any valid CSS blend mode
         :type fg: dict
 
-        :param bg: Nested dictionary of page background properties. {'color': str, 'gradient': {'kind': str, 'position': str, 'stops': list }, 'image': { 'url': str, 'width': int, 'height': int, 'blendMode': str }
+        :param bg: Nested dictionary of page background properties. { 'color': str, 'gradient': {'kind': str, 'position': str, 'stops': list }, 'image': { 'url': str, 'width': int, 'height': int, 'blendMode': str }
         :type bg: dict
 
         :param logo: Nested dictionary of logo properties. { 'url': str, 'autoInvert': bool, 'position': str, 'dimensions': { 'maxWidth': int, 'maxHeight': int }, 'crop': { 'top': int, 'left': int, 'bottom': int, 'right': int }, 'padding': { 'top': int, 'left': int, 'bottom': int, 'right': int}, 'style': str}        
@@ -309,15 +314,18 @@ def style(self, fg=None, bg=None, page=None, logo=None):
 
         **Example: Chained merge - results in url and blendMode being set, while color is dropped**
             ::
+
                 g2 =  g.style(bg={'color': 'black'}, fg={'blendMode': 'screen'})
                 g3 = g2.style(bg={'image': {'url': 'http://site.com/watermark.png'}})
                 
         **Example: Gradient background**
             ::
+
               g.style(bg={'gradient': {'kind': 'linear', 'position': 45, 'stops': [['rgb(0,0,0)', '0%'], ['rgb(255,255,255)', '100%']]}})
               
         **Example: Page settings**
             ::
+            
               g.style(page={'title': 'Site - {{ name }}', 'favicon': 'http://site.com/logo.ico'})
 
         """        
@@ -334,17 +342,10 @@ def style(self, fg=None, bg=None, page=None, logo=None):
     def encode_axis(self, rows=[]):
         """Render radial and linear axes with optional labels
 
-        :param rows: List of rows - {
-            label: Optional[str],
-            ?r: float,
-            ?x: float,
-            ?y: float,
-            ?internal: true,
-            ?external: true,
-            ?space: true
-        }
+        :param rows: List of rows - { label: Optional[str],?r: float, ?x: float, ?y: float, ?internal: true, ?external: true, ?space: true }
 
         :returns: Plotter
+        
         :rtype: Plotter
 
         **Example: Several radial axes**
@@ -537,9 +538,7 @@ def encode_point_icon(self, column,
             comparator=None,
             for_default=True, for_current=False,
             as_text=False, blend_mode=None, style=None, border=None, shape=None):
-        """Set node icon with more control than bind().
-        Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...).
-        When as_text=True is enabled, values are instead interpreted as raw strings.
+        """Set node icon with more control than bind(). Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
         :type column: str
@@ -606,9 +605,7 @@ def encode_edge_icon(self, column,
             comparator=None,
             for_default=True, for_current=False,
             as_text=False, blend_mode=None, style=None, border=None, shape=None):
-        """Set edge icon with more control than bind()
-        Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...).
-        When as_text=True is enabled, values are instead interpreted as raw strings.
+        """Set edge icon with more control than bind() Values from Font Awesome 4 such as "laptop": https://fontawesome.com/v4.7.0/icons/ , image URLs (http://...), and data URIs (data:...). When as_text=True is enabled, values are instead interpreted as raw strings.
 
         :param column: Data column name
         :type column: str
@@ -828,10 +825,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
              edge_source_color=None, edge_destination_color=None,
              point_title=None, point_label=None, point_color=None, point_weight=None, point_size=None, point_opacity=None, point_icon=None,
              point_x=None, point_y=None):
-        """Relate data attributes to graph structure and visual representation.
-
-        To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
-
+        """Relate data attributes to graph structure and visual representation. To facilitate reuse and replayable notebooks, the binding call is chainable. Invocation does not effect the old binding: it instead returns a new Plotter instance with the new bindings added to the existing ones. Both the old and new bindings can then be used for different graphs.
 
         :param source: Attribute containing an edge's source ID
         :type source: str
@@ -851,7 +845,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param edge_label: Attribute overriding edge's expanded label text. By default, scrollable list of attribute/value mappings.
         :type edge_label: str
 
-        :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
+        :param edge_color: Attribute overriding edge's color. rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
         :type edge_color: str
 
         :param edge_source_color: Attribute overriding edge's source color if no edge_color, as an rgba int64 value.
@@ -869,7 +863,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :param point_label: Attribute overriding node's expanded label text. By default, scrollable list of attribute/value mappings.
         :type point_label: str
 
-        :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see palette definitions <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ for values. Based on Color Brewer.
+        :param point_color: Attribute overriding node's color.rgba (int64) or int32 palette index, see `palette <https://graphistry.github.io/docs/legacy/api/0.9.2/api.html#extendedpalette>`_ definitions for values. Based on Color Brewer.
         :type point_color: str
 
         :param point_size: Attribute overriding node's size. By default, uses the node degree. The visualization will normalize point sizes and adjust dynamically using semantic zoom.
@@ -885,6 +879,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
         :rtype: Plotter
 
         **Example: Minimal**
+
             ::
 
                 import graphistry
@@ -892,6 +887,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g = g.bind(source='src', destination='dst')
 
         **Example: Node colors**
+
             ::
 
                 import graphistry
@@ -900,6 +896,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                            node='id', point_color='color')
 
         **Example: Chaining**
+
             ::
 
                 import graphistry
@@ -916,6 +913,7 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g3b = g2b.bind(point_size='size3b')
 
         In the above **Chaining** example, all bindings use src/dst/id. Colors and sizes bind to:
+
             ::
 
                 g: default/default
@@ -924,8 +922,6 @@ def bind(self, source=None, destination=None, node=None, edge=None,
                 g2b: color2b/size2b
                 g3a: color2a/size3a
                 g3b: color2b/size3b
-
-
         """
         res = copy.copy(self)
         res._source = source or self._source
@@ -1002,6 +998,7 @@ def nodes(self, nodes: Union[Callable, Any], node=None, *args, **kwargs) -> Plot
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_nodes(g, n):
@@ -1056,7 +1053,7 @@ def edges(self, edges: Union[Callable, Any], source=None, destination=None, edge
         If a callable, will be called with current Plotter and whatever positional+named arguments
 
         :param edges: Edges and their attributes, or transform from Plotter to edges
-        :type edges: Pandas dataframe, NetworkX graph, or IGraph graph.
+        :type edges: Pandas dataframe, NetworkX graph, or IGraph graph
 
         :returns: Plotter
         :rtype: Plotter
@@ -1101,6 +1098,7 @@ def edges(self, edges: Union[Callable, Any], source=None, destination=None, edge
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_edges(g, n):
diff --git a/graphistry/_version.py b/graphistry/_version.py
index c9b3980271..3bb0a87502 100644
--- a/graphistry/_version.py
+++ b/graphistry/_version.py
@@ -52,7 +52,7 @@ class NotThisMethod(Exception):
     """Exception raised if a method is not valid for the current scenario."""
 
 
-LONG_VERSION_PY = {}
+LONG_VERSION_PY = {}  # type: ignore
 HANDLERS = {}
 
 
diff --git a/graphistry/ai_utils.py b/graphistry/ai_utils.py
index 1b18dd4404..0f58cf3256 100644
--- a/graphistry/ai_utils.py
+++ b/graphistry/ai_utils.py
@@ -1,9 +1,17 @@
 import pandas as pd
+import numpy as np
 
 import graphistry
-from .util import setup_logger
 
-logger = setup_logger(__name__)
+from .constants import DISTANCE, WEIGHT, BATCH
+from logging import getLogger
+
+try:
+    import faiss  # type ignore
+except:
+    faiss = None
+
+logger = getLogger(__name__)
 
 
 # #################################################################################################
@@ -33,7 +41,7 @@ def search_to_df(word, col, df, as_string=False):
             res = df[df[col].str.contains(word, case=False)]
     except TypeError as e:
         logger.error(e)
-        return pd.DataFrame([], columns = df.columns)
+        return pd.DataFrame([], columns=df.columns)
     return res
 
 
@@ -127,3 +135,350 @@ def get_graphistry_from_milieu_search(
     ntdf = ndf[ndf[node_col].isin(gcols)]
     g = graphistry.edges(tdf, src, dst).nodes(ntdf, node_col)
     return g
+
+
+
+
+# #########################################################################################################################
+#
+#  Graphistry Vector Search Index
+#
+##########################################################################################################################
+
+
+class FaissVectorSearch:
+    def __init__(self, M):
+        import faiss
+        self.index = faiss.IndexFlatL2(M.shape[1])
+        self.index.add(M)
+
+    def search(self, q, k=5):
+        """
+        Search for the k nearest neighbors of a query vector q.
+
+        Parameters:
+        - q: the query vector to search for
+        - k: the number of nearest neighbors to return (default: 5)
+
+        Returns:
+        - Index: a numpy array of size (k,) containing the indices of the k nearest neighbors
+        - Distances: a numpy array of size (k,) containing the distances to the k nearest neighbors
+        """
+        q = np.asarray(q, dtype=np.float32)
+        Distances, Index = self.index.search(q.reshape(1, -1), k)
+        return Index[0], Distances[0]
+    
+    def search_df(self, q, df, k):
+        """ Query by vector using annoy index and append distance to results
+    
+        it is assumed len(vect) == len(df) == len(search_index)
+        args:
+            vect: query vector
+            df: dataframe to query
+            search_index: annoy index
+            top_n: number of results to return
+        returns:
+            sorted dataframe with top_n results and distance
+        """
+
+        indices, distances = self.search(q.values[0], k=k)
+
+        results = df.iloc[indices]
+        results.loc[:, DISTANCE] = distances
+        results = results.sort_values(by=[DISTANCE])
+
+        return results
+
+
+# #########################################################################################################################
+#
+#  Graphistry Graph Inference
+#
+##########################################################################################################################
+
+def edgelist_to_weighted_adjacency(g, weights=None):
+    """ Convert edgelist to weighted adjacency matrix in sparse coo_matrix"""
+    import scipy.sparse as ss
+    import numpy as np
+    res = g._edges[[g._source, g._destination]].values.astype(np.int64)
+    rows, cols = res.T[0], res.T[1]
+    if weights is None:
+        weights = np.ones(len(rows))
+    M = ss.coo_matrix((weights, (rows, cols)))
+    return M.tocsr()
+
+def hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_features, new_targets):
+    # #########################################################
+    g = res.nodes(new_nodes, node).edges(new_edges, src, dst)
+
+    # TODO this needs more work since edgelist_to_weighted_adjacency produces non square matrices (since infer_graph will add new nodes)
+    #g._weighted_adjacency = edgelist_to_weighted_adjacency(g)
+    g._node_embedding = new_emb
+    g._node_features = new_features
+    g._node_targets = new_targets
+    g = g.settings(url_params={'play': 0})
+    return g
+    
+
+def infer_graph(
+    res, emb, X, y, df, infer_on_umap_embedding=False, eps="auto", sample=None, n_neighbors=7, verbose=False, 
+):
+    """
+    Infer a graph from a graphistry object
+
+    args:
+        res: graphistry object
+        df: outside minibatch dataframe to add to existing graph
+        X: minibatch transformed dataframe
+        emb: minibatch UMAP embedding distance threshold for a minibatch point to cluster to existing graph
+        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatch point to cluster to existing graph
+        sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
+            This sets the global stickiness of the graph, and is a good way to control the number of edges incuded from the old graph.
+        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon.
+            This sets the local stickiness of the graph, and is a good way to control the number of edges between 
+            an added point and the existing graph.
+    returns:
+        graphistry Plottable object
+    """
+    #enhanced = is_notebook()
+    
+    print("-" * 50) if verbose else None
+    
+    if infer_on_umap_embedding and emb is not None:
+        X_previously_fit = res._node_embedding
+        X_new = emb
+        print("Infering edges over UMAP embedding") if verbose else None
+    else:  # can still be umap, but want to do the inference on the higher dimensional features
+        X_previously_fit = res._node_features
+        X_new = X
+        print("Infering edges over features embedding") if verbose else None
+
+    print("-" * 45) if verbose else None
+
+    FEATS = res._node_features
+    if FEATS is None:
+        raise ValueError("Must have node features to infer edges")
+    EMB = res._node_embedding if res._node_embedding is not None else FEATS.index
+    Y = res._node_target if res._node_target is not None else FEATS.index
+
+    assert (
+        df.shape[0] == X.shape[0]
+    ), "minibatches df and X must have same number of rows since f(df) = X"
+    if emb is not None:
+        assert (
+            emb.shape[0] == df.shape[0]
+        ), "minibatches emb and X must have same number of rows since h(df) = emb"
+        df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
+
+    # if umap, need to add '_n' as node id to df, adding new indices to existing graph
+    numeric_indices = range(
+        X_previously_fit.shape[0], X_previously_fit.shape[0] + X_new.shape[0]
+    )
+    df["_n"] = numeric_indices
+    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph
+    node = res._node
+
+    if node not in df.columns:
+        df[node] = numeric_indices
+
+    NDF = res._nodes
+    NDF[BATCH] = 0
+    EDF = res._edges
+    EDF[BATCH] = 0
+    src = res._source
+    dst = res._destination
+
+    #new_nodes = []
+    new_edges = []
+    old_edges = []
+    old_nodes = []
+    mdists = []
+
+    # check if pandas or cudf
+    if 'cudf.core.dataframe' in str(type(X_previously_fit)):
+        #  move it out of memory...
+        X_previously_fit = X_previously_fit.to_pandas()
+
+    for i in range(X_new.shape[0]):
+        diff = X_previously_fit - X_new.iloc[i, :]
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+        mdists.append(dist)
+
+    m, std = np.mean(mdists), np.std(mdists)
+    logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
+    print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
+    if eps == "auto":
+        eps = np.min([np.abs(m - std), m])
+    logger.info(
+        f"-epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
+    )
+    print(f' Max distance threshold; epsilon = {eps:.2f}') if verbose else None
+    
+    print(f' Finding {n_neighbors} nearest neighbors') if verbose else None
+    nn = []
+    for i, dist in enumerate(mdists):
+        record_df = df.iloc[i, :]
+        nearest = np.where(dist < eps)[0]
+        nn.append(len(nearest))
+        for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
+            this_ndf = NDF.iloc[j, :]
+            if sample:
+                local_edges = EDF[
+                    (EDF[src] == this_ndf[node]) | (EDF[dst] == this_ndf[node])
+                ]
+                if not local_edges.empty:
+                    old_edges.append(local_edges.sample(sample, replace=True))
+                    
+            weight = min(1 / (dist[j] + 1e-3), 1)
+            new_edges.append([this_ndf[node], record_df[node], weight, 1])
+            old_nodes.append(this_ndf)
+            #new_nodes.extend([record_df, this_ndf])
+            
+    print(f' {np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
+    
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
+
+    all_nodes = []
+    if len(old_edges):
+        old_edges = pd.concat(old_edges, axis=0).assign(_batch=0)
+        all_nodes = pd.concat([old_edges[src], old_edges[dst], new_edges[src], new_edges[dst]]).drop_duplicates()
+        print('', len(all_nodes), "nodes in new graph") if verbose else None
+
+    if sample:
+        new_edges = pd.concat([new_edges, old_edges], axis=0).drop_duplicates()
+        print(' Sampled', len(old_edges.drop_duplicates()), 'previous old edges') if verbose else None
+    new_edges = new_edges.drop_duplicates()
+    print('', len(new_edges), 'total edges after dropping duplicates') if verbose else None
+
+    if len(old_nodes):
+        old_nodes = pd.DataFrame(old_nodes)
+        old_nodes = pd.concat(
+            [old_nodes, NDF[NDF[node].isin(all_nodes)]], axis=0
+        ).drop_duplicates(subset=[node])
+    else:
+        old_nodes = NDF[NDF[node].isin(all_nodes)]
+
+    old_emb = None
+    if EMB is not None:
+        old_emb = EMB.loc[old_nodes.index]
+
+    new_emb = None
+    if emb is not None:
+        if 'cudf.core.dataframe.DataFrame' in str(type(old_emb)):  # convert to pd
+            old_emb = old_emb.to_pandas()
+        new_emb = pd.concat([emb, old_emb], axis=0)
+
+    new_features = pd.concat([X, FEATS.loc[old_nodes.index]], axis=0)
+
+    new_nodes = pd.concat([df, old_nodes], axis=0)  # append minibatch at top
+    print(" ** Final graph has", len(new_nodes), "nodes") if verbose else None
+    print(" - Batch has", len(df), "nodes") if verbose else None
+    print(" - Brought in", len(old_nodes), "nodes") if verbose else None
+
+    new_targets = pd.concat([y, Y.loc[old_nodes.index]]) if y is not None else Y
+
+    print("-" * 50) if verbose else None
+    return hydrate_graph(res, new_nodes, new_edges, node, src, dst, new_emb, new_features, new_targets)
+
+
+def infer_self_graph(res, 
+    emb, X, y, df, infer_on_umap_embedding=False, eps="auto", n_neighbors=7, verbose=False, 
+):
+    """
+    Infer a graph from a graphistry object
+
+    args:
+        df: outside minibatch dataframe to add to existing graph
+        X: minibatch transformed dataframe
+        emb: minibatch UMAP embedding distance threshold for a minibatch point to cluster to existing graph
+        eps: if 'auto' will find a good epsilon from the data; distance threshold for a minibatch point to cluster to existing graph
+        sample: number of nearest neighbors to add from existing graphs edges, if None, ignores existing edges.
+            This sets the global stickiness of the graph, and is a good way to control the number of edges incuded from the old graph.
+        n_neighbors, int: number of nearest neighbors to include per batch point within epsilon.
+            This sets the local stickiness of the graph, and is a good way to control the number of edges between 
+            an added point and the existing graph.
+    returns:
+        graphistry Plottable object
+    """
+    #enhanced = is_notebook()
+    
+    print("-" * 50) if verbose else None
+    
+    if infer_on_umap_embedding and emb is not None:
+        X_previously_fit = emb
+        X_new = emb
+        print("Infering edges over UMAP embedding") if verbose else None
+    else:  # can still be umap, but want to do the inference on the higher dimensional features
+        X_previously_fit = X
+        X_new = X
+        print("Infering edges over features embedding") if verbose else None
+
+    print("-" * 45) if verbose else None
+
+    assert (
+        df.shape[0] == X.shape[0]
+    ), "minibatches df and X must have same number of rows since f(df) = X"
+    if emb is not None:
+        assert (
+            emb.shape[0] == df.shape[0]
+        ), "minibatches emb and X must have same number of rows since h(df) = emb"
+        df = df.assign(x=emb.x, y=emb.y)  # add x and y to df for graphistry instance
+    else:  # if umap has been fit, but only transforming over features, need to add x and y or breaks plot binds of res
+        df['x'] = np.random.random(df.shape[0])
+        df['y'] = np.random.random(df.shape[0])
+
+    #  if umap, need to add '_n' as node id to df, adding new indices to existing graph
+    numeric_indices = np.arange(
+        X_previously_fit.shape[0],
+        dtype=np.float64  # this seems off but works
+        )
+    df["_n"] = numeric_indices
+    df[BATCH] = 1  # 1 for minibatch, 0 for existing graph, here should all be `1` 
+    node = res._node
+    if node not in df.columns:
+        df[node] = numeric_indices
+
+    src = res._source
+    dst = res._destination
+    
+    old_nodes = []
+    new_edges = []
+    mdists = []
+
+    for i in range(X_new.shape[0]):
+        diff = X_previously_fit - X_new.iloc[i, :]
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+        mdists.append(dist)
+
+    m, std = np.mean(mdists), np.std(mdists)
+    logger.info(f"--Mean distance to existing nodes  {m:.2f} +/- {std:.2f}")
+    print(f' Mean distance to existing nodes {m:.2f} +/- {std:.2f}') if verbose else None
+    if eps == "auto":
+        eps = np.min([np.abs(m - std), m])
+    logger.info(
+        f" epsilon = {eps:.2f} max distance threshold to be considered a neighbor"
+    )
+    print(f' Max distance threshold; epsilon = {eps:.2f}') if verbose else None
+    
+    print(f' Finding {n_neighbors} nearest neighbors') if verbose else None
+    nn = []
+    for i, dist in enumerate(mdists):
+        record_df = df.iloc[i, :]
+        nearest = np.where(dist < eps)[0]
+        nn.append(len(nearest))
+        for j in nearest[:n_neighbors]:  # add n_neighbors nearest neighbors, if any, super speedup hack
+            if i != j:
+                this_ndf = df.iloc[j, :]
+                weight = min(1 / (dist[j] + 1e-3), 1)
+                new_edges.append([this_ndf[node], record_df[node], weight, 1])
+                old_nodes.append(this_ndf)
+            
+    print(f' {np.mean(nn):.2f} neighbors per node within epsilon {eps:.2f}') if verbose else None
+    
+    new_edges = pd.DataFrame(new_edges, columns=[src, dst, WEIGHT, BATCH])
+    new_edges = new_edges.drop_duplicates()
+    print('', len(new_edges), 'total edges after dropping duplicates') if verbose else None
+    print(" ** Final graph has", len(df), "nodes") if verbose else None
+    # #########################################################
+    print("-" * 50) if verbose else None
+    return hydrate_graph(res, df, new_edges, node, src, dst, emb, X, y)
diff --git a/graphistry/compute/ComputeMixin.py b/graphistry/compute/ComputeMixin.py
index 8fd9895b95..7a9b2f71c7 100644
--- a/graphistry/compute/ComputeMixin.py
+++ b/graphistry/compute/ComputeMixin.py
@@ -347,6 +347,9 @@ def collapse(
         :param node: start `node` to begin traversal
         :param attribute: the given `attribute` to collapse over within `column`
         :param column: the `column` of nodes DataFrame that contains `attribute` to collapse over
+        :param self_edges: whether to include self edges in the collapsed graph
+        :param unwrap: whether to unwrap the collapsed graph into a single node
+        :param verbose: whether to print out collapse summary information
 
         :returns:A new Graphistry instance with nodes and edges DataFrame containing collapsed nodes and edges given by column attribute -- nodes and edges DataFrames contain six new columns `collapse_{node | edges}` and `final_{node | edges}`, while original (node, src, dst) columns are left untouched
         :rtype: Plottable
diff --git a/graphistry/compute/chain.py b/graphistry/compute/chain.py
index 057d56d328..4920b74c9f 100644
--- a/graphistry/compute/chain.py
+++ b/graphistry/compute/chain.py
@@ -90,28 +90,27 @@ def combine_steps(g: Plottable, kind: str, steps: List[Tuple[ASTObject,Plottable
 
 def chain(self: Plottable, ops: List[ASTObject]) -> Plottable:
     """
-
     Experimental: Chain a list of operations
 
     Return subgraph of matches according to the list of node & edge matchers
-
     If any matchers are named, add a correspondingly named boolean-valued column to the output
 
-    :param ops: List[ASTobject] Various node and edge matchers
-    :type fg: dict
+    :param ops: List[ASTObject] Various node and edge matchers
 
     :returns: Plotter
     :rtype: Plotter
 
     **Example: Find nodes of some type**
-        ::
+
+    ::
 
             from graphistry.ast import n
 
             people_nodes_df = g.chain([ n({"type": "person"}) ])._nodes
             
     **Example: Find 2-hop edge sequences with some attribute**
-        ::
+
+    ::
 
             from graphistry.ast import e_forward
 
diff --git a/graphistry/compute/cluster.py b/graphistry/compute/cluster.py
new file mode 100644
index 0000000000..585b17acd8
--- /dev/null
+++ b/graphistry/compute/cluster.py
@@ -0,0 +1,438 @@
+import logging
+import pandas as pd
+import numpy as np
+
+from typing import Any, List, Union, TYPE_CHECKING, Tuple, Optional
+from typing_extensions import Literal
+from collections import Counter
+
+from graphistry.Plottable import Plottable
+from graphistry.constants import CUML, UMAP_LEARN, DBSCAN  # noqa type: ignore
+from graphistry.features import ModelDict
+from graphistry.feature_utils import get_matrix_by_column_parts
+
+logger = logging.getLogger("compute.cluster")
+
+if TYPE_CHECKING:
+    MIXIN_BASE = Plottable
+else:
+    MIXIN_BASE = object
+
+DBSCANEngineConcrete = Literal["cuml", "umap_learn"]
+DBSCANEngine = Literal[DBSCANEngineConcrete, "auto"]
+
+
+def lazy_dbscan_import_has_dependency():
+    has_min_dependency = True
+    DBSCAN = None
+    try:
+        from sklearn.cluster import DBSCAN
+    except ImportError:
+        has_min_dependency = False
+        logger.info("Please install sklearn for CPU DBSCAN")
+
+    has_cuml_dependency = True
+    cuDBSCAN = None
+    try:
+        from cuml import DBSCAN as cuDBSCAN
+    except ImportError:
+        has_cuml_dependency = False
+        logger.info("Please install cuml for GPU DBSCAN")
+
+    return has_min_dependency, DBSCAN, has_cuml_dependency, cuDBSCAN
+
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
+
+
+def resolve_cpu_gpu_engine(
+    engine: DBSCANEngine,
+) -> DBSCANEngineConcrete:  # noqa
+    if engine in [CUML, UMAP_LEARN, 'sklearn']:
+        return engine  # type: ignore
+    if engine in ["auto"]:
+        (
+            has_min_dependency,
+            _,
+            has_cuml_dependency,
+            _,
+        ) = lazy_dbscan_import_has_dependency()
+        if has_cuml_dependency:
+            return "cuml"
+        if has_min_dependency:
+            return "umap_learn"
+
+    raise ValueError(  # noqa
+        f'engine expected to be "auto", '
+        '"umap_learn", "pandas", "sklearn", or  "cuml" '
+        f"but received: {engine} :: {type(engine)}"
+    )
+
+def make_safe_gpu_dataframes(X, y, engine):
+    """helper method to coerce a dataframe to the correct type (pd vs cudf)"""
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", 'sklearn', 'umap_learn']:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine == "cuml":
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
+
+    has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
+    if has_cudf_dependancy_:
+        # print('DBSCAN CUML Matrices')
+        return safe_cudf(X, y)
+    else:
+        return X, y
+
+
+def get_model_matrix(g, kind: str, cols: Optional[Union[List, str]], umap, target):
+    """
+        Allows for a single function to get the model matrix for both nodes and edges as well as targets, embeddings, and features
+
+    Args:
+            :g: graphistry graph
+            :kind: 'nodes' or 'edges'
+            :cols: list of columns to use for clustering given `g.featurize` has been run
+            :umap: whether to use UMAP embeddings or features dataframe
+            :target: whether to use the target dataframe or features dataframe
+
+    Returns:
+        pd.DataFrame: dataframe of model matrix given the inputs
+    """
+    assert kind in ["nodes", "edges"]
+    assert (
+        hasattr(g, "_node_encoder") if kind == "nodes" else hasattr(g, "_edge_encoder")
+    )
+
+    df = g.get_matrix(cols, kind=kind, target=target)
+
+    if umap and cols is None and g._umap is not None:
+        df = g._get_embedding(kind)            
+    
+    #if g.engine_dbscan in [CUML]:
+    df, _ = make_safe_gpu_dataframes(df, None, g.engine_dbscan)
+    #print('\n df:', df.shape, df.columns)
+    return df
+
+
+def dbscan_fit(g: Any, dbscan: Any, kind: str = "nodes", cols: Optional[Union[List, str]] = None, use_umap_embedding: bool = True, target: bool = False, verbose: bool = False):
+    """
+    Fits clustering on UMAP embeddings if umap is True, otherwise on the features dataframe
+        or target dataframe if target is True.
+
+    Args:
+        :g: graphistry graph
+        :kind: 'nodes' or 'edges'
+        :cols: list of columns to use for clustering given `g.featurize` has been run
+        :use_umap_embedding: whether to use UMAP embeddings or features dataframe for clustering (default: True)
+    """
+    X = get_model_matrix(g, kind, cols, use_umap_embedding, target)
+    
+    if X.empty:
+        raise ValueError("No features found for clustering")
+
+    dbscan.fit(X)
+    # this is a future feature one cuml supports it
+    if g.engine_dbscan == 'cuml':
+        labels = dbscan.labels_.to_numpy()
+        # dbscan.components_ = X[dbscan.core_sample_indices_.to_pandas()]  # can't believe len(samples) != unique(labels) ... #cumlfail
+    else:
+        labels = dbscan.labels_
+
+    if kind == "nodes":
+        g._nodes = g._nodes.assign(_dbscan=labels)
+    elif kind == "edges":
+        g._edges = g._edges.assign(_dbscan=labels)
+    else:
+        raise ValueError("kind must be one of `nodes` or `edges`")
+
+    kind = "node" if kind == "nodes" else "edge"
+    setattr(g, f"_{kind}_dbscan", dbscan)
+    
+    if cols is not None:  # set False since we used the features for verbose
+        use_umap_embedding = False
+
+    if verbose:
+        cnt = Counter(labels)
+        message = f"DBSCAN found {len(cnt)} clusters with {cnt[-1]} outliers"
+        print()
+        print('-' * len(message))
+        print(message)
+        print(f"--fit on {'umap embeddings' if use_umap_embedding else 'feature embeddings'} of size {X.shape}")
+        print('-' * len(message))
+
+    return g
+
+
+def dbscan_predict(X: pd.DataFrame, model: Any):
+    """
+    DBSCAN has no predict per se, so we reverse engineer one here
+    from https://stackoverflow.com/questions/27822752/scikit-learn-predicting-new-points-with-dbscan
+    
+    """
+    n_samples = X.shape[0]
+
+    y_new = np.ones(shape=n_samples, dtype=int) * -1
+
+    for i in range(n_samples):
+        diff = model.components_ - X.iloc[i, :].values  # NumPy broadcasting
+
+        dist = np.linalg.norm(diff, axis=1)  # Euclidean distance
+
+        shortest_dist_idx = np.argmin(dist)
+
+        if dist[shortest_dist_idx] < model.eps:
+            y_new[i] = model.labels_[model.core_sample_indices_[shortest_dist_idx]]
+
+    return y_new
+
+
+class ClusterMixin(MIXIN_BASE):
+    def __init__(self, *args, **kwargs):
+        pass
+
+    def _cluster_dbscan(
+        self, res, kind, cols, fit_umap_embedding, target, min_dist, min_samples, engine_dbscan, verbose, *args, **kwargs
+    ):
+        """DBSCAN clustering on cpu or gpu infered by .engine flag
+        """
+        _, DBSCAN, _, cuDBSCAN = lazy_dbscan_import_has_dependency()
+
+        if engine_dbscan in [CUML]:
+            print('`g.transform_dbscan(..)` not supported for engine=cuml, will return `g.transform_umap(..)` instead')
+
+        res.engine_dbscan = engine_dbscan  # resolve_cpu_gpu_engine(engine_dbscan)  # resolve_cpu_gpu_engine("auto")
+        res._dbscan_params = ModelDict(
+            "latest DBSCAN params",
+            kind=kind,
+            cols=cols,
+            target=target,
+            fit_umap_embedding=fit_umap_embedding,
+            min_dist=min_dist,
+            min_samples=min_samples,
+            engine_dbscan=engine_dbscan,
+            verbose=verbose,
+        )
+
+        dbscan = (
+            cuDBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
+            if res.engine_dbscan == CUML
+            else DBSCAN(eps=min_dist, min_samples=min_samples, *args, **kwargs)
+        )
+        # print('dbscan:', dbscan)
+
+        res = dbscan_fit(
+            res, dbscan, kind=kind, cols=cols, use_umap_embedding=fit_umap_embedding, verbose=verbose
+            )
+
+        return res
+
+    def dbscan(
+        self,
+        min_dist: float = 0.2,
+        min_samples: int = 1,
+        cols: Optional[Union[List, str]] = None,
+        kind: str = "nodes",
+        fit_umap_embedding: bool = True,
+        target: bool = False,
+        verbose: bool = False,
+        engine_dbscan: str = 'sklearn',
+        *args,
+        **kwargs,
+    ):
+        """DBSCAN clustering on cpu or gpu infered automatically. Adds a `_dbscan` column to nodes or edges.
+           NOTE: g.transform_dbscan(..) currently unsupported on GPU.
+
+        Examples:
+        ::
+
+            g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+
+            # cluster by UMAP embeddings
+            kind = 'nodes' | 'edges'
+            g2 = g.umap(kind=kind).dbscan(kind=kind)
+            print(g2._nodes['_dbscan']) | print(g2._edges['_dbscan'])
+
+            # dbscan in umap or featurize API
+            g2 = g.umap(dbscan=True, min_dist=1.2, min_samples=2, **kwargs)
+            # or, here dbscan is infered from features, not umap embeddings
+            g2 = g.featurize(dbscan=True, min_dist=1.2, min_samples=2, **kwargs)
+
+            # and via chaining,
+            g2 = g.umap().dbscan(min_dist=1.2, min_samples=2, **kwargs)
+
+            # cluster by feature embeddings
+            g2 = g.featurize().dbscan(**kwargs)
+
+            # cluster by a given set of feature column attributes, or with target=True
+            g2 = g.featurize().dbscan(cols=['ip_172', 'location', 'alert'], target=False, **kwargs)
+
+            # equivalent to above (ie, cols != None and umap=True will still use features dataframe, rather than UMAP embeddings)
+            g2 = g.umap().dbscan(cols=['ip_172', 'location', 'alert'], umap=True | False, **kwargs)
+
+            g2.plot() # color by `_dbscan` column
+
+        Useful:
+            Enriching the graph with cluster labels from UMAP is useful for visualizing clusters in the graph by color, size, etc, as well as assessing metrics per cluster, e.g. https://github.com/graphistry/pygraphistry/blob/master/demos/ai/cyber/cyber-redteam-umap-demo.ipynb
+
+        Args:
+            :min_dist float: The maximum distance between two samples for them to be considered as in the same neighborhood.
+            :kind str: 'nodes' or 'edges'
+            :cols: list of columns to use for clustering given `g.featurize` has been run, nice way to slice features or targets by fragments of interest, e.g. ['ip_172', 'location', 'ssh', 'warnings']
+            :fit_umap_embedding bool: whether to use UMAP embeddings or features dataframe to cluster DBSCAN
+            :min_samples: The number of samples in a neighborhood for a point to be considered as a core point. This includes the point itself.
+            :target: whether to use the target column as the clustering feature
+
+        """
+
+        res = self.bind()
+        res = res._cluster_dbscan(
+            res,
+            kind=kind,
+            cols=cols,
+            fit_umap_embedding=fit_umap_embedding,
+            target=target,
+            min_dist=min_dist,
+            min_samples=min_samples,
+            engine_dbscan=engine_dbscan,
+            verbose=verbose,
+            *args,
+            **kwargs,
+        )
+
+        return res
+
+    def _transform_dbscan(
+        self, df: pd.DataFrame, ydf, kind, verbose
+    ) -> Tuple[Union[pd.DataFrame, None], pd.DataFrame, pd.DataFrame, pd.DataFrame]:
+        
+        res = self.bind()
+        if hasattr(res, "_dbscan_params"):
+            # Assume that we are transforming to last fit of dbscan
+            cols = res._dbscan_params["cols"]
+            umap = res._dbscan_params["fit_umap_embedding"]
+            target = res._dbscan_params["target"]
+
+            dbscan = res._node_dbscan if kind == "nodes" else res._edge_dbscan
+            # print('DBSCAN TYPE IN TRANSFORM', type(dbscan))
+
+            emb = None
+            if umap and cols is None:
+                emb, X, y = res.transform_umap(df, ydf, kind=kind, return_graph=False)
+            else:
+                X, y = res.transform(df, ydf, kind=kind, return_graph=False)
+            XX = X
+            if target:
+                XX = y
+            if cols is not None:
+                XX = get_matrix_by_column_parts(XX, cols)
+
+            if umap:
+                X_ = emb
+            else:
+                X_ = XX
+            
+            if res.engine_dbscan == 'cuml':
+                print('Transform DBSCAN not yet supported for engine_dbscan=`cuml`, use engine=`umap_learn`, `pandas` or `sklearn` instead')
+                return emb, X, y, df
+            
+            X_, emb = make_safe_gpu_dataframes(X_, emb, 'pandas')  
+
+            labels = dbscan_predict(X_, dbscan)  # type: ignore
+            #print('after dbscan predict', type(labels))
+            if umap and cols is None:
+                df = df.assign(_dbscan=labels, x=emb.x, y=emb.y)  # type: ignore
+            else:
+                df = df.assign(_dbscan=labels)
+            
+            if verbose:
+                print(f"Transformed DBSCAN: {len(df[DBSCAN].unique())} clusters")
+
+            return emb, X, y, df  # type: ignore
+        else:
+            raise Exception("No dbscan model found. Please run `g.dbscan()` first")
+
+    def transform_dbscan(
+        self,
+        df: pd.DataFrame,
+        y: Optional[pd.DataFrame] = None,
+        min_dist: Union[float, str] = "auto",
+        infer_umap_embedding: bool = False,
+        sample: Optional[int] = None,
+        n_neighbors: Optional[int] = None,
+        kind: str = "nodes",
+        return_graph: bool = True,
+        verbose: bool = False,
+        ):  # type: ignore
+        """Transforms a minibatch dataframe to one with a new column '_dbscan' containing the DBSCAN cluster labels on the minibatch and generates a graph with the minibatch and the original graph, with edges between the minibatch and the original graph inferred from the umap embedding or features dataframe. Graph nodes | edges will be colored by '_dbscan' column.
+            
+            Examples:
+            ::
+
+                fit:
+                    g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                    g2 = g.featurize().dbscan()
+
+                predict:
+                ::
+
+                    emb, X, _, ndf = g2.transform_dbscan(ndf, return_graph=False)
+                    # or
+                    g3 = g2.transform_dbscan(ndf, return_graph=True)
+                    g3.plot()
+
+            likewise for umap:
+            ::
+
+                fit:
+                    g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node')
+                    g2 = g.umap(X=.., y=..).dbscan()
+
+                predict:
+                ::
+
+                    emb, X, y, ndf = g2.transform_dbscan(ndf, ndf, return_graph=False)
+                    # or
+                    g3 = g2.transform_dbscan(ndf, ndf, return_graph=True)
+                    g3.plot()
+
+
+        Args:
+            :df: dataframe to transform
+            :y: optional labels dataframe
+            :min_dist: The maximum distance between two samples for them to be considered as in the same neighborhood.
+                smaller values will result in less edges between the minibatch and the original graph.
+                Default 'auto', infers min_dist from the mean distance and std of new points to the original graph
+            :fit_umap_embedding: whether to use UMAP embeddings or features dataframe when inferring edges between
+                the minibatch and the original graph. Default False, uses the features dataframe
+            :sample: number of samples to use when inferring edges between the minibatch and the original graph,
+                if None, will only use closest point to the minibatch. If greater than 0, will sample the closest `sample` points
+                in existing graph to pull in more edges. Default None
+            :kind: 'nodes' or 'edges'
+            :return_graph: whether to return a graph or the (emb, X, y, minibatch df enriched with DBSCAN labels), default True
+                infered graph supports kind='nodes' only. 
+            :verbose: whether to print out progress, default False
+
+        """
+        emb, X, y, df = self._transform_dbscan(df, y, kind=kind, verbose=verbose)
+        if return_graph and kind not in ["edges"]:
+            df, y = make_safe_gpu_dataframes(df, y, 'pandas')
+            X, emb = make_safe_gpu_dataframes(X, emb, 'pandas')
+            g = self._infer_edges(emb, X, y, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,  # type: ignore
+                infer_on_umap_embedding=infer_umap_embedding
+                )
+            return g
+        return emb, X, y, df
diff --git a/graphistry/compute/collapse.py b/graphistry/compute/collapse.py
index ddf0885805..e9b06e512c 100644
--- a/graphistry/compute/collapse.py
+++ b/graphistry/compute/collapse.py
@@ -32,15 +32,15 @@
 
 
 def unpack(g: Plottable):
-    """
-        Helper method that unpacks graphistry instance
+    """Helper method that unpacks graphistry instance
+
     ex:
-        ndf, edf, src, dst, node = unpack(g)
 
-    -----------------------------------------------------------------------------------------
+        ndf, edf, src, dst, node = unpack(g)
 
     :param g: graphistry instance
-    :returns node DataFrame, edge DataFrame, source column, destination column, node column
+
+    :returns: node DataFrame, edge DataFrame, source column, destination column, node column
     """
     ndf = g._nodes
     edf = g._edges
@@ -51,10 +51,7 @@ def unpack(g: Plottable):
 
 
 def get_children(g: Plottable, node_id: Union[str, int], hops: int = 1):
-    """
-        Helper that gets children at k-hops from node `node_id`
-
-    ------------------------------------------------------------------
+    """Helper that gets children at k-hops from node `node_id`
 
     :returns graphistry instance of hops
     """
@@ -65,17 +62,14 @@ def get_children(g: Plottable, node_id: Union[str, int], hops: int = 1):
 def has_edge(
     g: Plottable, n1: Union[str, int], n2: Union[str, int], directed: bool = True
 ) -> bool:
-    """
-        Checks if `n1` and `n2` share an (directed or not) edge
-
-    ------------------------------------------------------------------
+    """Checks if `n1` and `n2` share an (directed or not) edge
 
     :param g: graphistry instance
     :param n1: `node` to check if has edge to `n2`
     :param n2: `node` to check if has edge to `n1`
     :param directed: bool, if True, checks only outgoing edges from `n1`->`n2`, else finds undirected edges
-    :returns bool, if edge exists between `n1` and `n2`
 
+    :returns: bool, if edge exists between `n1` and `n2`
     """
     ndf, edf, src, dst, node = unpack(g)
     if directed:
@@ -92,16 +86,14 @@ def has_edge(
 def get_edges_of_node(
     g: Plottable, node_id: Union[str, int], outgoing_edges: bool = True, hops: int = 1
 ):
-    """
-        Gets edges of node at k-hops from node
-
-    ----------------------------------------------------------------------------------
+    """Gets edges of node at k-hops from node
 
     :param g: graphistry instance
     :param node_id: `node` to find edges from
     :param outgoing_edges: bool, if true, finds all outgoing edges of `node`, default True
     :param hops: the number of hops from `node` to take, default = 1
-    :returns DataFrame of edges
+
+    :returns: DataFrame of edges
     """
     _, _, src, dst, _ = unpack(g)
     g2 = get_children(g, node_id, hops=hops)
@@ -119,11 +111,7 @@ def get_edges_in_out_cluster(
     column: Union[str, int],
     directed: bool = True,
 ):
-    """
-        Traverses children of `node_id` and separates them into incluster and outcluster sets depending if they have
-        `attribute` in node DataFrame `column`
-
-    --------------------------------------------------------------------------------------------------------------------
+    """Traverses children of `node_id` and separates them into incluster and outcluster sets depending if they have `attribute` in node DataFrame `column`
 
     :param g: graphistry instance
     :param node_id: `node` with `attribute` in `column`
@@ -157,67 +145,57 @@ def get_edges_in_out_cluster(
 
 
 def get_cluster_store_keys(ndf: pd.DataFrame, node: Union[str, int]):
-    """
-        Main innovation in finding and adding to super node.
-        Checks if node is a segment in any collapse_node in COLLAPSE column of nodes DataFrame
-
-    --------------------------------------------------------------------------------------------
+    """Main innovation in finding and adding to super node. Checks if node is a segment in any collapse_node in COLLAPSE column of nodes DataFrame
 
     :param ndf: node DataFrame
     :param node: node to find
-    :returns DataFrame of bools of where `wrap_key(node)` exists in COLLAPSE column
+
+    :returns: DataFrame of bools of where `wrap_key(node)` exists in COLLAPSE column
     """
     node = wrap_key(node)
     return ndf[COLLAPSE_NODE].astype(str).str.contains(node, na=False)
 
 
 def in_cluster_store_keys(ndf: pd.DataFrame, node: Union[str, int]) -> bool:
-    """
-        checks if node is in collapse_node in COLLAPSE column of nodes DataFrame
-
-    ------------------------------------------------------------------------------
+    """checks if node is in collapse_node in COLLAPSE column of nodes DataFrame
 
     :param ndf: nodes DataFrame
     :param node: node to find
-    :returns bool
+
+    :returns: bool
     """
     return any(get_cluster_store_keys(ndf, node))
 
 
 def reduce_key(key: Union[str, int]) -> str:
-    """
-        Takes "1 1 2 1 2 3" -> "1 2 3
-
-    ---------------------------------------------------
+    """Takes "1 1 2 1 2 3" -> "1 2 3
 
     :param key: node name
-    :returns new node name with duplicates removed
+
+    :returns: new node name with duplicates removed
     """
     uniques = " ".join(np.unique(str(key).split()))
     return uniques
 
 
 def unwrap_key(name: Union[str, int]) -> str:
-    """
-        Unwraps node name: ~name~ -> name
-
-    ----------------------------------------
+    """Unwraps node name: ~name~ -> name
 
     :param name: node to unwrap
-    :returns unwrapped node name
+
+    :returns: unwrapped node name
     """
     return str(name).replace(WRAP, "")
 
 
 def wrap_key(name: Union[str, int]) -> str:
-    """
-        Wraps node name -> ~name~
-
-    -----------------------------------
+    """Wraps node name -> ~name~
 
     :param name: node name
-    :returns wrapped node name
+
+    :returns: wrapped node name
     """
+
     name = str(name)
     if WRAP in name:  # idempotency
         return name
@@ -225,17 +203,16 @@ def wrap_key(name: Union[str, int]) -> str:
 
 
 def melt(ndf: pd.DataFrame, node: Union[str, int]) -> str:
-    """
-        Reduces node if in cluster store, otherwise passes it through.
+    """Reduces node if in cluster store, otherwise passes it through.
     ex:
+
         node = "4" will take any sequence from get_cluster_store_keys, "1 2 3", "4 3 6" and returns "1 2 3 4 6"
         when they have a common entry (3).
 
-    -------------------------------------------------------------------------------------------------------------
-
     :param ndf, node DataFrame
     :param node: node to melt
     :returns new_parent_name of super node
+
     """
     rdf = ndf[get_cluster_store_keys(ndf, node)]
     topkey = wrap_key(node)
@@ -259,14 +236,12 @@ def check_has_set(ndf, parent, child):
 def get_new_node_name(
     ndf: pd.DataFrame, parent: Union[str, int], child: Union[str, int]
 ) -> str:
-    """
-        If child in cluster group, melts name, else makes new parent_name from parent, child
-
-    ---------------------------------------------------------------------------------------------------------
+    """If child in cluster group, melts name, else makes new parent_name from parent, child
 
     :param ndf: node DataFrame
     :param parent: `node` with `attribute` in `column`
     :param child: `node` with `attribute` in `column`
+
     :returns new_parent_name
     """
     # THIS IS IMPORTANT FUNCTION -- it is where we wrap the parent/child in WRAP
@@ -300,8 +275,6 @@ def collapse_nodes_and_edges(
         # outside logic controls when that is the case
         # for example, it assumes parent is already in cluster keys of COLLAPSE node
 
-    ---------------------------------------------------------------------------------------
-
     :param g: graphistry instance
     :param parent: `node` with `attribute` in `column`
     :param child: `node` with `attribute` in `column`
@@ -328,29 +301,24 @@ def collapse_nodes_and_edges(
 def has_property(
     g: Plottable, ref_node: Union[str, int], attribute: Union[str, int], column: Union[str, int]
 ) -> bool:
-    """
-        Checks if ref_node is in node dataframe in column with attribute
-
-    -------------------------------------------------------------------------
-
+    """Checks if ref_node is in node dataframe in column with attribute
     :param attribute:
     :param column:
     :param g: graphistry instance
     :param ref_node: `node` to check if it as `attribute` in `column`
-    :returns bool"""
+
+    :returns: bool
+    """
     ndf, edf, src, dst, node = unpack(g)
     ref_node = unwrap_key(ref_node)
     return ref_node in ndf[ndf[column] == attribute][node].values
 
 
 def check_default_columns_present_and_coerce_to_string(g: Plottable):
-    """
-        Helper to set COLLAPSE columns to nodes and edges dataframe, while converting src, dst, node to dtype(str)
-
-    -------------------------------------------------------------------------
-
+    """Helper to set COLLAPSE columns to nodes and edges dataframe, while converting src, dst, node to dtype(str)
     :param g: graphistry instance
-    :returns graphistry instance
+
+    :returns: graphistry instance
     """
     ndf, edf, src, dst, node = unpack(g)
     if COLLAPSE_NODE not in ndf.columns:
@@ -376,32 +344,26 @@ def collapse_algo(
     column: Union[str, int],
     seen: dict,
 ):
-    """
-        Basically candy crush over graph properties in a topology aware manner
+    """Basically candy crush over graph properties in a topology aware manner
 
-        Checks to see if child node has desired property from parent, we will need to check if
-        (start_node=parent: has_attribute , children nodes: has_attribute) by case
-        (T, T), (F, T), (T, F) and (F, F),
-        we start recursive collapse (or not) on the children, reassigning nodes and edges.
+        Checks to see if child node has desired property from parent, we will need to check if (start_node=parent: has_attribute , children nodes: has_attribute) by case (T, T), (F, T), (T, F) and (F, F),we start recursive collapse (or not) on the children, reassigning nodes and edges.
 
         if (T, T), append children nodes to start_node, re-assign the name of the node, and update the edge table with new name,
 
-        if (F, T) start k-(potentially new) super nodes, with k the number of children of start_node.
-                Start node keeps k outgoing edges.
+        if (F, T) start k-(potentially new) super nodes, with k the number of children of start_node. Start node keeps k outgoing edges.
 
         if (T, F) it is the end of the cluster, and we keep new node as is; keep going
 
         if (F, F); keep going
-
-    --------------------------------------------------------------------------------------------------------------------
-
+  
     :param seen:
     :param g: graphistry instance
     :param child: child node to start traversal, for first traversal, set child=parent or vice versa.
     :param parent: parent node to start traversal, in main call, this is set to child.
     :param attribute: attribute to collapse by
     :param column: column in nodes dataframe to collapse over.
-    :returns graphistry instance with collapsed nodes.
+
+    :returns: graphistry instance with collapsed nodes.
     """
 
     compute_key = f"{parent} {child}"
@@ -456,16 +418,13 @@ def normalize_graph(
     self_edges: bool = False,
     unwrap: bool = False
 ) -> Plottable:
-    """
-        Final step after collapse traversals are done, removes duplicates and moves COLLAPSE columns into respective
-        (node, src, dst) columns of node, edges dataframe from Graphistry instance g.
-
-    --------------------------------------------------------------------------------------------------------------------
+    """Final step after collapse traversals are done, removes duplicates and moves COLLAPSE columns into respective(node, src, dst) columns of node, edges dataframe from Graphistry instance g.
 
     :param g: graphistry instance
     :param self_edges: bool, whether to keep duplicates from ndf, edf, default False
     :param unwrap: bool, whether to unwrap node text with `~`, default True
-    :returns final graphistry instance
+
+    :returns: final graphistry instance
     """
 
     ndf, edf, src, dst, node = unpack(g)
@@ -527,7 +486,6 @@ def collapse_by(
     """
         Main call in collapse.py, collapses nodes and edges by attribute, and returns normalized graphistry object.
 
-    --------------------------------------------------------------------------------------------------------------------
     :param self: graphistry instance
     :param parent: parent node to start traversal, in main call, this is set to child.
     :param start_node:
@@ -535,6 +493,7 @@ def collapse_by(
     :param column: column in nodes dataframe to collapse over.
     :param seen: dict of previously collapsed pairs -- {n1, n2) is seen as different from (n2, n1)
     :param verbose: bool, default True
+
     :returns graphistry instance with collapsed and normalized nodes.
     """
     from time import time
diff --git a/graphistry/compute/conditional.py b/graphistry/compute/conditional.py
index 101eee9829..df96c1c31f 100644
--- a/graphistry/compute/conditional.py
+++ b/graphistry/compute/conditional.py
@@ -66,7 +66,7 @@ def conditional_graph(self, x, given, kind='nodes', *args, **kwargs):
         Useful for finding the conditional probability of a node or edge attribute
         
         returned dataframe sums to 1 on each column
-        -----------------------------------------------------------
+   
         :param x: target column
         :param given: the dependent column
         :param kind: 'nodes' or 'edges'
diff --git a/graphistry/constants.py b/graphistry/constants.py
index 1e9f862e92..f6fda05fd9 100644
--- a/graphistry/constants.py
+++ b/graphistry/constants.py
@@ -7,22 +7,36 @@
 DST = "_dst_implicit"
 NODE = '_n_implicit'  # Is this being use anymore??
 WEIGHT = "_weight"
+BATCH = "_batch"
 # for UMAP reserved namespace
 X = "x"
 Y = "y"
 IMPLICIT_NODE_ID = (
     "_n"  # for g.featurize(..).umap(..) -> g.weighted_edges_from_nodes_df
 )
-DISTANCE = '_distance'  # for text search db column
+# for text search db column
+DISTANCE = '_distance'
+# Scalers
+SCALERS = ['quantile', 'standard', 'kbins', 'robust', 'minmax']
+
+# dbscan reserved namespace
+DBSCAN = '_dbscan'
+DBSCAN_PARAMS = '_dbscan_params'
+
 # ###############################################################
 # consistent clf pipelining and constructor methods across files
-DGL_GRAPH = "DGL_graph"
+DGL_GRAPH = "DGL_graph"  # TODO: change to _dgl_graph ? 
+KG_GRAPH = '_kg_graph'
 FEATURE = "feature"
 TARGET = "target"
 LABEL = "label"
 LABEL_NODES = "node_label"
 LABEL_EDGES = "edge_label"
 
+# ENGINES
+CUML = 'cuml'
+UMAP_LEARN = 'umap_learn'
+
 TRAIN_MASK = "train_mask"
 TEST_MASK = "test_mask"
 
@@ -38,12 +52,10 @@
 # scikit-learn params
 SKLEARN = "sklearn"
 
-
 # #############################################################
 # Caching and other internals
 CACHE_COERCION_SIZE = 100
 
-
 # #############################################################
 # Annoy defaults
 N_TREES = 10
diff --git a/graphistry/dgl_utils.py b/graphistry/dgl_utils.py
index f82614dff1..257c13a701 100644
--- a/graphistry/dgl_utils.py
+++ b/graphistry/dgl_utils.py
@@ -229,7 +229,7 @@ def dgl_lazy_init(self, train_split: float = 0.8, device: str = "cpu"):
             self.train_split = train_split
             self.device = device
             self._removed_edges_previously = False
-            self.DGL_graph = None
+            self._dgl_graph = None
             self.dgl_initialized = True
 
     def _prune_edge_target(self):
@@ -335,7 +335,7 @@ def _convert_edge_dataframe_to_DGL(
                 'destination column not set, try running g.bind(destination="my_col") or g.edges(df, destination="my_col")'
             )
 
-        res.DGL_graph, res._adjacency, res._entity_to_index = pandas_to_dgl_graph(
+        res._dgl_graph, res._adjacency, res._entity_to_index = pandas_to_dgl_graph(
             res._edges,
             res._source,
             res._destination,
@@ -370,7 +370,7 @@ def _featurize_nodes_to_dgl(
 
         ndata = convert_to_torch(X_enc, y_enc)
         # add ndata to the graph
-        res.DGL_graph.ndata.update(ndata)
+        res._dgl_graph.ndata.update(ndata)
         res._mask_nodes()
         return res
 
@@ -396,7 +396,7 @@ def _featurize_edges_to_dgl(
 
         edata = convert_to_torch(X_enc, y_enc)
         # add edata to the graph
-        res.DGL_graph.edata.update(edata)
+        res._dgl_graph.edata.update(edata)
         res._mask_edges()
         return res
 
@@ -443,7 +443,6 @@ def build_gnn(
         :param inplace: default, False, whether to return Graphistry instance in place or not.
 
         """
-
         if inplace:
             res = self
         else:
@@ -504,30 +503,21 @@ def build_gnn(
             return res
 
     def _mask_nodes(self):
-        if config.FEATURE in self.DGL_graph.ndata:
-            n = self.DGL_graph.ndata[config.FEATURE].shape[0]
+        if config.FEATURE in self._dgl_graph.ndata:
+            n = self._dgl_graph.ndata[config.FEATURE].shape[0]
             (
-                self.DGL_graph.ndata[config.TRAIN_MASK],
-                self.DGL_graph.ndata[config.TEST_MASK],
+                self._dgl_graph.ndata[config.TRAIN_MASK],
+                self._dgl_graph.ndata[config.TEST_MASK],
             ) = get_torch_train_test_mask(n, self.train_split)
 
     def _mask_edges(self):
-        if config.FEATURE in self.DGL_graph.edata:
-            n = self.DGL_graph.edata[config.FEATURE].shape[0]
+        if config.FEATURE in self._dgl_graph.edata:
+            n = self._dgl_graph.edata[config.FEATURE].shape[0]
             (
-                self.DGL_graph.edata[config.TRAIN_MASK],
-                self.DGL_graph.edata[config.TEST_MASK],
+                self._dgl_graph.edata[config.TRAIN_MASK],
+                self._dgl_graph.edata[config.TEST_MASK],
             ) = get_torch_train_test_mask(n, self.train_split)
 
-    def __getitem__(self, idx):
-        # get one example by index, here we have only one graph. #todo parameterize case if we have RGNN
-        if self.DGL_graph is None:
-            logger.warning("DGL graph is not built, run `g.build_gnn(...)` first")
-        return self.DGL_graph
-
-    # def __len__(self): # this messes up scope.
-    #     # number of data examples
-    #     return 1
 
 
 # if __name__ == "__main__":
@@ -607,7 +597,7 @@ def __getitem__(self, idx):
 #         use_edge_scaler="zscale",
 #     )
 #     # the DGL graph
-#     G = g2.DGL_graph
+#     G = g2._dgl_graph
 #     print('G', G)
 #     # to get a sense of the different parts in training loop above
 #     # labels = torch.tensor(T.values, dtype=torch.float)
diff --git a/graphistry/embed_utils.py b/graphistry/embed_utils.py
index 10798d70a3..9e64fdfa10 100644
--- a/graphistry/embed_utils.py
+++ b/graphistry/embed_utils.py
@@ -6,6 +6,7 @@
 from .PlotterBase import Plottable
 from .compute.ComputeMixin import ComputeMixin
 
+
 def lazy_embed_import_dep():
     try:
         import torch
@@ -20,6 +21,13 @@ def lazy_embed_import_dep():
     except:
         return False, None, None, None, None, None, None, None
 
+def check_cudf():
+    try:
+        import cudf
+        return True, cudf
+    except:
+        return False, object
+        
 
 if TYPE_CHECKING:
     _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
@@ -30,6 +38,8 @@ def lazy_embed_import_dep():
     MIXIN_BASE = object
     torch = Any
 
+has_cudf, cudf = check_cudf()
+
 XSymbolic = Optional[Union[List[str], str, pd.DataFrame]]
 ProtoSymbolic = Optional[Union[str, Callable[[TT, TT, TT], TT]]]  # type: ignore
 
@@ -89,7 +99,8 @@ def __init__(self):
         self._device = "cpu"
 
     def _preprocess_embedding_data(self, res, train_split:Union[float, int] = 0.8) -> Plottable:
-        _, torch, _, _, _, _, F, _ = lazy_embed_import_dep()
+        #_, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
+        import torch
         log('Preprocessing embedding data')
         src, dst = res._source, res._destination
         relation = res._relation
@@ -125,7 +136,7 @@ def _preprocess_embedding_data(self, res, train_split:Union[float, int] = 0.8) -
             log(msg="--Splitting data")
             train_size = int(train_split * len(triplets))
             test_size = len(triplets) - train_size
-            train_dataset, test_dataset = torch.utils.data.random_split(triplets, [train_size, test_size])
+            train_dataset, test_dataset = torch.utils.data.random_split(triplets, [train_size, test_size])  # type: ignore
             res._train_idx = train_dataset.indices
             res._test_idx = test_dataset.indices
 
@@ -153,13 +164,13 @@ def _build_graph(self, res) -> Plottable:
             g_dgl.edata[dgl.ETYPE] = r
 
         g_dgl.edata["norm"] = dgl.norm_by_dst(g_dgl).unsqueeze(-1)
-        res.g_dgl = g_dgl
+        res._kg_dgl = g_dgl
         return res
 
 
     def _init_model(self, res, batch_size:int, sample_size:int, num_steps:int, device):
         _, _, _, _, GraphDataLoader, HeteroEmbed, _, _ = lazy_embed_import_dep()
-        g_iter = SubgraphIterator(res.g_dgl, sample_size, num_steps)
+        g_iter = SubgraphIterator(res._kg_dgl, sample_size, num_steps)
         g_dataloader = GraphDataLoader(
             g_iter, batch_size=batch_size, collate_fn=lambda x: x[0]
         )
@@ -209,7 +220,7 @@ def _train_embedding(self, res, epochs:int, batch_size:int, lr:float, sample_siz
                 )
 
             model.eval()
-            res._kg_embeddings = model(res.g_dgl.to(device)).detach()
+            res._kg_embeddings = model(res._kg_dgl.to(device)).detach()
             res._embed_model = model
             if res._eval_flag and res._train_idx is not None:
                 score = res._eval(threshold=0.5)
@@ -222,7 +233,7 @@ def _train_embedding(self, res, epochs:int, batch_size:int, lr:float, sample_siz
     @property
     def _gcn_node_embeddings(self):
         _, torch, _, _, _, _, _, _ = lazy_embed_import_dep()
-        g_dgl = self.g_dgl.to(self._device)
+        g_dgl = self._kg_dgl.to(self._device)
         em = self._embed_model(g_dgl).detach()
         torch.cuda.empty_cache()
         return em
@@ -288,6 +299,17 @@ def embed(
         -------
             self : graphistry instance
         """
+        # this is temporary, will be fixed in future releases
+        try:
+            if isinstance(self._nodes, cudf.DataFrame):
+                self._nodes = self._nodes.to_pandas()
+        except:
+            pass
+        try:
+            if isinstance(self._edges, cudf.DataFrame):
+                self._edges = self._edges.to_pandas()
+        except:
+            pass
         if inplace:
             res = self
         else:
@@ -404,25 +426,44 @@ def predict_links(
             where score >= threshold if anamalous if False else score <= threshold, or a dataframe
             
         """
-
+        logging.warning("currently `predict_links` is cpu only, gpu compatibility will be added in \
+                future releases") 
         all_nodes = self._node2id.values()
         all_relations = self._relation2id.values()
 
         if source is None:
             src = pd.Series(all_nodes)
         else:
+            # this is temporary, will be removed after gpu feature utils
+            try:
+                if isinstance(source, cudf.DataFrame):
+                    source = source.to_pandas()  # type: ignore
+            except:
+                pass
             src = pd.Series(source)
             src = src.map(self._node2id)
 
         if relation is None:
             rel = pd.Series(all_relations)
         else:
+            # this is temporary, will be removed after gpu feature utils
+            try:
+                if isinstance(relation, cudf.DataFrame):
+                    relation = relation.to_pandas()  # type: ignore
+            except:
+                pass
             rel = pd.Series(relation)
             rel = rel.map(self._relation2id)
 
         if destination is None:
             dst = pd.Series(all_nodes)
         else:
+            # this is temporary, will be removed after gpu feature utils
+            try:
+                if isinstance(destination, cudf.DataFrame):
+                    destination = destination.to_pandas()  # type: ignore
+            except:
+                pass
             dst = pd.Series(destination)
             dst = dst.map(self._node2id)
 
diff --git a/graphistry/feature_utils.py b/graphistry/feature_utils.py
index ba6227da29..2a60194ca2 100644
--- a/graphistry/feature_utils.py
+++ b/graphistry/feature_utils.py
@@ -3,6 +3,7 @@
 import os
 import pandas as pd
 from time import time
+from inspect import getmodule
 import warnings
 from functools import partial
 
@@ -23,6 +24,7 @@
 from . import constants as config
 from .PlotterBase import WeakValueDictionary, Plottable
 from .util import setup_logger, check_set_memoize
+from .ai_utils import infer_graph, infer_self_graph
 
 # add this inside classes and have a method that can set log level
 logger = setup_logger(name=__name__, verbose=config.VERBOSE)
@@ -47,6 +49,16 @@
         SuperVectorizer = Any
         GapEncoder = Any
         SimilarityEncoder = Any
+    try:
+        from cu_cat import (
+            SuperVectorizer,
+            GapEncoder,
+            SimilarityEncoder,
+        )  # type: ignore
+    except:
+        SuperVectorizer = Any
+        GapEncoder = Any
+        SimilarityEncoder = Any
     try:
         from sklearn.preprocessing import FunctionTransformer
         from sklearn.base import BaseEstimator, TransformerMixin
@@ -54,6 +66,13 @@
         FunctionTransformer = Any
         BaseEstimator = object
         TransformerMixin = object
+    # try:
+    #     from cuml.preprocessing import FunctionTransformer
+    #     from sklearn.base import BaseEstimator, TransformerMixin
+    # except:
+    #     FunctionTransformer = Any
+    #     BaseEstimator = object
+    #     TransformerMixin = object
 else:
     MIXIN_BASE = object
     Pipeline = Any
@@ -91,6 +110,28 @@ def lazy_import_has_min_dependancy():
     except ModuleNotFoundError as e:
         return False, e
 
+def lazy_import_has_cu_cat_dependancy():
+    import warnings
+    warnings.filterwarnings("ignore")
+    try:
+        import scipy.sparse  # noqa
+        from scipy import __version__ as scipy_version
+        from cu_cat import __version__ as cu_cat_version
+        import cu_cat
+        from sklearn import __version__ as sklearn_version
+        from cuml import __version__ as cuml_version
+        import cuml
+        from cudf import __version__ as cudf_version
+        import cudf
+        logger.debug(f"SCIPY VERSION: {scipy_version}")
+        logger.debug(f"Cuda CAT VERSION: {cu_cat_version}")
+        logger.debug(f"sklearn VERSION: {sklearn_version}")
+        logger.debug(f"cuml VERSION: {cuml_version}")
+        logger.debug(f"cudf VERSION: {cudf_version}")
+
+        return True, 'ok', cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 def assert_imported_text():
     has_dependancy_text_, import_text_exn, _ = lazy_import_has_dependancy_text()
@@ -101,7 +142,6 @@ def assert_imported_text():
         )
         raise import_text_exn
 
-
 def assert_imported():
     has_min_dependancy_, import_min_exn = lazy_import_has_min_dependancy()
     if not has_min_dependancy_:
@@ -110,7 +150,36 @@ def assert_imported():
                      "`pip install graphistry[ai]`"  # noqa
         )
         raise import_min_exn
+        
+def assert_cuml_cucat():
+    has_cuml_dependancy_, import_cuml_exn, cudf = lazy_import_has_cu_cat_dependancy()
+    if not has_cuml_dependancy_:
+        logger.error(  # noqa
+                     "cuml not found, trying running"  # noqa
+                     "`pip install rapids`"  # noqa
+        )
+        raise import_cuml_exn
+
+def make_safe_gpu_dataframes(X, y, engine):
+
+    def safe_cudf(X, y):
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "dirty_cat", "torch"]:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
 
+    has_cudf_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
+    if has_cudf_dependancy_:
+        print(f"Using GPU: {engine}")
+        return safe_cudf(X, y)
+    else:
+        return X, y
 
 # ############################################################################
 #
@@ -135,7 +204,8 @@ def assert_imported():
 #
 #      _featurize_or_get_edges_dataframe_if_X_is_None
 
-FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch"]
+
+FeatureEngineConcrete = Literal["none", "pandas", "dirty_cat", "torch", "cu_cat"]
 FeatureEngine = Literal[FeatureEngineConcrete, "auto"]
 
 
@@ -143,13 +213,16 @@ def resolve_feature_engine(
     feature_engine: FeatureEngine,
 ) -> FeatureEngineConcrete:  # noqa
 
-    if feature_engine in ["none", "pandas", "dirty_cat", "torch"]:
+    if feature_engine in ["none", "pandas", "dirty_cat", "torch", "cu_cat"]:
         return feature_engine  # type: ignore
 
     if feature_engine == "auto":
         has_dependancy_text_, _, _ = lazy_import_has_dependancy_text()
         if has_dependancy_text_:
             return "torch"
+        has_cuml_dependancy_, _, cudf = lazy_import_has_cu_cat_dependancy()
+        if has_cuml_dependancy_:
+            return "cu_cat"
         has_min_dependancy_, _ = lazy_import_has_min_dependancy()
         if has_min_dependancy_:
             return "dirty_cat"
@@ -157,7 +230,7 @@ def resolve_feature_engine(
 
     raise ValueError(  # noqa
         f'feature_engine expected to be "none", '
-        '"pandas", "dirty_cat", "torch", or "auto"'
+        '"pandas", "dirty_cat", "torch", "cu_cat", or "auto"'
         f'but received: {feature_engine} :: {type(feature_engine)}'
     )
 
@@ -167,8 +240,9 @@ def resolve_feature_engine(
 
 def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
-    if isinstance(y, pd.DataFrame):
-        return y
+    if isinstance(y, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(y)):
+
+        return y  # type: ignore
 
     if df is None:
         raise ValueError("Missing data for featurization")
@@ -188,9 +262,8 @@ def resolve_y(df: Optional[pd.DataFrame], y: YSymbolic) -> pd.DataFrame:
 
 def resolve_X(df: Optional[pd.DataFrame], X: XSymbolic) -> pd.DataFrame:
 
-    if isinstance(X, pd.DataFrame):
-        return X
-
+    if isinstance(X, pd.DataFrame) or 'cudf.core.dataframe' in str(getmodule(X)):
+        return X  # type: ignore
     if df is None:
         raise ValueError("Missing data for featurization")
 
@@ -222,10 +295,7 @@ def safe_divide(a, b):
 def features_without_target(
     df: pd.DataFrame, y: Optional[Union[List, str, pd.DataFrame]] = None
 ) -> pd.DataFrame:
-    """
-        Checks if y DataFrame column name is in df, and removes it
-        from df if so
-    ___________________________________________________________________
+    """Checks if y DataFrame column name is in df, and removes it from df if so
 
     :param df: model DataFrame
     :param y: target DataFrame
@@ -266,17 +336,15 @@ def remove_node_column_from_symbolic(X_symbolic, node):
             logger.info(f"Removing `{node}` from input X_symbolic list")
             X_symbolic.remove(node)
         return X_symbolic
-    if isinstance(X_symbolic, pd.DataFrame):
+    if isinstance(X_symbolic, pd.DataFrame) or 'cudf' in str(getmodule(X_symbolic)):
         logger.info(f"Removing `{node}` from input X_symbolic DataFrame")
         return X_symbolic.drop(columns=[node], errors="ignore")
 
-
 def remove_internal_namespace_if_present(df: pd.DataFrame):
     """
         Some tranformations below add columns to the DataFrame,
         this method removes them before featurization
         Will not drop if suffix is added during UMAP-ing
-    ______________________________________________________________
 
     :param df: DataFrame
     :return: DataFrame with dropped columns in reserved namespace
@@ -396,9 +464,8 @@ def is_dataframe_all_numeric(df: pd.DataFrame) -> bool:
 
 
 def find_bad_set_columns(df: pd.DataFrame, bad_set: List = ["[]"]):
-    """
-        Finds columns that if not coerced to strings, will break processors.
-    -------------------------------------------------------------------------
+    """Finds columns that if not coerced to strings, will break processors.
+    
     :param df: DataFrame
     :param bad_set: List of strings to look for.
     :return: list
@@ -428,9 +495,7 @@ def check_if_textual_column(
     confidence: float = 0.35,
     min_words: float = 2.5,
 ) -> bool:
-    """
-        Checks if `col` column of df is textual or not using basic heuristics
-    __________________________________________________________________________
+    """Checks if `col` column of df is textual or not using basic heuristics
 
     :param df: DataFrame
     :param col: column name
@@ -469,9 +534,7 @@ def check_if_textual_column(
 def get_textual_columns(
     df: pd.DataFrame, min_words: float = 2.5
 ) -> List:
-    """
-        Collects columns from df that it deems are textual.
-    _____________________________________________________________________
+    """Collects columns from df that it deems are textual.
 
     :param df: DataFrame
     :return: list of columns names
@@ -498,7 +561,6 @@ class Embedding:
     """
     Generates random embeddings of a given dimension 
     that aligns with the index of the dataframe
-    _____________________________________________________________________
     
     """
     def __init__(self, df: pd.DataFrame):
@@ -536,14 +598,12 @@ def get_preprocessing_pipeline(
     encode: str = "ordinal",
     strategy: str = "quantile",
 ) -> Pipeline:  # noqa
-    """
-        Helper function for imputing and scaling np.ndarray data
-        using different scaling transformers.
-    -----------------------------------------------------------------
+    """Helper function for imputing and scaling np.ndarray data using different scaling transformers.
+
     :param X: np.ndarray
     :param impute: whether to run imputing or not
     :param use_scaler: string in None or
-            ["minmax", "quantile", "zscale", "robust", "kbins"],
+            ["minmax", "quantile", "standard", "robust", "kbins"],
             selects scaling transformer, default None
     :param n_quantiles: if use_scaler = 'quantile',
             sets the quantile bin size.
@@ -563,16 +623,24 @@ def get_preprocessing_pipeline(
         KBinsDiscretizer,
         MinMaxScaler,
         MultiLabelBinarizer,
-        QuantileTransformer,
+        QuantileTransformer, 
         RobustScaler,
         StandardScaler,
     )
+    # from cuml.preprocessing import (
+    #     # FunctionTransformer,
+    #     KBinsDiscretizer,
+    #     MinMaxScaler,
+    #     # QuantileTransformer,  ## cuml 23 only
+    #     RobustScaler,
+    #     StandardScaler,
+    # )
     from sklearn.pipeline import Pipeline
     from sklearn.impute import SimpleImputer
     available_preprocessors = [
         "minmax",
         "quantile",
-        "zscale",
+        "standard",
         "robust",
         "kbins",
     ]
@@ -599,7 +667,7 @@ def get_preprocessing_pipeline(
         scaler = QuantileTransformer(
             n_quantiles=n_quantiles, output_distribution=output_distribution
         )
-    elif use_scaler == "zscale":
+    elif use_scaler == "standard":
         scaler = StandardScaler()
     elif use_scaler == "robust":
         scaler = RobustScaler(quantile_range=quantile_range)
@@ -621,25 +689,28 @@ def get_preprocessing_pipeline(
 def fit_pipeline(
     X: pd.DataFrame, transformer, keep_n_decimals: int = 5
 ) -> pd.DataFrame:
-    """
-     Helper to fit DataFrame over transformer pipeline.
-     Rounds resulting matrix X by keep_n_digits if not 0,
-     which helps for when transformer pipeline is scaling or imputer
-     which sometime introduce small negative numbers,
-     and umap metrics like Hellinger need to be positive
-    :param X, DataFrame to transform.
+    """Helper to fit DataFrame over transformer pipeline. Rounds resulting matrix X by keep_n_digits if not 0, which helps for when transformer pipeline is scaling or imputer which sometime introduce small negative numbers, and umap metrics like Hellinger need to be positive
+    :param X: DataFrame to transform.
     :param transformer: Pipeline object to fit and transform
-    :param keep_n_decimals: Int of how many decimal places to keep in
-    rounded transformed data
+    :param keep_n_decimals: Int of how many decimal places to keep in rounded transformed data
     """
     columns = X.columns
     index = X.index
-
-    X = transformer.fit_transform(X)
-    if keep_n_decimals:
-        X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
-
-    return pd.DataFrame(X, columns=columns, index=index)
+    X_type = str(getmodule(X))
+    if 'cudf' not in X_type:
+        X = transformer.fit_transform(X)
+        if keep_n_decimals:
+            X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
+        X = pd.DataFrame(X, columns=columns, index=index)
+    else:
+        X = transformer.fit_transform(X.to_numpy())
+        if keep_n_decimals:
+            X = np.round(X, decimals=keep_n_decimals)  #  type: ignore  # noqa
+        # import cudf
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
+        X = cudf.DataFrame(X, columns=columns, index=index)
+    return X
 
 
 def impute_and_scale_df(
@@ -794,8 +865,8 @@ def encoder(X, use_scaler):  # noqa: E301
 
 
 def get_cardinality_ratio(df: pd.DataFrame):
-    """Calculates ratio of unique values to total number of rows of DataFrame
-    -------------------------------------------------------------------------
+    """Calculates the ratio of unique values to total number of rows of DataFrame
+   
     :param df: DataFrame
     """
     ratios = {}
@@ -864,6 +935,7 @@ def process_dirty_dataframes(
     similarity: Optional[str] = None,  # "ngram",
     categories: Optional[str] = "auto",
     multilabel: bool = False,
+    feature_engine: Optional[str] = "dirty_cat",
 ) -> Tuple[
     pd.DataFrame,
     Optional[pd.DataFrame],
@@ -873,8 +945,7 @@ def process_dirty_dataframes(
     """
         Dirty_Cat encoder for record level data. Will automatically turn
         inhomogeneous dataframe into matrix using smart conversion tricks.
-    ______________________________________________________________________
-
+   
     :param ndf: node DataFrame
     :param y: target DataFrame or series
     :param cardinality_threshold: For ndf columns, below this threshold,
@@ -883,17 +954,22 @@ def process_dirty_dataframes(
             threshold, encoder is OneHot, above, it is GapEncoder
     :param n_topics: number of topics for GapEncoder, default 42
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param similarity: one of 'ngram', 'levenshtein-ratio', 'jaro',
             or'jaro-winkler'}) – The type of pairwise string similarity
             to use. If None or False, uses a SuperVectorizer
     :return: Encoded data matrix and target (if not None),
             the data encoder, and the label encoder.
     """
-    from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
-    from sklearn.preprocessing import FunctionTransformer
+    if feature_engine != 'cu_cat':
+        from dirty_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+        from sklearn.preprocessing import FunctionTransformer
+    elif feature_engine == 'cu_cat':
+        lazy_import_has_cu_cat_dependancy()  # tried to use this rather than importing below
+        from cu_cat import SuperVectorizer, GapEncoder, SimilarityEncoder
+        from cuml.preprocessing import FunctionTransformer
     t = time()
-
+    
     if not is_dataframe_all_numeric(ndf):
         data_encoder = SuperVectorizer(
             auto_cast=True,
@@ -906,7 +982,6 @@ def process_dirty_dataframes(
         )
 
         logger.info(":: Encoding DataFrame might take a few minutes ------")
-        
         X_enc = data_encoder.fit_transform(ndf, y)
         X_enc = make_array(X_enc)
 
@@ -929,11 +1004,17 @@ def process_dirty_dataframes(
         #  now just set the feature names, since dirty cat changes them in
         #  a weird way...
         data_encoder.get_feature_names_out = callThrough(features_transformed)
-        
-        X_enc = pd.DataFrame(
-            X_enc, columns=features_transformed, index=ndf.index
-        )
-        X_enc = X_enc.fillna(0.0)
+        if 'cudf' not in str(getmodule(ndf)):
+            X_enc = pd.DataFrame(
+                X_enc, columns=features_transformed, index=ndf.index
+            )
+            X_enc = X_enc.fillna(0.0)  # TODO -- this is a hack in cuml version
+        else:
+            # X_enc = cudf.DataFrame.from_arrow(X_enc)
+            X_enc.index = ndf.index
+            X_enc.columns = np.array(features_transformed)
+            X_enc = X_enc.fillna(0.0)
+
     else:
         logger.info("-*-*- DataFrame is completely numeric")
         X_enc, _, data_encoder, _ = get_numeric_transformers(ndf, None)
@@ -1024,6 +1105,8 @@ def process_nodes_dataframes(
     feature_engine: FeatureEngineConcrete = "pandas"
     # test_size: Optional[bool] = None,
 ) -> Tuple[
+    pd.DataFrame,
+    Any,
     pd.DataFrame,
     Any,
     SuperVectorizer,
@@ -1033,15 +1116,12 @@ def process_nodes_dataframes(
     Any,
     List[str],
 ]:
-    """
-        Automatic Deep Learning Embedding/ngrams of Textual Features,
-        with the rest of the columns taken care of by dirty_cat
-    _________________________________________________________________________
+    """Automatic Deep Learning Embedding/ngrams of Textual Features, with the rest of the columns taken care of by dirty_cat
 
     :param df: pandas DataFrame of data
     :param y: pandas DataFrame of targets
     :param use_scaler: None or string in
-            ['minmax', 'zscale', 'robust', 'quantile']
+            ['minmax', 'standard', 'robust', 'quantile']
     :param n_topics: number of topics in Gap Encoder
     :param use_scaler:
     :param confidence: Number between 0 and 1, will pass
@@ -1055,6 +1135,7 @@ def process_nodes_dataframes(
     :param model_name: SentenceTransformer model name. See available list at
             https://www.sbert.net/docs/pretrained_models.
             html#sentence-embedding-models
+
     :return: X_enc, y_enc, data_encoder, label_encoder,
         scaling_pipeline,
         scaling_pipeline_target,
@@ -1067,7 +1148,7 @@ def process_nodes_dataframes(
         X_enc, y_enc, data_encoder, label_encoder = get_numeric_transformers(
             df, y
         )
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1089,6 +1170,8 @@ def process_nodes_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
@@ -1132,7 +1215,8 @@ def process_nodes_dataframes(
         n_topics_target=n_topics_target,
         similarity=similarity,
         categories=categories,
-        multilabel=multilabel
+        multilabel=multilabel,
+        feature_engine=feature_engine,
     )
 
     if embedding:
@@ -1153,8 +1237,7 @@ def process_nodes_dataframes(
     logger.debug(
         f"--The entire Encoding process took {(time()-t)/60:.2f} minutes"
     )
-
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
         X_enc,
         y_enc,
         use_scaler,
@@ -1168,10 +1251,11 @@ def process_nodes_dataframes(
         strategy=strategy,
         keep_n_decimals=keep_n_decimals,
     )
-
     return (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         data_encoder,
         label_encoder,
         scaling_pipeline,
@@ -1183,7 +1267,7 @@ class FastMLB:
     def __init__(self, mlb, in_column, out_columns):
         if isinstance(in_column, str):
             in_column = [in_column]
-        self.columns = in_column  # should be singe entry list ['cats']
+        self.columns = in_column  # should be single entry list ['cats']
         self.mlb = mlb
         self.out_columns = out_columns
         self.feature_names_in_ = in_column
@@ -1241,28 +1325,43 @@ def encode_edges(edf, src, dst, mlb, fit=False):
         edf (pd.DataFrame): edge dataframe
         src (string): source column
         dst (string): destination column
-        mlb (sklearn): multilabelBinarizer
-        fit (bool, optional): If true, fits multilabelBinarizer.
-            Defaults to False.
-    Returns:
-        tuple: pd.DataFrame, multilabelBinarizer
+        mlb (sklearn): multilabelBinarizer ##not in cuml yet so cast down to pandas
+        fit (bool, optional): If true, fits multilabelBinarizer. Defaults to False.
+
+    :Returns: tuple: pd.DataFrame, multilabelBinarizer
     """
     # uses mlb with fit=T/F so we can use it in transform mode
     # to recreate edge feature concat definition
+
+    logger.debug("Encoding Edges using MultiLabelBinarizer")
+    edf_type = str(getmodule(edf))
     source = edf[src]
     destination = edf[dst]
-    logger.debug("Encoding Edges using MultiLabelBinarizer")
-    if fit:
+    source_dtype = str(getmodule(source))
+                       
+    if fit and 'cudf' not in source_dtype:
         T = mlb.fit_transform(zip(source, destination))
-    else:
+    elif fit and 'cudf' in source_dtype:
+        T = mlb.fit_transform(zip(source.to_pandas(), destination.to_pandas()))
+    elif not fit and 'cudf' not in source_dtype:
         T = mlb.transform(zip(source, destination))
+    elif not fit and 'cudf' in source_dtype:
+        T = mlb.transform(zip(source.to_pandas(), destination.to_pandas()))
+                                   
     T = 1.0 * T  # coerce to float
     columns = [
         str(k) for k in mlb.classes_
     ]  # stringify the column names or scikits.base throws error
     mlb.get_feature_names_out = callThrough(columns)
     mlb.columns_ = [src, dst]
-    T = pd.DataFrame(T, columns=columns, index=edf.index)
+    if 'cudf' in edf_type:
+        # lazy_import_has_cu_cat_dependancy()
+        # import cudf
+        # assert_cuml_cucat()
+        _, _, cudf = lazy_import_has_cu_cat_dependancy()
+        T = cudf.DataFrame(T, columns=columns, index=edf.index)
+    else:
+        T = pd.DataFrame(T, columns=columns, index=edf.index)
     logger.info(f"Shape of Edge Encoding: {T.shape}")
     return T, mlb
 
@@ -1273,7 +1372,7 @@ def process_edge_dataframes(
     src: str,
     dst: str,
     cardinality_threshold: int = 40,
-    cardinality_threshold_target: int = 100,
+    cardinality_threshold_target: int = 400,
     n_topics: int = config.N_TOPICS_DEFAULT,
     n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
     use_scaler: Optional[str] = None,
@@ -1283,7 +1382,6 @@ def process_edge_dataframes(
     ngram_range: tuple = (1, 3),
     max_df: float = 0.2,
     min_df: int = 3,
-    #confidence: float = 0.35,
     min_words: float = 2.5,
     model_name: str = "paraphrase-MiniLM-L6-v2",
     similarity: Optional[str] = None,
@@ -1298,6 +1396,8 @@ def process_edge_dataframes(
     keep_n_decimals: int = 5,
     feature_engine: FeatureEngineConcrete = "pandas",
 ) -> Tuple[
+    pd.DataFrame,
+    pd.DataFrame,
     pd.DataFrame,
     pd.DataFrame,
     List[Any],
@@ -1319,9 +1419,9 @@ def process_edge_dataframes(
     :param src: source column to select in edf
     :param dst: destination column to select in edf
     :param use_scaler: None or string in
-        ['minmax', 'zscale', 'robust', 'quantile']
-    :return: Encoded data matrix and target (if not None),
-        the data encoders, and the label encoder.
+        ['minmax', 'standard', 'robust', 'quantile']
+
+    :return: Encoded data matrix and target (if not None), the data encoders, and the label encoder.
     """
     lazy_import_has_min_dependancy()
     from sklearn.preprocessing import (
@@ -1334,6 +1434,9 @@ def process_edge_dataframes(
         MultiLabelBinarizer()
     )  # create new one so we can use encode_edges later in
     # transform with fit=False
+    _, _, cudf = lazy_import_has_cu_cat_dependancy()
+    # assert_cuml_cucat()
+    
     T, mlb_pairwise_edge_encoder = encode_edges(
         edf, src, dst, mlb_pairwise_edge_encoder, fit=True
     )
@@ -1354,7 +1457,7 @@ def process_edge_dataframes(
         # add the two datasets together
         X_enc = pd.concat([T, X_enc], axis=1)
         # then scale them
-        X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
+        X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(  # noqa
             X_enc,
             y_enc,
             use_scaler,
@@ -1374,6 +1477,8 @@ def process_edge_dataframes(
         return (
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             [mlb_pairwise_edge_encoder, data_encoder],
             label_encoder,
             scaling_pipeline,
@@ -1385,6 +1490,8 @@ def process_edge_dataframes(
     (
         X_enc,
         y_enc,
+        _,
+        _,
         data_encoder,
         label_encoder,
         _,
@@ -1415,7 +1522,11 @@ def process_edge_dataframes(
     if not X_enc.empty and not T.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found Edges and Dirty_cat encoding =>")
-        X_enc = pd.concat([T, X_enc], axis=1)
+        T_type = str(getmodule(T))
+        if 'cudf' not in T_type:
+            X_enc = pd.concat([T, X_enc], axis=1)
+        else:
+            X_enc = cudf.concat([T, X_enc], axis=1)
     elif not T.empty and X_enc.empty:
         logger.debug("-" * 60)
         logger.debug("<= Found only Edges =>")
@@ -1426,7 +1537,7 @@ def process_edge_dataframes(
         f" {(time()-t)/60:.2f} minutes"
     )
 
-    X_enc, y_enc, scaling_pipeline, scaling_pipeline_target = smart_scaler(
+    X_encs, y_encs, scaling_pipeline, scaling_pipeline_target = smart_scaler(
         X_enc,
         y_enc,
         use_scaler,
@@ -1444,6 +1555,8 @@ def process_edge_dataframes(
     res = (
         X_enc,
         y_enc,
+        X_encs,
+        y_encs,
         [mlb_pairwise_edge_encoder, data_encoder],
         label_encoder,
         scaling_pipeline,
@@ -1501,22 +1614,23 @@ def transform_dirty(
     data_encoder: Union[SuperVectorizer, FunctionTransformer],  # type: ignore
     name: str = "",
 ) -> pd.DataFrame:
-    from sklearn.preprocessing import MultiLabelBinarizer
+    # from sklearn.preprocessing import MultiLabelBinarizer
     logger.debug(f"-{name} Encoder:")
     logger.debug(f"\t{data_encoder}\n")
     # print(f"-{name} Encoder:")
     # print(f"\t{data_encoder}\n")
-    try:
-        logger.debug(f"{data_encoder.get_feature_names_in}")
-    except Exception as e:
-        logger.warning(e)
-        pass
+    # try:
+    #     logger.debug(f"{data_encoder.get_feature_names_in}")
+    # except Exception as e:
+    #     logger.warning(e)
+    #     pass
     logger.debug(f"TRANSFORM pre as df -- \t{df.shape}")
 
     # #####################################  for dirty_cat 0.3.0
     use_columns = getattr(data_encoder, 'columns_', [])
     if len(use_columns):
-        X = data_encoder.transform(df[use_columns])
+        #print(f"Using columns: {use_columns}")
+        X = data_encoder.transform(df[df.columns.intersection(use_columns)])
     # #####################################  with dirty_cat 0.2.0
     else:
         X = data_encoder.transform(df)
@@ -1544,20 +1658,21 @@ def transform(
     # this function aligns with what is computed during
     # processing nodes or edges.
     (
-        X_enc,
-        y_enc,
+        _,
+        _,
+        _,
+        _,
         data_encoder,
         label_encoder,
-        scaling_pipeline,
-        scaling_pipeline_target,
+        _,
+        _,
         text_model,
         text_cols,
     ) = res
 
-    # feature_columns = X_enc.columns
-    # feature_columns_target = y_enc.columns
     logger.info("-" * 90)
-
+    
+    # index = df.index
     y = pd.DataFrame([])
     T = pd.DataFrame([])
     # encode nodes
@@ -1613,14 +1728,14 @@ def transform(
     logger.info(f"--Features matrix shape: {X.shape}")
     logger.info(f"--Target matrix shape: {y.shape}")
 
-    if scaling_pipeline and not X.empty:
-        logger.info("--Scaling Features")
-        X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns)
-    if scaling_pipeline_target and not y.empty:
-        logger.info(f"--Scaling Target {scaling_pipeline_target}")
-        y = pd.DataFrame(
-            scaling_pipeline_target.transform(y), columns=y.columns
-        )
+    # if scaling_pipeline and not X.empty:
+    #     logger.info("--Scaling Features")
+    #     X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=index)
+    # if scaling_pipeline_target and not y.empty:
+    #     logger.info(f"--Scaling Target {scaling_pipeline_target}")
+    #     y = pd.DataFrame(
+    #         scaling_pipeline_target.transform(y), columns=y.columns, index=index
+    #     )
 
     return X, y
 
@@ -1665,7 +1780,6 @@ def _hecho(self, res):
         logger.info("\n-- Setting Encoder Parts from Fit ::")
         logger.info(f'Feature Columns In: {self.feature_names_in}')
         logger.info(f'Target Columns In: {self.target_names_in}')
-
         for name, value in zip(self.res_names, res):
             if name not in ["X_enc", "y_enc"]:
                 logger.info("-" * 90)
@@ -1676,6 +1790,8 @@ def _set_result(self, res):
         [
             X_enc,
             y_enc,
+            X_encs,
+            y_encs,
             data_encoder,
             label_encoder,
             scaling_pipeline,
@@ -1689,8 +1805,10 @@ def _set_result(self, res):
         # label_encoder.target_names_in = self.target_names_in
         self.feature_columns = X_enc.columns
         self.feature_columns_target = y_enc.columns
-        self.X = X_enc
-        self.y = y_enc
+        self.X = X_encs
+        self.y = y_encs
+        self.X_orignal = X_enc
+        self.y_orignal = y_enc
         self.data_encoder = data_encoder  # is list for edges
         self.label_encoder = label_encoder
         self.scaling_pipeline = scaling_pipeline
@@ -1707,40 +1825,67 @@ def fit(self, src=None, dst=None, *args, **kwargs):
         self._set_result(res)
 
     def transform(self, df, ydf=None):
+        "Raw transform, no scaling."
+        X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
+        return X, y
+    
+    def _transform_scaled(self, df, ydf, scaling_pipeline, scaling_pipeline_target):
+        """Transform with scaling fit durning fit."""
         X, y = transform(df, ydf, self.res, self.kind, self.src, self.dst)
+        if scaling_pipeline is not None and not X.empty:
+            X = pd.DataFrame(scaling_pipeline.transform(X), columns=X.columns, index=X.index)
+        if scaling_pipeline_target is not None and y is not None and not y.empty:
+            y = pd.DataFrame(scaling_pipeline_target.transform(y), columns=y.columns, index=y.index)
         return X, y
+    
+    def transform_scaled(self, df, ydf=None, scaling_pipeline=None, scaling_pipeline_target=None):
+        if scaling_pipeline is None:
+            scaling_pipeline = self.scaling_pipeline
+        if scaling_pipeline_target is None:
+            scaling_pipeline_target = self.scaling_pipeline_target
+        return self._transform_scaled(df, ydf, scaling_pipeline, scaling_pipeline_target)
 
     def fit_transform(self, src=None, dst=None, *args, **kwargs):
         self.fit(src=src, dst=dst, *args, **kwargs)
         return self.X, self.y
 
-    def scale(self, df, ydf=None, set_scaler=False, *args, **kwargs):
-        # pretty hacky but gets job done --
-        """Fits new scaling functions on df, ydf via args-kwargs
-        (ie use downstream as X_train, X_test ,... or batch 
-        when different scaling on the outputs is required)
+    def scale(self, X=None, y=None, return_pipeline=False, *args, **kwargs):
+        """Fits new scaling functions on df, y via args-kwargs
+        
+        **Example:**
+            ::
+
+                from graphisty.features import SCALERS, SCALER_OPTIONS
+                print(SCALERS)
+                g = graphistry.nodes(df)
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scaled=False)  # unscaled transformer output
+                # now scale with new settings
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
+                
+            args:
+            ::
+
+                ;X: pd.DataFrame of features
+                :y: pd.DataFrame of target features
+                :kind: str, one of 'nodes' or 'edges'
+                *args, **kwargs: passed to smart_scaler pipeline
+
+            returns:
+                scaled X, y
         """
-        # pop off the previous scaler so that .transform won't use it
-        self.res[4] = None
-        self.res[5] = None
-
-        X, y = self.transform(df, ydf)  # these are the raw transforms,
         logger.info("-Fitting new scaler on raw features")
         X, y, scaling_pipeline, scaling_pipeline_target = smart_scaler(
             X_enc=X, y_enc=y, *args, **kwargs
         )
-
-        if set_scaler:
-            logger.info("--Setting fit scaler to self")
-            self.res[4] = scaling_pipeline
-            self.res[5] = scaling_pipeline_target
-            self.scaling_pipeline = scaling_pipeline
-            self.scaling_pipeline_target = scaling_pipeline_target
-        else:  # add the original back
-            self.res[4] = self.scaling_pipeline
-            self.res[5] = self.scaling_pipeline_target
-
-        return X, y, scaling_pipeline, scaling_pipeline_target
+        if return_pipeline:
+            return X, y, scaling_pipeline, scaling_pipeline_target
+        return X, y
 
 
 # ######################################################################################################################
@@ -1753,9 +1898,7 @@ def scale(self, df, ydf=None, set_scaler=False, *args, **kwargs):
 def prune_weighted_edges_df_and_relabel_nodes(
     wdf: pd.DataFrame, scale: float = 0.1, index_to_nodes_dict: Optional[Dict] = None
 ) -> pd.DataFrame:
-    """
-        Prune the weighted edge DataFrame so to return high
-        fidelity similarity scores.
+    """Prune the weighted edge DataFrame so to return high fidelity similarity scores.
 
     :param wdf: weighted edge DataFrame gotten via UMAP
     :param scale: lower values means less edges > (max - scale * std)
@@ -1815,21 +1958,40 @@ def reuse_featurization(
         memoize=memoize,
     )
 
+def get_matrix_by_column_part(X: pd.DataFrame, column_part: str) -> pd.DataFrame:
+    """Get the feature matrix by column part existing in column names."""
+    transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
+    return X[transformed_columns] 
+
+def get_matrix_by_column_parts(X: pd.DataFrame, column_parts: Optional[Union[list, str]]) -> pd.DataFrame:
+    """Get the feature matrix by column parts list existing in column names."""
+    if column_parts is None:
+        return X
+    if isinstance(column_parts, str):
+        column_parts = [column_parts]
+    res = pd.concat([get_matrix_by_column_part(X, column_part) for column_part in column_parts], axis=1)  # type: ignore
+    res = res.loc[:, ~res.columns.duplicated()]  # type: ignore
+    return res
+
 
 class FeatureMixin(MIXIN_BASE):
-    """
-    FeatureMixin for automatic featurization of nodes and edges DataFrames.
-    Subclasses UMAPMixin for umap-ing of automatic features.
+    """FeatureMixin for automatic featurization of nodes and edges DataFrames. Subclasses UMAPMixin for umap-ing of automatic features.
 
     Usage:
+    ::
+
         g = graphistry.nodes(df, 'node_column')
         g2 = g.featurize()
 
     or for edges,
+    ::
+
         g = graphistry.edges(df, 'src', 'dst')
         g2 = g.featurize(kind='edges')
 
-    or chain them,
+    or chain them for both nodes and edges,
+    ::
+
         g = graphistry.edges(edf, 'src', 'dst').nodes(ndf, 'node_column')
         g2 = g.featurize().featurize(kind='edges')
 
@@ -1842,25 +2004,25 @@ def __init__(self, *args, **kwargs):
         pass
 
     def _get_feature(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_features')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_features')
         return x
     
     def _get_target(self, kind):
-        kind = kind.replace('s', '')
-        assert kind in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
-        x = getattr(self, f'_{kind}_target')
+        kind2 = kind.replace('s', '')
+        assert kind2 in ['node', 'edge'], f'kind needs to be in `nodes` or `edges`, found {kind}'
+        x = getattr(self, f'_{kind2}_target')
         return x
     
     def _featurize_nodes(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 120,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -1869,7 +2031,6 @@ def _featurize_nodes(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[str] = None,
@@ -1885,15 +2046,19 @@ def _featurize_nodes(
         remove_node_column: bool = True,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
-        res = self.copy()
+        res = self.copy() 
         ndf = res._nodes
         node = res._node
-
+        # print(['ndf:',ndf])
+        # print(['X:',X])
+        # print(['node:',res._node])
+        
         if remove_node_column:
             ndf = remove_node_column_from_symbolic(ndf, node)
             X = remove_node_column_from_symbolic(X, node)
-
+        
         if ndf is None:
             logger.info(
                 "! Materializing Nodes and setting `embedding=True`"
@@ -1913,9 +2078,14 @@ def _featurize_nodes(
         X_resolved = resolve_X(ndf, X)
         y_resolved = resolve_y(ndf, y)
 
-        feature_engine = resolve_feature_engine(feature_engine)
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
 
-        fkwargs = dict(
+        #feature_engine = resolve_feature_engine(feature_engine)
+        res.feature_engine = feature_engine
+        
+        from .features import ModelDict
+
+        fkwargs = ModelDict("Featurize Params",
             X=X_resolved,
             y=y_resolved,
             use_scaler=use_scaler,
@@ -1930,7 +2100,6 @@ def _featurize_nodes(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -1954,6 +2123,7 @@ def _featurize_nodes(
 
         old_res = reuse_featurization(res, memoize, fkwargs)
         if old_res:
+            print("--- [[ RE-USING NODE FEATURIZATION ]]") if verbose else None
             logger.info("--- [[ RE-USING NODE FEATURIZATION ]]")
             fresh_res = copy.copy(res)
             for attr in ["_node_features", "_node_target", "_node_encoder"]:
@@ -1965,21 +2135,24 @@ def _featurize_nodes(
         X_resolved = remove_internal_namespace_if_present(X_resolved)
 
         keys_to_remove = ["X", "y", "remove_node_column"]
-        nfkwargs = {}
+        nfkwargs = dict()
         for key, value in fkwargs.items():
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
-        #############################################################
+        print('-' * 80) if verbose else None
+        print("** Featuring nodes") if verbose else None
+        # ############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="nodes")
         encoder.fit(**nfkwargs)
-        ############################################################
+        # ###########################################################
 
         # if changing, also update fresh_res
         res._node_features = encoder.X
+        res._node_features_raw = encoder.X_orignal  # .copy()
         res._node_target = encoder.y
+        res._node_target_raw = encoder.y_orignal  # .copy()
         res._node_encoder = encoder  # now this does
-        
         # all the work `._node_encoder.transform(df, y)` etc
 
         return res
@@ -1988,17 +2161,16 @@ def _featurize_edges(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         multilabel: bool = False,
         model_name: str = "paraphrase-MiniLM-L6-v2",
@@ -2014,6 +2186,7 @@ def _featurize_edges(
         keep_n_decimals: int = 5,
         feature_engine: FeatureEngineConcrete = "pandas",
         memoize: bool = True,
+        verbose: bool = False,
     ):
 
         res = self.copy()
@@ -2032,6 +2205,11 @@ def _featurize_edges(
                 **{res._destination: res._edges[res._destination]}
             )
 
+        res.feature_engine = feature_engine
+
+        X_resolved, y_resolved = make_safe_gpu_dataframes(X_resolved, y_resolved, engine=feature_engine)
+
+
         # now that everything is set
         fkwargs = dict(
             X=X_resolved,
@@ -2046,7 +2224,6 @@ def _featurize_edges(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2063,6 +2240,7 @@ def _featurize_edges(
             feature_engine=feature_engine,
         )
 
+
         res._feature_params = {
             **getattr(res, "_feature_params", {}),
             "edges": fkwargs,
@@ -2088,6 +2266,7 @@ def _featurize_edges(
             if key not in keys_to_remove:
                 nfkwargs[key] = value
 
+        print("** Featuring edges") if verbose else None
         ###############################################################
         encoder = FastEncoder(X_resolved, y_resolved, kind="edges")
         encoder.fit(src=res._source, dst=res._destination, **nfkwargs)
@@ -2095,13 +2274,30 @@ def _featurize_edges(
 
         # if editing, should also update fresh_res
         res._edge_features = encoder.X
+        res._edge_features_raw = encoder.X_orignal  # .copy()
         res._edge_target = encoder.y
+        res._edge_target_raw = encoder.y_orignal  # .copy()
         res._edge_encoder = encoder
 
         return res
+    
+    def _infer_edges(self, emb, X, y, df, eps='auto', n_neighbors=4, sample=None, infer_on_umap_embedding=False, 
+                     verbose=False, merge_policy=False, **kwargs):
+        res = self.bind()
+        if merge_policy:
+            # useful to cluster onto existing graph
+            g = infer_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                            n_neighbors=n_neighbors, eps=eps, sample=sample, verbose=verbose, **kwargs) 
+        else:
+            # useful to cluster onto self
+            g = infer_self_graph(res, emb, X, y, df, infer_on_umap_embedding=infer_on_umap_embedding, 
+                                 n_neighbors=n_neighbors, eps=eps, verbose=verbose, **kwargs)
+        return g
 
-    def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
+    def _transform(self, encoder: str, df: pd.DataFrame, ydf: Optional[pd.DataFrame], scaled):
         if getattr(self, encoder) is not None:
+            if scaled:
+                return getattr(self, encoder).transform_scaled(df, ydf)
             return getattr(self, encoder).transform(df, ydf)
         else:
             logger.debug(
@@ -2109,45 +2305,126 @@ def _transform(self, encoder: str, df: pd.DataFrame, ydf: pd.DataFrame):
                 "before being able to transform data"
             )
 
-    def transform(self, df, ydf, kind):
-        """Transform new data"""
+    def transform(self, df: pd.DataFrame, 
+                  y: Optional[pd.DataFrame] = None, 
+                  kind: str = 'nodes', 
+                  min_dist: Union[str, float, int] = 'auto', 
+                  n_neighbors: int = 7,
+                  merge_policy: bool = False,
+                  sample: Optional[int] = None, 
+                  return_graph: bool = True,
+                  scaled: bool = True,
+                  verbose: bool = False):
+        """Transform new data and append to existing graph, or return dataframes
+        
+            **args:**
+
+                :df: pd.DataFrame, raw data to transform
+                :ydf: pd.DataFrame, optional
+                :kind: str  # one of `nodes`, `edges`
+                :return_graph: bool, if True, will return a graph with inferred edges.
+                :merge_policy: bool, if True, adds batch to existing graph nodes via nearest neighbors. If False, will infer edges only between nodes in the batch, default False
+                :min_dist: float, if return_graph is True, will use this value in NN search, or 'auto' to infer a good value. min_dist represents the maximum distance between two samples for one to be considered as in the neighborhood of the other.
+                :sample: int, if return_graph is True, will use sample edges of existing graph to fill out the new graph
+                :n_neighbors: int, if return_graph is True, will use this value for n_neighbors in Nearest Neighbors search
+                :scaled: bool, if True, will use scaled transformation of data set during featurization, default True
+                :verbose: bool, if True, will print metadata about the graph construction, default False
+
+            **Returns:**
+
+                X, y: pd.DataFrame, transformed data if return_graph is False
+                or a graphistry Plottable with inferred edges if return_graph is True
+        """
         if kind == "nodes":
-            return self._transform("_node_encoder", df, ydf)
+            X, y_ = self._transform("_node_encoder", df, y, scaled=scaled)
         elif kind == "edges":
-            return self._transform("_edge_encoder", df, ydf)
+            X, y_ = self._transform("_edge_encoder", df, y, scaled=scaled)
         else:
             logger.debug("kind must be one of `nodes`,"
                          f"`edges`, found {kind}")
+            
+        if return_graph and kind not in ["edges"]:
+            emb = None  # will not be able to infer graph from umap coordinates, 
+            # but will be able to infer graph from features of existing edges
+            g = self._infer_edges(emb, X, y_, df, eps=min_dist, sample=sample, n_neighbors=n_neighbors,
+                                  infer_on_umap_embedding=False, merge_policy=merge_policy,
+                                  verbose=verbose)
+            return g
+        return X, y_
 
     def scale(
         self,
-        df,
-        ydf,
-        kind,
-        use_scaler,
-        use_scaler_target,
-        set_scaler=False,
+        df: Optional[pd.DataFrame] = None,
+        y: Optional[pd.DataFrame] = None,
+        kind: str = "nodes",
+        use_scaler: Union[str, None] = None,
+        use_scaler_target: Union[str, None] = None,
         impute: bool = True,
         n_quantiles: int = 10,
         output_distribution: str = "normal",
         quantile_range=(25, 75),
-        n_bins: int = 2,
+        n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
         keep_n_decimals: int = 5,
+        return_scalers: bool = False,
     ):
+        """Scale data using the same scalers as used in the featurization step.
+        
+            **Example**
+            ::
+
+                g = graphistry.nodes(df)
+                X, y = g.featurize().scale(kind='nodes', use_scaler='robust', use_scaler_target='kbins', n_bins=3)
+                
+                # or 
+                g = graphistry.nodes(df)
+                # set a scaling strategy for features and targets -- umap uses those and produces different results depending.
+                g2 = g.umap(use_scaler='standard', use_scaler_target=None)
+                
+                # later if you want to scale new data, you can do so
+                X, y = g2.transform(df, df, scale=False)
+                X_scaled, y_scaled = g2.scale(X, y, use_scaler='minmax', use_scaler_target='kbins', n_bins=5)
+                # fit some other pipeline
+                clf.fit(X_scaled, y_scaled)
+
+            **Args:**
+
+                :df: pd.DataFrame, raw data to transform, if None, will use data from featurization fit
+                :y: pd.DataFrame, optional target data
+                :kind: str, one of `nodes`, `edges`
+                :use_scaler: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :use_scaler_target: str, optional, one of `minmax`, `robust`, `standard`, `kbins`, `quantile`
+                :impute: bool, if True, will impute missing values
+                :n_quantiles: int, number of quantiles to use for quantile scaler
+                :output_distribution: str, one of `normal`, `uniform`, `lognormal` 
+                :quantile_range: tuple, range of quantiles to use for quantile scaler
+                :n_bins: int, number of bins to use for KBinsDiscretizer
+                :encode: str, one of `ordinal`, `onehot`, `onehot-dense`, `binary`    
+                :strategy: str, one of `uniform`, `quantile`, `kmeans`
+                :keep_n_decimals: int, number of decimals to keep after scaling
+                :return_scalers: bool, if True, will return the scalers used to scale the data
+
+            **Returns:**
+
+                (X, y) transformed data if return_graph is False or a graph with inferred edges if return_graph is True, or (X, y, scaler, scaler_target) if return_scalers is True
+        """
+                
+        if df is None:  # use the original data
+            X, y = (self._node_features_raw, self._node_target_raw) if kind == "nodes" else (self._edge_features_raw, self._edge_target_raw)  # type: ignore
+        else:
+            X, y = self.transform(df, y, kind=kind, return_graph=False, scaled=False)
 
         if kind == "nodes" and hasattr(self, "_node_encoder"):  # type: ignore
             if self._node_encoder is not None:  # type: ignore
                 (
                     X,
                     y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    scaler,
+                    scaler_target
                 ) = self._node_encoder.scale(
-                    df,
-                    ydf,
-                    set_scaler=set_scaler,
+                    X,
+                    y,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
@@ -2158,6 +2435,7 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
@@ -2171,12 +2449,11 @@ def scale(
                 (
                     X,
                     y,
-                    scaling_pipeline,
-                    scaling_pipeline_target,
+                    scaler,
+                    scaler_target
                 ) = self._edge_encoder.scale(
-                    df,
-                    ydf,
-                    set_scaler=set_scaler,
+                    X,
+                    y,
                     use_scaler=use_scaler,
                     use_scaler_target=use_scaler_target,
                     impute=impute,
@@ -2187,14 +2464,17 @@ def scale(
                     encode=encode,
                     strategy=strategy,
                     keep_n_decimals=keep_n_decimals,
+                    return_pipeline=True
                 )  # type: ignore
             else:
                 raise AttributeError(
                     'Please run g.featurize(kind="edges", *args, **kwargs) '
                     'first before scaling matrices and targets is possible.'
                 )
+        if return_scalers:
+            return X, y, scaler, scaler_target
+        return X, y
 
-        return X, y, scaling_pipeline, scaling_pipeline_target
 
     def featurize(
         self,
@@ -2213,29 +2493,29 @@ def featurize(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        min_words: float = 2.5,
+        min_words: float = 4.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         impute: bool = True,
         n_quantiles: int = 100,
         output_distribution: str = "normal",
-        quantile_range=(25, 75),
+        quantile_range = (25, 75),
         n_bins: int = 10,
         encode: str = "ordinal",
         strategy: str = "uniform",
-        similarity: Optional[
-            str
-        ] = None,  # turn this off in favor of Gap Encoder
+        similarity: Optional[str] = None,  # turn this off in favor of Gap Encoder
         categories: Optional[str] = "auto",
         keep_n_decimals: int = 5,
         remove_node_column: bool = True,
         inplace: bool = False,
         feature_engine: FeatureEngine = "auto",
+        dbscan: bool = False,
+        min_dist: float = 0.5,  # DBSCAN eps
+        min_samples: int = 1,  # DBSCAN min_samples
         memoize: bool = True,
+        verbose: bool = False,
     ):
-        r"""
-            Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
-        ______________________________________________________________________
-
+        r"""Featurize Nodes or Edges of the underlying nodes/edges DataFrames.
+        
         :param kind: specify whether to featurize `nodes` or `edges`.
                 Edge featurization includes a pairwise
                 src-to-dst feature block using a MultiLabelBinarizer,
@@ -2248,11 +2528,11 @@ def featurize(
         :param use_scaler: selects which scaler (and automatically imputes
                 missing values using mean strategy)
                 to scale the data. Options are;
-                "minmax", "quantile", "zscale", "robust",
+                "minmax", "quantile", "standard", "robust",
                 "kbins", default None.
                 Please see scikits-learn documentation
                 https://scikit-learn.org/stable/modules/preprocessing.html
-                Here 'zscale' corresponds to 'StandardScaler' in scikits.
+                Here 'standard' corresponds to 'StandardScaler' in scikits.
         :param cardinality_threshold: dirty_cat threshold on cardinality of
                 categorical labels across columns.
                 If value is greater than threshold, will run GapEncoder
@@ -2291,20 +2571,21 @@ def featurize(
                 but at cost of encoding time. If faster encoding is needed,
                 `average_word_embeddings_komninos` is useful
                 and produces less semantically relevant vectors.
-                Please see www.huggingface.co or sentence_transformer
+                Please see sentence_transformer
                 (https://www.sbert.net/) library for all available models.
         :param multilabel: if True, will encode a *single* target column composed of
                 lists of lists as multilabel outputs. 
                 This only works with y=['a_single_col'], default False
         :param embedding: If True, produces a random node embedding of size `n_topics`
-                default, False.
+                default, False. If no node features are provided, will produce random embeddings 
+                (for GNN models, for example)
         :param use_ngrams: If True, will encode textual columns as TfIdf Vectors,
                 default, False.
         :param ngram_range: if use_ngrams=True, can set ngram_range, eg: tuple = (1, 3)
         :param max_df:  if use_ngrams=True, set max word frequency to consider in vocabulary
                 eg: max_df = 0.2,
         :param min_df:  if use_ngrams=True, set min word count to consider in vocabulary
-                eg: min_df = 3    
+                eg: min_df = 3 or 0.00001
         :param categories: Optional[str] in ["auto", "k-means", "most_frequent"], decides which 
                 category to select in Similarity Encoding, default 'auto'
         :param impute: Whether to impute missing values, default True
@@ -2314,7 +2595,7 @@ def featurize(
                 can return distribution as ["normal", "uniform"]
         :param quantile_range: if use_scaler = 'robust'|'quantile', 
                 sets the quantile range.
-        :param n_bins: number of bins to use in kbins discretizer
+        :param n_bins: number of bins to use in kbins discretizer, default 10
         :param encode: encoding for KBinsDiscretizer, can be one of
                 `onehot`, `onehot-dense`, `ordinal`, default 'ordinal'
         :param strategy: strategy for KBinsDiscretizer, can be one of
@@ -2322,6 +2603,9 @@ def featurize(
         :param n_quantiles: if use_scaler = "quantile", sets the number of quantiles, default=100
         :param output_distribution: if use_scaler="quantile"|"robust", 
                 choose from ["normal", "uniform"]
+        :param dbscan: whether to run DBSCAN, default False.
+        :param min_dist: DBSCAN eps parameter, default 0.5.
+        :param min_samples: DBSCAN min_samples parameter, default 5.
         :param keep_n_decimals: number of decimals to keep                
         :param remove_node_column: whether to remove node column so it is
                 not featurized, default True.
@@ -2329,16 +2613,22 @@ def featurize(
                 not, default False.
         :param memoize: whether to store and reuse results across runs,
                 default True.
-        :return: self, with new attributes set by the featurization process.
+        :return: graphistry instance with new attributes set by the featurization process.
         """
-        assert_imported()
+        feature_engine = resolve_feature_engine(feature_engine)
+
+        print('Featurizing nodes with feature_engine=' + feature_engine)
+
+        if feature_engine == 'dirty_cat':
+            assert_imported()
+        elif feature_engine == 'cu_cat':
+            assert_cuml_cucat()
+
         if inplace:
             res = self
         else:
             res = self.bind()
 
-        feature_engine = resolve_feature_engine(feature_engine)
-
         if kind == "nodes":
             res = res._featurize_nodes(
                 X=X,
@@ -2355,11 +2645,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity,  
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2371,6 +2660,7 @@ def featurize(
                 remove_node_column=remove_node_column,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         elif kind == "edges":
             res = res._featurize_edges(
@@ -2387,11 +2677,10 @@ def featurize(
                 ngram_range=ngram_range,
                 max_df=max_df,
                 min_df=min_df,
-                #confidence=confidence,  # deprecated
                 min_words=min_words,
                 model_name=model_name,
-                similarity=similarity,  # deprecated
-                categories=categories,  # deprecated
+                similarity=similarity, 
+                categories=categories,
                 impute=impute,
                 n_quantiles=n_quantiles,
                 quantile_range=quantile_range,
@@ -2402,12 +2691,17 @@ def featurize(
                 keep_n_decimals=keep_n_decimals,
                 feature_engine=feature_engine,
                 memoize=memoize,
+                verbose=verbose
             )
         else:
             logger.warning(
                 f"One may only featurize `nodes` or `edges`, got {kind}"
             )
             return self
+        
+        if dbscan:  # this adds columns to the dataframe, will break tests of pure featurization & umap, so set to False in those
+            res = res.dbscan(min_dist=min_dist, min_samples=min_samples, kind=kind, fit_umap_embedding=False, verbose=verbose)  # type: ignore
+
         if not inplace:
             return res
 
@@ -2415,19 +2709,18 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "zscale",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
         cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
-        embedding=False,
+        embedding: bool = False,
         use_ngrams: bool = False,
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2446,13 +2739,9 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, pd.DataFrame, MIXIN_BASE]:
-        """
-        helper method gets node feature and target matrix if X, y
-        are not specified.
-        if X, y are specified will set them as `_node_target` and
-        `_node_target` attributes
-        -----------------------------------------------------------
+        """helper method gets node feature and target matrix if X, y are not specified. if X, y are specified will set them as `_node_target` and `_node_target` attributes
         """
 
         res = self.bind()
@@ -2462,7 +2751,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             res._node_target = None
 
         if reuse_if_existing and res._node_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Node Featurization')
             return res._node_features, res._node_target, res
 
         res = res._featurize_nodes(
@@ -2480,7 +2769,6 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2496,6 +2784,7 @@ def _featurize_or_get_nodes_dataframe_if_X_is_None(
             remove_node_column=remove_node_column,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._node_features is not None  # ensure no infinite loop
@@ -2511,10 +2800,10 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         self,
         X: XSymbolic = None,
         y: YSymbolic = None,
-        use_scaler: Optional[str] = "robust",
-        use_scaler_target: Optional[str] = "kbins",
+        use_scaler: Optional[str] = None,
+        use_scaler_target: Optional[str] = None,
         cardinality_threshold: int = 40,
-        cardinality_threshold_target: int = 20,
+        cardinality_threshold_target: int = 400,
         n_topics: int = config.N_TOPICS_DEFAULT,
         n_topics_target: int = config.N_TOPICS_TARGET_DEFAULT,
         multilabel: bool = False,
@@ -2522,7 +2811,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         ngram_range: tuple = (1, 3),
         max_df: float = 0.2,
         min_df: int = 3,
-        #confidence: float = 0.35,
         min_words: float = 2.5,
         model_name: str = "paraphrase-MiniLM-L6-v2",
         similarity: Optional[
@@ -2540,11 +2828,10 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
         feature_engine: FeatureEngineConcrete = "pandas",
         reuse_if_existing=False,
         memoize: bool = True,
+        verbose: bool = False,
     ) -> Tuple[pd.DataFrame, Optional[pd.DataFrame], MIXIN_BASE]:
-        """
-        helper method gets edge feature and target matrix if X, y
-        are not specified
-        -----------------------------------------------------------
+        """ helper method gets edge feature and target matrix if X, y are not specified
+      
         :param X: Data Matrix
         :param y: target, default None
         :return: data `X` and `y`
@@ -2557,7 +2844,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             res._edge_target = None
 
         if reuse_if_existing and res._edge_features is not None:
-            # logger.info('-Reusing Existing Featurization')
+            logger.info('-Reusing Existing Edge Featurization')
             return res._edge_features, res._edge_target, res
 
         res = res._featurize_edges(
@@ -2574,7 +2861,6 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             ngram_range=ngram_range,
             max_df=max_df,
             min_df=min_df,
-            #confidence=confidence,
             min_words=min_words,
             model_name=model_name,
             similarity=similarity,
@@ -2589,6 +2875,7 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             keep_n_decimals=keep_n_decimals,
             feature_engine=feature_engine,
             memoize=memoize,
+            verbose=verbose,
         )
 
         assert res._edge_features is not None  # ensure no infinite loop
@@ -2600,39 +2887,43 @@ def _featurize_or_get_edges_dataframe_if_X_is_None(
             memoize=memoize,
         )
 
-    def _features_by_col(self, column_part: str, kind: str):
-        if kind == 'nodes' and hasattr(self, '_node_features'):
-            X = self._node_features
-        elif kind == 'edges' and hasattr(self, '_edge_features'):
-            X = self._edge_features
-        else:
-            raise ValueError('make sure to call `featurize` or `umap` before calling `get_features_by_cols`')
-        
-        transformed_columns = X.columns[X.columns.map(lambda x: True if column_part in x else False)]  # type: ignore
-        return X[transformed_columns]  # type: ignore
     
-    def get_features_by_cols(self, columns: Union[List, str], kind: str = 'nodes'):
-        """Returns feature matrix with only the columns that contain the string `column_part` in their name.
-        
-            `X = g.get_features_by_cols(['feature1', 'feature2'])`
-            will retrieve a feature matrix with only the columns that contain the string 
-            `feature1` or `feature2` in their name.
+    def get_matrix(self, columns: Optional[Union[List, str]] = None, kind: str = 'nodes', target: bool = False) -> pd.DataFrame:
+        """Returns feature matrix, and if columns are specified, returns matrix with only the columns that contain the string `column_part` in their name.`X = g.get_matrix(['feature1', 'feature2'])` will retrieve a feature matrix with only the columns that contain the string `feature1` or `feature2` in their name. Most useful for topic modeling, where the column names are of the form `topic_0: descriptor`, `topic_1: descriptor`, etc. Can retrieve unique columns in original dataframe, or actual topic features like [ip_part, shoes, preference_x, etc]. Powerful way to retrieve features from a featurized graph by column or (top) features of interest.
             
-            example:
-                res = g2.get_features_by_cols(['172', 'percent'])
-                res.columns
+            **Example:**
+            ::
+            
+                # get the full feature matrices
+                X = g.get_matrix()
+                y = g.get_matrix(target=True)
+            
+                # get subset of features, or topics, given topic model encoding
+                X = g2.get_matrix(['172', 'percent'])
+                X.columns
                     => ['ip_172.56.104.67', 'ip_172.58.129.252', 'item_percent']
+                # or in targets
+                y = g2.get_matrix(['total', 'percent'], target=True)
+                y.columns
+                    => ['basket_price_total', 'conversion_percent', 'CTR_percent', 'CVR_percent']
+
+                # not as useful for sbert features. 
+
+            Caveats:
+                - if you have a column name that is a substring of another column name, you may get unexpected results.
 
         Args:
-            columns (Union[List, str]): list of column names or a single column name that may exist in columns 
-                of the feature matrix.
-            kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+            :columns (Union[List, str]): list of column names or a single column name that may exist in columns of the feature matrix. If None, returns original feature matrix
+            :kind (str, optional): Node or Edge features. Defaults to 'nodes'.
+            :target (bool, optional): If True, returns the target matrix. Defaults to False.
 
         Returns:
             pd.DataFrame: feature matrix with only the columns that contain the string `column_part` in their name.
         """
-        if isinstance(columns, str):
-            columns = [columns]
-        X = pd.concat([self._features_by_col(col, kind=kind) for col in columns], axis=1)  # type: ignore
-        X = X.loc[:, ~X.columns.duplicated()]  # type: ignore
-        return X
+
+        if target:
+            X = self._get_target(kind)
+        else:
+            X = self._get_feature(kind)
+
+        return get_matrix_by_column_parts(X, columns)
diff --git a/graphistry/features.py b/graphistry/features.py
index 7567b159db..32e83a3a28 100644
--- a/graphistry/features.py
+++ b/graphistry/features.py
@@ -1,6 +1,6 @@
-from collections import UserDict
 from .util import setup_logger
 from .constants import VERBOSE, TRACE
+from .util import ModelDict
 
 logger = setup_logger("graphistry.features", verbose=VERBOSE, fullpath=TRACE)
 
@@ -21,8 +21,8 @@
 # ################# graphistry featurization config constants #################
 N_TOPICS = 42
 N_TOPICS_TARGET = 10
-HIGH_CARD = 4e7  # forces one hot encoding
-MID_CARD = 2e3  # todo: forces hashing
+HIGH_CARD = 1e9  # forces one hot encoding
+MID_CARD = 1e3  # todo: force hashing
 LOW_CARD = 2
 
 CARD_THRESH = 40
@@ -30,29 +30,61 @@
 
 FORCE_EMBEDDING_ALL_COLUMNS = 0  # min_words
 HIGH_WORD_COUNT = 1024
+MID_WORD_COUNT = 128
 LOW_WORD_COUNT = 3
 
 NGRAMS_RANGE = (1, 3)
 MAX_DF = 0.2
 MIN_DF = 3
 
-N_BINS = 10
 KBINS_SCALER = "kbins"
+STANDARD = 'standard'
+ROBUST = 'robust'
+MINMAX = 'minmax'
+QUANTILE = 'quantile'
+# for Optuna
+ERROR = "error"
+
+SCALERS = [STANDARD, ROBUST, MINMAX, KBINS_SCALER, QUANTILE]
+NO_SCALER = None
+# Scaler options
+N_BINS = 10
 IMPUTE = "median"  # set to
 N_QUANTILES = 100
 OUTPUT_DISTRIBUTION = "normal"
-QUANTILES_RANGE = (25, 75)
-N_BINS = 10
+QUANTILES_RANGE = (5, 95)
 ENCODE = "ordinal"  # kbins, onehot, ordinal, label
 STRATEGY = "uniform"  # uniform, quantile, kmeans
 SIMILARITY = None  # 'ngram' , default None uses Gap
 CATEGORIES = "auto"
-KEEP_N_DECIMALS = 5
+SCALER_OPTIONS = {'impute': ['median', None], 'n_quantiles': [10,100], 'output_distribution': ['normal', 'uniform'],
+                  'quantile_range': QUANTILES_RANGE, 
+                  'encode': ['kbins', 'onehot', 'ordinal', 'label'], 
+                  'strategy': ['uniform', 'quantile', 'kmeans'],
+                  'similarity':[None, 'ngram'], 'categories': CATEGORIES, 'n_bins': [2, 100], 
+                  'use_scaler': SCALERS, 'use_scaler_target': SCALERS
+}
 
+# precision in decimal places
+KEEP_N_DECIMALS = 5  # TODO: check to see if this takes a lot of time
+BATCH_SIZE_SMALL = 32
 BATCH_SIZE = 1000
-NO_SCALER = None
 EXTRA_COLS_NEEDED = ["x", "y", "_n"]
 # ###############################################################
+# ################# graphistry umap config constants #################
+N_COMPONENTS = 2
+N_NEIGHBORS = 20
+MIN_DIST = 0.1
+SPREAD = 1
+LOCAL_CONNECTIVITY = 1
+REPULSION_STRENGTH = 2
+NEGATIVE_SAMPLING_RATE = 5
+METRIC = "euclidean"
+
+UMAP_OPTIONS = {'n_components': [2, 10], 'n_neighbors': [2, 30], 'min_dist': [0.01, 0.99], 'spread': [0.5, 5], 'local_connectivity': [1, 30],
+                'repulsion_strength': [1, 10], 'negative_sampling_rate': [5, 20], 
+                'metric': ['euclidean', 'cosine', 'manhattan', 'l1', 'l2', 'cityblock', 'braycurtis', 'canberra', 'chebyshev', 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule']
+}
 
 # ###############################################################
 # ################# enrichments
@@ -72,12 +104,14 @@
 NGRAMS = "ngrams"
 # ############ Embedding Models
 PARAPHRASE_SMALL_MODEL = "sentence-transformers/paraphrase-albert-small-v2"
-PARAPHRASE_MULTILINGUAL_MODEL = (
-    "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
-)
+PARAPHRASE_MULTILINGUAL_MODEL = "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
 MSMARCO2 = "sentence-transformers/msmarco-distilbert-base-v2"  # 768
 MSMARCO3 = "sentence-transformers/msmarco-distilbert-base-v3"  # 512
 QA_SMALL_MODEL = "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"
+LLM_SMALL = "sentence-transformers/llm-en-dim128"
+LLM_LARGE = "sentence-transformers/llm-en-dim512"
+
+EMBEDDING_MODELS = [PARAPHRASE_SMALL_MODEL, PARAPHRASE_MULTILINGUAL_MODEL, MSMARCO2, MSMARCO3, QA_SMALL_MODEL, LLM_SMALL, LLM_LARGE]
 # #############################################################################
 # Model Training Constants
 # Used for seeding random state
@@ -87,41 +121,30 @@
 SPLIT_HIGH = 0.5
 
 # #############################################################################
-class ModelDict(UserDict):
-    """Helper class to print out model names
-
-    Args:
-        message: description of model
-        verbose: print out model names, logging happens regardless
-    """
-
-    def __init__(self, message, verbose=True, *args, **kwargs):
-        self._message = message
-        self._verbose = verbose
-        self._print_length = min(LENGTH_PRINT, len(message))
-        self._updates = []
-        super().__init__(*args, **kwargs)
-
-    def __repr__(self):
-        logger.info(self._message)
-        if self._verbose:
-            print("_" * self._print_length)
-            print()
-            print(self._message)
-            print("_" * self._print_length)
-            print()
-        return super().__repr__()
-
-    def update(self, *args, **kwargs):
-        self._updates.append(args[0])
-        if len(self._updates) > 1:  # don't take first update since its the init/default
-            self._message += (
-                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
-            )
-        return super().update(*args, **kwargs)
-
-
-default_featurize_parameters = dict(
+# model training options
+
+FEATURE_OPTIONS = {
+    'kind': ['nodes', 'edges'],
+    'cardinality_threshold': [1, HIGH_CARD],
+    'cardinality_threshold_target': [1, HIGH_CARD],
+    'n_topics': [4, 100],
+    'n_topics_target': [4, 100],
+    'multilabel': [True, False],
+    'embedding': [True, False],
+    'use_ngrams': [True, False],
+    'ngram_range': (1, 5),
+    'max_df': [0.1, 0.9],
+    'min_df': [1, 10],
+    'min_words': [0, 100],
+    'model_name': [MSMARCO2, MSMARCO3, PARAPHRASE_SMALL_MODEL, PARAPHRASE_MULTILINGUAL_MODEL, QA_SMALL_MODEL],
+}
+
+
+# #############################################################################
+# Model Training {params}
+
+default_featurize_parameters = ModelDict(
+    "Featurize Parameters",
     kind="nodes",
     use_scaler=NO_SCALER,
     use_scaler_target=NO_SCALER,
@@ -154,60 +177,87 @@ def update(self, *args, **kwargs):
 )
 
 
+default_umap_parameters = ModelDict("Umap Parameters",
+        {"n_components": N_COMPONENTS,
+        **({"metric": METRIC} if True else {}),
+        "n_neighbors": N_NEIGHBORS,
+        "min_dist": MIN_DIST,
+        "spread": SPREAD,
+        "local_connectivity": LOCAL_CONNECTIVITY,
+        "repulsion_strength": REPULSION_STRENGTH,
+        "negative_sample_rate": NEGATIVE_SAMPLING_RATE,
+    }
+)
+
+
+umap_hellinger = ModelDict("Umap Parameters Hellinger",    
+        {"n_components": N_COMPONENTS,
+        "metric": "hellinger",  # info metric, can't use on
+        # textual encodings since they contain negative values...
+        "n_neighbors": 15,
+        "min_dist": 0.3,
+        "spread": 0.5,
+        "local_connectivity": 1,
+        "repulsion_strength": 1,
+        "negative_sample_rate": 5
+        }
+)
+
+umap_euclidean = ModelDict("Umap Parameters Euclidean",
+        {"n_components": N_COMPONENTS,
+        "metric": "euclidean",
+        "n_neighbors": 12,
+        "min_dist": 0.1,
+        "spread": 0.5,
+        "local_connectivity": 1,
+        "repulsion_strength": 1,
+        "negative_sample_rate": 5
+        }
+)
+
 # #############################################################################
 # Create useful presets for the user
 # makes naming and encoding models consistently and testing different models against eachother easy
 # customize the default parameters for each model you want to test
 
 # Ngrams Model over features
-ngrams_model = ModelDict("Ngrams Model", verbose=True, **default_featurize_parameters)
-ngrams_model.update(dict(use_ngrams=True, min_words=HIGH_CARD))
+ngrams_model = ModelDict(
+    "Ngrams Model", use_ngrams=True, min_words=HIGH_CARD, verbose=True
+)
 
 # Topic Model over features
-topic_model = ModelDict("Topic Model", verbose=True, **default_featurize_parameters)
-topic_model.update(
-    dict(
-        cardinality_threshold=LOW_CARD,  # force topic model
-        cardinality_threshold_target=LOW_CARD,  # force topic model
-        n_topics=N_TOPICS,
-        n_topics_target=N_TOPICS_TARGET,
-        min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
-    )
+topic_model = ModelDict(
+    "Reliable Topic Models on Features and Target",
+    cardinality_threshold=LOW_CARD,  # force topic model
+    cardinality_threshold_target=LOW_CARD,  # force topic model
+    n_topics=N_TOPICS,
+    n_topics_target=N_TOPICS_TARGET,
+    min_words=HIGH_CARD,  # make sure it doesn't turn into sentence model, but rather topic models
+    verbose=True,
 )
 
 # useful for text data that you want to paraphrase
 embedding_model = ModelDict(
-    f"{PARAPHRASE_SMALL_MODEL} Embedding Model",
+    f"{PARAPHRASE_SMALL_MODEL} sbert Embedding Model",
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
     verbose=True,
-    **default_featurize_parameters,
-)
-embedding_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=PARAPHRASE_SMALL_MODEL,  # if we need multilingual support, use PARAPHRASE_MULTILINGUAL_MODEL
-    )
 )
 
 # useful for when search input is much smaller than the encoded documents
 search_model = ModelDict(
-    f"{MSMARCO2} Search Model", verbose=True, **default_featurize_parameters
-)
-search_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=MSMARCO2,
-    )
+    f"{MSMARCO2} Search Model",
+    verbose=True,
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=MSMARCO2,
 )
 
 # Question Answering encodings for search
 qa_model = ModelDict(
-    f"{QA_SMALL_MODEL} QA Model", verbose=True, **default_featurize_parameters
-)
-qa_model.update(
-    dict(
-        min_words=FORCE_EMBEDDING_ALL_COLUMNS,
-        model_name=QA_SMALL_MODEL,
-    )
+    f"{QA_SMALL_MODEL} QA Model",
+    min_words=FORCE_EMBEDDING_ALL_COLUMNS,
+    model_name=QA_SMALL_MODEL,
+    verbose=True,
 )
 
 
@@ -221,7 +271,7 @@ def update(self, *args, **kwargs):
 
 
 if __name__ == "__main__":
-    # python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'
+    """python3 -m graphistry.features -m 'my awesome edge encoded model' -p '{"kind":"edges"}'"""
     import argparse
     import json
 
diff --git a/graphistry/hyper_dask.py b/graphistry/hyper_dask.py
index 5da16298f9..1b4ee15647 100644
--- a/graphistry/hyper_dask.py
+++ b/graphistry/hyper_dask.py
@@ -602,7 +602,7 @@ def df_coercion(  # noqa: C901
     if engine == Engine.DASK:
         import dask.dataframe
         if isinstance(df, pd.DataFrame):
-            out = dask.dataframe.from_pandas(df, **{
+            out = dask.dataframe.from_pandas(df, **{  # type: ignore
                 **({'npartitions': npartitions} if npartitions is not None else {}) , 
                 **({'chunksize': chunksize} if chunksize is not None else {})
             })
diff --git a/graphistry/layout/graph/graph.py b/graphistry/layout/graph/graph.py
index b04dd4842e..4cbeac9182 100644
--- a/graphistry/layout/graph/graph.py
+++ b/graphistry/layout/graph/graph.py
@@ -8,31 +8,55 @@
 
 
 class Graph(object):
-    """
-        The graph is stored in disjoint-sets holding each connected component in `components` as a list of graph_core objects.
-
-        **Attributes**
-            C (list[GraphBase]): list of graph_core components.
-
-        **Methods**
-            add_vertex(v): add vertex v into the Graph as a new component
-            add_edge(e): add edge e and its vertices into the Graph possibly merging the
-            associated graph_core components
-            get_vertices_count(): see order()
-            vertices(): see graph_core
-            edges(): see graph_core
-            remove_edge(e): remove edge e possibly spawning two new cores
-            if the graph_core that contained e gets disconnected.
-            remove_vertex(v): remove vertex v and all its edges.
-            order(): the order of the graph (number of vertices)
-            norm(): the norm of the graph (number of edges)
-            deg_min(): the minimum degree of vertices
-            deg_max(): the maximum degree of vertices
-            deg_avg(): the average degree of vertices
-            eps(): the graph epsilon value (norm/order), average number of edges per vertex.
-            connected(): returns True if the graph is connected (i.e. it has only one component).
-            components(): returns the list of components
-    """
+    # """
+    #     The graph is stored in disjoint-sets holding each connected component in `components` as a list of graph_core objects.
+
+    #     **Attributes**
+    #         C (list[GraphBase]): list of graph_core components.
+
+
+    #     **add_edge(e):**
+    #         add edge e and its vertices into the Graph possibly merging the associated graph_core components
+
+    #     **get_vertices_count():** 
+    #         see order()
+
+    #     **vertices():** 
+    #         see graph_core
+        
+    #     **edges():** 
+    #         see graph_core
+
+    #     **remove_edge(e):** 
+    #         remove edge e possibly spawning two new cores if the graph_core that contained e gets disconnected.
+
+    #     **remove_vertex(v):** 
+    #         remove vertex v and all its edges.
+
+    #     **order():** 
+    #         the order of the graph (number of vertices)
+
+    #     **norm():** 
+    #         the norm of the graph (number of edges)
+
+    #     **deg_min():** 
+    #         the minimum degree of vertices
+        
+    #     **deg_max():** 
+    #         the maximum degree of vertices
+
+    #     **deg_avg():** 
+    #         the average degree of vertices
+
+    #     **eps():** 
+    #         the graph epsilon value (norm/order), average number of edges per vertex.
+
+    #     **connected():** 
+    #         returns True if the graph is connected (i.e. it has only one component).
+
+    #     **components():**
+    #         returns the list of components
+    # """
 
     component_class = GraphBase
 
@@ -73,16 +97,22 @@ def __init__(self, vertices = None, edges = None, directed = True):
             self.components.append(self.component_class(vertices, edge_set, directed))
 
     def add_vertex(self, v):
+        """
+        add vertex v into the Graph as a new component
+        """
         for c in self.components:
             if v in c.verticesPoset:
                 return c.verticesPoset.get(v)
         g = self.component_class(directed = self.directed)
         v = g.add_single_vertex(v)
         self.components.append(g)
+        print("add vertex v into the Graph as a new component")
         return v
 
     def add_edge(self, e):
-
+        """
+        add edge e and its vertices into the Graph possibly merging the associated graph_core components
+        """
         x = e.v[0]
         y = e.v[1]
         x = self.add_vertex(x)
@@ -116,6 +146,9 @@ def get_vertex_from_data(self, data):
         return None
 
     def vertices(self):
+        """
+         see graph_core
+        """
         for c in self.components:
             vertices = c.verticesPoset
             for v in vertices:
@@ -128,6 +161,9 @@ def edges(self):
                 yield e
 
     def remove_edge(self, e):
+        """
+        remove edge e possibly spawning two new cores if the graph_core that contained e gets disconnected.
+        """
         # get the GraphBase:
         c = e.v[0].component
         assert c == e.v[1].component
@@ -147,6 +183,9 @@ def remove_edge(self, e):
         return e
 
     def remove_vertex(self, x):
+        """
+        remove vertex v and all its edges.
+        """
         c = x.component
         if c not in self.components:
             return None
@@ -165,24 +204,42 @@ def remove_vertex(self, x):
         return x
 
     def order(self):
+        """
+        the order of the graph (number of vertices)
+        """
         return sum([c.order() for c in self.components])
 
     def norm(self):
+        """
+        the norm of the graph (number of edges)
+        """
         return sum([c.norm() for c in self.components])
 
     def deg_min(self):
+        """
+         the minimum degree of vertices
+        """
         return min([c.deg_min() for c in self.components])
 
     def deg_max(self):
+        """
+         the maximum degree of vertices
+        """
         return max([c.deg_max() for c in self.components])
 
     def deg_avg(self):
+        """
+        the average degree of vertices
+        """
         t = 0.0
         for c in self.components:
             t += sum([v.degree() for v in c.verticesPoset])
         return t / float(self.order())
 
     def eps(self):
+        """
+        the graph epsilon value (norm/order), average number of edges per vertex.
+        """
         return float(self.norm()) / self.order()
 
     def path(self, x, y, f_io = 0, hook = None):
@@ -203,4 +260,7 @@ def __contains__(self, G):
         return r
 
     def connected(self):
+        """
+        returns the list of components
+        """
         return len(self.components) == 1
diff --git a/graphistry/layout/graph/graphBase.py b/graphistry/layout/graph/graphBase.py
index 0e1b8f51e4..725f45daf9 100644
--- a/graphistry/layout/graph/graphBase.py
+++ b/graphistry/layout/graph/graphBase.py
@@ -13,41 +13,6 @@ class GraphBase(object):
             loops (set[Edge]): the set of *loop* edges (of degree 0).
             directed (bool): indicates if the graph is considered *oriented* or not.
 
-        Methods:
-            vertices(cond=None): generates an iterator over vertices, with optional filter
-            edges(cond=None): generates an iterator over edges, with optional filter
-            matrix(cond=None): returns the associativity matrix of the graph component
-            order(): the order of the graph (number of vertices)
-            norm(): the norm of the graph (number of edges)
-            deg_min(): the minimum degree of vertices
-            deg_max(): the maximum degree of vertices
-            deg_avg(): the average degree of vertices
-            eps(): the graph epsilon value (norm/order), average number of edges per vertex.
-            path(x,y,f_io=0,hook=None): shortest path between vertices x and y by breadth-first descent,
-            contrained by f_io direction if provided. The path is returned as a list of Vertex objects.
-            If a *hook* function is provided, it is called at every vertex added to the path, passing
-            the vertex object as argument.
-            roots(): returns the list of *roots* (vertices with no inward edges).
-            leaves(): returns the list of *leaves* (vertices with no outward edges).
-            add_single_vertex(v): allow a GraphBase to hold a single vertex.
-            add_edge(e): add edge e. At least one of its vertex must belong to the graph,
-            the other being added automatically.
-            remove_edge(e): remove Edge e, asserting that the resulting graph is still connex.
-            remove_vertex(x): remove Vertex x and all associated edges.
-            dijkstra(x,f_io=0,hook=None): shortest weighted-edges paths between x and all other vertices
-            by dijkstra's algorithm with heap used as priority queue.
-            get_scs_with_feedback(): returns the set of strongly connected components
-            ("scs") by using Tarjan algorithm.
-            These are maximal sets of vertices such that there is a path from each
-            vertex to every other vertex.
-            The algorithm performs a DFS from the provided list of root vertices.
-            A cycle is of course a strongly connected component,
-            but a strongly connected component can include several cycles.
-            The Feedback Acyclic Set of edge to be removed/reversed is provided by
-            marking the edges with a "feedback" flag.
-            Complexity is O(V+E).
-            partition(): returns a *partition* of the connected graph as a list of lists.
-            neighbors(v): returns neighbours of a vertex v.
     """
 
     def __init__(self, vertices = None, edges = None, directed = True):
@@ -96,12 +61,21 @@ def __init__(self, vertices = None, edges = None, directed = True):
                 v.component = self
 
     def roots(self):
+        """
+        returns the list of *roots* (vertices with no inward edges).
+        """
         return list(filter(lambda v: len(v.e_in()) == 0, self.verticesPoset))
 
     def leaves(self):
+        """
+        returns the list of *leaves* (vertices with no outward edges).
+        """
         return list(filter(lambda v: len(v.e_out()) == 0, self.verticesPoset))
 
     def add_single_vertex(self, v):
+        """
+        allow a GraphBase to hold a single vertex.
+        """
         if len(self.edgesPoset) == 0 and len(self.verticesPoset) == 0:
             v = self.verticesPoset.add(v)
             v.component = self
@@ -109,6 +83,9 @@ def add_single_vertex(self, v):
         return None
 
     def add_edge(self, e):
+        """
+        add edge e. At least one of its vertex must belong to the graph, the other being added automatically.
+        """
         if e in self.edgesPoset:
             return self.edgesPoset.get(e)
         x = e.v[0]
@@ -127,6 +104,9 @@ def add_edge(self, e):
         return e
 
     def remove_edge(self, e):
+        """
+        remove Edge e, asserting that the resulting graph is still connex.
+        """
         if e not in self.edgesPoset:
             return
         e.detach()
@@ -143,6 +123,9 @@ def remove_edge(self, e):
             return e
 
     def remove_vertex(self, x):
+        """
+        remove Vertex x and all associated edges.
+        """
         if x not in self.verticesPoset:
             return
         vertices = x.neighbors()  # get all neighbor vertices to check paths
@@ -168,6 +151,9 @@ def constant_function(self, value):
         return lambda x: value
 
     def vertices(self, cond = None):
+        """
+        generates an iterator over vertices, with optional filter
+        """
         vertices = self.verticesPoset
         if cond is None:
             cond = self.constant_function(True)
@@ -176,6 +162,9 @@ def vertices(self, cond = None):
                 yield v
 
     def edges(self, cond = None):
+        """
+        generates an iterator over edges, with optional filter
+        """
         edges = self.edgesPoset
         if cond is None:
             cond = self.constant_function(True)
@@ -185,7 +174,7 @@ def edges(self, cond = None):
 
     def matrix(self, cond = None):
         """
-            This associativity matrix is like the adjacency matrix but antisymmetric.
+            This associativity matrix is like the adjacency matrix but antisymmetric. Returns the associativity matrix of the graph component
 
         :param cond: same a the condition function in vertices().
         :return: array
@@ -207,27 +196,46 @@ def matrix(self, cond = None):
         return mat
 
     def order(self):
+        """
+        the order of the graph (number of vertices)
+        """
         return len(self.verticesPoset)
 
     def norm(self):
         """
-            The size of the edge poset.
+            The size of the edge poset (number of edges).
         """
         return len(self.edgesPoset)
 
     def deg_min(self):
+        """
+        the minimum degree of vertices
+        """
         return min([v.degree() for v in self.verticesPoset])
 
     def deg_max(self):
+        """
+         the maximum degree of vertices
+        """
         return max([v.degree() for v in self.verticesPoset])
 
     def deg_avg(self):
+        """
+         the average degree of vertices
+        """
         return sum([v.degree() for v in self.verticesPoset]) / float(self.order())
 
     def eps(self):
+        """
+         the graph epsilon value (norm/order), average number of edges per vertex.
+        """
         return float(self.norm()) / self.order()
 
     def path(self, x, y, f_io = 0, hook = None):
+        """
+        shortest path between vertices x and y by breadth-first descent, contrained by f_io direction if provided. The path is returned as a list of Vertex objects.
+        If a *hook* function is provided, it is called at every vertex added to the path, passing the vertex object as argument.
+        """
         assert x in self.verticesPoset
         assert y in self.verticesPoset
         x = self.verticesPoset.get(x)
@@ -263,6 +271,9 @@ def path(self, x, y, f_io = 0, hook = None):
         return p
 
     def dijkstra(self, x, f_io = 0, hook = None):
+        """
+        shortest weighted-edges paths between x and all other vertices by dijkstra's algorithm with heap used as priority queue.
+        """
         from collections import defaultdict
         from heapq import heappop, heappush
 
@@ -300,7 +311,11 @@ def dijkstra(self, x, f_io = 0, hook = None):
 
     def get_scs_with_feedback(self, roots = None):
         """
-            Minimum FAS algorithm (feedback arc set) creating a DAG.
+            Minimum FAS algorithm (feedback arc set) creating a DAG. Returns the set of strongly connected components
+            ("scs") by using Tarjan algorithm. These are maximal sets of vertices such that there is a path from each vertex to every other vertex.
+            The algorithm performs a DFS from the provided list of root vertices. A cycle is of course a strongly connected component,but a strongly connected component can include several cycles.
+            The Feedback Acyclic Set of edge to be removed/reversed is provided by marking the edges with a "feedback" flag.
+            Complexity is O(V+E).
 
         :param roots:
         :return:
diff --git a/graphistry/layout/graph/vertexBase.py b/graphistry/layout/graph/vertexBase.py
index 07cb8d6794..1a950273f0 100644
--- a/graphistry/layout/graph/vertexBase.py
+++ b/graphistry/layout/graph/vertexBase.py
@@ -7,17 +7,6 @@ class VertexBase(object):
         **Attributes**
             e (list[Edge]): list of edges associated with this vertex.
 
-        **Methods**
-            degree() : degree of the vertex (number of edges).
-            e_in() : list of edges directed toward this vertex.
-            e_out(): list of edges directed outward this vertex.
-            e_dir(int): either e_in, e_out or all edges depending on provided direction parameter (>0 means outward).
-            neighbors(f_io=0): list of neighbor vertices in all directions (default) or in filtered f_io direction (>0 means outward).
-            e_to(v): returns the Edge from this vertex directed toward vertex v.
-            e_from(v): returns the Edge from vertex v directed toward this vertex.
-            e_with(v): return the Edge with both this vertex and vertex v
-            detach(): removes this vertex from all its edges and returns this list of edges.
-
     """
 
     def __init__(self):
@@ -25,15 +14,27 @@ def __init__(self):
         self.e = []
 
     def degree(self):
+        """
+        degree() : degree of the vertex (number of edges).
+        """
         return len(self.e)
 
     def e_in(self):
+        """
+        e_in() : list of edges directed toward this vertex.
+        """
         return list(filter((lambda e: e.v[1] == self), self.e))
 
     def e_out(self):
+        """
+        e_out(): list of edges directed outward this vertex.
+        """
         return list(filter((lambda e: e.v[0] == self), self.e))
 
     def e_dir(self, dir):
+        """
+        either e_in, e_out or all edges depending on provided direction parameter (>0 means outward).
+        """
         if dir > 0:
             return self.e_out()
         if dir < 0:
@@ -42,7 +43,7 @@ def e_dir(self, dir):
 
     def neighbors(self, direction = 0):
         """
-            Returns the neighbors of this vertex.
+            Returns the neighbors of this vertex. List of neighbor vertices in all directions (default) or in filtered f_io direction (>0 means outward).
 
             :param direction:
                 - 0: parent and children
@@ -58,24 +59,36 @@ def neighbors(self, direction = 0):
         return arr
 
     def e_to(self, y):
+        """
+        returns the Edge from this vertex directed toward vertex v.
+        """
         for e in self.e_out():
             if e.v[1] == y:
                 return e
         return None
 
     def e_from(self, x):
+        """
+         returns the Edge from vertex v directed toward this vertex.
+        """
         for e in self.e_in():
             if e.v[0] == x:
                 return e
         return None
 
     def e_with(self, v):
+        """
+        return the Edge with both this vertex and vertex v
+        """
         for e in self.e:
             if v in e.v:
                 return e
         return None
 
     def detach(self):
+        """
+        removes this vertex from all its edges and returns this list of edges.
+        """
         E = self.e[:]
         for e in E:
             e.detach()
diff --git a/graphistry/plotter.py b/graphistry/plotter.py
index 3ed7c06118..00f32dfdeb 100644
--- a/graphistry/plotter.py
+++ b/graphistry/plotter.py
@@ -8,13 +8,14 @@
 from .embed_utils import HeterographEmbedModuleMixin  # type: ignore
 from .text_utils import SearchToGraphMixin  # type: ignore
 from .compute.conditional import ConditionalMixin  # type: ignore
+from .compute.cluster import ClusterMixin  # type: ignore
 
 
 mixins = ([
     CosmosMixin, NeptuneMixin, GremlinMixin,
     HeterographEmbedModuleMixin,
     SearchToGraphMixin,
-    DGLGraphMixin,
+    DGLGraphMixin, ClusterMixin,
     UMAPMixin,
     FeatureMixin, ConditionalMixin,
     LayoutsMixin,
@@ -32,6 +33,7 @@ def __init__(self, *args, **kwargs):
         ConditionalMixin.__init__(self, *args, **kwargs)
         FeatureMixin.__init__(self, *args, **kwargs)
         UMAPMixin.__init__(self, *args, **kwargs)
+        ClusterMixin.__init__(self, *args, **kwargs)
         DGLGraphMixin.__init__(self, *args, **kwargs)
         SearchToGraphMixin.__init__(self, *args, **kwargs)
         HeterographEmbedModuleMixin.__init__(self, *args, **kwargs)
diff --git a/graphistry/plugins/cugraph.py b/graphistry/plugins/cugraph.py
index b5f070af72..da68452d11 100644
--- a/graphistry/plugins/cugraph.py
+++ b/graphistry/plugins/cugraph.py
@@ -223,14 +223,19 @@ def compute_cugraph(
 
     :param alg: algorithm name
     :type alg: str
+
     :param out_col: node table output column name, defaults to alg param
     :type out_col: Optional[str]
+
     :param params: algorithm parameters passed to cuGraph as kwargs
     :type params: dict
+
     :param kind: kind of cugraph to use
     :type kind: CuGraphKind
+
     :param directed: whether graph is directed
     :type directed: bool
+
     :param G: cugraph graph to use; if None, use self
     :type G: Optional[cugraph.Graph]
 
@@ -239,16 +244,19 @@ def compute_cugraph(
     
     **Example: Pagerank**
         ::
+
             g2 = g.compute_cugraph('pagerank')
             assert 'pagerank' in g2._nodes.columns
 
     **Example: Katz centrality with rename**
         ::
+
             g2 = g.compute_cugraph('katz_centrality', out_col='katz_centrality_renamed')
             assert 'katz_centrality_renamed' in g2._nodes.columns
 
     **Example: Pass params to cugraph**
         ::
+        
             g2 = g.compute_cugraph('k_truss', params={'k': 2})
             assert 'k_truss' in g2._nodes.columns
 
@@ -360,6 +368,7 @@ def layout_cugraph(
 
     **Example: ForceAtlas2 layout**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -367,6 +376,7 @@ def layout_cugraph(
 
     **Example: Change which column names are generated**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -377,6 +387,7 @@ def layout_cugraph(
 
     **Example: Pass parameters to layout methods**
         ::
+        
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
diff --git a/graphistry/plugins/igraph.py b/graphistry/plugins/igraph.py
index a5bab3ac19..b7bdc0d405 100644
--- a/graphistry/plugins/igraph.py
+++ b/graphistry/plugins/igraph.py
@@ -49,10 +49,10 @@ def from_igraph(self,
     :param merge_if_existing: bool
 
     :returns: Plotter
-    :rtype: Plotter
 
     **Example: Convert from igraph, including all node/edge properties**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'v': [101, 102, 103, 104]})
             g = graphistry.edges(edges, 's', 'd').materialize_nodes().get_degrees()
@@ -62,6 +62,7 @@ def from_igraph(self,
 
     **Example: Enrich from igraph, but only load in 1 node attribute**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a', 'b', 'c', 'd'], 'd': ['b', 'c', 'd', 'e'], 'v': [101, 102, 103, 104]})
             g = graphistry.edges(edges, 's', 'd').materialize_nodes().get_degree()
@@ -198,7 +199,8 @@ def from_igraph(self,
     return g
 
 
-def to_igraph(self: Plottable, 
+def to_igraph(
+    self: Plottable, 
     directed: bool = True,
     include_nodes: bool = True,
     node_attributes: Optional[List[str]] = None,
@@ -309,8 +311,8 @@ def compute_igraph(
     :rtype: Plotter
 
     **Example: Pagerank**
-
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -319,6 +321,7 @@ def compute_igraph(
 
     **Example: Pagerank with custom name**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -327,6 +330,7 @@ def compute_igraph(
 
     **Example: Pagerank on an undirected**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -334,7 +338,8 @@ def compute_igraph(
             assert 'pagerank' in g2._nodes.columns
 
     **Example: Pagerank with custom parameters**
-            ::
+        ::
+
                 import graphistry, pandas as pd
                 edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['c','c','e','e']})
                 g = graphistry.edges(edges, 's', 'd')
@@ -447,6 +452,7 @@ def layout_igraph(
 
     **Example: Sugiyama layout**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -456,6 +462,7 @@ def layout_igraph(
 
     **Example: Change which column names are generated**
         ::
+
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
@@ -466,6 +473,7 @@ def layout_igraph(
 
     **Example: Pass parameters to layout methods - Sort nodes by degree**
         ::
+        
             import graphistry, pandas as pd
             edges = pd.DataFrame({'s': ['a','b','c','d'], 'd': ['b','c','d','e']})
             g = graphistry.edges(edges, 's', 'd')
diff --git a/graphistry/pygraphistry.py b/graphistry/pygraphistry.py
index 0a8dd76310..2051a32523 100644
--- a/graphistry/pygraphistry.py
+++ b/graphistry/pygraphistry.py
@@ -1942,6 +1942,7 @@ def nodes(nodes: Union[Callable, Any], node=None, *args, **kwargs) -> Plottable:
 
         **Example**
             ::
+            
                 import graphistry
 
                 def sample_nodes(g, n):
@@ -1992,6 +1993,7 @@ def edges(
 
         **Example**
             ::
+
                 import graphistry
 
                 def sample_edges(g, n):
diff --git a/graphistry/tests/test_compute_cluster.py b/graphistry/tests/test_compute_cluster.py
new file mode 100644
index 0000000000..c93d0e279d
--- /dev/null
+++ b/graphistry/tests/test_compute_cluster.py
@@ -0,0 +1,73 @@
+import pandas as pd
+import unittest
+import pytest
+import graphistry
+from graphistry.constants import DBSCAN
+from graphistry.util import ModelDict
+from graphistry.compute.cluster import lazy_dbscan_import_has_dependency
+
+has_dbscan, _, has_gpu_dbscan, _ = lazy_dbscan_import_has_dependency()
+
+
+ndf = edf = pd.DataFrame({'src': [1, 2, 1, 4], 'dst': [4, 5, 6, 1], 'label': ['a', 'b', 'b', 'c']})
+
+class TestComputeCluster(unittest.TestCase):
+    
+    def _condition(self, g, kind):
+        if kind == 'nodes':
+            self.assertTrue(g._node_dbscan is not None, 'instance has no `_node_dbscan` method')
+            self.assertTrue(DBSCAN in g._nodes, 'node df has no `_dbscan` attribute')
+            #self.assertTrue(g._point_color is not None, 'instance has no `_point_color` method')
+        else:
+            self.assertTrue(g._edge_dbscan is not None, 'instance has no `_edge_dbscan` method')
+            self.assertTrue(DBSCAN in g._edges, 'edge df has no `_dbscan` attribute')
+    
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_umap_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        for kind in ['nodes', 'edges']:
+            g2 = g.umap(kind=kind, n_topics=2, dbscan=False).dbscan(kind=kind, verbose=True)
+            self._condition(g2, kind)
+            g3 = g.umap(kind=kind, n_topics=2, dbscan=True)
+            self._condition(g3, kind)
+            if kind == 'nodes':
+                self.assertEqual(g2._nodes[DBSCAN].tolist(), g3._nodes[DBSCAN].tolist())
+            else:
+                self.assertEqual(g2._edges[DBSCAN].tolist(), g3._edges[DBSCAN].tolist())
+
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_featurize_cluster(self):
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        for kind in ['nodes', 'edges']:
+            g = g.featurize(kind=kind, n_topics=2).dbscan(kind=kind, verbose=True)
+            self._condition(g, kind)
+            
+    @pytest.mark.skipif(not has_dbscan, reason="requires ai dependencies")
+    def test_dbscan_params(self):
+        dbscan_params = [ModelDict('Testing UMAP', kind='nodes', min_dist=0.2, min_samples=1, cols=None, target=False, 
+                                   fit_umap_embedding=False, verbose=True, engine_dbscan='sklearn'), 
+                         ModelDict('Testing UMAP target', kind='nodes', min_dist=0.1, min_samples=1, cols=None, 
+                                   fit_umap_embedding=True, target=True, verbose=True, engine_dbscan='sklearn'),
+
+        ]
+        for params in dbscan_params:
+            g = graphistry.nodes(ndf).edges(edf, 'src', 'dst').umap(y='label', n_topics=2)
+            g2 = g.dbscan(**params)
+            self.assertTrue(g2._dbscan_params == params, f'dbscan params not set correctly, found {g2._dbscan_params} but expected {params}')
+        
+    @pytest.mark.skipif(not has_gpu_dbscan, reason="requires ai dependencies")
+    def test_transform_dbscan(self):
+        kind = 'nodes'
+        g = graphistry.nodes(ndf).edges(edf, 'src', 'dst')
+        g2 = g.umap(y='label', n_topics=2, kind=kind).dbscan(fit_umap_embedding=True)
+        
+        _, _, _, df = g2.transform_dbscan(ndf, kind=kind, verbose=True, return_graph=False)
+        self.assertTrue(DBSCAN in df, f'transformed df has no `{DBSCAN}` attribute')
+                
+        g3 = g2.transform_dbscan(ndf, ndf, verbose=True)
+        self._condition(g3, kind)
+        
+            
+if __name__ == '__main__':
+    unittest.main()
+    
diff --git a/graphistry/tests/test_dgl_utils.py b/graphistry/tests/test_dgl_utils.py
index 7aaf424b7c..baaef46135 100644
--- a/graphistry/tests/test_dgl_utils.py
+++ b/graphistry/tests/test_dgl_utils.py
@@ -74,7 +74,7 @@
 class TestDGL(unittest.TestCase):
     def _test_cases_dgl(self, g):
         # simple test to see if DGL graph was set during different featurization + umap strategies
-        G = g.DGL_graph
+        G = g._dgl_graph
         keys = ["feature", "target", "train_mask", "test_mask"]
         keys_without_target = ["feature", "train_mask", "test_mask"]
 
diff --git a/graphistry/tests/test_embed_utils.py b/graphistry/tests/test_embed_utils.py
index e979f6f26f..307bdd0266 100644
--- a/graphistry/tests/test_embed_utils.py
+++ b/graphistry/tests/test_embed_utils.py
@@ -1,51 +1,157 @@
+import os
 import pytest
 import pandas as pd
 import unittest
 import graphistry
 import numpy as np
 
-from graphistry.embed_utils import lazy_embed_import_dep
+from graphistry.embed_utils import lazy_embed_import_dep, check_cudf
 
 import logging
 logger = logging.getLogger(__name__)
 
 dep_flag, _, _, _, _, _, _, _ = lazy_embed_import_dep()
+has_cudf, cudf = check_cudf()
 
-edf = pd.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
-        columns=['src', 'dst', 'rel']
-)
-ndf_no_ids = pd.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
-ndf_with_ids = pd.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
-        columns = ['id', 'feat1']
-)
+# enable tests if has cudf and env didn't explicitly disable
+is_test_cudf = has_cudf and os.environ["TEST_CUDF"] != "0"
 
-graph_no_feat = graphistry.edges(edf, 'src', 'dst')
-graph_with_feat_no_ids = graph_no_feat.nodes(ndf_no_ids)
-graph_with_feat_with_ids = graph_no_feat.nodes(ndf_with_ids, 'id')
-graphs = [('no_feat', graph_no_feat), ('with_feat_no_ids', graph_with_feat_no_ids), ('with_feat_with_ids', graph_with_feat_with_ids)]
-d = 4
+class TestEmbed(unittest.TestCase):
 
-kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def setUp(self):
+        self.edf = pd.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
+            columns=['src', 'dst', 'rel']
+        )
+        ndf_no_ids = pd.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
+        ndf_with_ids = pd.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
+            columns = ['id', 'feat1']
+        )
+
+        self.graph_no_feat = graphistry.edges(self.edf, 'src', 'dst')
+        self.graph_with_feat_no_ids = self.graph_no_feat.nodes(ndf_no_ids)
+        self.graph_with_feat_with_ids = self.graph_no_feat.nodes(ndf_with_ids, 'id')
+        self.graphs = [
+            ('no_feat', self.graph_no_feat),
+            ('with_feat_no_ids', self.graph_with_feat_no_ids),
+            ('with_feat_with_ids', self.graph_with_feat_with_ids)
+        ]
+        self.d = 4
+
+        self.kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+        
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_embed_out_basic(self):
+        for name, g in self.graphs:
+            g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
+            num_nodes = len(set(g._edges['src'] + g._edges['dst']))
+            logging.debug('name: %s basic tests', name)
+            self.assertEqual(g._edges.shape, self.edf.shape)
+            self.assertEqual(set(g._edges[g._relation]), set(g._edges['rel']))
+            self.assertEqual(g._kg_embeddings.shape,(num_nodes, self.d))
 
-class TestEmbed(unittest.TestCase):
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_predict_links(self):
+        source = pd.Series([0,2])
+        relation = None
+        destination = pd.Series([1])
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
+
+        g_new = g.predict_links(source, relation, destination, threshold=0, anomalous=False)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+        
+        g_new = g.predict_links(source, relation, destination, threshold=1, anomalous=True)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+    
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_predict_links_all(self):
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
+        g_new = g.predict_links_all(threshold=0)
+        self.assertTrue( g_new._edges.shape[0] > 0)
+        self.assertIn("score", g_new._edges.columns)
+
+        
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    def test_chaining(self):
+        for name, g in self.graphs:
+            logging.debug('name: %s test changing embedding dim with feats' % name)
+            g = g.embed('rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * self.d, **self.kwargs)
+            self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test changing embedding dim without feats', name)
+            g = g.embed('rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * self.d, **self.kwargs)
+            self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test relationship change', name)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='src', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+            self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
+
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
+            logging.debug('name: %s test relationship change', name)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
+            self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
+
+
+class TestEmbedCUDF(unittest.TestCase):
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
+    def setUp(self):
+        self.edf = cudf.DataFrame([[0, 1, 0], [1, 2, 0], [2, 0, 1]],
+            columns=['src', 'dst', 'rel']
+        )
+        ndf_no_ids = cudf.DataFrame([['a'], ['a'], ['b']], columns=['feat'])
+        ndf_with_ids = cudf.DataFrame([[0, 'a'], [1, 'a'], [2, 'b']],
+            columns = ['id', 'feat1']
+        )
+
+        self.graph_no_feat = graphistry.edges(self.edf, 'src', 'dst')
+        self.graph_with_feat_no_ids = self.graph_no_feat.nodes(ndf_no_ids)
+        self.graph_with_feat_with_ids = self.graph_no_feat.nodes(ndf_with_ids, 'id')
+        self.graphs = [
+            ('no_feat', self.graph_no_feat),
+            ('with_feat_no_ids', self.graph_with_feat_no_ids),
+            ('with_feat_with_ids', self.graph_with_feat_with_ids)
+        ]
+        self.d = 4
+
+        self.kwargs = {'n_topics': 6, 'cardinality_threshold':10, 'epochs': 1, 'sample_size':10, 'num_steps':10}
+        
+
+    @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_embed_out_basic(self):
-        for name, g in graphs:
-            g = g.embed('rel', embedding_dim=d, **kwargs)
+        for name, g in self.graphs:
+            g = g.embed('rel', embedding_dim=self.d, **self.kwargs)
             num_nodes = len(set(g._edges['src'] + g._edges['dst']))
             logging.debug('name: %s basic tests', name)
-            self.assertEqual(g._edges.shape, edf.shape)
+            self.assertEqual(g._edges.shape, self.edf.shape)
             self.assertEqual(set(g._edges[g._relation]), set(g._edges['rel']))
-            self.assertEqual(g._kg_embeddings.shape,(num_nodes, d))
+            self.assertEqual(g._kg_embeddings.shape,(num_nodes, self.d))
 
 
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_predict_links(self):
         source = pd.Series([0,2])
         relation = None
         destination = pd.Series([1])
-        g = graph_no_feat.embed('rel', embedding_dim=d, **kwargs)
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
 
         g_new = g.predict_links(source, relation, destination, threshold=0, anomalous=False)
         self.assertTrue( g_new._edges.shape[0] > 0)
@@ -56,44 +162,47 @@ def test_predict_links(self):
         self.assertIn("score", g_new._edges.columns)
     
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_predict_links_all(self):
-        g = graph_no_feat.embed('rel', embedding_dim=d, **kwargs)
+        g = self.graph_no_feat.embed('rel', embedding_dim=self.d, **self.kwargs)
         g_new = g.predict_links_all(threshold=0)
         self.assertTrue( g_new._edges.shape[0] > 0)
         self.assertIn("score", g_new._edges.columns)
 
         
     @pytest.mark.skipif(not dep_flag, reason="requires ai feature dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
     def test_chaining(self):
-        for name, g in graphs:
+        for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim with feats' % name)
-            g = g.embed('rel', use_feat=True, embedding_dim=d, **kwargs)
-            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * d, **kwargs)
+            g = g.embed('rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=True, embedding_dim=2 * self.d, **self.kwargs)
             self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test changing embedding dim without feats', name)
-            g = g.embed('rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * d, **kwargs)
+            g = g.embed('rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed('rel', use_feat=False, embedding_dim=2 * self.d, **self.kwargs)
             self.assertNotEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test relationship change', name)
-            g = g.embed(relation='rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed(relation='src', use_feat=False, embedding_dim=d, **kwargs)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='src', use_feat=False, embedding_dim=self.d, **self.kwargs)
             self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
             self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
 
-        [g.reset_caches() for _, g in graphs]
-        for name, g in graphs:
+        [g.reset_caches() for _, g in self.graphs]
+        for name, g in self.graphs:
             logging.debug('name: %s test relationship change', name)
-            g = g.embed(relation='rel', use_feat=False, embedding_dim=d, **kwargs)
-            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=d, **kwargs)
+            g = g.embed(relation='rel', use_feat=False, embedding_dim=self.d, **self.kwargs)
+            g2 = g.embed(relation='rel', use_feat=True, embedding_dim=self.d, **self.kwargs)
             self.assertEqual(g._kg_embeddings.shape, g2._kg_embeddings.shape)
             self.assertNotEqual(np.linalg.norm(g._kg_embeddings - g2._kg_embeddings), 0)
 
 
+
 if __name__ == "__main__":
     unittest.main()
diff --git a/graphistry/tests/test_feature_utils.py b/graphistry/tests/test_feature_utils.py
index f3738b3707..c357381365 100644
--- a/graphistry/tests/test_feature_utils.py
+++ b/graphistry/tests/test_feature_utils.py
@@ -15,13 +15,27 @@
     process_nodes_dataframes,
     resolve_feature_engine,
     lazy_import_has_min_dependancy,
+    lazy_import_has_cu_cat_dependancy,
     lazy_import_has_dependancy_text,
     FastEncoder
 )
 
+from graphistry.features import topic_model, ngrams_model
+from graphistry.constants import SCALERS
+
+np.random.seed(137)
 
 has_min_dependancy, _ = lazy_import_has_min_dependancy()
 has_min_dependancy_text, _, _ = lazy_import_has_dependancy_text()
+has_cu_cat_dependancy_text, _, _ = lazy_import_has_cu_cat_dependancy()
+
+HAS_CUCAT = False
+try:
+    import cu_cat, cudf
+    HAS_CUCAT = True
+except:
+    cu_cat = object
+    cudf = pd
 
 logger = logging.getLogger(__name__)
 warnings.filterwarnings("ignore")
@@ -127,7 +141,7 @@
 target_names_node = [['label'], ['label', 'type']]
 # test also sending in a dataframe for target
 double_target_reddit = pd.DataFrame(
-    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}
+    {"label": ndf_reddit.label.values, "type": ndf_reddit["type"].values}, index=ndf_reddit.index
 )
 single_target_reddit = pd.DataFrame({"label": ndf_reddit.label.values})
 
@@ -136,6 +150,14 @@
 edge_df2['dst'] = np.random.random_integers(0, 120, size=len(edge_df2))
 edge2_target_df = pd.DataFrame({'label': edge_df2.label})
 
+# #############################################################################################################
+what = ['whatever', 'on what', 'what do', 'what do you', 'what do you think', 
+        'to what', 'but what', 'what is', 'what it', 'what kind', 'what kind of', 
+        'of what', 'know what', 'what are', 'what are the', 'what to', 'what to do', 
+        'from what', 'with what', 'and what', 'what you', 'whats', 'know what to', 'don know what', 'what the']
+freedom = ['title: dyslexics, experience, language',
+       'label: languagelearning, agile, leaves',
+       'title: freedom, finally, moved']
 # ################################################
 # data to test textual and numeric DataFrame
 # ndf_stocks, price_df_stocks = get_stocks_dataframe()
@@ -162,6 +184,44 @@ def check_allclose_fit_transform_on_same_data(X, x, Y=None, y=None):
                 allclose_stats(Y, y, value, name)
 
 
+class TestFeaturizeGetMethods(unittest.TestCase):
+    
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def setUp(self) -> None:
+        g = graphistry.nodes(ndf_reddit)
+        g2 = g.featurize(y=double_target_reddit,  # ngrams
+                use_ngrams=True,
+                ngram_range=(1, 4)
+                )
+        
+        g3 = g.featurize(**topic_model  # topic model       
+        )
+        self.g = g
+        self.g2 = g2
+        self.g3 = g3
+        
+    @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
+    def test_get_col_matrix(self):
+        # no edges so this should be None
+        assert self.g2.get_matrix(kind='edges') is None
+        
+        # test target methods
+        assert all(self.g2.get_matrix(target=True).columns == self.g2._node_target.columns)
+        assert self.g2.get_matrix('Anxiety', target=True).shape[0] == len(self.g2._node_target)
+        # test str vs list 
+        assert (self.g2.get_matrix('Anxiety', target=True) == self.g2.get_matrix(['Anxiety'], target=True)).all().values[0]
+
+        # assert list(self.g2.get_matrix(['Anxiety', 'education', 'computer'], target=True).columns) == ['label_Anxiety', 'label_education', 'label_computervision']
+    
+        # test feature methods
+        # ngrams
+        assert (self.g2.get_matrix().columns == self.g2._node_features.columns).all()
+        assert list(self.g2.get_matrix('what').columns) == what, list(self.g2.get_matrix('what').columns)
+        
+        # topic
+        assert all(self.g3.get_matrix().columns == self.g3._node_features.columns)
+        assert list(self.g3.get_matrix(['language', 'freedom']).columns) == freedom, self.g3.get_matrix(['language', 'freedom']).columns
+
 class TestFastEncoder(unittest.TestCase):
     # we test how far off the fit returned values different from the transformed
     
@@ -237,7 +297,8 @@ def test_process_node_dataframes_min_words(self):
                 2,
                 4000,
             ]:  # last one should skip encoding, and throw all to dirty_cat
-                X_enc, y_enc, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
+
+                X_enc, y_enc, X_encs, y_encs, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
                     ndf_reddit,
                     y=double_target_reddit,
                     use_scaler=None,
@@ -260,6 +321,74 @@ def test_multi_label_binarizer(self):
         assert y.shape == (4, 4)
         assert sum(y.sum(1).values - np.array([1., 2., 1., 0.])) == 0
         
+class TestFeatureCUMLProcessors(unittest.TestCase):
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
+    def cases_tests(self, x, y, data_encoder, target_encoder, name, value):
+        self.assertIsInstance(
+            x,
+            cudf.DataFrame,
+            f"Returned data matrix is not cudf DataFrame for {name} {value}",
+        )
+        self.assertFalse(
+            x.empty,
+            f"cudf DataFrame should not be empty for {name} {value}",
+        )
+        self.assertIsInstance(
+            y,
+            pd.DataFrame,
+            f"Returned Target is not a cudf DataFrame for {name} {value}",
+        )
+        self.assertFalse(
+            y.empty,
+            f"cudf Target DataFrame should not be empty for {name} {value}",
+        )
+        self.assertIsInstance(
+            data_encoder,
+            cu_cat.super_vectorizer.TableVectorizer,
+            f"Data Encoder is not a cu_cat.super_vectorizer.TableVectorizer instance for {name} {value}",
+        )
+        self.assertIsInstance(
+            target_encoder,
+            cu_cat.super_vectorizer.TableVectorizer,
+            f"Data Target Encoder is not a cu_cat.super_vectorizer.TableVectorizer instance for {name} {value}",
+        )
+
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires cu_cat feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
+    def test_process_node_dataframes_min_words(self):
+        # test different target cardinality
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            for min_words in [
+                2,
+                4000,
+            ]:  # last one should skip encoding, and throw all to dirty_cat
+
+                X_enc, y_enc, X_encs, y_encs, data_encoder, label_encoder, ordinal_pipeline, ordinal_pipeline_target, text_model, text_cols = process_nodes_dataframes(
+                    ndf_reddit,
+                    y=double_target_reddit,
+                    use_scaler=None,
+                    cardinality_threshold=40,
+                    cardinality_threshold_target=40,
+                    n_topics=20,
+                    min_words=min_words,
+                    model_name=model_avg_name,
+                    feature_engine=resolve_feature_engine('auto')
+                )
+                self.cases_tests(X_enc, y_enc, data_encoder, label_encoder, "min_words", min_words)
+    
+    @pytest.mark.skipif(not lazy_import_has_cu_cat_dependancy, reason="requires minimal feature dependencies")
+    @pytest.mark.skipif(not HAS_CUCAT, reason="requires cu_cat, cudf")
+    def test_multi_label_binarizer(self):
+        g = graphistry.nodes(bad_df)  # can take in a list of lists and convert to multiOutput
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            g2 = g.featurize(y=['list_str'], X=['src'], multilabel=True)
+        y = g2._get_target('node')
+        assert y.shape == (4, 4)
+        assert sum(y.sum(1).values - np.array([1., 2., 1., 0.])) == 0
+
 class TestFeatureMethods(unittest.TestCase):
 
     def _check_attributes(self, g, attributes):
@@ -370,19 +499,21 @@ def test_edge_featurization(self):
     def test_node_scaling(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.featurize(X="title", y='label', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
-            a, b, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
-
-        
+        for scaler in SCALERS:
+            X, y, c, d = g2.scale(ndf_reddit, single_target_reddit, kind='nodes', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(SCALERS), 
+                                  return_scalers=True)
 
     @pytest.mark.skipif(not has_min_dependancy or not has_min_dependancy_text, reason="requires ai feature dependencies")
     def test_edge_scaling(self):
         g = graphistry.edges(edge_df2, "src", "dst")
         g2 = g.featurize(y='label', kind='edges', use_scaler=None, use_scaler_target=None)
-        scalers = ['quantile', 'zscale', 'kbins', 'robust', 'minmax']
-        for scaler in scalers:
-            a, b, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', use_scaler=scaler, use_scaler_target=np.random.choice(scalers))
+        for scaler in SCALERS:
+            X, y, c, d = g2.scale(edge_df2, edge2_target_df, kind='edges', 
+                                  use_scaler=scaler, 
+                                  use_scaler_target=np.random.choice(SCALERS), 
+                                  return_scalers=True)
 
 
 
diff --git a/graphistry/tests/test_text_utils.py b/graphistry/tests/test_text_utils.py
index b4ecc713af..710bdc5ece 100644
--- a/graphistry/tests/test_text_utils.py
+++ b/graphistry/tests/test_text_utils.py
@@ -55,18 +55,18 @@ def setUp(self):
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_query(self):
         for g in [self.g_ngrams, self.g_emb]:
-            res, _ = g.query('How to set up DNS', thresh=100)
+            res, _ = g.search('How to set up DNS', thresh=100)
             assert not res.empty, f'Results DataFrame should not be empty, found {res}'
        
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies") 
     def test_query_graph(self):
         for name, g in zip(['ngrams', 'embedding'], [self.g_ngrams, self.g_emb]):
-            res = g.query_graph('How to set up DNS', thresh=100)
+            res = g.search_graph('How to set up DNS', thresh=100)
             assert not res._nodes.empty, f'{name}-Results DataFrame should not be empty, found {res._nodes}'
             #url = res.plot(render=False)
             #logger.info(f'{name}: {url}')
             
-        res = self.g_with_edges.query_graph('Wife', thresh=100)
+        res = self.g_with_edges.search_graph('Wife', thresh=100)
         assert not res._nodes.empty, f'Results DataFrame should not be empty, found {res._nodes}'
         #url = res.plot(render=False)
         #logger.info(f'With Explicit Edges: {url}')
diff --git a/graphistry/tests/test_umap_utils.py b/graphistry/tests/test_umap_utils.py
index ca0c3897ba..c6b4f0aa74 100644
--- a/graphistry/tests/test_umap_utils.py
+++ b/graphistry/tests/test_umap_utils.py
@@ -4,9 +4,12 @@
 import warnings
 
 import graphistry
+
+import os
 import logging
 import numpy as np
 import pandas as pd
+from graphistry import Plottable
 from graphistry.feature_utils import remove_internal_namespace_if_present
 from graphistry.tests.test_feature_utils import (
     ndf_reddit,
@@ -20,18 +23,31 @@
     edge2_target_df,
     model_avg_name,
     lazy_import_has_min_dependancy,
-    check_allclose_fit_transform_on_same_data
+    check_allclose_fit_transform_on_same_data,
+)
+from graphistry.umap_utils import (
+    lazy_umap_import_has_dependancy,
+    lazy_cuml_import_has_dependancy,
+    lazy_cudf_import_has_dependancy,
 )
-from graphistry.umap_utils import lazy_umap_import_has_dependancy, lazy_cuml_import_has_dependancy
 
 has_dependancy, _ = lazy_import_has_min_dependancy()
 has_cuml, _, _ = lazy_cuml_import_has_dependancy()
+has_cudf, _, _ = lazy_cudf_import_has_dependancy()
 has_umap, _, _ = lazy_umap_import_has_dependancy()
+has_cudf, _, cudf = lazy_cudf_import_has_dependancy()
+
+# print('has_dependancy', has_dependancy)
+# print('has_cuml', has_cuml)
+# print('has_cudf', has_cudf)
+# print('has_umap', has_umap)
 
 logger = logging.getLogger(__name__)
 
-warnings.filterwarnings('ignore')
+warnings.filterwarnings("ignore")
 
+# enable tests if has cudf and env didn't explicitly disable
+is_test_cudf = has_cudf and os.environ["TEST_CUDF"] != "0"
 
 triangleEdges = pd.DataFrame(
     {
@@ -65,74 +81,228 @@ class TestUMAPFitTransform(unittest.TestCase):
     # check to see that .fit and transform gives similar embeddings on same data
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def setUp(self):
-
+        verbose = True
         g = graphistry.nodes(ndf_reddit)
+        self.gn = g
         
+        self.test = ndf_reddit.sample(5)
+
+
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(y=double_target_reddit, 
-                        use_ngrams=True, 
-                        ngram_range=(1, 2), 
-                        use_scaler='robust', 
-                        cardinality_threshold=2)
-            
+            g2 = g.umap(
+                y=['label', 'type'],
+                use_ngrams=True,
+                ngram_range=(1, 2),
+                use_scaler="robust",
+                cardinality_threshold=2,
+                verbose=verbose,
+            )
+
+        self.g2 = g2
         fenc = g2._node_encoder
         self.X, self.Y = fenc.X, fenc.y
         self.EMB = g2._node_embedding
-        self.emb, self.x, self.y = g2.transform_umap(ndf_reddit, ydf=double_target_reddit, kind='nodes')
+        self.emb, self.x, self.y = g2.transform_umap(
+            ndf_reddit, ndf_reddit, kind="nodes", return_graph=False, verbose=verbose
+        )
+        self.g3 = g2.transform_umap(
+            ndf_reddit, ndf_reddit, kind="nodes", return_graph=True, verbose=verbose
+        )
 
+        # do the same for edges
         edge_df22 = edge_df2.copy()
-        edge_df22['rando'] = np.random.rand(edge_df2.shape[0])
-        g = graphistry.edges(edge_df22, 'src', 'dst')
+        edge_df22["rando"] = np.random.rand(edge_df2.shape[0])
+        g = graphistry.edges(edge_df22, "src", "dst")
+        self.ge = g
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(y=edge2_target_df, kind='edges',
-                 use_ngrams=True, 
-                 ngram_range=(1, 2),
-                 use_scaler=None,
-                 use_scaler_target=None,
-                 cardinality_threshold=2, n_topics=4)
-        
+            g2 = g.umap(
+                y=['label'],
+                kind="edges",
+                use_ngrams=True,
+                ngram_range=(1, 2),
+                use_scaler=None,
+                use_scaler_target=None,
+                cardinality_threshold=2,
+                n_topics=4,
+                verbose=verbose,
+            )
+
         fenc = g2._edge_encoder
         self.Xe, self.Ye = fenc.X, fenc.y
         self.EMBe = g2._edge_embedding
-        self.embe, self.xe, self.ye = g2.transform_umap(edge_df22, ydf=edge2_target_df, kind='edges')
-
-    # @pytest.mark.skipif(not has_dependancy, reason="requires umap feature dependencies")
-    # def test_allclose_fit_transform_on_same_data(self):
-    #     check_allclose_fit_transform_on_same_data(self.X, self.x, self.Y, self.y)
-    #     check_allclose_fit_transform_on_same_data(self.Xe, self.xe, self.Ye, self.ye)
+        self.embe, self.xe, self.ye = g2.transform_umap(
+            edge_df22, y=edge2_target_df, kind="edges", return_graph=False, verbose=verbose
+        )        
+        self.g2e = g2
 
-    #     check_allclose_fit_transform_on_same_data(self.EMB, self.emb, None, None)
-    #     check_allclose_fit_transform_on_same_data(self.EMBe, self.embe, None, None)
 
     @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
     def test_columns_match(self):
-        assert all(self.X.columns == self.x.columns), 'Node Feature Columns do not match'
-        assert all(self.Y.columns == self.y.columns), 'Node Target Columns do not match'
-        assert all(self.Xe.columns == self.xe.columns), 'Edge Feature Columns do not match'
-        assert all(self.Ye.columns == self.ye.columns), 'Edge Target Columns do not match'
+        assert set(self.X.columns) == set(self.x.columns), "Node Feature Columns do not match"
+        assert set(self.Y.columns) == set(self.y.columns), "Node Target Columns do not match"
+        assert set(self.Xe.columns) == set(self.xe.columns), "Edge Feature Columns do not match"
+        assert set(self.Ye.columns) == set(self.ye.columns), "Edge Target Columns do not match"
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_index_match(self):
+        # nodes
+        d = self.g2._nodes.shape[0]
+        de = self.g2e._edges.shape[0]
+        assert (self.gn._nodes.index == self.g2._nodes.index).sum() == d, "Node Indexes do not match"
+        assert (self.gn._nodes.index == self.EMB.index).sum() == d, "Emb Indexes do not match"
+        assert (self.gn._nodes.index == self.emb.index).sum() == d, "Transformed Emb Indexes do not match"
+        assert (self.gn._nodes.index == self.X.index).sum() == d, "Transformed Node features Indexes do not match"
+        assert (self.gn._nodes.index == self.y.index).sum() == d, "Transformed Node target Indexes do not match"
+
+        # edges
+        assert (self.ge._edges.index == self.g2e._edges.index).sum() == de, "Edge Indexes do not match"
+        assert (self.ge._edges.index == self.EMBe.index).sum() == de, "Edge Emb Indexes do not match"
+        assert (self.ge._edges.index == self.embe.index).sum() == de, "Edge Transformed Emb Indexes do not match"
+        assert (self.ge._edges.index == self.Xe.index).sum() == de, "Edge Transformed features Indexes do not match"
+        assert (self.ge._edges.index == self.ye.index).sum() == de, "Edge Transformed target Indexes do not match"
+
+        # make sure the indexes match at transform time internally as well
+        assert (self.X.index == self.x.index).sum() == d, "Node Feature Indexes do not match"
+        assert (self.Y.index == self.y.index).sum() == d, "Node Target Indexes do not match"
+        assert (self.Xe.index == self.xe.index).sum() == de, "Edge Feature Indexes do not match"
+        assert (self.Ye.index == self.ye.index).sum() == de, "Edge Target Indexes do not match"
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_node_index_match_in_infered_graph(self):
+        # nodes
+        g3 = self.g2._nodes
+        assert (g3.index == self.EMB.index).sum() == len(g3), "Node Emb Indexes do not match"
+        assert (g3.index == self.emb.index).sum() == len(g3), "Node Transformed Emb Indexes do not match"
+        assert (g3.index == self.X.index).sum() == len(g3), "Node Transformed features Indexes do not match"
+        assert (g3.index == self.y.index).sum() == len(g3), "Node Transformed target Indexes do not match"
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_edge_index_match_in_infered_graph(self):
+        g3 = self.g2e._edges
+        assert (g3.index == self.EMBe.index).sum() == len(g3), "Edge Emb Indexes do not match"
+        assert (g3.index == self.embe.index).sum() == len(g3), "Edge Transformed Emb Indexes do not match"
+        assert (g3.index == self.Xe.index).sum() == len(g3), "Edge Transformed Node features Indexes do not match"
+        assert (g3.index == self.ye.index).sum() == len(g3), "Edge Transformed Node target Indexes do not match"
+
+    
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_umap_kwargs(self):
+        umap_kwargs = {
+            "n_components": 2,
+            "metric": "euclidean",
+            "n_neighbors": 3,
+            "min_dist": 1,
+            "spread": 1,
+            "local_connectivity": 1,
+            "repulsion_strength": 1,
+            "negative_sample_rate": 5,
+        }
+
+        umap_kwargs2 = {k: v + 1 for k, v in umap_kwargs.items() if k not in ['metric']}  # type: ignore
+        umap_kwargs2['metric'] = 'euclidean'
+        g = graphistry.nodes(self.test)
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            g2 = g.umap(**umap_kwargs, engine='umap_learn')
+            g3 = g.umap(**umap_kwargs2, engine='umap_learn')
+        assert g2._umap_params == umap_kwargs
+        assert (
+            g2._umap_params == umap_kwargs
+        ), f"Umap params do not match, found {g2._umap_params} vs {umap_kwargs}"
+        assert len(g2._node_embedding.columns) == 2, f"Umap params do not match, found {len(g2._node_embedding.columns)} vs 2"
+
+        assert (
+            g3._umap_params == umap_kwargs2
+        ), f"Umap params do not match, found {g3._umap_params} vs {umap_kwargs2}"
+        assert len(g3._node_embedding.columns) == 3, f"Umap params do not match, found {len(g3._node_embedding.columns)} vs 3"
+        
+        g4 = g2.transform_umap(self.test)
+        assert (
+            g4._umap_params == umap_kwargs
+        ), f"Umap params do not match, found {g4._umap_params} vs {umap_kwargs}"
+        assert g4._n_components == 2, f"Umap params do not match, found {g2._n_components} vs 2"
+
+        g5 = g3.transform_umap(self.test)
+        assert (
+            g5._umap_params == umap_kwargs2
+        ), f"Umap params do not match, found {g5._umap_params} vs {umap_kwargs2}"
+
+    @pytest.mark.skipif(not has_umap, reason="requires umap feature dependencies")
+    def test_transform_umap(self):
+        np.random.seed(41)
+        test = self.test
+        assert ( 
+            self.g2._node_embedding.shape[0] <= self.g3._node_embedding.shape[0]
+        ), "Node Embedding Lengths do not match, found {} and {}".format(
+            self.g2._node_embedding.shape[0], self.g3._node_embedding.shape[0]
+        )
+        # now feed it args
+        min_dist = ["auto", 10]
+        sample = [None, 2]
+        return_graph = [True, False]
+        fit_umap_embedding = [True, False]
+        n_neighbors = [2, None]
+        for ep in min_dist:
+            g4 = self.g2.transform_umap(test, test, min_dist=ep)
+            assert True
+        for return_g in return_graph:
+            g4 = self.g2.transform_umap(test, test, return_graph=return_g)
+            if return_g:
+                assert True
+            else:
+                objs = (pd.DataFrame,)
+                if has_cudf:
+                    objs = (pd.DataFrame, cudf.DataFrame)
+                assert len(g4) == 3
+                assert isinstance(g4[0], objs)
+                assert isinstance(g4[1], objs)
+                assert isinstance(g4[2], objs)
+                assert g4[0].shape[1] == 2
+                assert g4[1].shape[1] >= 2
+                assert g4[2].shape[0] == test.shape[0]
+        for n_neigh in n_neighbors:
+            g4 = self.g2.transform_umap(test, n_neighbors=n_neigh)
+            assert True
+        for sample_ in sample:
+            print("sample", sample_)
+            g4 = self.g2.transform_umap(test, sample=sample_)
+            assert True
+        for fit_umap_embedding_ in fit_umap_embedding:
+            g4 = self.g2.transform_umap(test, fit_umap_embedding=fit_umap_embedding_)
+            assert True
 
 
 class TestUMAPMethods(unittest.TestCase):
     def _check_attributes(self, g, attributes):
         msg = "Graphistry instance after umap should have `{}` as attribute"
         msg2 = "Graphistry instance after umap should not have None values for `{}`"
+        objs = (pd.DataFrame,)
+        if has_cudf:
+            objs = (pd.DataFrame, cudf.DataFrame)
 
         for attribute in attributes:
             self.assertTrue(hasattr(g, attribute), msg.format(attribute))
             self.assertTrue(getattr(g, attribute) is not None, msg2.format(attribute))
-            if 'df' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-            if 'node_' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-            if 'edge_' in attribute:
-                self.assertIsInstance(getattr(g, attribute), pd.DataFrame, msg.format(attribute))
-
+            if "df" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), objs, msg.format(attribute)
+                )
+            if "node_" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), objs, msg.format(attribute)
+                )
+            if "edge_" in attribute:
+                self.assertIsInstance(
+                    getattr(g, attribute), objs, msg.format(attribute)
+                )
 
     def cases_check_node_attributes(self, g):
         attributes = [
@@ -195,6 +365,7 @@ def _test_umap(self, g, use_cols, targets, name, kind, df):
                         model_name=model_avg_name,
                         feature_engine=feature_engine,
                         n_neighbors=2,
+                        dbscan=False,
                     )
 
                     self.cases_test_graph(g2, kind=kind, df=df)
@@ -227,7 +398,9 @@ def test_edge_umap(self):
             df=triangleEdges,
         )
 
-    @pytest.mark.skipif(not has_dependancy or not has_umap, reason="requires umap feature dependencies")
+    @pytest.mark.skipif(
+        not has_dependancy or not has_umap, reason="requires umap feature dependencies"
+    )
     def test_filter_edges(self):
         for kind, g in [("nodes", graphistry.nodes(triangleNodes))]:
             g2 = g.umap(kind=kind, feature_engine="none")
@@ -240,7 +413,9 @@ def test_filter_edges(self):
                     f"{kind} -- scale: {scale}: resulting edges dataframe shape: {shape}"
                 )
                 logger.debug("-" * 80)
-                self.assertGreaterEqual(shape[0], last_shape)  # should return more and more edges
+                self.assertGreaterEqual(
+                    shape[0], last_shape
+                )  # should return more and more edges
                 last_shape = shape[0]
 
 
@@ -252,27 +427,36 @@ class TestUMAPAIMethods(TestUMAPMethods):
     def _test_umap(self, g, use_cols, targets, name, kind, df):
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
-            for scaler in ['kbins', 'robust']:
+            for scaler in ["kbins", "robust"]:
                 for cardinality in [2, 200]:
                     for use_ngram in [True, False]:
                         for use_col in use_cols:
                             for target in targets:
                                 logger.debug("*" * 90)
-                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                value = [
+                                    scaler,
+                                    cardinality,
+                                    use_ngram,
+                                    target,
+                                    use_col,
+                                ]
                                 logger.debug(f"{value}")
                                 logger.debug("-" * 80)
-                            
-                                g2 = g.umap(kind=kind,
+
+                                g2 = g.umap(
+                                    kind=kind,
                                     X=use_col,
                                     y=target,
                                     model_name=model_avg_name,
                                     use_scaler=scaler,
                                     use_scaler_target=scaler,
                                     use_ngrams=use_ngram,
-                                    engine='umap_learn',
+                                    engine="umap_learn",
                                     cardinality_threshold=cardinality,
                                     cardinality_threshold_target=cardinality,
-                                    n_neighbors=3)
+                                    n_neighbors=3,
+                                    dbscan=False,
+                                )
 
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
@@ -305,8 +489,8 @@ def test_node_umap(self):
     )
     def test_edge_umap(self):
         g = graphistry.edges(edge_df2, "src", "dst")
-        targets = [None, 'label']
-        use_cols = [None, 'title']
+        targets = [None, "label"]
+        use_cols = [None, "title"]
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -327,21 +511,23 @@ def test_edge_umap(self):
     )
     def test_chaining_nodes(self):
         g = graphistry.nodes(ndf_reddit)
-        g2 = g.umap()
+        g2 = g.umap(dbscan=False)
 
-        logger.debug('======= g.umap() done ======')
+        logger.debug("======= g.umap() done ======")
         g3a = g2.featurize()
-        logger.debug('======= g3a.featurize() done ======')
-        g3 = g3a.umap()
-        logger.debug('======= g3.umap() done ======')
+        logger.debug("======= g3a.featurize() done ======")
+        g3 = g3a.umap(dbscan=False)
+        logger.debug("======= g3.umap() done ======")
         assert g2._node_features.shape == g3._node_features.shape
         # since g3 has feature params with x and y.
-        g3._feature_params['nodes']['X'].pop('x')
-        g3._feature_params['nodes']['X'].pop('y')
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X'])
-        assert g2._feature_params['nodes']['y'].shape == g3._feature_params['nodes']['y'].shape  # None
+        g3._feature_params["nodes"]["X"].pop("x")
+        g3._feature_params["nodes"]["X"].pop("y")
+        assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
+        assert (
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
+        )  # None
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
-        
+
     @pytest.mark.skipif(
         not has_dependancy or not has_umap,
         reason="requires ai+umap feature dependencies",
@@ -352,11 +538,13 @@ def test_chaining_edges(self):
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
-            g2 = g.umap(kind='edges')
-            g3 = g.featurize(kind='edges').umap(kind='edges')
-            
-        assert all(g2._feature_params['edges']['X'] == g3._feature_params['edges']['X'])
-        assert all(g2._feature_params['edges']['y'] == g3._feature_params['edges']['y'])  # None
+            g2 = g.umap(kind="edges", dbscan=False)
+            g3 = g.featurize(kind="edges").umap(kind="edges", dbscan=False)
+
+        assert all(g2._feature_params["edges"]["X"] == g3._feature_params["edges"]["X"])
+        assert all(
+            g2._feature_params["edges"]["y"] == g3._feature_params["edges"]["y"]
+        )  # None
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
@@ -366,19 +554,32 @@ def test_chaining_edges(self):
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
         g = graphistry.nodes(ndf_reddit)
         n_topics_target = 6
-    
+
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
             warnings.filterwarnings("ignore", category=FutureWarning)
 
-            g2 = g.umap(X="type", y="label", cardinality_threshold_target=3, n_topics_target=n_topics_target)  # makes a GapEncoded Target
-            g3 = g.umap(X="type", y="label", cardinality_threshold_target=30000)  # makes a one-hot-encoded target
-            
-        assert all(g2._feature_params['nodes']['X'] == g3._feature_params['nodes']['X']), "features should be the same"
-        assert all(g2._feature_params['nodes']['y'] != g3._feature_params['nodes']['y']), "targets in memoize should be different"  # None
-        assert g2._node_target.shape[1] != g3._node_target.shape[1], 'Targets should be different'
-        assert g2._node_target.shape[1] == n_topics_target, 'Targets '
+            g2 = g.umap(
+                X="type",
+                y="label",
+                cardinality_threshold_target=3,
+                n_topics_target=n_topics_target,
+            )  # makes a GapEncoded Target
+            g3 = g.umap(
+                X="type", y="label", cardinality_threshold_target=30000
+            )  # makes a one-hot-encoded target
+
+        assert all(
+            g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"]
+        ), "features should be the same"
+        assert all(
+            g2._feature_params["nodes"]["y"] != g3._feature_params["nodes"]["y"]
+        ), "targets in memoize should be different"  # None
+        assert (
+            g2._node_target.shape[1] != g3._node_target.shape[1]
+        ), "Targets should be different"
+        assert g2._node_target.shape[1] == n_topics_target, "Targets "
 
     @pytest.mark.skipif(
         not has_dependancy or not has_umap,
@@ -412,27 +613,35 @@ class TestCUMLMethods(TestUMAPMethods):
     def _test_umap(self, g, use_cols, targets, name, kind, df):
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
-            for scaler in ['kbins', 'robust']:
+            for scaler in ["kbins", "robust"]:
                 for cardinality in [2, 200]:
                     for use_ngram in [True, False]:
                         for use_col in use_cols:
                             for target in targets:
                                 logger.debug("*" * 90)
-                                value = [scaler, cardinality, use_ngram, target, use_col]
-                                logger.debug(f"{value}")
+                                value = [
+                                    scaler,
+                                    cardinality,
+                                    use_ngram,
+                                    target,
+                                    use_col,
+                                ]
+                                logger.debug(f"{name}:\n{value}")
                                 logger.debug("-" * 80)
-                            
-                                g2 = g.umap(kind=kind,
+
+                                g2 = g.umap(
+                                    kind=kind,
                                     X=use_col,
                                     y=target,
                                     model_name=model_avg_name,
                                     use_scaler=scaler,
                                     use_scaler_target=scaler,
                                     use_ngrams=use_ngram,
-                                    engine='cuml',
+                                    engine="cuml",
                                     cardinality_threshold=cardinality,
                                     cardinality_threshold_target=cardinality,
-                                    n_neighbors=3)
+                                    n_neighbors=3,
+                                )
 
                                 self.cases_test_graph(g2, kind=kind, df=df)
 
@@ -465,8 +674,8 @@ def test_node_umap(self):
     )
     def test_edge_umap(self):
         g = graphistry.edges(edge_df2, "src", "dst")
-        targets = [None, 'label']
-        use_cols = [None, 'title']
+        targets = [None, "label"]
+        use_cols = [None, "title"]
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -489,6 +698,200 @@ def test_chaining_nodes(self):
         g = graphistry.nodes(ndf_reddit)
         g2 = g.umap()
 
+        logger.debug("======= g.umap() done ======")
+        g3a = g2.featurize()
+        logger.debug("======= g3a.featurize() done ======")
+        g3 = g3a.umap()
+        logger.debug("======= g3.umap() done ======")
+        assert g2._node_features.shape == g3._node_features.shape, f"featurize() should be idempotent, found {g2._node_features.shape} != {g3._node_features.shape}"
+        # since g3 has feature params with x and y.
+        g3._feature_params["nodes"]["X"].pop("x")
+        g3._feature_params["nodes"]["X"].pop("y")
+        assert all(g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"])
+        assert (
+            g2._feature_params["nodes"]["y"].shape == g3._feature_params["nodes"]["y"].shape
+        )  # None
+        assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cuml,
+        reason="requires cuml feature dependencies",
+    )
+    def test_chaining_edges(self):
+        g = graphistry.edges(edge_df, "src", "dst")
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+            g2 = g.umap(kind="edges")
+            g3 = g.featurize(kind="edges").umap(kind="edges")
+
+        assert all(g2._feature_params["edges"]["X"] == g3._feature_params["edges"]["X"])
+        assert all(
+            g2._feature_params["edges"]["y"] == g3._feature_params["edges"]["y"]
+        )  # None
+        assert all(g2._edge_features == g3._edge_features)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cuml,
+        reason="requires cuml feature dependencies",
+    )
+    def test_feature_kwargs_yield_different_values_using_umap_api(self):
+        g = graphistry.nodes(ndf_reddit)
+        n_topics_target = 6
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            g2 = g.umap(
+                X="type",
+                y="label",
+                cardinality_threshold_target=3,
+                n_topics_target=n_topics_target,
+            )  # makes a GapEncoded Target
+            g3 = g.umap(
+                X="type", y="label", cardinality_threshold_target=30000
+            )  # makes a one-hot-encoded target
+
+        assert all(
+            g2._feature_params["nodes"]["X"] == g3._feature_params["nodes"]["X"]
+        ), "features should be the same"
+        assert all(
+            g2._feature_params["nodes"]["y"] != g3._feature_params["nodes"]["y"]
+        ), "targets in memoize should be different"  # None
+        assert (
+            g2._node_target.shape[1] != g3._node_target.shape[1]
+        ), "Targets should be different"
+        assert g2._node_target.shape[1] == n_topics_target, "Targets "
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_umap,
+        reason="requires cuml feature dependencies",
+    )
+    def test_filter_edges(self):
+        for kind, g in [("nodes", graphistry.nodes(ndf_reddit))]:
+            g2 = g.umap(kind=kind, model_name=model_avg_name)
+            last_shape = 0
+            for scale in np.linspace(0, 1, 8):  # six sigma in 8 steps
+                g3 = g2.filter_weighted_edges(scale=scale)
+                shape = g3._edges.shape
+                logger.debug("*" * 90)
+                logger.debug(
+                    f"{kind} -- scale: {scale}: resulting edges dataframe shape: {shape}"
+                )
+                logger.debug("-" * 80)
+                self.assertGreaterEqual(shape[0], last_shape)
+                last_shape = shape[0]
+
+class TestCudfUmap(unittest.TestCase):
+    # temporary tests for cudf pass thru umap
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
+    def setUp(self):
+        self.samples = 1000
+        df = pd.DataFrame(np.random.randint(18,75,size=(self.samples, 1)), columns=['age'])
+        df['user_id'] = np.random.randint(0,200,size=(self.samples, 1))
+        df['profile'] = np.random.randint(0,1000,size=(self.samples, 1))
+        self.df = cudf.from_pandas(df)
+    
+    @pytest.mark.skipif(not has_dependancy or not has_cuml, reason="requires cuml dependencies")
+    @pytest.mark.skipif(not is_test_cudf, reason="requires cudf")
+    def test_base(self):
+        graphistry.nodes(self.df).umap('auto')._node_embedding.shape == (self.samples, 2)
+        graphistry.nodes(self.df).umap('engine')._node_embedding.shape == (self.samples, 2)
+
+
+@pytest.mark.skipif(
+    not has_dependancy or not has_cudf,
+    reason="requires cudf feature dependencies",
+)
+class TestCUDFMethods(TestUMAPMethods):
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def _test_umap(self, g, use_cols, targets, name, kind, df):
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            for scaler in ['kbins', 'robust']:
+                for cardinality in [2, 200]:
+                    for use_ngram in [True, False]:
+                        for use_col in use_cols:
+                            for target in targets:
+                                logger.debug("*" * 90)
+                                value = [scaler, cardinality, use_ngram, target, use_col]
+                                logger.debug(f"{value}")
+                                logger.debug("-" * 80)
+
+                                g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+                                g2 = g.umap(kind=kind,
+                                    X=use_col,
+                                    y=target,
+                                    model_name=model_avg_name,
+                                    use_scaler=scaler,
+                                    use_scaler_target=scaler,
+                                    use_ngrams=use_ngram,
+                                    engine='cudf',
+                                    cardinality_threshold=cardinality,
+                                    cardinality_threshold_target=cardinality,
+                                    n_neighbors=3)
+
+                                self.cases_test_graph(g2, kind=kind, df=df)
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_node_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        use_cols = [None, text_cols_reddit, good_cols_reddit, meta_cols_reddit]
+        targets = [None, single_target_reddit, double_target_reddit]
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Node UMAP with `(target, use_col)=`",
+                kind="nodes",
+                df=ndf_reddit,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_edge_umap(self):
+        g = graphistry.nodes(cudf.from_pandas(edge_df2), "src", "dst")
+        targets = [None, 'label']
+        use_cols = [None, 'title']
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", category=UserWarning)
+            warnings.filterwarnings("ignore", category=DeprecationWarning)
+            warnings.filterwarnings("ignore", category=FutureWarning)
+
+            self._test_umap(
+                g,
+                use_cols=use_cols,
+                targets=targets,
+                name="Edge UMAP with `(target, use_col)=`",
+                kind="edges",
+                df=edge_df2,
+            )
+
+    @pytest.mark.skipif(
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
+    )
+    def test_chaining_nodes(self):
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
+        g2 = g.umap()
+
         logger.debug('======= g.umap() done ======')
         g3a = g2.featurize()
         logger.debug('======= g3a.featurize() done ======')
@@ -503,11 +906,11 @@ def test_chaining_nodes(self):
         assert g2._node_embedding.shape == g3._node_embedding.shape  # kinda weak sauce
         
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
-        reason="requires cuml feature dependencies",
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
     )
     def test_chaining_edges(self):
-        g = graphistry.edges(edge_df, "src", "dst")
+        g = graphistry.nodes(cudf.from_pandas(edge_df), "src", "dst")
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", category=UserWarning)
             warnings.filterwarnings("ignore", category=DeprecationWarning)
@@ -520,11 +923,11 @@ def test_chaining_edges(self):
         assert all(g2._edge_features == g3._edge_features)
 
     @pytest.mark.skipif(
-        not has_dependancy or not has_cuml,
-        reason="requires cuml feature dependencies",
+        not has_dependancy or not has_cudf,
+        reason="requires cudf feature dependencies",
     )
     def test_feature_kwargs_yield_different_values_using_umap_api(self):
-        g = graphistry.nodes(ndf_reddit)
+        g = graphistry.nodes(cudf.from_pandas(ndf_reddit))
         n_topics_target = 6
     
         with warnings.catch_warnings():
@@ -545,7 +948,7 @@ def test_feature_kwargs_yield_different_values_using_umap_api(self):
         reason="requires ai+umap feature dependencies",
     )
     def test_filter_edges(self):
-        for kind, g in [("nodes", graphistry.nodes(ndf_reddit))]:
+        for kind, g in [("nodes", graphistry.nodes(cudf.from_pandas(ndf_reddit)))]:
             g2 = g.umap(kind=kind, model_name=model_avg_name)
             last_shape = 0
             for scale in np.linspace(0, 1, 8):  # six sigma in 8 steps
diff --git a/graphistry/text_utils.py b/graphistry/text_utils.py
index 6af12f3655..63fa5031d0 100644
--- a/graphistry/text_utils.py
+++ b/graphistry/text_utils.py
@@ -1,32 +1,23 @@
-import os
-from time import time
-import numpy as np
 import pandas as pd
 
 from .feature_utils import FeatureMixin
-from .ai_utils import search_to_df, setup_logger
-from .constants import WEIGHT, N_TREES, DISTANCE, VERBOSE, TRACE
+from .ai_utils import search_to_df, FaissVectorSearch
+from .constants import WEIGHT, DISTANCE
+from logging import getLogger
 
 from typing import (
-    Hashable,
-    List,
-    Union,
-    Dict,
-    Any,
-    Optional,
-    Tuple,
-    TYPE_CHECKING, 
-    Type
+    TYPE_CHECKING,
 )  # noqa
 
 
-logger = setup_logger(__name__, verbose=VERBOSE, fullpath=TRACE)
-
 if TYPE_CHECKING:
     MIXIN_BASE = FeatureMixin
 else:
     MIXIN_BASE = object
 
+logger = getLogger(__name__)
+
+
 class SearchToGraphMixin(MIXIN_BASE):
     def __init__(self, *args, **kwargs) -> None:
         super().__init__(*args, **kwargs)
@@ -34,162 +25,156 @@ def __init__(self, *args, **kwargs) -> None:
     def assert_fitted(self):
         # assert self._umap is not None, 'Umap needs to be fit first, run g.umap(..) to fit a model'
         assert (
-            self._get_feature('nodes') is not None
-        ), "Graphistry Instance is not fit, run g.featurize(kind='nodes', ..) to fit a model' \
+            self._get_feature("nodes") is not None
+        ), "Graphistry Instance is not fit, run g.featurize(kind='nodes', ..) to fit a model ' \
         'if you have nodes & edges dataframe or g.umap(kind='nodes', ..) if you only have nodes dataframe"
 
     def assert_features_line_up_with_nodes(self):
         ndf = self._nodes
-        X = self._get_feature('nodes')
+        X = self._get_feature("nodes")
         a, b = ndf.shape[0], X.shape[0]
-        assert a == b, 'Nodes dataframe and feature vectors are not same size, '\
-        f'found nodes: {a}, feats: {b}. Did you mutate nodes between fit?'
+        assert a == b, (
+            "Nodes dataframe and feature vectors are not same size, "
+            f"found nodes: {a}, feats: {b}. Did you mutate nodes between fit?"
+        )
 
     def build_index(self, angular=False, n_trees=None):
-        from annoy import AnnoyIndex  # type: ignore
         # builds local index
         self.assert_fitted()
         self.assert_features_line_up_with_nodes()
-        
-        X = self._get_feature('nodes')
-
-        logger.info(f"Building Index of size {X.shape}")
-
-        if angular:
-            logger.info('-using angular metric')
-            metric = 'angular'
-        else:
-            logger.info('-using euclidean metric')
-            metric = 'euclidean'
-            
-        search_index = AnnoyIndex(X.shape[1], metric)
-        # Add all the feature vectors to the search index
-        for i in range(len(X)):
-            search_index.add_item(i, X.values[i])
-        if n_trees is None:
-            n_trees = N_TREES
-
-        logger.info(f'-building index with {n_trees} trees')
-        search_index.build(n_trees)
-
-        self.search_index = search_index
+        X = self._get_feature("nodes")
+        if type(X) != pd.DataFrame:
+            print(f"Converting from {type(X)} to pandas for semantic search index")
+            X = X.to_pandas()
+        self.search_index = FaissVectorSearch(
+            X.values
+        )  # self._build_search_index(X, angular, n_trees, faiss=False)
 
     def _query_from_dataframe(self, qdf: pd.DataFrame, top_n: int, thresh: float):
         # Use the loaded featurizers to transform the dataframe
-        vect, _ = self.transform(qdf, None, kind="nodes")
-        
-        indices, distances = self.search_index.get_nns_by_vector(
-            vect.values[0], top_n, include_distances=True
-        )
-        
-        results = self._nodes.iloc[indices]
-        results[DISTANCE] = distances
-        results = results.query(f"{DISTANCE} < {thresh}")
+        vect, _ = self.transform(qdf, None, kind="nodes", return_graph=False)
 
-        results = results.sort_values(by=[DISTANCE])
+        results = self.search_index.search_df(vect, self._nodes, top_n)
+        results = results.query(f"{DISTANCE} < {thresh}")
 
         return results, vect
-    
+
     def _query(self, query: str, top_n: int, thresh: float):
         # build the query dataframe
-        if not hasattr(self, 'search_index'):
+        if not hasattr(self, "search_index"):
             self.build_index()
 
         qdf = pd.DataFrame([])
-                
+
         cols_text = self._node_encoder.text_cols  # type: ignore
 
         if len(cols_text) == 0:
-            logger.warn('** Querying is only possible using Transformer/Ngrams embeddings')    
+            logger.warn(
+                "** Querying is only possible using Transformer/Ngrams embeddings"
+            )
             return pd.DataFrame([]), None
-            
+
         qdf[cols_text[0]] = [query]
         if len(cols_text) > 1:
             for col in cols_text[1:]:
-                qdf[col] = ['']   
+                qdf[col] = [""]
 
         # this is hookey and needs to be fixed on dirty_cat side (with errors='ignore')
-        # if however min_words = 0, all columns will be textual, 
+        # if however min_words = 0, all columns will be textual,
         # and no other data_encoder will be generated
-        if hasattr(self._node_encoder.data_encoder, 'columns_'):  # type: ignore
+        if hasattr(self._node_encoder.data_encoder, "columns_"):  # type: ignore
 
             other_cols = self._node_encoder.data_encoder.columns_  # type: ignore
 
             if other_cols is not None and len(other_cols):
-                logger.warn('** There is no easy way to encode categorical or other features at query time. '
-                            f'Set `thresh` to a large value if no results show up.\ncolumns: {other_cols}')
+                logger.warn(
+                    "** There is no easy way to encode categorical or other features at query time. "
+                    f"Set `thresh` to a large value if no results show up.\ncolumns: {other_cols}"
+                )
                 df = self._nodes
                 dt = df[other_cols].dtypes
                 for col, v in zip(other_cols, dt.values):
                     if str(v) in ["string", "object", "category"]:
-                        qdf[col] = df.sample(1)[col].values  # so hookey 
+                        qdf[col] = df.sample(1)[col].values  # so hookey
                     elif str(v) in [
-                    "int",
-                    "float", 
-                    "float64",
-                    "float32",
-                    "float16",
-                    "int64",
-                    "int32",
-                    "int16",
-                    "uint64",
-                    "uint32",
-                    "uint16",
-                ]:
+                        "int",
+                        "float",
+                        "float64",
+                        "float32",
+                        "float16",
+                        "int64",
+                        "int32",
+                        "int16",
+                        "uint64",
+                        "uint32",
+                        "uint16",
+                    ]:
                         qdf[col] = df[col].mean()
 
         return self._query_from_dataframe(qdf, thresh=thresh, top_n=top_n)
 
     def search(
-        self, query: str, cols = None, thresh: float = 5000, fuzzy: bool = True, top_n: int = 10
-    ):  
+        self,
+        query: str,
+        cols=None,
+        thresh: float = 5000,
+        fuzzy: bool = True,
+        top_n: int = 10,
+    ):
         """Natural language query over nodes that returns a dataframe of results sorted by relevance column "distance".
 
-            If node data is not yet feature-encoded (and explicit edges are given), 
-            run automatic feature engineering: 
-            ```
-                g2 = g.featurize(kind='nodes', X=['text_col_1', ..], 
+            If node data is not yet feature-encoded (and explicit edges are given),
+            run automatic feature engineering:
+            ::
+
+                g2 = g.featurize(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
-                )  
-            ```    
-            
-            If edges do not yet exist, generate them via 
-            ```
-                g2 = g.umap(kind='nodes', X=['text_col_1', ..], 
+                )
+
+            If edges do not yet exist, generate them via
+            ::
+
+                g2 = g.umap(kind='nodes', X=['text_col_1', ..],
                 min_words=0 # forces all named columns are textually encoded
-                )  
-            ``` 
+                )
+            
             If an index is not yet built, it is generated `g2.build_index()` on the fly at search time.
-            Otherwise, can set `g2.build_index()` and then subsequent `g2.search(...)` 
-            calls will be not rebuilt index.
+            Otherwise, can set `g2.build_index()` to build it ahead of time.
 
         Args:
-            query (str): natural language query.
-            cols (list or str, optional): if fuzzy=False, select which column to query. 
+            :query (str): natural language query.
+            :cols (list or str, optional): if fuzzy=False, select which column to query.
                                             Defaults to None since fuzzy=True by defaul.
-            thresh (float, optional): distance threshold from query vector to returned results.
-                                        Defaults to 5000, set large just in case, 
+            :thresh (float, optional): distance threshold from query vector to returned results.
+                                        Defaults to 5000, set large just in case,
                                         but could be as low as 10.
-            fuzzy (bool, optional): if True, uses embedding + annoy index for recall, 
-                                        otherwise does string matching over given `cols` 
+            :fuzzy (bool, optional): if True, uses embedding + annoy index for recall,
+                                        otherwise does string matching over given `cols`
                                         Defaults to True.
-            top_n (int, optional): how many results to return. Defaults to 100.
+            :top_n (int, optional): how many results to return. Defaults to 100.
 
         Returns:
-            pd.DataFrame, vector_encoding_of_query: 
-                * rank ordered dataframe of results matching query
-                * vector encoding of query via given transformer/ngrams model if fuzzy=True
-                    else None
+            **pd.DataFrame, vector_encoding_of_query:**
+            rank ordered dataframe of results matching query
+
+            vector encoding of query via given transformer/ngrams model if fuzzy=True else None
         """
         if not fuzzy:
             if cols is None:
-                logger.error(f'Columns to search for `{query}` \
-                             need to be given when fuzzy=False, found {cols}')
-                
+                logger.error(
+                    f"Columns to search for `{query}` \
+                             need to be given when fuzzy=False, found {cols}"
+                )
+
             logger.info(f"-- Word Match: [[ {query} ]]")
             return (
-                pd.concat([search_to_df(query, col, self._nodes, as_string=True) for col in cols]),
-                None
+                pd.concat(
+                    [
+                        search_to_df(query, col, self._nodes, as_string=True)
+                        for col in cols
+                    ]
+                ),
+                None,
             )
         else:
             logger.info(f"-- Search: [[ {query} ]]")
@@ -204,19 +189,19 @@ def search_graph(
         broader: bool = False,
         inplace: bool = False,
     ):
-        """Input a natural language query and return a graph of results. 
+        """Input a natural language query and return a graph of results.
             See help(g.search) for more information
 
         Args:
-            query (str): query input eg "coding best practices"
-            scale (float, optional): edge weigh threshold,  Defaults to 0.5.
-            top_n (int, optional): how many results to return. Defaults to 100.
-            thresh (float, optional): distance threshold from query vector to returned results.
-                                        Defaults to 5000, set large just in case, 
+            :query (str): query input eg "coding best practices"
+            :scale (float, optional): edge weigh threshold,  Defaults to 0.5.
+            :top_n (int, optional): how many results to return. Defaults to 100.
+            :thresh (float, optional): distance threshold from query vector to returned results.
+                                        Defaults to 5000, set large just in case,
                                         but could be as low as 10.
-            broader (bool, optional): if True, will retrieve entities connected via an edge
+            :broader (bool, optional): if True, will retrieve entities connected via an edge
                 that were not necessarily bubbled up in the results_dataframe. Defaults to False.
-            inplace (bool, optional): whether to return new instance (default) or mutate self.
+            :inplace (bool, optional): whether to return new instance (default) or mutate self.
                                         Defaults to False.
 
         Returns:
@@ -226,9 +211,11 @@ def search_graph(
             res = self
         else:
             res = self.bind()
-                    
+
         edf = edges = res._edges
+        # print('shape of edges', edf.shape)
         rdf = df = res._nodes
+        # print('shape of nodes', rdf.shape)
         node = res._node
         indices = rdf[node]
         src = res._source
@@ -240,43 +227,55 @@ def search_graph(
                 indices = rdf[node]
                 # now get edges from indices
                 if broader:  # this will make a broader graph, finding NN in src OR dst
-                    edges = edf[
-                        (edf[src].isin(indices)) | (edf[dst].isin(indices))
-                    ]
-                else:  # finds only edges between results from query, if they exist, 
+                    edges = edf[(edf[src].isin(indices)) | (edf[dst].isin(indices))]
+                else:  # finds only edges between results from query, if they exist,
                     # default smaller graph
-                    edges = edf[
-                        (edf[src].isin(indices)) & (edf[dst].isin(indices))
-                    ]
+                    edges = edf[(edf[src].isin(indices)) & (edf[dst].isin(indices))]
             else:
-                logger.warn('**No results found due to empty DataFrame, returning original graph')
+                logger.warn(
+                    "**No results found due to empty DataFrame, returning original graph"
+                )
                 return res
-            
+
         try:  # for umap'd edges
             edges = edges.query(f"{WEIGHT} > {scale}")
         except:  # for explicit edges
             pass
-        
+
         found_indices = pd.concat([edges[src], edges[dst], indices], axis=0).unique()
+        emb = None
         try:
             tdf = rdf.iloc[found_indices]
-        except:  # for explicit relabeled nodes
+            feats = res._node_features.iloc[found_indices]  # type: ignore
+            if res._umap is not None:
+                emb = res._node_embedding.iloc[found_indices]  # type: ignore
+        except Exception as e:  # for explicit relabeled nodes
+            logger.exception(e)
             tdf = rdf[df[node].isin(found_indices)]
+            feats = res._node_features.loc[tdf.index]  # type: ignore
+            if res._umap is not None:
+                emb = res._node_embedding[df[node].isin(found_indices)]  # type: ignore
         logger.info(f" - Returning edge dataframe of size {edges.shape[0]}")
         # get all the unique nodes
-        logger.info(f" - Returning {tdf.shape[0]} unique nodes given scale {scale} and thresh {thresh}")
-        
+        logger.info(
+            f" - Returning {tdf.shape[0]} unique nodes given scale {scale} and thresh {thresh}"
+        )
+
         g = res.edges(edges, src, dst).nodes(tdf, node)
-        
+        # add them back so they sync with .dbscan etc calls
+        g._node_features = feats
+        g._node_embedding = emb
+
         if g._name is not None:
-            name = f'{g._name}-query:{query}'
+            name = f"{g._name}-query:{query}"
         else:
-            name = f'query:{query}'
+            name = f"query:{query}"
         g = g.name(name)  # type: ignore
         return g
 
     def save_search_instance(self, savepath):
         from joblib import dump  # type: ignore   # need to make this onnx or similar
+
         self.build_index()
         search = self.search_index
         del self.search_index  # can't pickle Annoy
@@ -287,6 +286,7 @@ def save_search_instance(self, savepath):
     @classmethod
     def load_search_instance(self, savepath):
         from joblib import load  # type: ignore   # need to make this onnx or similar
+
         cls = load(savepath)
         cls.build_index()
         return cls
diff --git a/graphistry/umap_utils.py b/graphistry/umap_utils.py
index 5cd092f29d..f9a133ef26 100644
--- a/graphistry/umap_utils.py
+++ b/graphistry/umap_utils.py
@@ -1,17 +1,21 @@
 import copy
 from time import time
 from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+from inspect import getmodule
 
 import pandas as pd
 
 from . import constants as config
+from .constants import CUML, UMAP_LEARN
 from .feature_utils import (FeatureMixin, Literal, XSymbolic, YSymbolic,
                             prune_weighted_edges_df_and_relabel_nodes,
                             resolve_feature_engine)
 from .PlotterBase import Plottable, WeakValueDictionary
-from .util import check_set_memoize, setup_logger
+from .util import check_set_memoize
 
-logger = setup_logger(name=__name__, verbose=config.VERBOSE)
+import logging
+
+logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     MIXIN_BASE = FeatureMixin
@@ -39,22 +43,34 @@ def lazy_cuml_import_has_dependancy():
         import warnings
 
         warnings.filterwarnings("ignore")
-        import cuml  # type: ignore
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore")
+            import cuml  # type: ignore
 
         return True, "ok", cuml
     except ModuleNotFoundError as e:
         return False, e, None
 
+def lazy_cudf_import_has_dependancy():
+    try:
+        import warnings
+
+        warnings.filterwarnings("ignore")
+        import cudf  # type: ignore
+
+        return True, "ok", cudf
+    except ModuleNotFoundError as e:
+        return False, e, None
 
 def assert_imported():
-    has_dependancy_, import_exn, umap_learn = lazy_umap_import_has_dependancy()
+    has_dependancy_, import_exn, _ = lazy_umap_import_has_dependancy()
     if not has_dependancy_:
         logger.error("UMAP not found, trying running " "`pip install graphistry[ai]`")
         raise import_exn
 
 
 def assert_imported_cuml():
-    has_cuml_dependancy_, import_cuml_exn, cuml = lazy_cuml_import_has_dependancy()
+    has_cuml_dependancy_, import_cuml_exn, _ = lazy_cuml_import_has_dependancy()
     if not has_cuml_dependancy_:
         logger.warning("cuML not found, trying running " "`pip install cuml`")
         raise import_cuml_exn
@@ -73,22 +89,22 @@ def is_legacy_cuml():
         return False
 
 
-UMAPEngineConcrete = Literal["cuml", "umap_learn"]
+UMAPEngineConcrete = Literal['cuml', 'umap_learn']
 UMAPEngine = Literal[UMAPEngineConcrete, "auto"]
 
 
 def resolve_umap_engine(
     engine: UMAPEngine,
 ) -> UMAPEngineConcrete:  # noqa
-    if engine in ["cuml", "umap_learn"]:
+    if engine in [CUML, UMAP_LEARN]:
         return engine  # type: ignore
     if engine in ["auto"]:
-        has_cuml_dependancy_, _, cuml = lazy_cuml_import_has_dependancy()
+        has_cuml_dependancy_, _, _ = lazy_cuml_import_has_dependancy()
         if has_cuml_dependancy_:
-            return "cuml"
+            return 'cuml'
         has_umap_dependancy_, _, _ = lazy_umap_import_has_dependancy()
         if has_umap_dependancy_:
-            return "umap_learn"
+            return 'umap_learn'
 
     raise ValueError(  # noqa
         f'engine expected to be "auto", '
@@ -97,34 +113,34 @@ def resolve_umap_engine(
     )
 
 
-###############################################################################
+def make_safe_gpu_dataframes(X, y, engine):
 
+    def safe_cudf(X, y):
+        # remove duplicate columns
+        if len(X.columns) != len(set(X.columns)):
+            X = X.loc[:, ~X.columns.duplicated()]
+        try:
+            y = y.loc[:, ~y.columns.duplicated()]
+        except:
+            pass
+        new_kwargs = {}
+        kwargs = {'X': X, 'y': y}
+        for key, value in kwargs.items():
+            if isinstance(value, cudf.DataFrame) and engine in ["pandas", "umap_learn", "dirty_cat"]:
+                new_kwargs[key] = value.to_pandas()
+            elif isinstance(value, pd.DataFrame) and engine in ["cuml", "cu_cat"]:
+                new_kwargs[key] = cudf.from_pandas(value)
+            else:
+                new_kwargs[key] = value
+        return new_kwargs['X'], new_kwargs['y']
 
-umap_kwargs_probs = {
-    "n_components": 2,
-    "metric": "hellinger",  # info metric, can't use on
-    # textual encodings since they contain negative values...
-    # unless scaling min max etc
-    "n_neighbors": 15,
-    "min_dist": 0.3,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
-
-umap_kwargs_euclidean = {
-    "n_components": 2,
-    "metric": "euclidean",
-    "n_neighbors": 12,
-    "min_dist": 0.1,
-    "verbose": True,
-    "spread": 0.5,
-    "local_connectivity": 1,
-    "repulsion_strength": 1,
-    "negative_sample_rate": 5,
-}
+    has_cudf_dependancy_, _, cudf = lazy_cudf_import_has_dependancy()
+    if has_cudf_dependancy_:
+        return safe_cudf(X, y)
+    else:
+        return X, y
+
+###############################################################################
 
 # #############################################################################
 #
@@ -157,43 +173,47 @@ def umap_graph_to_weighted_edges(umap_graph, engine, is_legacy, cfg=config):
 class UMAPMixin(MIXIN_BASE):
     """
     UMAP Mixin for automagic UMAPing
-
     """
     # FIXME where is this used? 
     _umap_memoize: WeakValueDictionary = WeakValueDictionary()
 
     def __init__(self, *args, **kwargs):
-        self.umap_initialized = False
+        #self._umap_initialized = False
+        #self.engine = self.engine if hasattr(self, "engine") else None
+        pass
+
 
     def umap_lazy_init(
         self,
+        res,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
-        spread=0.5,
-        local_connectivity=1,
-        repulsion_strength=1,
-        negative_sample_rate=5,
+        spread: float = 0.5,
+        local_connectivity: int = 1,
+        repulsion_strength: float = 1,
+        negative_sample_rate: int = 5,
         n_components: int = 2,
         metric: str = "euclidean",
         engine: UMAPEngine = "auto",
         suffix: str = "",
+        verbose: bool = False,
     ):
+        from graphistry.features import ModelDict
+
         engine_resolved = resolve_umap_engine(engine)
         # FIXME remove as set_new_kwargs will always replace?
-        if engine_resolved == "umap_learn":
+        if engine_resolved == UMAP_LEARN:
             _, _, umap_engine = lazy_umap_import_has_dependancy()
-        elif engine_resolved == "cuml":
+        elif engine_resolved == CUML:
             _, _, umap_engine = lazy_cuml_import_has_dependancy()
         else:
             raise ValueError(
                 "No umap engine, ensure 'auto', 'umap_learn', or 'cuml', and the library is installed"
             )
-
-        if not self.umap_initialized:
-            umap_kwargs = dict(
-                {
+        umap_kwargs = ModelDict("UMAP Parameters",
+                **{
                     "n_components": n_components,
-                    **({"metric": metric} if engine_resolved == "umap_learn" else {}),
+                    **({"metric": metric} if engine_resolved == UMAP_LEARN else {}),  # type: ignore
                     "n_neighbors": n_neighbors,
                     "min_dist": min_dist,
                     "spread": spread,
@@ -202,20 +222,31 @@ def umap_lazy_init(
                     "negative_sample_rate": negative_sample_rate,
                 }
             )
+        
+        if getattr(res, '_umap_params', None) == umap_kwargs:
+            print('Same umap params as last time, skipping new init') if verbose else None
+            return res
+        
+        print('lazy init') if verbose else None
+        print(umap_kwargs) if verbose else None
+        # set new umap kwargs
+        res._umap_params = umap_kwargs  
+
+        res._n_components = n_components
+        res._metric = metric
+        res._n_neighbors = n_neighbors
+        res._min_dist = min_dist
+        res._spread = spread
+        res._local_connectivity = local_connectivity
+        res._repulsion_strength = repulsion_strength
+        res._negative_sample_rate = negative_sample_rate
+        res._umap = umap_engine.UMAP(**umap_kwargs)
+        res.engine = engine_resolved
+        res._suffix = suffix
+                                                            
+        return res
 
-            self.n_components = n_components
-            self.metric = metric
-            self.n_neighbors = n_neighbors
-            self.min_dist = min_dist
-            self.spread = spread
-            self.local_connectivity = local_connectivity
-            self.repulsion_strength = repulsion_strength
-            self.negative_sample_rate = negative_sample_rate
-            self._umap = umap_engine.UMAP(**umap_kwargs)
-            self.umap_initialized = True
-            self.engine = engine_resolved
-            self.suffix = suffix
-
+    #@safe_gpu_dataframes
     def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
         if y is None:
             return None
@@ -229,8 +260,16 @@ def _check_target_is_one_dimensional(self, y: Union[pd.DataFrame, None]):
                 "as it is not one dimensional"
             )
             return None
+    
+    def _get_embedding(self, kind='nodes'):
+        if kind == 'nodes':
+            return self._node_embedding
+        elif kind == 'edges':
+            return self._edge_embedding
+        else:
+            raise ValueError('kind must be one of `nodes` or `edges`')
 
-    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+    def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
         t = time()
@@ -238,21 +277,20 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info("-" * 90)
         logger.info(f"Starting UMAP-ing data of shape {X.shape}")
 
-        if self.engine == "cuml" and is_legacy_cuml():
+        if self.engine == CUML and is_legacy_cuml():  # type: ignore
             from cuml.neighbors import NearestNeighbors
 
-            knn = NearestNeighbors(n_neighbors=self.n_neighbors)
+            knn = NearestNeighbors(n_neighbors=self._n_neighbors)  # type: ignore
             cc = self._umap.fit(X, y, knn_graph=knn)
             knn.fit(cc.embedding_)
             self._umap.graph_ = knn.kneighbors_graph(cc.embedding_)
-            self._weighted_adjacency = self._umap.graph_
-
         else:
             self._umap.fit(X, y)
-            self._weighted_adjacency = self._umap.graph_
+            
+        self._weighted_adjacency = self._umap.graph_
         # if changing, also update fresh_res
         self._weighted_edges_df = umap_graph_to_weighted_edges(
-            self._umap.graph_, self.engine, is_legacy_cuml()
+            self._umap.graph_, self.engine, is_legacy_cuml()  # type: ignore
         )
 
         mins = (time() - t) / 60
@@ -260,35 +298,75 @@ def umap_fit(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
         logger.info(f" - or {X.shape[0]/mins:.2f} rows per minute")
         return self
 
-    def umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None):
+
+    def _umap_fit_transform(self, X: pd.DataFrame, y: Union[pd.DataFrame, None] = None, verbose=False):
         if self._umap is None:
             raise ValueError("UMAP is not initialized")
-        self.umap_fit(X, y)
+        self.umap_fit(X, y, verbose=verbose)
         emb = self._umap.transform(X)
         emb = self._bundle_embedding(emb, index=X.index)
         return emb
 
-    def transform_umap(  # noqa: E303
-        self, df: pd.DataFrame, ydf: pd.DataFrame, kind: str = "nodes"
-    ) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
-        try:
-            logger.debug(f"Going into Transform umap {df.shape}, {ydf.shape}")
-        except:
-            pass
-        x, y = self.transform(df, ydf, kind=kind)
-        emb = self._umap.transform(x)  # type: ignore
+
+    def transform_umap(self, df: pd.DataFrame, 
+                    y: Optional[pd.DataFrame] = None, 
+                    kind: str = 'nodes', 
+                    min_dist: Union[str, float, int] = 'auto', 
+                    n_neighbors: int = 7,
+                    merge_policy: bool = False,
+                    sample: Optional[int] = None, 
+                    return_graph: bool = True,
+                    fit_umap_embedding: bool = True,
+                    verbose: bool = False
+    ) -> Union[Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame], Plottable]:
+        """Transforms data into UMAP embedding
+        
+        Args:
+            :df: Dataframe to transform
+            :y: Target column
+            :kind: One of `nodes` or `edges`
+            :min_dist: Epsilon for including neighbors in infer_graph
+            :n_neighbors: Number of neighbors to use for contextualization
+            :merge_policy: if True, use previous graph, adding new batch to existing graph's neighbors
+                useful to contextualize new data against existing graph. If False, `sample` is irrelevant.
+
+            sample: Sample number of existing graph's neighbors to use for contextualization -- helps make denser graphs
+            return_graph: Whether to return a graph or just the embeddings
+            fit_umap_embedding: Whether to infer graph from the UMAP embedding on the new data, default True
+            verbose: Whether to print information about the graph inference
+        """
+        df, y = make_safe_gpu_dataframes(df, y, 'pandas')
+        X, y_ = self.transform(df, y, kind=kind, return_graph=False, verbose=verbose)
+        X, y_ = make_safe_gpu_dataframes(X, y_, self.engine)  # type: ignore
+        emb = self._umap.transform(X)  # type: ignore
         emb = self._bundle_embedding(emb, index=df.index)
-        return emb, x, y
+        if return_graph and kind not in ["edges"]:
+            emb, _ = make_safe_gpu_dataframes(emb, None, 'pandas')  # for now so we don't have to touch infer_edges, force to pandas
+            X, y_ = make_safe_gpu_dataframes(X, y_, 'pandas')
+            g = self._infer_edges(emb, X, y_, df, 
+                                  infer_on_umap_embedding=fit_umap_embedding, merge_policy=merge_policy,
+                                  eps=min_dist, sample=sample, n_neighbors=n_neighbors,
+                                  verbose=verbose) 
+            return g
+        return emb, X, y_
 
     def _bundle_embedding(self, emb, index):
         # Converts Embedding into dataframe and takes care if emb.dim > 2
-        if emb.shape[1] == 2:
+        if emb.shape[1] == 2 and 'cudf.core.dataframe' not in str(getmodule(emb)) and not hasattr(emb, 'device'):
             emb = pd.DataFrame(emb, columns=[config.X, config.Y], index=index)
+        elif emb.shape[1] == 2 and 'cudf.core.dataframe' in str(getmodule(emb)):
+            emb.rename(columns={0: config.X, 1: config.Y}, inplace=True)
+        elif emb.shape[1] == 2 and hasattr(emb, 'device'):
+            import cudf
+            emb = cudf.DataFrame(emb, columns=[config.X, config.Y], index=index)
         else:
             columns = [config.X, config.Y] + [
-                f"umap_{k}" for k in range(2, emb.shape[1] - 2)
+                f"umap_{k}" for k in range(2, emb.shape[1])
             ]
-            emb = pd.DataFrame(emb, columns=columns, index=index)
+            if 'cudf.core.dataframe' not in str(getmodule(emb)):
+                emb = pd.DataFrame(emb, columns=columns, index=index)
+            elif 'cudf.core.dataframe' in str(getmodule(emb)):
+                emb.columns = columns
         return emb
 
     def _process_umap(
@@ -299,31 +377,41 @@ def _process_umap(
         kind,
         memoize: bool,
         featurize_kwargs,
+        verbose = False,
         **umap_kwargs,
     ):
         """
         Returns res mutated with new _xy
         """
-        res._umap = self._umap
+        #from .features import ModelDict
+        umap_kwargs_pure = umap_kwargs.copy()
 
         logger.debug("process_umap before kwargs: %s", umap_kwargs)
         umap_kwargs.update({"kind": kind, "X": X_, "y": y_})
-        umap_kwargs = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
-        logger.debug("process_umap after kwargs: %s", umap_kwargs)
+        umap_kwargs_reuse = {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+        logger.debug("process_umap after kwargs: %s", umap_kwargs_reuse)
 
         old_res = reuse_umap(
-            res, memoize, {**umap_kwargs, "featurize_kwargs": featurize_kwargs or {}}
+            res, memoize, {**umap_kwargs_reuse, "featurize_kwargs": featurize_kwargs or {}}
         )
         if old_res:
+            print(" --- [[ RE-USING UMAP ]]") if verbose else None
             logger.info(" --- [[ RE-USING UMAP ]]")
+            print('umap previous n_components', umap_kwargs['n_components']) if verbose else None
             fresh_res = copy.copy(res)
             for attr in ["_xy", "_weighted_edges_df", "_weighted_adjacency"]:
                 setattr(fresh_res, attr, getattr(old_res, attr))
             # have to set _raw_data attribute on umap?
             fresh_res._umap = old_res._umap  # this saves the day!
+            #fresh_res._umap_initialized = True
+            fresh_res._umap_params = umap_kwargs_pure
             return fresh_res
 
-        emb = res.umap_fit_transform(X_, y_)
+        print('-' * 60) if verbose else None
+        print('** Fitting UMAP') if verbose else None
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs_pure)
+        
+        emb = res._umap_fit_transform(X_, y_, verbose=verbose)
         res._xy = emb
         return res
 
@@ -366,9 +454,9 @@ def _set_features(  # noqa: E303
 
     def umap(
         self,
-        kind: str = "nodes",
         X: XSymbolic = None,
         y: YSymbolic = None,
+        kind: str = "nodes",
         scale: float = 1.0,
         n_neighbors: int = 12,
         min_dist: float = 0.1,
@@ -382,58 +470,72 @@ def umap(
         play: Optional[int] = 0,
         encode_position: bool = True,
         encode_weight: bool = True,
+        dbscan: bool = False,
         engine: UMAPEngine = "auto",
-        inplace: bool = False,
         feature_engine: str = "auto",
+        inplace: bool = False,
         memoize: bool = True,
+        verbose: bool = False,
         **featurize_kwargs,
     ):
-        """
-            UMAP the featurized node or edges data,
-            or pass in your own X, y (optional).
-
-        :param kind: `nodes` or `edges` or None.
-                If None, expects explicit X, y (optional) matrices,
-                and will Not associate them to nodes or edges.
-                If X, y (optional) is given, with kind = [nodes, edges],
-                it will associate new matrices to nodes or edges attributes.
-        :param feature_engine: How to encode data
-                ("none", "auto", "pandas", "dirty_cat", "torch")
-        :param encode_weight: if True, will set new edges_df from
-                implicit UMAP, default True.
-        :param encode_position: whether to set default plotting bindings
-                -- positions x,y from umap for .plot()
-        :param X: either an ndarray of features, or column names to featurize
-        :param y: either an ndarray of targets, or column names to featurize
-                targets
-        :param scale: multiplicative scale for pruning weighted edge DataFrame
-                gotten from UMAP, between [0, ..) with high end meaning keep
-                all edges
-        :param n_neighbors: UMAP number of nearest neighbors to include for
-                UMAP connectivity, lower makes more compact layouts. Minimum 2
-        :param min_dist: UMAP float between 0 and 1, lower makes more compact
-                layouts.
-        :param spread: UMAP spread of values for relaxation
-        :param local_connectivity: UMAP connectivity parameter
-        :param repulsion_strength: UMAP repulsion strength
-        :param negative_sample_rate: UMAP negative sampling rate
-        :param n_components: number of components in the UMAP projection,
-                default 2
-        :param metric: UMAP metric, default 'euclidean'.
-                see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
-                en/latest/parameters.html] documentation for more.
-        :param suffix: optional suffix to add to x, y attributes of umap.
-        :param play: Graphistry play parameter, default 0, how much to evolve
-                the network during clustering
-        :param engine: selects which engine to use to calculate UMAP:
-                NotImplemented yet, default UMAP-LEARN
-        :param memoize: whether to memoize the results of this method,
-                default True.
+        """UMAP the featurized nodes or edges data, or pass in your own X, y (optional) dataframes of values
+        
+        Example
+
+        >>> import graphistry   
+        >>> g = graphistry.nodes(pd.DataFrame({'node': [0,1,2], 'data': [1,2,3], 'meta': ['a', 'b', 'c']}))
+        >>> g2 = g.umap(n_components=3, spread=1.0, min_dist=0.1, n_neighbors=12, negative_sample_rate=5, local_connectivity=1, repulsion_strength=1.0, metric='euclidean', suffix='', play=0, encode_position=True, encode_weight=True, dbscan=False, engine='auto', feature_engine='auto', inplace=False, memoize=True, verbose=False)
+        >>> g2.plot()
+        
+        Parameters
+ 
+            :X: either a dataframe ndarray of features, or column names to featurize
+            :y: either an dataframe ndarray of targets, or column names to featurize
+                    targets
+            :kind: `nodes` or `edges` or None.
+                    If None, expects explicit X, y (optional) matrices,
+                    and will Not associate them to nodes or edges.
+                    If X, y (optional) is given, with kind = [nodes, edges],
+                    it will associate new matrices to nodes or edges attributes.
+            :scale: multiplicative scale for pruning weighted edge DataFrame
+                    gotten from UMAP, between [0, ..) with high end meaning keep
+                    all edges
+            :n_neighbors: UMAP number of nearest neighbors to include for
+                    UMAP connectivity, lower makes more compact layouts. Minimum 2
+            :min_dist: UMAP float between 0 and 1, lower makes more compact
+                    layouts.
+            :spread: UMAP spread of values for relaxation
+            :local_connectivity: UMAP connectivity parameter
+            :repulsion_strength: UMAP repulsion strength
+            :negative_sample_rate: UMAP negative sampling rate
+            :n_components: number of components in the UMAP projection,
+                    default 2
+            :metric: UMAP metric, default 'euclidean'.
+                    see (UMAP-LEARN)[https://umap-learn.readthedocs.io/
+                    en/latest/parameters.html] documentation for more.
+            :suffix: optional suffix to add to x, y attributes of umap.
+            :play: Graphistry play parameter, default 0, how much to evolve
+                    the network during clustering. 0 preserves the original UMAP layout.
+            :encode_weight: if True, will set new edges_df from
+                    implicit UMAP, default True.
+            :encode_position: whether to set default plotting bindings
+                    -- positions x,y from umap for .plot(), default True
+            :dbscan: whether to run DBSCAN on the UMAP embedding, default False.
+            :engine: selects which engine to use to calculate UMAP:
+                    default "auto" will use cuML if available, otherwise UMAP-LEARN.
+            :feature_engine: How to encode data
+                    ("none", "auto", "pandas", "dirty_cat", "torch")
+            :inplace: bool = False, whether to modify the current object, default False.
+                    when False, returns a new object, useful for chaining in a functional paradigm.
+            :memoize: whether to memoize the results of this method,
+                    default True.
+            :verbose: whether to print out extra information, default False.
+
         :return: self, with attributes set with new data
         """
-        if engine == "umap_learn":
+        if engine == UMAP_LEARN:
             assert_imported()
-        elif engine == "cuml":
+        elif engine == CUML:
             assert_imported_cuml()
 
         umap_kwargs = dict(
@@ -446,16 +548,32 @@ def umap(
             repulsion_strength=repulsion_strength,
             negative_sample_rate=negative_sample_rate,
             engine=engine,
+            suffix=suffix,
         )
         logger.debug("umap_kwargs: %s", umap_kwargs)
 
+        # temporary until we have full cudf support in feature_utils.py
+        has_cudf, _, cudf = lazy_cudf_import_has_dependancy()
+
+        if has_cudf:
+            flag_nodes_cudf = isinstance(self._nodes, cudf.DataFrame)
+            flag_edges_cudf = isinstance(self._edges, cudf.DataFrame)
+
+            if flag_nodes_cudf or flag_edges_cudf:
+                res = self
+                if flag_nodes_cudf:
+                    res._nodes = res._nodes.to_pandas()
+                if flag_edges_cudf:
+                    res._edges = res._edges.to_pandas()
+                res = res.umap(X=self._nodes, y=self._edges, **umap_kwargs)  # type: ignore
+                return res
+
         if inplace:
             res = self
         else:
             res = self.bind()
 
-        res.umap_lazy_init(engine=engine, suffix=suffix)
-        # res.suffix = suffix
+        res = res.umap_lazy_init(res, verbose=verbose, **umap_kwargs)  # type: ignore
 
         logger.debug("umap input X :: %s", X)
         logger.debug("umap input y :: %s", y)
@@ -463,12 +581,10 @@ def umap(
         featurize_kwargs = self._set_features(
             res, X, y, kind, feature_engine, {**featurize_kwargs, "memoize": memoize}
         )
-        # umap_kwargs = {**umap_kwargs,
-        # 'featurize_kwargs': featurize_kwargs or {}}
 
         if kind == "nodes":
+            index = res._nodes.index
             if res._node is None:
-
                 logger.debug("-Writing new node name")
                 res = res.nodes(  # type: ignore
                     res._nodes.reset_index(drop=True)
@@ -476,9 +592,9 @@ def umap(
                     .rename(columns={"index": config.IMPLICIT_NODE_ID}),
                     config.IMPLICIT_NODE_ID,
                 )
+                res._nodes.index = index
 
             nodes = res._nodes[res._node].values
-            index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
 
             logger.debug("propagating with featurize_kwargs: %s", featurize_kwargs)
             (
@@ -491,16 +607,23 @@ def umap(
 
             logger.debug("umap X_: %s", X_)
             logger.debug("umap y_: %s", y_)
+            logger.debug("data is type :: %s", (type(X_)))
+            if isinstance(X_, pd.DataFrame):
+                index_to_nodes_dict = dict(zip(range(len(nodes)), nodes))
+            elif 'cudf.core.dataframe' in str(getmodule(X_)):
+                index_to_nodes_dict = nodes  # {}?
+
+            # add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
 
             res = res._process_umap(
-                res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
+                res, X_, y_, kind, memoize, featurize_kwargs, verbose, **umap_kwargs
             )
 
             res._weighted_adjacency_nodes = res._weighted_adjacency
             if res._xy is None:
                 raise RuntimeError("This should not happen")
             res._node_embedding = res._xy
-            # TODO add edge filter so graph doesn't have double edges
             # TODO user-guidable edge merge policies like upsert?
             res._weighted_edges_df_from_nodes = (
                 prune_weighted_edges_df_and_relabel_nodes(
@@ -520,6 +643,9 @@ def umap(
                 **featurize_kwargs
             )
 
+            # add the safe coercion here 
+            X_, y_ = make_safe_gpu_dataframes(X_, y_, res.engine)  # type: ignore
+
             res = res._process_umap(
                 res, X_, y_, kind, memoize, featurize_kwargs, **umap_kwargs
             )
@@ -539,9 +665,9 @@ def umap(
                 "kind should be one of `nodes` or `edges` unless"
                 "you are passing explicit matrices"
             )
-            if X is not None and isinstance(X, pd.DataFrame):
+            if X is not None and isinstance(X, pd.DataFrame) or '':
                 logger.info("New Matrix `X` passed in for UMAP-ing")
-                xy = res.umap_fit_transform(X, y)
+                xy = res._umap_fit_transform(X, y, verbose=verbose)
                 res._xy = xy
                 res._weighted_edges_df = prune_weighted_edges_df_and_relabel_nodes(
                     res._weighted_edges_df, scale=scale
@@ -553,7 +679,7 @@ def umap(
             else:
                 logger.error(
                     "If `kind` is `None`, `X` and optionally `y`"
-                    "must be given and be of type pd.DataFrame"
+                    "must be given."
                 )
         else:
             raise ValueError(
@@ -563,9 +689,12 @@ def umap(
             res, kind, encode_position, encode_weight, play
         )  # noqa: E501
 
-        if res.engine == "cuml" and is_legacy_cuml():
+        if res.engine == CUML and is_legacy_cuml():  # type: ignore
             res = res.prune_self_edges()
 
+        if dbscan:
+            res = res.dbscan(min_dist=min_dist, kind=kind, fit_umap_embedding=True, verbose=verbose)  # type: ignore
+
         if not inplace:
             return res
 
@@ -580,23 +709,26 @@ def _bind_xy_from_umap(
         df = res._nodes if kind == "nodes" else res._edges
 
         df = df.copy(deep=False)
-        x_name = config.X + res.suffix
-        y_name = config.Y + res.suffix
+        x_name = config.X + res._suffix
+        y_name = config.Y + res._suffix
         if kind == "nodes":
             emb = res._node_embedding
         else:
             emb = res._edge_embedding
+            
+        if type(df) == type(emb):
+            df[x_name] = emb.values.T[0]
+            df[y_name] = emb.values.T[1]
+        elif isinstance(df, pd.DataFrame) and 'cudf.core.dataframe' in str(getmodule(emb)):
+            df[x_name] = emb.to_numpy().T[0]
+            df[y_name] = emb.to_numpy().T[1]
 
-        df[x_name] = emb.values.T[0]  # if embedding is greater
-        # than two dimensions will only take first two coordinates
-        df[y_name] = emb.values.T[1]
-        #
         res = res.nodes(df) if kind == "nodes" else res.edges(df)
 
         if encode_weight and kind == "nodes":
             # adds the implicit edge dataframe and binds it to
             # graphistry instance
-            w_name = config.WEIGHT + res.suffix
+            w_name = config.WEIGHT + res._suffix
             umap_edges_df = res._weighted_edges_df_from_nodes.copy(deep=False)
             umap_edges_df = umap_edges_df.rename(columns={config.WEIGHT: w_name})
             res = res.edges(umap_edges_df, config.SRC, config.DST)
@@ -625,6 +757,7 @@ def filter_weighted_edges(
     ):
         """
         Filter edges based on _weighted_edges_df (ex: from .umap())
+
         """
         if inplace:
             res = self
diff --git a/graphistry/util.py b/graphistry/util.py
index ce7249c934..025e8853b3 100644
--- a/graphistry/util.py
+++ b/graphistry/util.py
@@ -10,28 +10,31 @@
 import warnings
 from functools import lru_cache
 from typing import Any
+from collections import UserDict
 
 from .constants import VERBOSE, CACHE_COERCION_SIZE, TRACE
 
 
 # #####################################
 
+
 def global_logger():
     logger = logging.getLogger()
     return logger
 
+
 def setup_logger(name, verbose=VERBOSE, fullpath=TRACE):
-    #if fullpath:
+    # if fullpath:
     #    FORMAT = "[%(filename)s:%(lineno)s - %(funcName)20s() ]\n   %(message)s\n"
-    #else:
+    # else:
     #    FORMAT = " %(message)s\n"
-    #logging.basicConfig(format=FORMAT)
-    #logger = logging.getLogger()#f'graphistry.{name}')
-    #if verbose is None:
+    # logging.basicConfig(format=FORMAT)
+    # logger = logging.getLogger()#f'graphistry.{name}')
+    # if verbose is None:
     #    logger.setLevel(logging.ERROR)
-    #else:
+    # else:
     #    logger.setLevel(logging.INFO if verbose else logging.DEBUG)
-    #return logger
+    # return logger
     return global_logger()
 
 
@@ -39,6 +42,8 @@ def setup_logger(name, verbose=VERBOSE, fullpath=TRACE):
 # Caching utils
 
 _cache_coercion_val = None
+
+
 @lru_cache(maxsize=CACHE_COERCION_SIZE)
 def cache_coercion_helper(k):
     return _cache_coercion_val
@@ -46,8 +51,8 @@ def cache_coercion_helper(k):
 
 def cache_coercion(k, v):
     """
-        Holds references to last 100 used coercions
-        Use with weak key/value dictionaries for actual lookups
+    Holds references to last 100 used coercions
+    Use with weak key/value dictionaries for actual lookups
     """
     global _cache_coercion_val
     _cache_coercion_val = v
@@ -65,30 +70,37 @@ def __init__(self, v):
 def hash_pdf(df: pd.DataFrame) -> str:
     # can be 20% faster via to_parquet (see lmeyerov issue in pandas gh), but unclear if always available
     return (
-        hashlib.sha256(putil.hash_pandas_object(df, index=True).to_numpy().tobytes()).hexdigest()
-        + hashlib.sha256(str(df.columns).encode('utf-8')).hexdigest()  # noqa: W503
+        hashlib.sha256(
+            putil.hash_pandas_object(df, index=True).to_numpy().tobytes()
+        ).hexdigest()
+        + hashlib.sha256(str(df.columns).encode("utf-8")).hexdigest()  # noqa: W503
     )
 
 
 def hash_memoize_helper(v: Any) -> str:
 
     if isinstance(v, dict):
-        rolling = '{'
+        rolling = "{"
+        for k2, v2 in v.items():
+            rolling += f"{k2}:{hash_memoize_helper(v2)},"
+        rolling += "}"
+    elif isinstance(v, ModelDict):
+        rolling = "{"
         for k2, v2 in v.items():
-            rolling += f'{k2}:{hash_memoize_helper(v2)},'
-        rolling += '}'
+            rolling += f"{k2}:{hash_memoize_helper(v2)},"
+        rolling += "}"
     elif isinstance(v, list):
-        rolling = '['
+        rolling = "["
         for i in v:
-            rolling += f'{hash_memoize_helper(i)},'
-        rolling += ']'
+            rolling += f"{hash_memoize_helper(i)},"
+        rolling += "]"
     elif isinstance(v, tuple):
-        rolling = '('
+        rolling = "("
         for i in v:
-            rolling += f'{hash_memoize_helper(i)},'
-        rolling += ')'
+            rolling += f"{hash_memoize_helper(i)},"
+        rolling += ")"
     elif isinstance(v, bool):
-        rolling = 'T' if v else 'F'
+        rolling = "T" if v else "F"
     elif isinstance(v, int):
         rolling = str(v)
     elif isinstance(v, float):
@@ -96,49 +108,54 @@ def hash_memoize_helper(v: Any) -> str:
     elif isinstance(v, str):
         rolling = v
     elif v is None:
-        rolling = 'N'
+        rolling = "N"
     elif isinstance(v, pd.DataFrame):
         rolling = hash_pdf(v)
     else:
-        raise TypeError(f'Unsupported memoization type: {type(v)}')
+        raise TypeError(f"Unsupported memoization type: {type(v)}")
 
     return rolling
 
+
 def hash_memoize(v: Any) -> str:
-    return hashlib.sha256(hash_memoize_helper(v).encode('utf-8')).hexdigest()
+    return hashlib.sha256(hash_memoize_helper(v).encode("utf-8")).hexdigest()
+
 
-def check_set_memoize(g, metadata, attribute, name: str = '', memoize: bool = True):  # noqa: C901
+def check_set_memoize(
+    g, metadata, attribute, name: str = "", memoize: bool = True
+):  # noqa: C901
     """
-        Helper Memoize function that checks if metadata args have changed for object g -- which is unconstrained save
-        for the fact that it must have `attribute`. If they have not changed, will return memoized version,
-        if False, will continue with whatever pipeline it is in front.
+    Helper Memoize function that checks if metadata args have changed for object g -- which is unconstrained save
+    for the fact that it must have `attribute`. If they have not changed, will return memoized version,
+    if False, will continue with whatever pipeline it is in front.
     """
-    
-    logger = setup_logger(f'{__name__}.memoization')
+
+    logger = setup_logger(f"{__name__}.memoization")
 
     if not memoize:
-        logger.debug('Memoization disabled')
+        logger.debug("Memoization disabled")
         return False
-    
+
     hashed = None
     weakref = getattr(g, attribute)
     try:
-        hashed = hash_memoize(metadata)
+        hashed = hash_memoize(dict(data=metadata))
     except TypeError:
         logger.warning(
-            f'! Failed {name} speedup attempt. Continuing without memoization speedups.'
+            f"! Failed {name} speedup attempt. Continuing without memoization speedups."
         )
     try:
         if hashed in weakref:
-            logger.debug(f'{name} memoization hit: %s', hashed)
+            logger.debug(f"{name} memoization hit: %s", hashed)
             return weakref[hashed].v
         else:
-            logger.debug(f'{name} memoization miss for id (of %s): %s',
-                         len(weakref), hashed)
+            logger.debug(
+                f"{name} memoization miss for id (of %s): %s", len(weakref), hashed
+            )
     except:
-        logger.debug(f'Failed to hash {name} kwargs', exc_info=True)
+        logger.debug(f"Failed to hash {name} kwargs", exc_info=True)
         pass
-    
+
     if memoize and (hashed is not None):
         w = WeakValueWrapper(g)
         cache_coercion(hashed, w)
@@ -281,6 +298,90 @@ def deprecated_func(*args, **kwargs):
     return deprecated_decorator
 
 
+# #############################################################################
+# MODEL Parameter HELPERS
+def get_timestamp():
+    import datetime
+
+    return datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+
+class ModelDict(UserDict):
+    """Helper class to print out model names and keep track of updates
+
+    Args:
+        message: description of model
+        verbose: print out model names, logging happens regardless
+    """
+
+    def __init__(self, message, verbose=True, _timestamp=False, *args, **kwargs):
+        self._message = message
+        self._verbose = verbose
+        self._timestamp = _timestamp  # do no use this inside the class, as it will trigger memoization. Only use outside of class.
+        L = (
+            len(message)
+            if _timestamp is False
+            else max(len(message), len(get_timestamp()) + 1)
+        )
+        self._print_length = min(80, L)
+        self._updates = []
+        super().__init__(*args, **kwargs)
+
+    def print(self, message):
+        if self._timestamp:
+            message = f"{message}\n{get_timestamp()}"
+        if self._verbose:
+            print("_" * self._print_length)
+            print()
+            print(message)
+            print("_" * self._print_length)
+            print()
+
+    def __repr__(self):
+        # logger.info(self._message)
+        self.print(self._message)
+        return super().__repr__()
+
+    # def __setitem__(self, key, value):  # can't get this to work properly as it doesn't get called on update
+    #     self._updates.append({key: value})
+    #     if len(self._updates) > 1:
+    #         self._message += (
+    #             "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+    #         )
+    #     return super().__setitem__(key, value)
+
+    def update(self, *args, **kwargs):
+        self._updates.append(args[0])
+        if len(self._updates) > 1:  # don't take first update since its the init/default
+            self._message += (
+                "\n" + "_" * self._print_length + f"\n\nUpdated: {self._updates[-1]}"
+            )
+        return super().update(*args, **kwargs)
+
+
+def is_notebook():
+    """Check if running in a notebook"""
+    try:
+        from IPython import get_ipython
+
+        if "IPKernelApp" not in get_ipython().config:  # pragma: no cover
+            raise ImportError("console")
+            return False
+        if "VSCODE_PID" in os.environ:  # pragma: no cover
+            raise ImportError("vscode")
+            return False
+    except:
+        return False
+    else:  # pragma: no cover
+        return True
+    
+    
+def printmd(string, color=None, size=20):
+    """Print markdown string in notebook"""
+    from IPython.display import Markdown, display
+    colorstr = "<span style='color:{};font-weight:200;font-size:{}px'>{}</span>".format(color, size, string)
+    display(Markdown(colorstr))
+
 #
 # def inspect_decorator(func, args, kwargs):
 #     import inspect
diff --git a/mypy.ini b/mypy.ini
index fd6b4d9ece..5b4403e91f 100644
--- a/mypy.ini
+++ b/mypy.ini
@@ -1,5 +1,5 @@
 [mypy]
-python_version = 3.7
+python_version = 3.8
 
 # TODO check tests
 exclude = graph_vector_pb2|versioneer|_version|graphistry/tests
@@ -31,6 +31,9 @@ ignore_missing_imports = True
 [mypy-dgl.*]
 ignore_missing_imports = True
 
+[mypy-faiss.*]
+ignore_missing_imports = True
+
 [mypy-igraph.*]
 ignore_missing_imports = True
 
@@ -90,4 +93,7 @@ ignore_missing_imports = True
 ignore_missing_imports = True
 
 [mypy-cuml.*]
-ignore_missing_imports = True
\ No newline at end of file
+ignore_missing_imports = True
+
+[mypy-cu_cat.*]
+ignore_missing_imports = true
diff --git a/setup.py b/setup.py
index 5817609f6a..3ad1513235 100755
--- a/setup.py
+++ b/setup.py
@@ -10,12 +10,13 @@ def unique_flatten_dict(d):
 core_requires = [
   'numpy',
   'palettable >= 3.0',
-  'pandas >= 0.17.0',
+  'pandas < 2.0.0',
   'pyarrow >= 0.15.0',
   'requests',
   'squarify',
   'typing-extensions',
-  'packaging >= 20.1'
+  'packaging >= 20.1',
+  'setuptools < 60.0.0',
 ]
 
 stubs = [
@@ -33,14 +34,18 @@ def unique_flatten_dict(d):
     'networkx': ['networkx>=2.5'],
     'gremlin': ['gremlinpython'],
     'bolt': ['neo4j', 'neotime'],
-    'nodexl': ['openpyxl', 'xlrd'],
+    'nodexl': ['openpyxl==3.1.0', 'xlrd'],
     'jupyter': ['ipython'],
 }
 
 base_extras_heavy = {
   'umap-learn': ['umap-learn', 'dirty-cat==0.2.0', 'scikit-learn>=1.0'],
 }
-base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch', 'sentence-transformers', 'annoy', 'joblib']
+
+# https://github.com/facebookresearch/faiss/issues/1589 for faiss-cpu 1.6.1, #'setuptools==67.4.0' removed
+base_extras_heavy['ai'] = base_extras_heavy['umap-learn'] + ['scipy', 'dgl', 'torch<2', 'sentence-transformers', 'faiss-cpu', 'joblib']
+
+base_extras_heavy['cu_cat'] = base_extras_heavy['ai'] + ['cu_cat @ git+http://github.com/graphistry/cu-cat.git@0.03.0']
 
 base_extras = {**base_extras_light, **base_extras_heavy}