[WIP] - Anywidget

package.json

@@ -13,6 +13,7 @@
     "coverage": "vitest --coverage"
   },
   "dependencies": {
+    "@anywidget/react": "^0.2.0",
     "@czi-sds/components": "^21.6.3",
     "@emotion/css": "^11.11.2",
     "@emotion/react": "^11.11.4",

python/src/intracktive/_tests/test_widget.py

@@ -6,7 +6,7 @@
 import napari
 import pandas as pd
 import pytest
-from intracktive.widget import IntracktiveWidget
+from intracktive.napari_widget import IntracktiveWidget
 
 
 def test_intracktive_widget_2D(

python/src/intracktive/convert.py

@@ -290,20 +290,10 @@ def convert_dataframe_to_zarr(
     n_time_points = df["t"].max() + 1
     max_values_per_time_point = df.groupby("t").size().max()
 
-    uniq_track_ids = df["track_id"].unique()
-    extended_uniq_track_ids = np.append(
-        uniq_track_ids, -1
-    )  # include -1 for orphaned tracklets
-    fwd_map = ArrayMap(
-        extended_uniq_track_ids, np.append(np.arange(1, 1 + len(uniq_track_ids)), -1)
-    )
-
-    # relabeling from 0 to N-1
-    df["track_id"] = fwd_map[df["track_id"].to_numpy()]
-    # orphaned are set to 0 according to skimage convention
-    df["parent_track_id"] = fwd_map[df["parent_track_id"].to_numpy()]
-
+    # Replace the manual remapping code with the new function call
+    df = make_track_ids_consecutive(df, inplace=True)
     n_tracklets = df["track_id"].nunique()
+
     # (z, y, x) + extra_cols
     num_values_per_point = 4 if add_radius else 3
 
@@ -595,8 +585,26 @@ def dataframe_to_browser(
         pre_normalized=pre_normalized,
     )
 
-    # Use the new zarr_to_browser function
-    zarr_to_browser(zarr_dir_with_storename, flag_open_browser)
+    hostURL = serve_directory(
+        path=zarr_dir,
+        threaded=True,
+    )
+
+    LOG.info("localhost successfully launched, serving: %s", zarr_dir_with_storename)
+    baseUrl = "https://intracktive.sf.czbiohub.org"  # inTRACKtive application
+    dataUrl = (
+        hostURL + "/" + zarr_path.name + "/"
+    )  # exact path of the data (on localhost)
+    fullUrl = baseUrl + generate_viewer_state_hash(
+        data_url=str(dataUrl)
+    )  # full hash that encodes viewerState
+    LOG.info("Copy the following URL into the Google Chrome browser:")
+    LOG.info("full URL: %s", fullUrl)
+
+    if flag_open_browser:
+        webbrowser.open(fullUrl)
+    else:
+        return dataUrl
 
 
 def check_if_columns_exist(
@@ -647,6 +655,41 @@ def get_col_type(column: pd.Series) -> str:
         return "continuous"
 
 
+def make_track_ids_consecutive(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame:
+    """
+    Remap track IDs to be consecutive starting from 1, preserving parent-child relationships.
+    Uses ArrayMap from skimage for efficient array-based mapping.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        DataFrame containing 'track_id' and 'parent_track_id' columns
+    inplace : bool, optional
+        If True, modifies the dataframe in place. If False, returns a copy.
+
+    Returns
+    -------
+    pd.DataFrame
+        DataFrame with remapped consecutive track IDs
+    """
+    if not inplace:
+        df = df.copy()
+
+    uniq_track_ids = df["track_id"].unique()
+    extended_uniq_track_ids = np.append(
+        uniq_track_ids, -1
+    )  # include -1 for orphaned tracklets
+    fwd_map = ArrayMap(
+        extended_uniq_track_ids, np.append(np.arange(1, 1 + len(uniq_track_ids)), -1)
+    )
+
+    # relabeling from 1 to N
+    df["track_id"] = fwd_map[df["track_id"].to_numpy()]
+    df["parent_track_id"] = fwd_map[df["parent_track_id"].to_numpy()]
+
+    return df
+
+
 @click.command(name="convert")
 @click.argument(
     "input_file",

python/src/intracktive/examples/anywidget_example.ipynb

@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8dee98d4-c8b1-4ba9-8c10-8e326db0a138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%env ANYWIDGET_HMR=1\n",
+    "import anywidget\n",
+    "import traitlets\n",
+    "\n",
+    "\n",
+    "class Widget(anywidget.AnyWidget):\n",
+    "    get_selected_tracks = traitlets.List().tag(sync=True)\n",
+    "    dataset_url = traitlets.Unicode(\n",
+    "        \"https://public.czbiohub.org/royerlab/zoo/Zebrafish/tracks_zebrafish_bundle.zarr/\"\n",
+    "    ).tag(sync=True)\n",
+    "    selected_cells = traitlets.List().tag(sync=True)\n",
+    "\n",
+    "    def select_tracks(self, cell_ids):\n",
+    "        print(\"in class widget1, cell_ids = \", self.selected_cells)\n",
+    "        self.selected_cells = cell_ids\n",
+    "        print(\"in class widget2, cell_ids = \", self.selected_cells)\n",
+    "\n",
+    "    _esm = \"static/widget.js\"\n",
+    "    _css = \"static/widget.css\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1ebd6233-decd-434f-87d7-7648f962e5dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# default widget has zebrafish\n",
+    "w = Widget()\n",
+    "w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59317ec0",
+   "metadata": {},
+   "source": [
+    "# get/set data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "555439b3-5509-4743-bee3-2400712d15f9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w = Widget(\n",
+    "    dataset_url=\"https://public.czbiohub.org/royerlab/zoo/Ascidian/tracks_ascidian_attributes_bundle.zarr/\"\n",
+    ")\n",
+    "w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12f4f980-7c9f-4247-8989-be73971a9f26",
+   "metadata": {},
+   "source": [
+    "Get track_ids of selected cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b187affc-bc62-46dd-a531-4c84d0aeae2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"tracks:\", w.get_selected_tracks)\n",
+    "print(len(w.get_selected_tracks), \"tracks selected\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "08962eca-f237-4597-93b5-7bd6b9ca1d7b",
+   "metadata": {},
+   "source": [
+    "Select tracks progammatically"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72729521-3c62-42dc-80e7-a4743c24ed31",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w.select_tracks([39])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b971a2d0-4d83-46bb-afab-6bac63d8765f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "w.select_tracks([40, 1, 39])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "03099cc5-7e0e-4466-97bc-249499fc01d9",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

python/src/intracktive/examples/anywidget_example2.ipynb

@@ -0,0 +1,126 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "217c2911-87bc-4e7c-8198-1a8d252904e9",
+   "metadata": {},
+   "source": [
+    "# Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f06c4f19-d756-44f1-8559-9f5e79f68081",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import traitlets\n",
+    "import anywidget\n",
+    "import pandas as pd\n",
+    "\n",
+    "from pathlib import Path\n",
+    "from IPython.display import clear_output\n",
+    "from intracktive.convert import dataframe_to_browser\n",
+    "\n",
+    "\n",
+    "class Widget(anywidget.AnyWidget):\n",
+    "    selected_points = traitlets.List().tag(sync=True)\n",
+    "    dataset_url = traitlets.Unicode(\n",
+    "        \"https://public.czbiohub.org/royerlab/zoo/Zebrafish/tracks_zebrafish_bundle.zarr/\"\n",
+    "    ).tag(sync=True)\n",
+    "\n",
+    "    _esm = \"static/widget.js\"\n",
+    "    _css = \"static/widget.css\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f416127-f17e-4816-b2ce-03317fee1605",
+   "metadata": {},
+   "source": [
+    "# Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c19cf75c-5bb5-4c29-ab83-6c29d0d6b4c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"https://public.czbiohub.org/royerlab/zoo/C_elegans/tracks.csv\")\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d23d870-b89b-4b3a-a228-3c52646a6469",
+   "metadata": {},
+   "source": [
+    "# Open inTRACKtive within notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "127be4fb-3e81-4583-aa1a-0a5d205129fb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "zarr_data_url = dataframe_to_browser(df, Path(), flag_open_browser=False)\n",
+    "clear_output()\n",
+    "\n",
+    "wid = Widget(dataset_url=zarr_data_url)\n",
+    "wid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d6b86bf-cd77-4f8d-8fd2-ce6040f00f62",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "wid.selected_points"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee233f4a-74bf-400d-aaba-4036d2c73066",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9db1b2a0-f304-429a-9e5e-db7685912e10",
+   "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": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}