diff --git a/.github/workflows/pr_checks_backend.yml b/.github/workflows/pr_checks_backend.yml
index c6420a60..764af50d 100644
--- a/.github/workflows/pr_checks_backend.yml
+++ b/.github/workflows/pr_checks_backend.yml
@@ -68,10 +68,6 @@ jobs:
           echo "Using Python: $(which python)"
           pipenv --python $(which python) install --dev
 
-      - name: Install awkde
-        working-directory: data/src/awkde
-        run: pipenv run pip install .
-
       - name: Run Pytest
         working-directory: data/src
         run: PYTHONPATH=$PYTHONPATH:. pipenv run pytest
diff --git a/.gitignore b/.gitignore
index a4fe2518..4e250002 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,7 @@ data/src/app/service-account-key.json
 # compiled python files
 *.pyc
 
-# awkde build files
-data/src/awkde/build/
-tmp/
\ No newline at end of file
+tmp/
+
+# Local python development files
+.python-version
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index c54c5fe9..bc19a61e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -9,7 +9,6 @@ repos:
         language: python
         types_or: [python, pyi, jupyter]
         files: \.py$
-        exclude: ^data/src/awkde/
 
       # Ruff for Python formatting
       - id: ruff-format
@@ -18,7 +17,6 @@ repos:
         language: python
         types_or: [python, pyi, jupyter]
         files: \.py$
-        exclude: ^data/src/awkde/
 
       # Dependency checks for Python
       - id: check-python-dependencies
@@ -35,14 +33,13 @@ repos:
           radon hal {} -s | awk "/effort/ { if (\$3 > 2000) print FILENAME \": High Halstead effort:\" \$3 }"'
         language: system
         files: \.py$
-        exclude: ^data/src/awkde/
 
   - repo: https://github.com/jendrikseipp/vulture
     rev: 'v2.3' # or any later Vulture version
     hooks:
       - id: vulture
         name: vulture (Dead code detection)
-        entry: vulture data/ --exclude=data/src/awkde
+        entry: vulture data/
         language: python
         types: [python]
         files: ^data/
diff --git a/data/Dockerfile b/data/Dockerfile
index d5cf8d40..b6676ac9 100644
--- a/data/Dockerfile
+++ b/data/Dockerfile
@@ -29,12 +29,12 @@ ENV GDAL_CONFIG=/usr/bin/gdal-config
 # Install Pipenv
 RUN pip install pipenv
 
-# update pipfile
-RUN pipenv lock
-
 # Copy the Pipfile and Pipfile.lock from the src directory
 COPY src/Pipfile src/Pipfile.lock ./
 
+# update pipfile
+RUN pipenv lock
+
 # Install the dependencies from Pipfile
 RUN pipenv install --deploy --ignore-pipfile
 
@@ -44,17 +44,9 @@ RUN git clone https://github.com/felt/tippecanoe.git \
     && make \
     && make install
 
-# Copy the src directory (including awkde)
+# Copy the src directory
 COPY src/ .
 
-# Install awkde within the Pipenv environment
-# Assuming awkde is located in /usr/src/app/awkde
-WORKDIR /usr/src/app/awkde
-RUN pipenv run pip install .
-
-# Change back to the original working directory
-WORKDIR /usr/src/app
-
 # Use Pipenv to run the script
 # Adjust the path to your main Python script if needed
 CMD ["pipenv", "run", "python", "./script.py"]
diff --git a/data/docker-compose.yml b/data/docker-compose.yml
index 5737d39a..e3707af2 100644
--- a/data/docker-compose.yml
+++ b/data/docker-compose.yml
@@ -26,7 +26,7 @@ services:
       context: .
     volumes:
       - ./src:/usr/src/app
-    command: sh -c "pip install ruff && ruff format --exclude '/usr/src/app/awkde/'"
+    command: sh -c "pip install ruff && ruff format"
     network_mode: 'host'
 
   linter:
@@ -34,7 +34,7 @@ services:
       context: .
     volumes:
       - ./src:/usr/src/app
-    command: sh -c "pip install ruff && ruff check --fix --exclude '/usr/src/app/awkde/'"
+    command: sh -c "pip install ruff && ruff check --fix"
     network_mode: 'host'
 
   streetview:
diff --git a/data/pyproject.toml b/data/pyproject.toml
index 0970a1fa..679fa5e2 100644
--- a/data/pyproject.toml
+++ b/data/pyproject.toml
@@ -1,25 +1,14 @@
-[tool.pylint]
-ignore = ["awkde/"]
-output-format = "colorized,parseable"
-
-[tool.pylint.'MESSAGES CONTROL']
-disable = ["all"]
-enable = ["duplicate-code"]
-
 [tool.vulture]
-exclude = ["awkde/"]
 make_whitelist = true
 # min_confidence = 80
 sort_by_size = true
 
 [tool.mypy]
 ignore_missing_imports = true
-exclude = 'awkde/'
 
 [tool.ruff]
 # Exclude a variety of commonly ignored directories, plus our own
 exclude = [
-    "awkde/",
     ".bzr",
     ".direnv",
     ".eggs",
diff --git a/data/src/Pipfile b/data/src/Pipfile
index ebe4fd46..c0b119be 100644
--- a/data/src/Pipfile
+++ b/data/src/Pipfile
@@ -4,38 +4,38 @@ verify_ssl = true
 name = "pypi"
 
 [packages]
-jupyter = "*"
-requests = "*"
-pandas = "==2.2.2"
+awkde = { git = 'https://github.com/mennthor/awkde.git', ref = '5b601fe4d92229d5deb8737fd121dce193bac552' }
+esridump = "~=1.13.0"
+fiona = "~=1.10.1"
+future = "~=1.0.0"
+geoalchemy2 = "~=0.16.0"
 geopandas = "==1.0.1"
-shapely = "*"
-matplotlib = "*"
-rasterio = "*"
-scikit-learn = "*"
-mapclassify = "*"
-fiona = "*"
-esridump = "*"
-sqlalchemy = "*"
-psycopg2-binary = "*"
-mapbox = "*"
-google-cloud-storage = "*"
+google-cloud-storage = "~=2.19.0"
+jenkspy = "~=0.4.1"
+jupyter = "~=1.1.1"
+libpysal = "~=4.12.1"
+mapbox = "~=0.18.1"
+mapclassify = "~=2.8.1"
+matplotlib = "~=3.9.3"
+networkx = "~=3.4.2"
+pandas = "==2.2.2"
+psycopg2-binary = "~=2.9.10"
+pyarrow = "~=18.1.0"
 pydantic = "==2.8.2"
-future = "*"
-slack-sdk = "*"
-networkx = "*"
-libpysal = "*"
-jenkspy = "*"
-pyarrow = "*"
-tqdm = "*"
-geoalchemy2 ="*"
+rasterio = "~=1.4.3"
+requests = "~=2.32.3"
+scikit-learn = "~=1.6.0"
+shapely = "~=2.0.6"
+slack-sdk = "~=3.33.5"
+sqlalchemy = "~=2.0.36"
+tqdm = "~=4.67.1"
+
 
 [dev-packages]
-black = "*"
-pytest = "*"
-vulture = "*"
-pylint = "*"
-radon = "*"
-ruff = "*"
+pytest = "~=8.3.4"
+vulture = "~=2.14"
+radon = "~=6.0.1"
+ruff = "~=0.8.2"
 
 [requires]
 python_version = "3.11"
diff --git a/data/src/Pipfile.lock b/data/src/Pipfile.lock
index 726d2430..f5ec22ce 100644
--- a/data/src/Pipfile.lock
+++ b/data/src/Pipfile.lock
@@ -1,3087 +1,2930 @@
 {
-  "_meta": {
-    "hash": {
-      "sha256": "b0cc0a48b0d260c226c0a714f60c880ea2b05027dcaba59b3eff007abe2ac1c9"
-    },
-    "pipfile-spec": 6,
-    "requires": {
-      "python_full_version": "3.11.4",
-      "python_version": "3.11"
-    },
-    "sources": [
-      {
-        "name": "pypi",
-        "url": "https://pypi.org/simple",
-        "verify_ssl": true
-      }
-    ]
-  },
-  "default": {
-    "affine": {
-      "hashes": [
-        "sha256:8a3df80e2b2378aef598a83c1392efd47967afec4242021a0b06b4c7cbc61a92",
-        "sha256:a24d818d6a836c131976d22f8c27b8d3ca32d0af64c1d8d29deb7bafa4da1eea"
-      ],
-      "markers": "python_version >= '3.7'",
-      "version": "==2.4.0"
-    },
-    "annotated-types": {
-      "hashes": [
-        "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53",
-        "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==0.7.0"
-    },
-    "anyio": {
-      "hashes": [
-        "sha256:2f834749c602966b7d456a7567cafcb309f96482b5081d14ac93ccd457f9dd48",
-        "sha256:ea60c3723ab42ba6fff7e8ccb0488c898ec538ff4df1f1d5e642c3601d07e352"
-      ],
-      "markers": "python_version >= '3.9'",
-      "version": "==4.7.0"
-    },
-    "argon2-cffi": {
-      "hashes": [
-        "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08",
-        "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"
-      ],
-      "markers": "python_version >= '3.7'",
-      "version": "==23.1.0"
-    },
-    "argon2-cffi-bindings": {
-      "hashes": [
-        "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670",
-        "sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f",
-        "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583",
-        "sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194",
-        "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c",
-        "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a",
-        "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082",
-        "sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5",
-        "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f",
-        "sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7",
-        "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d",
-        "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f",
-        "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae",
-        "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3",
-        "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86",
-        "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367",
-        "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d",
-        "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93",
-        "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb",
-        "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e",
-        "sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351"
-      ],
-      "markers": "python_version >= '3.6'",
-      "version": "==21.2.0"
-    },
-    "arrow": {
-      "hashes": [
-        "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80",
-        "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==1.3.0"
-    },
-    "asttokens": {
-      "hashes": [
-        "sha256:0dcd8baa8d62b0c1d118b399b2ddba3c4aff271d0d7a9e0d4c1681c79035bbc7",
-        "sha256:e3078351a059199dd5138cb1c706e6430c05eff2ff136af5eb4790f9d28932e2"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==3.0.0"
-    },
-    "async-lru": {
-      "hashes": [
-        "sha256:b8a59a5df60805ff63220b2a0c5b5393da5521b113cd5465a44eb037d81a5627",
-        "sha256:ff02944ce3c288c5be660c42dbcca0742b32c3b279d6dceda655190240b99224"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==2.0.4"
-    },
-    "attrs": {
-      "hashes": [
-        "sha256:5cfb1b9148b5b086569baec03f20d7b6bf3bcacc9a42bebf87ffaaca362f6346",
-        "sha256:81921eb96de3191c8258c199618104dd27ac608d9366f5e35d011eae1867ede2"
-      ],
-      "markers": "python_version >= '3.7'",
-      "version": "==24.2.0"
-    },
-    "babel": {
-      "hashes": [
-        "sha256:368b5b98b37c06b7daf6696391c3240c938b37767d4584413e8438c5c435fa8b",
-        "sha256:d1f3554ca26605fe173f3de0c65f750f5a42f924499bf134de6423582298e316"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==2.16.0"
-    },
-    "beautifulsoup4": {
-      "hashes": [
-        "sha256:74e3d1928edc070d21748185c46e3fb33490f22f52a3addee9aee0f4f7781051",
-        "sha256:b80878c9f40111313e55da8ba20bdba06d8fa3969fc68304167741bbf9e082ed"
-      ],
-      "markers": "python_full_version >= '3.6.0'",
-      "version": "==4.12.3"
-    },
-    "bleach": {
-      "hashes": [
-        "sha256:117d9c6097a7c3d22fd578fcd8d35ff1e125df6736f554da4e432fdd63f31e5e",
-        "sha256:123e894118b8a599fd80d3ec1a6d4cc7ce4e5882b1317a7e1ba69b56e95f991f"
-      ],
-      "markers": "python_version >= '3.9'",
-      "version": "==6.2.0"
-    },
-    "boto3": {
-      "hashes": [
-        "sha256:5ef7166fe5060637b92af8dc152cd7acecf96b3fc9c5456706a886cadb534391",
-        "sha256:fc8001519c8842e766ad3793bde3fbd0bb39e821a582fc12cf67876b8f3cf7f1"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==1.35.78"
-    },
-    "botocore": {
-      "hashes": [
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-      "markers": "python_version >= '3.8'",
-      "version": "==1.35.78"
-    },
-    "cachecontrol": {
-      "hashes": [
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-        "sha256:65e3abd62b06382ce3894df60dde9e0deb92aeb734724f68fa4f3b91e97206b9"
-      ],
-      "markers": "python_version >= '3.8'",
-      "version": "==0.14.1"
-    },
-    "cachetools": {
-      "hashes": [
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-      ],
-      "markers": "python_version >= '3.7'",
-      "version": "==5.5.0"
-    },
-    "certifi": {
-      "hashes": [
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-        "sha256:bec941d2aa8195e248a60b31ff9f0558284cf01a52591ceda73ea9afffd69fd9"
-      ],
-      "markers": "python_version >= '3.6'",
-      "version": "==2024.8.30"
-    },
-    "cffi": {
-      "hashes": [
-        "sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8",
-        "sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2",
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-        "sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17",
-        "sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf",
-        "sha256:31000ec67d4221a71bd3f67df918b1f88f676f1c3b535a7eb473255fdc0b83fc",
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-        "sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1",
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-        "sha256:9755e4345d1ec879e3849e62222a18c7174d65a6a92d5b346b1863912168b595",
-        "sha256:98e3969bcff97cae1b2def8ba499ea3d6f31ddfdb7635374834cf89a1a08ecf0",
-        "sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65",
-        "sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41",
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+            "version": "==1.17.0"
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+        "vulture": {
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+                "sha256:cb8277902a1138deeab796ec5bef7076a6e0248ca3607a3f3dee0b6d9e9b8415",
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+            "markers": "python_version >= '3.8'",
+            "version": "==2.14"
+        }
     }
-  }
 }
diff --git a/data/src/awkde/LICENSE b/data/src/awkde/LICENSE
deleted file mode 100644
index cedffc1a..00000000
--- a/data/src/awkde/LICENSE
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2017 Thorben Menne
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/data/src/awkde/README.md b/data/src/awkde/README.md
deleted file mode 100644
index 2afd1dfe..00000000
--- a/data/src/awkde/README.md
+++ /dev/null
@@ -1,65 +0,0 @@
-# Adaptive Width KDE with Gaussian Kernels
-
-## Installation
-
-This uses the awesome [pybind11](https://github.com/pybind/pybind11) package which makes creating C++ bindings super convenient.
-Only the evaluation is written in a small C++ snippet to speed it up, the rest is a pure python implementation.
-
-The setup is taken from the example at https://github.com/pybind/python_example
-Just clone the repository and invoke `pip`:
-
-```bash
-git clone https://github.com/mennthor/awkde
-pip install [--user] [-e] ./awkde
-```
-
-Note: `--user` and `-e` are optional. Use `--user` to install in your user site-package directory (recommended) and `-e` if you want to only link the files to the site-package directories, so that when you make any changes in your local copy, you don't have to reinstall all the time.
-
-Try to run the `example/examply.py`:
-
-```
-cd awkde/example
-python example.py
-```
-
-You should get this plot in the examples folder (you need matplotlib for the plot):
-
-![example plot](example/example.png)
-
-## Algorithm
-
-The unweighted kernel density estimator is defined as
-
-![kernel density formula](tex/kernel_dens.png)
-
-where the product `h * lambda` takes the role of a local `sigma`.
-
-The kernel bandwith is choosen locally to account for variations in the
-density of the data.
-Areas with large density gets smaller kernels and vice versa.
-This smoothes the tails and gets high resolution in high statistics regions.
-The local bandwidth paramter is defined as
-
-![kernel density formula](tex/lambda.png)
-
-where
-
-![kernel density formula](tex/log_g.png)
-
-is some normalization and `^f(X_i)` the KDE estimate at the data point `X_i`.
-The local bandwidth is multiplied to the global bandwidth for each kernel.
-
-Furthermore different scales in data is accounted for by scaling it via its
-covariance matrix to an equal spread.
-First a global kernel bandwidth is applied to the transformed data and then
-based on that density a local bandwidth parameter is applied.
-
-All credit for the method goes to [1] and to S. Schoenen and L. Raedel for
-huge parts of the implementation.
-For information on Silverman or Scott rule, see [2] or [3].
-
-## References
-
-- [1] B. Wang and X. Wang, "Bandwidth Selection for Weighted Kernel Density Estimation", Sep. 2007, DOI: 10.1214/154957804100000000.
-- [2] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and Visualization", John Wiley & Sons, New York, Chicester, 1992.
-- [3] B.W. Silverman, "Density Estimation for Statistics and Data Analysis", Vol. 26, Monographs on Statistics and Applied Probability, Chapman and Hall, London, 1986.
diff --git a/data/src/awkde/awkde.egg-info/PKG-INFO b/data/src/awkde/awkde.egg-info/PKG-INFO
deleted file mode 100644
index d657a2b1..00000000
--- a/data/src/awkde/awkde.egg-info/PKG-INFO
+++ /dev/null
@@ -1,13 +0,0 @@
-Metadata-Version: 2.1
-Name: awkde
-Version: 0.1
-Summary: Adaptive width gaussian KDE
-Home-page: https://github.com/mennthor/awkde
-Author: Thorben Menne
-Author-email: thorben.menne@tu-dortmund.de
-License-File: LICENSE
-Requires-Dist: numpy
-Requires-Dist: scipy
-Requires-Dist: scikit-learn
-Requires-Dist: pybind11
-Requires-Dist: future
diff --git a/data/src/awkde/awkde.egg-info/SOURCES.txt b/data/src/awkde/awkde.egg-info/SOURCES.txt
deleted file mode 100644
index e3893b75..00000000
--- a/data/src/awkde/awkde.egg-info/SOURCES.txt
+++ /dev/null
@@ -1,13 +0,0 @@
-LICENSE
-README.md
-pyproject.toml
-setup.py
-awkde/__init__.py
-awkde/awkde.py
-awkde/tools.py
-awkde.egg-info/PKG-INFO
-awkde.egg-info/SOURCES.txt
-awkde.egg-info/dependency_links.txt
-awkde.egg-info/requires.txt
-awkde.egg-info/top_level.txt
-cpp/backend.cpp
\ No newline at end of file
diff --git a/data/src/awkde/awkde.egg-info/dependency_links.txt b/data/src/awkde/awkde.egg-info/dependency_links.txt
deleted file mode 100644
index 8b137891..00000000
--- a/data/src/awkde/awkde.egg-info/dependency_links.txt
+++ /dev/null
@@ -1 +0,0 @@
-
diff --git a/data/src/awkde/awkde.egg-info/requires.txt b/data/src/awkde/awkde.egg-info/requires.txt
deleted file mode 100644
index 3ac2e261..00000000
--- a/data/src/awkde/awkde.egg-info/requires.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy
-scipy
-scikit-learn
-pybind11
-future
diff --git a/data/src/awkde/awkde.egg-info/top_level.txt b/data/src/awkde/awkde.egg-info/top_level.txt
deleted file mode 100644
index 436fa0a6..00000000
--- a/data/src/awkde/awkde.egg-info/top_level.txt
+++ /dev/null
@@ -1 +0,0 @@
-awkde
diff --git a/data/src/awkde/awkde/__init__.py b/data/src/awkde/awkde/__init__.py
deleted file mode 100644
index dca7c77c..00000000
--- a/data/src/awkde/awkde/__init__.py
+++ /dev/null
@@ -1,10 +0,0 @@
-# coding: utf-8
-
-from __future__ import print_function
-from __future__ import division
-from __future__ import absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
-
-from .awkde import GaussianKDE
diff --git a/data/src/awkde/awkde/awkde.py b/data/src/awkde/awkde/awkde.py
deleted file mode 100644
index e979acf0..00000000
--- a/data/src/awkde/awkde/awkde.py
+++ /dev/null
@@ -1,461 +0,0 @@
-# coding: utf-8
-
-from __future__ import division, print_function, absolute_import, unicode_literals
-from builtins import int, open
-from future import standard_library
-
-standard_library.install_aliases()
-
-import os
-import sys
-import json
-import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.utils import check_random_state
-
-from awkde.tools import standardize_nd_sample, shift_and_scale_nd_sample
-import awkde.backend as backend
-
-
-class GaussianKDE(BaseEstimator):
-    """
-    GaussianKDE
-
-    Kernel denstiy estimate using gaussian kernels and a local kernel bandwidth.
-    Implements the ``sklearn.BaseEstimator`` class and can be used in a cross-
-    validation gridsearch (``sklearn.model_selection``).
-
-    Parameters
-    ----------
-    glob_bw : float or str, optional
-        The global bandwidth of the kernel, must be a float ``> 0`` or one of
-        ``['silverman'|'scott']``. If ``alpha`` is not ``None``, this is the
-        bandwidth used for the first estimate KDE from which the local bandwidth
-        is calculated. ``If ['silverman'|'scott']`` a rule of thumb is used to
-        estimate the global bandwidth. (default: 'silverman')
-    alpha : float or None, optional
-        If ``None``, only the global bandwidth ``glob_bw`` is used. If
-        ``0 <= alpha <= 1``, an adaptive local kernel bandwith is used as
-        described in [1]_. (default: 0.5)
-    diag_cov : bool, optional
-        If ``True``, scale fit sample by variance only, which means using a
-        diagonal covariance matrix. (default: False)
-
-    Notes
-    -----
-    The unweighted kernel density estimator is defined as
-
-    .. math:
-
-      \hat{f}(x) = \sum_i \frac{1}{h\lambda_i}\cdot
-                     K\left(\frac{x - X_i}{h\lambda_i}\right)
-
-
-    where the product :math:`h\lambda_i` takes the role of a local
-    variance :math`\sigma_i^2`.
-
-    The kernel bandwith is choosen locally to account for variations in the
-    density of the data.
-    Areas with large density gets smaller kernels and vice versa.
-    This smoothes the tails and gets high resolution in high statistics regions.
-    The local bandwidth parameter is defined as
-
-    .. math: \lambda_i = (\hat{f}(X_i) / g)^{-\alpha}
-
-    where :math:`\log g = n^{-1}\sum_i \log\hat{f}(X_i)` is some normalization
-    and :math:`\hat{f}(X_i)` the KDE estimate at the data point :math:`X_i`.
-    The local bandwidth is multiplied to the global bandwidth for each kernel.
-
-    Furthermore different scales in data is accounted for by scaling it via its
-    covariance matrix to an equal spread.
-    First a global kernel bandwidth is applied to the transformed data and then
-    based on that density a local bandwidth parameter is applied.
-
-    All credit for the method goes to [1]_ and to S. Schoenen and L. Raedel for
-    huge parts of the implementation :+1:.
-    For information on Silverman or Scott rule, see [2]_ or [3]_.
-
-    References
-    ----------
-    .. [1] B. Wang and X. Wang, "Bandwidth Selection for Weighted Kernel Density
-           Estimation", Sep. 2007, DOI: 10.1214/154957804100000000.
-    .. [2] D.W. Scott, "Multivariate Density Estimation: Theory, Practice, and
-           Visualization", John Wiley & Sons, New York, Chicester, 1992.
-    .. [3] B.W. Silverman, "Density Estimation for Statistics and Data
-           Analysis", Vol. 26, Monographs on Statistics and Applied Probability,
-           Chapman and Hall, London, 1986.
-    """
-
-    def __init__(self, glob_bw="silverman", alpha=0.5, diag_cov=False):
-        if type(glob_bw) is str:
-            if glob_bw not in ["silverman", "scott"]:
-                raise ValueError("glob_bw can be one of ['silverman'|'scott'].")
-            pass
-        elif glob_bw <= 0:
-            raise ValueError("Global bandwidth must be > 0.")
-
-        # List class attributes. Setup indicating that no fit was done yet
-        self._n_kernels = None
-        self._n_features = None
-        self._std_X = None
-        self._mean = None
-        self._cov = None
-        self._kde_values = None
-        self._inv_loc_bw = None
-        self._adaptive = None
-
-        self.alpha = alpha
-        self._glob_bw = glob_bw
-        self._diag_cov = diag_cov
-
-        return
-
-    # Properties
-    @property
-    def alpha(self):
-        return self._alpha
-
-    @alpha.setter
-    def alpha(self, alpha):
-        """
-        The adaptive width can easily be changed after the model has been fit,
-        because the computation only needs the cached ``_kde_values``.
-        """
-        if alpha is None:
-            self._adaptive = False
-        else:
-            if alpha < 0 or alpha > 1:
-                raise ValueError("alpha must be in [0, 1]")
-            self._adaptive = True
-
-        self._alpha = alpha
-
-        if self._std_X is not None and self._adaptive:
-            # Recalculate local bandwidth if we already have a fitted model
-            self._calc_local_bandwidth()
-
-    @property
-    def glob_bw(self):
-        return self._glob_bw
-
-    @property
-    def diag_cov(self):
-        return self._diag_cov
-
-    # Public Methods
-    def __call__(self, X):
-        # Does the same as `predict`, only copy docstring here
-        self.__call__.__func__.__doc__ = self.predict.__doc__
-        return self.predict(X)
-
-    def fit(self, X, bounds=None, weights=None):
-        """
-        Prepare KDE to describe the data.
-
-        Data is transformed via global covariance matrix to equalize scales in
-        different features.
-        Then a symmetric kernel with cov = diag(1) is used to describe the pdf
-        at each point.
-
-        Parameters
-        -----------
-        X : array-like, shape (n_samples, n_features)
-            Data points defining each kernel position. Each row is a point, each
-            column is a feature.
-        bounds : array-like, shape (n_features, 2)
-            Boundary condition for each dimension. The method of mirrored points
-            is used to improve prediction close to bounds. If no bound shall be
-            given in a specific direction use ``None``, eg.
-            ``[[0, None], ...]``. If ``bounds`` is ``None`` no bounds are used
-            in any direction. (default: ``None``)
-        weights : array-like, shape (n_samples), optional
-            Per event weights to consider for ``X``. If ``None`` all weights are
-            set to one. (default: ``None``)
-
-        Returns
-        -------
-        mean : array-like, shape (n_features)
-            The (weighted) mean of the given data.
-        cov : array-like, shape (n_features, n_features)
-            The (weighted) covariance matrix of the given data.
-
-        Raises
-        ------
-        ``NotImplementedError`` if ``bounds`` or ``weights`` are not ``None``.
-        """
-        if bounds is not None:
-            # TODO: Use mirroring of points near boundary regions and then
-            #       constrain KDE to values inside Region but taking all kernels
-            #       into account. (only neccessary on hard cuts)
-            raise NotImplementedError("TODO: Boundary conditions.")
-        if weights is not None:
-            # TODO: Implement weighted statitistics
-            raise NotImplementedError("TODO: Implement weighted statistics.")
-
-        if len(X.shape) != 2:
-            raise ValueError("`X` must have shape (n_samples, n_features).")
-
-        # Transform sample to zero mean and unity covariance matrix
-        self._n_kernels, self._n_features = X.shape
-        self._std_X, self._mean, self._cov = standardize_nd_sample(
-            X, cholesky=True, ret_stats=True, diag=self._diag_cov
-        )
-
-        # Get global bandwidth number
-        self._glob_bw = self._get_glob_bw(self._glob_bw)
-
-        # Build local bandwidth parameter if alpha is set
-        if self._adaptive:
-            self._kde_values = self._evaluate(self._std_X, adaptive=False)
-            self._calc_local_bandwidth()
-
-        return self._mean, self._cov
-
-    def predict(self, X):
-        """
-        Evaluate KDE at given points X.
-
-        Parameters
-        -----------
-        X : array-like, shape (n_samples, n_features)
-            Data points we want to evaluate the KDE at. Each row is a point,
-            each column is a feature.
-
-        Returns
-        -------
-        prob : array-like, shape (len(X))
-            The probability from the KDE pdf for each point in X.
-        """
-        if self._std_X is None:
-            raise ValueError("KDE has not been fitted to data yet.")
-
-        X = np.atleast_2d(X)
-        _, n_feat = X.shape
-        if n_feat != self._n_features:
-            raise ValueError("Dimensions of given points and KDE don't match.")
-
-        # Standardize given points to be in the same space as the KDE
-        X = standardize_nd_sample(
-            X,
-            mean=self._mean,
-            cov=self._cov,
-            cholesky=True,
-            ret_stats=False,
-            diag=self._diag_cov,
-        )
-
-        # No need to backtransform, because we only return y-values
-        return self._evaluate(X, adaptive=self._adaptive)
-
-    def sample(self, n_samples, random_state=None):
-        """
-        Get random samples from the KDE model.
-
-        Parameters
-        ----------
-        n_samples : int, optional
-            Number of samples to generate. (default: 1)
-        random_state : RandomState, optional
-            Turn seed into a `np.random.RandomState` instance. Method from
-            `sklearn.utils`. Can be None, int or RndState. (default: None)
-
-        Returns
-        -------
-        X : array_like, shape (n_samples, n_features)
-            Generated samples from the fitted model.
-        """
-        if self._std_X is None:
-            raise ValueError("KDE has not been fitted to data yet.")
-
-        rndgen = check_random_state(random_state)
-
-        # Select randomly all kernels to sample from
-        idx = rndgen.randint(0, self._n_kernels, size=n_samples)
-
-        # Because we scaled to standard normal dist, we can draw uncorrelated
-        # and the cov is only the inverse bandwidth of each kernel.
-        means = self._std_X[idx]
-        invbw = np.ones(n_samples) / self._glob_bw
-        if self._adaptive:
-            invbw *= self._inv_loc_bw[idx]
-        invbw = invbw.reshape(n_samples, 1)
-
-        # Retransform to original space
-        sample = np.atleast_2d(rndgen.normal(means, 1.0 / invbw))
-        return shift_and_scale_nd_sample(sample, self._mean, self._cov)
-
-    def score(self, X):
-        """
-        Compute the total ln-probability of points X under the KDE model.
-
-        Parameters
-        ----------
-        X : array-like, shape (n_samples, n_features)
-            Data points included in the score calculation. Each row is a point,
-            each column is a feature.
-
-        Returns
-        -------
-        lnprob : float
-            Total ln-likelihood of the data ``X`` given the KDE model.
-        """
-        if self._std_X is None:
-            raise ValueError("KDE has not been fitted to data yet.")
-
-        X = np.atleast_2d(X)
-        _, n_feat = X.shape
-        if n_feat != self._n_features:
-            raise ValueError("Dimensions of given points and KDE don't match.")
-
-        probs = self.predict(np.atleast_2d(X))
-        if np.any(probs <= 0):
-            return -np.inf
-        else:
-            return np.sum(np.log(probs))
-
-    def to_json(self, fpath):
-        """
-        Write out the relevant parameters for the KDE model as a JSON file,
-        which can be used to reconstruct the whole model with ``from_json``.
-
-        Parameters
-        ----------
-        fpath : string
-            File path where to save the JSON dump.
-        """
-        if self._std_X is None:
-            raise ValueError("KDE has not been fitted to data yet.")
-
-        out = self.get_params()  # From implementing sklearnBaseEstimator
-        out["kde_X_std"] = [list(Xi) for Xi in self._std_X]
-        out["kde_X_mean"] = list(self._mean)
-        out["kde_X_cov"] = [list(Xi) for Xi in self._cov]
-
-        if self._kde_values is not None:
-            out["kde_Y"] = list(self._kde_values)
-        else:
-            out["kde_Y"] = None
-
-        # json seems to behave differently in py2 vs py3 ...
-        if sys.version_info[0] < 3:
-            mode = "wb"
-        else:
-            mode = "w"
-        with open(os.path.abspath(fpath), mode) as f:
-            json.dump(obj=out, fp=f, indent=2)
-
-        return
-
-    @classmethod
-    def from_json(cls, fpath, verb=False):
-        """
-        Build a awKDE object from a JSON dict with the needed parts.
-
-        Parameters
-        ----------
-        fpath : string
-            Path to the JSON file. Must have keys:
-
-            - 'alpha', 'diag_cov', 'glob_bw': See GaussianKDE docstring.
-            - 'kde_Y': KDE function values at points 'kde_X_std' used for the
-                       adaptive kernel computation.
-            - 'kde_X_std': Standardized sample in shape
-                           ``(nsamples, nfeatures)``.
-            - 'kde_X_mean': Mean vector of the standardized sample.
-            - 'kde_X_cov': Covariance matrix of the stadardized sample.
-        verb : bool, optional
-            If ``True`` print model summary. (default: ``False``)
-
-        Returns
-        -------
-        kde : KDE.GaussianKDE
-            KDE object in fitted state, ready to evaluate or sample from.
-        """
-        with open(os.path.abspath(fpath), "rb") as f:
-            d = json.load(f)
-
-        kde = cls(glob_bw=d["glob_bw"], alpha=d["alpha"], diag_cov=d["diag_cov"])
-
-        # Reconstruct all internals without using fit again
-        kde._std_X = np.atleast_2d(d["kde_X_std"])
-        kde._n_kernels, kde._n_features = kde._std_X.shape
-        kde._mean = np.atleast_1d(d["kde_X_mean"])
-        kde._cov = np.atleast_2d(d["kde_X_cov"])
-
-        if len(kde._mean) != kde._n_features:
-            raise ValueError(
-                "'kde_X_mean' has not the same dimension " + "as the X values."
-            )
-        if kde._cov.shape != (kde._n_features, kde._n_features):
-            raise ValueError("'kde_X_cov' has not shape " + "(n_features, n_features).")
-
-        if d["kde_Y"] is not None:
-            if d["alpha"] is None:
-                raise ValueError("Saved 'alpha' is None, but 'kde_Y' is not.")
-            # Set kde values and alpha to restore inverse bandwidth internally
-            kde._kde_values = np.atleast_1d(d["kde_Y"])
-            kde.alpha = d["alpha"]
-
-        if len(kde._kde_values) != kde._n_kernels:
-            raise ValueError("'kde_Y' has not the same length as 'kde_X_std'.")
-
-        if verb:
-            print("Loaded KDE model from {}".format(fpath))
-            print("- glob_bw         : {:.3f}".format(kde._glob_bw))
-            print("- alpha           : {:.3f}".format(kde._alpha))
-            print("- adaptive        : {}".format(kde._adaptive))
-            print("- Nr. of kernels  : {:d}".format(kde._n_kernels))
-            print("- Nr. of data dim : {:d}".format(kde._n_features))
-
-        return kde
-
-    # Private Methods
-    def _evaluate(self, X, adaptive):
-        """
-        Evaluate KDE at given points, returning the log-probability.
-
-        Parameters
-        -----------
-        X : array-like, shape (n_samples, n_features)
-            Data points we want to evaluate the KDE at. Each row is a point,
-            each column is a feature.
-        adaptive : bool, optional
-            Wether to evaluate with fixed or with adaptive kernel.
-            (default: True)
-
-        Returns
-        -------
-        prob : array-like, shape (len(X))
-            The probability from the KDE PDF for each point in X.
-        """
-        n = self._n_kernels
-        d = self._n_features
-
-        # Get fixed or adaptive bandwidth
-        invbw = np.ones(n) / self._glob_bw
-        if adaptive:
-            invbw *= self._inv_loc_bw
-
-        # Total norm, including gaussian kernel norm with data covariance
-        norm = invbw**d / np.sqrt(np.linalg.det(2 * np.pi * self._cov)) / n
-
-        return backend.kernel_sum(self._std_X, X, invbw, norm)
-
-    def _get_glob_bw(self, glob_bw):
-        """Simple wrapper to handle string args given for global bw."""
-        dim = self._n_features
-        nsam = self._n_kernels
-        if glob_bw == "silverman":
-            return np.power(nsam * (dim + 2.0) / 4.0, -1.0 / (dim + 4))
-        elif glob_bw == "scott":
-            return np.power(nsam, -1.0 / (dim + 4))
-        else:
-            return self._glob_bw
-
-    def _calc_local_bandwidth(self):
-        """Build the local bandwidth from cached ``_kde_values``."""
-        # Get local bandwidth from local "density" g
-        g = np.exp(np.sum(np.log(self._kde_values)) / self._n_kernels)
-        # Needed inverted so use power of (+alpha), shape (n_samples)
-        self._inv_loc_bw = (self._kde_values / g) ** (self._alpha)
-        return
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diff --git a/data/src/awkde/awkde/tools.py b/data/src/awkde/awkde/tools.py
deleted file mode 100644
index 153adf3e..00000000
--- a/data/src/awkde/awkde/tools.py
+++ /dev/null
@@ -1,147 +0,0 @@
-# coding: utf8
-
-"""
-Part of awkde package
----------------------
-
-Helper tools for standardizing a data sample.
-"""
-
-from __future__ import print_function, division, absolute_import
-from future import standard_library
-
-standard_library.install_aliases()
-
-import numpy as _np
-
-
-def standardize_nd_sample(
-    sam, mean=None, cov=None, cholesky=True, ret_stats=False, diag=False
-):
-    """
-    Standardizes a n-dimensional sample using the Mahalanobis distance.
-
-    .. math:: x' = \Sigma^{-1/2} (x - y)
-
-    The resulting sample :math:`x'` has a zero mean vector and an identity
-    covariance.
-
-    Parameters
-    ----------
-    sam : array-like, shape (n_samples, n_features)
-        Data points in the sample, each column is a feature, each row a point.
-    mean : array-like, shape (n_features), optional
-        If explicitely given, use this mean vector for the transformation. If
-        None, the estimated mean from data is used. (default: None)
-    cov : array-like, shape (n_features, n_features), optional
-        If explicitely given, use this covariance matrix for the transformation.
-        If None, the estimated cov from data is used. (default: None)
-    cholesky : bool, optional
-        If true, use fast Cholesky decomposition to calculate the sqrt of the
-        inverse covariance matrix. Else use eigenvalue decomposition (Can be
-        numerically unstable, not recommended). (default: True)
-    ret_stats : bool, optional
-        If True, the mean vector and covariance matrix of the input sample are
-        returned, too. (default: False)
-    diag : bool
-        If True, only scale by variance, diagonal cov matrix. (default: False)
-
-    Returns
-    -------
-    stand_sam : array-like, shape (n_samples, n_features)
-        Standardized sample, with mean = [0., ..., 0.] and cov = identity.
-
-    Optional Returns
-    ----------------
-    mean : array-like, shape(n_features)
-        Mean vector of the input data, only if ret_stats is True.
-    cov : array-like, shape(n_features, n_features)
-        Covariance matrix of the input data, only if ret_stats is True.
-
-    Example
-    -------
-    >>> mean = [10, -0.01, 1]
-    >>> cov = [[14, -.2, 0], [-.2, .1, -0.1], [0, -0.1, 1]]
-    >>> sam = np.random.multivariate_normal(mean, cov, size=1000)
-    >>> std_sam = standardize_nd_sample(sam)
-    >>> print(np.mean(std_sam, axis=0))
-    >>> print(np.cov(std_sam, rowvar=False))
-    """
-    if len(sam.shape) != 2:
-        raise ValueError("Shape of `sam` must be (n_samples, n_features).")
-    if mean is None and cov is None:
-        # Mean and cov over the first axis
-        mean = _np.mean(sam, axis=0)
-        cov = _np.atleast_2d(_np.cov(sam, rowvar=False))
-    elif mean is not None and cov is not None:
-        mean = _np.atleast_1d(mean)
-        cov = _np.atleast_2d(cov)
-        if len(mean) != sam.shape[1]:
-            raise ValueError("Dimensions of mean and sample don't match.")
-        if cov.shape[0] != sam.shape[1]:
-            raise ValueError("Dimensions of cov and sample don't match.")
-
-    if diag:
-        cov = _np.diag(cov) * _np.eye(cov.shape[0])
-
-    if cholesky:
-        # Cholesky produces a tridiagonal matrix from A with: L L^T = A
-        # To get the correct trafo, we need to transpose the returned L:
-        #   L.L^t
-        sqrtinvcov = _np.linalg.cholesky(_np.linalg.inv(cov)).T
-    else:
-        # The naive sqrt of eigenvalues. Is (at least) instable for > 3d
-        # A = Q lam Q^-1. If A is symmetric: A = Q lam Q^T
-        lam, Q = _np.linalg.eig(_np.linalg.inv(cov))
-        sqrtlam = _np.sqrt(lam)
-        sqrtinvcov = _np.dot(sqrtlam * Q, Q.T)
-
-    # Transform each sample point and reshape result (n_samples, n_features)
-    stand_sam = _np.dot(sqrtinvcov, (sam - mean).T).T
-
-    if ret_stats:
-        return stand_sam, mean, cov
-    else:
-        return stand_sam
-
-
-def shift_and_scale_nd_sample(sam, mean, cov, cholesky=True):
-    """
-    Shift and scale a nD sample by given mean and covariance matrix.
-
-    This is the inverse operation of `standardize_nd_sample`. If a
-    standardized sample :math:`x'` with zero mean vector and identity covariance
-    matrix is given, it is rescaled and shifted using
-
-    .. math:: x = (\Sigma^{1/2} x) + y
-
-    then having a mean vector `mean` and a covariance matrix `cov`.
-
-    Parameters
-    ----------
-    sam : array-like, shape (n_samples, n_features)
-        Data points in the sample, each column is a feature, each row a point.
-    mean : array-like, shape (n_features)
-        Mean vector used for the transformation.
-    cov : array-like, shape (n_features, n_features)
-        Covariance matrix used for the transformation.
-
-    Returns
-    -------
-    scaled_sam : array-like, shape (n_samples, n_features)
-        Scaled sample using the transformation with the given mean and cov.
-    """
-    if len(sam.shape) != 2:
-        raise ValueError("Shape of `sam` must be (n_samples, n_features).")
-    mean = _np.atleast_1d(mean)
-    cov = _np.atleast_2d(cov)
-    if len(mean) != sam.shape[1]:
-        raise ValueError("Dimensions of mean and sample don't match.")
-    if cov.shape[0] != sam.shape[1]:
-        raise ValueError("Dimensions of cov and sample don't match.")
-
-    # Transformation matrix: inverse of original trafo
-    sqrtinvcov = _np.linalg.cholesky(_np.linalg.inv(cov)).T
-    sqrtcov = _np.linalg.inv(sqrtinvcov)
-
-    return _np.dot(sqrtcov, sam.T).T + mean
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+++ /dev/null
@@ -1,115 +0,0 @@
-#include <pybind11/pybind11.h>
-#include <pybind11/numpy.h>
-#include <cmath>
-
-
-namespace py = pybind11;
-
-// Actual C++ snippet. See `docstr` below for argument info.
-template <typename T>
-py::array_t<T> kernel_sum (const py::array_t<T>& X,
-                           const py::array_t<T>& Y,
-                           const py::array_t<T>& invbw,
-                           const py::array_t<T>& norm) {
-    // Gather input shape info
-    auto bX = X.template unchecked<2>();
-    auto bY = Y.template unchecked<2>();
-    auto lenX = bX.shape(0);
-    auto ndim = bX.shape(1);
-    auto lenY = bY.shape(0);
-    auto ndimY = bY.shape(1);
-    auto binvbw = invbw.template unchecked<1>();
-    auto bnorm = norm.template unchecked<1>();
-
-    // Check data integrity
-    if (ndim != ndimY) {
-        throw std::runtime_error(
-            "Points in X and Y must have the same number of dimensions.");
-    }
-    if (lenX != binvbw.shape(0) || lenX != bnorm.shape(0)) {
-        throw std::runtime_error(
-            "X, invbw and norm must have the same lengths.");
-    }
-
-    // Set up output: shape (lenY)
-    py::array_t<T> eval(lenY);
-    auto beval = eval.template mutable_unchecked<1>();
-
-    // Get squared distances for all in Y to all in X and sum up the PDF value
-    // from the gaussian kernel PDF for each Y.
-    T diff_ij;
-    T dist2_ij;
-    for (unsigned int i = 0; i < lenY; ++i) {  // Loop over Y elements
-        beval(i) = 0.;
-        for (unsigned int j = 0; j < lenX; ++j) {  // Loop over X elements
-            dist2_ij = 0.;
-            for (unsigned int k = 0; k < ndim; ++k) {  // Loop over features
-                diff_ij = bY(i, k) - bX(j, k);
-                dist2_ij += diff_ij * diff_ij;
-            }
-            // PDF_i = sum_{j in X} (norm_j * exp(-0.5 * dist2_ij * invbw_j^2)
-            beval(i) += bnorm(j) * std::exp(-0.5 * dist2_ij *
-                                            binvbw(j) * binvbw(j));
-        }
-    }
-
-    return eval;
-}
-
-
-// ////////////////////////////////////////////////////////////////////////////
-// pybind11 binding stuff
-PYBIND11_MODULE(backend, m) {
-    // Module docstring
-    m.doc() = R"pbdoc(
-        Pybind11 C++ backend for awkde
-        ------------------------------
-
-        .. currentmodule:: awkde_backend
-
-        .. autosummary::
-           :toctree: _generate
-
-           kernel_sum
-    )pbdoc";
-
-    // Method docstrings (easier to read when separated)
-    // Note: using C++ multi-line literals (pbdoc delimiter)
-    auto docstr = R"pbdoc(
-    kernel_sum
-
-    Takes an array of kernel points `X` and points `Y` to
-    evaluate the KDE at and returns the KDE PDF values for
-    each point in `Y`.
-
-    Parameters
-    ----------
-    X : double array, shape (len(X), ndim)
-        Data points defining each kernel position. Each row is
-        a point, each column is a feature.
-    Y : double array, shape (len(Y), ndim)
-        Data points we want to evaluate the KDE at. Each row is
-        a point, each column is a feature.
-    invbw : double array, shape (len(X))
-        Inverse kernel bandwidth, acting as :math:`1 / sigma^2`.
-    norm : double array, shape (len(X))
-        Kernel gaussian norm for `ndim` dimensions.
-
-    Returns
-    -------
-    eval : float array, shape (len(Y))
-        The probability from the KDE PDF for each point in `Y`.
-    )pbdoc";
-
-    // Define the actual template typess
-    m.def("kernel_sum", &kernel_sum<double>, docstr,
-          py::arg("X"), py::arg("Y"), py::arg("invbw"), py::arg("norm"));
-    m.def("kernel_sum", &kernel_sum<float>, "",
-          py::arg("X"), py::arg("Y"), py::arg("invbw"), py::arg("norm"));
-
-#ifdef VERSION_INFO
-    m.attr("__version__") = py::str(VERSION_INFO);
-#else
-    m.attr("__version__") = py::str("dev");
-#endif
-}
\ No newline at end of file
diff --git a/data/src/awkde/example/example.png b/data/src/awkde/example/example.png
deleted file mode 100644
index 4a3562a3..00000000
Binary files a/data/src/awkde/example/example.png and /dev/null differ
diff --git a/data/src/awkde/example/example.py b/data/src/awkde/example/example.py
deleted file mode 100644
index 15f8c513..00000000
--- a/data/src/awkde/example/example.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# coding: utf-8
-
-"""
-A 2D example for the adaptive width KDE.
-
-Sample x1 from a gaussian and x2 from x*exp(-x).
-Make a plot with the prediction and save the model to a JSON dump.
-"""
-
-import numpy as np
-import scipy.stats as scs
-import matplotlib.pyplot as plt
-import matplotlib.gridspec as gridspec
-from matplotlib.colors import LogNorm
-
-from awkde import GaussianKDE
-
-
-rndgen = np.random.RandomState(seed=3575)  # ESEL
-# Gaussian
-mean = 3.0
-sigma = 0.25
-# a^2 * x * exp(-a * x)
-a = 100.0
-
-n_samples = 1000
-logE_sam = rndgen.normal(mean, sigma, size=n_samples)
-
-# From pythia8: home.thep.lu.se/~torbjorn/doxygen/Basics_8h_source.html
-u1, u2 = rndgen.uniform(size=(2, n_samples))
-sigma_sam = -np.log(u1 * u2) / a
-
-# Shape must be (n_points, n_features)
-sample = np.vstack((logE_sam, sigma_sam)).T
-
-# Create KDE and fit it. Save model in JSON format
-print("Fitting model to {} sample points.".format(n_samples))
-kde = GaussianKDE(glob_bw="silverman", alpha=0.5, diag_cov=True)
-kde.fit(sample)
-
-# Save and load the model
-outf = "./example_KDE.json"
-print("Saving model to {}".format(outf))
-kde.to_json(outf)
-print("Loading same model from {}".format(outf))
-kde = GaussianKDE.from_json(outf)
-
-# Evaluate at dense grid
-minx, maxx = np.amin(sample[:, 0]), np.amax(sample[:, 0])
-miny, maxy = np.amin(sample[:, 1]), np.amax(sample[:, 1])
-
-x = np.linspace(minx, maxx, 100)
-y = np.linspace(miny, maxy, 100)
-
-XX, YY = np.meshgrid(x, y)
-grid_pts = np.array(list(map(np.ravel, [XX, YY]))).T
-
-zz = kde.predict(grid_pts)
-ZZ = zz.reshape(XX.shape)
-
-# These are bin edges for pcolormesh, len = len(x) + 1
-dx2, dy2 = (x[1] - x[0]) / 2.0, (y[1] - y[0]) / 2.0
-bx = np.concatenate((x - dx2, [x[-1] + dx2]))
-by = np.concatenate((y - dy2, [y[-1] + dy2]))
-
-
-# Evaluate true PDFs at same grid
-def fx(x):
-    return scs.norm.pdf(x, mean, sigma)
-
-
-def fy(y):
-    return a**2 * y * np.exp(-a * y)
-
-
-fZ = (fx(grid_pts[:, 0]) * fy(grid_pts[:, 1])).reshape(XX.shape)
-
-# Sample new points from KDE model
-kde_sam = kde.sample(n_samples=100000, random_state=rndgen)
-
-print("Making example plot.")
-# Big plot on the left (2D KDE + points ) and three right (1D margins + hist)
-fig = plt.figure(figsize=(10, 6))
-gs = gridspec.GridSpec(3, 3)
-axl = fig.add_subplot(gs[:, :2])
-axrt = fig.add_subplot(gs[0, 2])
-axrc = fig.add_subplot(gs[1, 2])
-axrb = fig.add_subplot(gs[2, 2])
-
-color_of_points = "#351322"
-color_of_dist = "#1e90ff"
-
-# Main plot
-axl.pcolormesh(bx, by, ZZ, cmap="Blues", norm=LogNorm(), shading="flat")
-axl.scatter(
-    logE_sam, sigma_sam, marker=".", color=color_of_points, edgecolor="none", s=30
-)
-axl.set_title("KDE log PDF + original sample")
-
-# Top right: truth with scatter
-axrt.pcolormesh(bx, by, fZ, cmap="Blues", norm=LogNorm(), shading="flat")
-axrt.scatter(logE_sam, sigma_sam, marker=".", color=color_of_points, s=1)
-axrt.set_title("True log PDF + KDE sample")
-
-# 1D x1, x2 hists. Hist very fine, so we get the shape of the PDF and don't
-# have to integrate the KDE PDF numerically.
-axrc.hist(kde_sam[:, 0], bins=250, density=True, color=color_of_points)
-axrc.plot(x, fx(x), color=color_of_dist)
-axrb.hist(kde_sam[:, 1], bins=250, density=True, color=color_of_points)
-axrb.plot(y, fy(y), color=color_of_dist)
-axrc.set_title("True 1D PDF + KDE sample")
-axrb.set_title("True 1D PDF + KDE sample")
-
-for axi in (axl, axrt):
-    axi.set_xlim(minx, maxx)
-    axi.set_ylim(0, maxy)
-    axi.set_xlabel("x1")
-    axi.set_ylabel("x2")
-
-axrc.set_xlim(minx, maxx)
-axrb.set_xlim(miny, maxy)
-axrc.set_xlabel("x1")
-axrb.set_xlabel("x2")
-
-fig.tight_layout()
-fig.savefig("example.png", dpi=50)
-# plt.show()
diff --git a/data/src/awkde/example/example_KDE.json b/data/src/awkde/example/example_KDE.json
deleted file mode 100644
index 8e4994c4..00000000
--- a/data/src/awkde/example/example_KDE.json
+++ /dev/null
@@ -1,1346 +0,0 @@
-{
-  "kde_X_cov": [
-    [0.055394409593707866, 0.0],
-    [0.0, 0.000189259752803009]
-  ],
-  "glob_bw": 0.31622776601683794,
-  "kde_X_std": [
-    [-0.038772367739634397, -1.1254927149286837],
-    [0.91283236066410689, -1.0203477536431722],
-    [-0.053052130849891473, 0.043403576093835834],
-    [-1.0756456141606578, 0.42504425101493282],
-    [0.12079924319366782, 0.84469339896658147],
-    [-1.0539632687647917, -0.5483372178811039],
-    [1.5767696954279486, -0.78224177248956683],
-    [-1.2190236670242769, -0.99111259730203682],
-    [1.4931503939595865, -1.300980586705534],
-    [0.1220377935327538, 0.061519148878938459],
-    [2.2069432312913313, -0.7143264313133022],
-    [1.9341883310230343, 0.99504117628753064],
-    [0.72551610777568942, 0.28456885096661361],
-    [1.5285619237773624, -0.55287318744585356],
-    [0.16333960386251034, -0.53061949111115592],
-    [-0.5050524027018567, 1.2524600532486772],
-    [0.23234176914316912, -0.18131358604187067],
-    [-0.90780269926272628, 0.036142650371662817],
-    [0.36800071918168614, -0.0070031795904719179],
-    [-0.10064751715853765, 1.5304585212365116],
-    [-0.35122794397872431, 0.27943077479246325],
-    [-0.80382666045113094, -0.12898312177038987],
-    [0.41224958215799318, 0.64365480467782732],
-    [2.7129398019058595, -0.32810905962283138],
-    [-2.2361614923831334, 0.16002049630640255],
-    [-1.3206348042010336, 0.98041885279393159],
-    [-0.34139280736010874, -1.2286699964943877],
-    [1.7729512081167225, 0.91163034764109918],
-    [0.57505284564283687, -1.0972390183490282],
-    [0.19825977243802756, -1.0873055838346457],
-    [-0.654932972471994, -1.3078385530466843],
-    [-0.58475208340517848, -1.0580303697727917],
-    [0.42983230302562808, 2.0044995350940944],
-    [-0.27815222403779749, -0.43678173076992693],
-    [-0.60728286225878769, -1.1700500534079419],
-    [-0.78891057551636723, 0.59585812659142612],
-    [-0.016170617822073137, -0.95961686832095305],
-    [-2.1426063401230699, -1.3315591188358176],
-    [-0.30090661571572924, 2.6456443611317471],
-    [-0.15802001927584688, 0.45495088779220688],
-    [-0.83488247617177946, -0.44589633575639986],
-    [0.2479730662335021, -0.084092313195468629],
-    [1.5254718117934776, -1.1404409003946978],
-    [1.2238974647235652, 0.34090541177725814],
-    [0.91465263273951047, 0.44160441828566543],
-    [-0.066259199584315018, 0.56380852126987846],
-    [-0.39542212734122262, 0.17718601155493119],
-    [0.17652094257423095, -0.39508111489101982],
-    [0.068107825083629031, 0.69008735825929512],
-    [-0.56695604118475917, 0.3275177676362917],
-    [-0.22399671484983688, -0.001379326037921005],
-    [-0.15367203570186891, 1.3294631530719758],
-    [-0.23324842028993184, -0.93250741110297264],
-    [0.98047025711942104, 1.8479876249111546],
-    [1.0238816220332314, -0.62935347216543913],
-    [-0.41792921871027139, 1.7055333860412465],
-    [1.6228851342570911, 1.7892960657909092],
-    [-0.028484886684660357, 0.86975888426881254],
-    [0.33489307530858553, -0.46931069767422345],
-    [-0.081242722847126303, 0.18488730963420796],
-    [-0.5007790087922257, -0.79155910992885026],
-    [-0.72034839456750921, -0.6650074954682067],
-    [-0.41252933420480936, -0.48949394331785628],
-    [0.38996746421843415, -0.64509175234983829],
-    [-0.85012548480957884, -0.034286906329572403],
-    [1.9687233064963148, 2.3046159837380054],
-    [0.72823689131001434, -0.19626461186956734],
-    [-1.2118822992970855, -0.76934394415088303],
-    [-0.19297178364046058, -0.41834660295120024],
-    [0.61563343534856618, -0.11845238952535454],
-    [0.70167061759060212, -0.74833812899924113],
-    [-0.070465004791077632, 1.8055424732738568],
-    [-1.3638929226972762, -0.89510132739248938],
-    [-1.4529943556801053, -1.1818187779871725],
-    [-1.2606917362347754, -0.4301030229653996],
-    [-1.2273726958298214, 0.36584025502615763],
-    [1.0863784937705043, -0.20155908611548315],
-    [0.24309476807382935, 0.4109723885746695],
-    [1.1036479238383679, -1.0412773345339248],
-    [-0.99185219253367318, -0.86210030402568805],
-    [0.94724982938884028, -0.97758633409625328],
-    [-0.12875083391666997, -1.0597380645788717],
-    [1.2944206187944147, 0.53650163036127041],
-    [-1.293252604981163, -0.85211866718453977],
-    [-0.18609554341345411, -1.0489853388664461],
-    [-2.5534850985478732, -0.27068344556963386],
-    [-0.091969682364326386, -0.23420189267828612],
-    [0.17298936823033406, -0.19084454636041906],
-    [0.79166305555512406, 0.12303226027725996],
-    [0.83417560704215055, 0.42771761436946582],
-    [-0.72406631107421193, 1.6492482353640665],
-    [2.5296769249474353, -1.1944429926515672],
-    [-1.3351024481102842, 2.2501007625819249],
-    [-1.7266120099747977, -1.2066642818074598],
-    [-0.65803886018625757, -0.42458375854079838],
-    [-1.4751932272875081, 0.03627627608030197],
-    [0.50870519076321097, -1.1041803060942859],
-    [2.3876324293900817, -0.065491912365537905],
-    [0.46050388632359573, -0.90306586825358603],
-    [-1.3157209197658335, -0.048096838642655057],
-    [-0.47008421513190757, 0.26771642480144492],
-    [0.58373521102510872, -0.65413820068944328],
-    [-0.89548301995423063, -1.2534087588670748],
-    [-1.8304030833253615, -0.85349112885425837],
-    [0.40778211224941996, -1.1891623150576829],
-    [-1.5063201088328682, -0.034693614602703704],
-    [0.077394827047095063, -0.48079377456933525],
-    [1.8872348565628514, -0.99100401556364426],
-    [-0.57983754184705638, -0.069222549088807261],
-    [0.63205639406195957, 0.040974523293963346],
-    [0.69070940799810543, -0.52952487183260122],
-    [-2.0183416947523241, -1.1738751914291645],
-    [-0.23232863884416557, 1.1253437050027864],
-    [-0.33270088596271385, 0.17169354028770778],
-    [-1.7776536530548805, -0.67581426204984585],
-    [-0.18813186215523445, -0.055577446545041619],
-    [0.80496678542421618, 0.34579195436671761],
-    [-0.31887618979112076, -1.3471343588011564],
-    [0.46671827236945357, 1.120698581857722],
-    [-1.080246617945092, -0.8367469372976718],
-    [1.8684702823371175, -0.24514232476644246],
-    [0.63284900258848775, -0.065054649027471823],
-    [-0.0506976364495378, -0.20743569170279827],
-    [-2.1322566738075071, 0.23179887530253848],
-    [1.1446042978333772, 3.07776901701085],
-    [0.66843336391437047, -1.0031320601347922],
-    [0.016509218339861579, 4.0513080405612758],
-    [2.1004373648957362, -0.44531771019951666],
-    [0.78075616290166516, -0.32842613834560586],
-    [0.13016871926236007, -0.048037283489279596],
-    [0.48696002404338373, 0.11079036304780647],
-    [-0.35852091006074049, -0.62481725704504065],
-    [-0.84856541980158517, -0.18807486230713741],
-    [-0.73713678884965439, 0.20361340058431293],
-    [-0.55901765955143923, 1.3966559490697048],
-    [1.0245902185486961, 0.57016475567687619],
-    [0.12439137474745653, 0.57804562782782098],
-    [0.9227204032733437, 1.7272179211285759],
-    [1.1129534258487503, -0.62880004779541732],
-    [-0.56260201865246617, -0.77449635413316964],
-    [1.0695428278985164, 0.24135785164700549],
-    [0.10511656252554125, 0.22227012884682679],
-    [0.66899149347343356, -0.74326663440881935],
-    [0.57594460914975154, -1.0352070625914438],
-    [0.63140022559718145, -0.47914365212599352],
-    [-0.36175692574754931, -1.1111035181890572],
-    [0.45918854118962094, 0.90291757666583505],
-    [0.62797180607299585, -1.0022068876321306],
-    [-0.80998512772424813, -1.0099204639373431],
-    [-1.1525043094121921, -1.2384537226113088],
-    [1.2171438489881916, 0.11207717513910258],
-    [1.3166105228343228, 1.7449027854895383],
-    [0.061434181754760481, 0.68786128655135514],
-    [-0.76594256403931316, -0.49398561381701117],
-    [1.7362652563759693, -0.17398681290053361],
-    [-0.85423174450574246, 0.27616823819484776],
-    [-0.69683213650374265, 2.3795666222152803],
-    [1.0615877681027768, 0.18585301362333759],
-    [1.3241362247841066, 0.40681190078073048],
-    [-0.23617873940978798, -0.9250586245365261],
-    [0.30614001089815185, 1.8155252075806645],
-    [1.1447902753480921, -0.35350137720339486],
-    [2.2193115033287838, -0.12501181744103168],
-    [-0.36161163527524959, -0.83512028922215076],
-    [0.85769413844023845, -0.5964211893654342],
-    [1.3263983911040482, 1.4300922383927743],
-    [-0.14044019719883249, 4.0847224381413456],
-    [-1.9164805785535866, 0.26176065966185952],
-    [-1.2205965197881774, -0.448947175875897],
-    [-0.95929931312859262, -0.17727592645617476],
-    [-1.8260296395509554, 0.92890503993689955],
-    [0.31033945951677444, 0.18070744806028158],
-    [-0.46473371296099525, 1.1221774850395263],
-    [-0.43048835312942246, -0.81807681983944036],
-    [-0.084383655173623381, -0.48378913724778372],
-    [0.32579630937407844, 0.041551188262265462],
-    [-1.2982712510279686, -0.66307225202100273],
-    [0.4317412641500768, 0.81972401222948443],
-    [-0.37127360085483413, 0.12639698660058676],
-    [0.69692819441339326, -0.23773434981674194],
-    [-0.0074801683471671983, -0.20104741922358621],
-    [-0.055147440662418543, -0.43452629558626565],
-    [0.50478587778940565, -0.84443531229231206],
-    [1.1307622274138558, 1.5853231181599845],
-    [1.1959743113386254, -1.0787095913829654],
-    [1.5696343300299844, -1.1747629367630215],
-    [0.2816675394476203, -0.40299502508143409],
-    [0.23151506141696973, 1.8706673665136597],
-    [1.3883010472771509, 0.32315693057847727],
-    [-0.54460716881955906, 1.5688309903376627],
-    [-0.5317213266115034, -1.0166494084974111],
-    [-2.0984918740299756, -1.26639178175762],
-    [-0.18360212569648834, 0.64294180715265281],
-    [0.10365598178725659, -1.0318580774405659],
-    [-1.1398433327839337, 0.18709003014587619],
-    [-1.1322231761991095, 1.67457090495573],
-    [-1.7083367159353426, 0.5069947817121021],
-    [1.0463254795378605, -1.0178800522694997],
-    [0.30177844720871189, -0.82392407631122222],
-    [0.078450619741106495, 0.12711093961029016],
-    [-0.55829413029875252, -0.62384473059007384],
-    [0.59651665633358031, 1.8770321860630763],
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-}
diff --git a/data/src/awkde/pyproject.toml b/data/src/awkde/pyproject.toml
deleted file mode 100644
index 38a53fd1..00000000
--- a/data/src/awkde/pyproject.toml
+++ /dev/null
@@ -1,3 +0,0 @@
-[build-system]
-requires = ["setuptools>=42", "wheel", "pybind11"]
-build-backend = "setuptools.build_meta"
diff --git a/data/src/awkde/setup.py b/data/src/awkde/setup.py
deleted file mode 100644
index d77c9bfd..00000000
--- a/data/src/awkde/setup.py
+++ /dev/null
@@ -1,36 +0,0 @@
-"""
-Note: `pip install --user pybind11` mandatory. Did not really test if the
-instal_requires and import workaround below works. If not, just install it
-manually first.
-
-Credits:
-- https://pybind11.readthedocs.io/en/latest/compiling.html
-- https://github.com/pybind/python_example
-"""
-
-from setuptools import setup, find_packages
-
-from pybind11.setup_helpers import Pybind11Extension, build_ext
-
-
-ext_modules = [
-    Pybind11Extension(
-        # Module name (toplevel same as python, then same as in C++ file)
-        name="awkde.backend",
-        sources=["cpp/backend.cpp"],
-        extra_compile_args=["-O3"],  # "-ggdb" for debugging stuff
-    ),
-]
-
-setup(
-    name="awkde",
-    version="0.1",
-    author="Thorben Menne",
-    author_email="thorben.menne@tu-dortmund.de",
-    url="https://github.com/mennthor/awkde",
-    description="Adaptive width gaussian KDE",
-    packages=find_packages(),
-    install_requires=["numpy", "scipy", "scikit-learn", "pybind11", "future"],
-    ext_modules=ext_modules,  # Compiled external modules
-    cmdclass={"build_ext": build_ext},  # Finds highest supported C++ version
-)
diff --git a/data/src/awkde/tex/kernel_dens.png b/data/src/awkde/tex/kernel_dens.png
deleted file mode 100644
index 6affc9b7..00000000
Binary files a/data/src/awkde/tex/kernel_dens.png and /dev/null differ
diff --git a/data/src/awkde/tex/lambda.png b/data/src/awkde/tex/lambda.png
deleted file mode 100644
index 7e062ea8..00000000
Binary files a/data/src/awkde/tex/lambda.png and /dev/null differ
diff --git a/data/src/awkde/tex/log_g.png b/data/src/awkde/tex/log_g.png
deleted file mode 100644
index b6f01fd1..00000000
Binary files a/data/src/awkde/tex/log_g.png and /dev/null differ
diff --git a/data/src/main.py b/data/src/main.py
index 096d85cc..25176201 100644
--- a/data/src/main.py
+++ b/data/src/main.py
@@ -1,4 +1,3 @@
-import sys
 import traceback
 
 import pandas as pd
@@ -42,11 +41,6 @@
 )
 from new_etl.database import to_postgis_with_schema
 
-# Ensure the directory containing awkde is in the Python path
-awkde_path = "/usr/src/app"
-if awkde_path not in sys.path:
-    sys.path.append(awkde_path)
-
 
 try:
     print("Starting ETL process.")
diff --git a/data/src/new_etl/data_utils/pwd_parcels.py b/data/src/new_etl/data_utils/pwd_parcels.py
index 7d7b3f75..b0c51b41 100644
--- a/data/src/new_etl/data_utils/pwd_parcels.py
+++ b/data/src/new_etl/data_utils/pwd_parcels.py
@@ -44,6 +44,7 @@ def merge_pwd_parcels_gdf(
     pwd_parcels_gdf_indexed = pwd_parcels_gdf_unique_opa_id.set_index("opa_id")
     merged_gdf_indexed = primary_featurelayer_gdf_unique_opa_id.set_index("opa_id")
 
+    # ISSUE: This update and the other transformations might be incorrect
     merged_gdf_indexed.update(
         pwd_parcels_gdf_indexed[["geometry"]],
     )
diff --git a/data/src/script.py b/data/src/script.py
index cf42ace6..32a525cb 100644
--- a/data/src/script.py
+++ b/data/src/script.py
@@ -1,4 +1,3 @@
-import sys
 import time
 
 from classes.backup_archive_database import BackupArchiveDatabase
@@ -34,11 +33,6 @@
 
 from classes.slack_error_reporter import send_error_to_slack
 
-# Ensure the directory containing awkde is in the Python path
-awkde_path = "/usr/src/app"
-if awkde_path not in sys.path:
-    sys.path.append(awkde_path)
-
 try:
     services = [
         city_owned_properties,
diff --git a/docs/TECHNOLOGIES.md b/docs/TECHNOLOGIES.md
new file mode 100644
index 00000000..a1167791
--- /dev/null
+++ b/docs/TECHNOLOGIES.md
@@ -0,0 +1,102 @@
+# Project Technologies
+
+## Overview
+
+This document is mean to summarize all of the various technologies and tools used across the Clean & Green Philly project, encompassing both
+frontend, backend, ETL pipeline, devops/infrastructure, and miscellaneous tools. The following will include a brief description of each technology
+as well as its function within our project as well as links to external resource to help new contributors familiarize themselves.
+
+## Frontend
+
+### Next.js
+
+Our frontend web app is built with the Next.js framework, which is a React-based framework with additional, opinionated solutions for how to accomplish
+common web application tasks including routing, data fetching, optimizations for serving media files, and a range of other features. The best place to learn more about Next.js is directly on their [docs](https://nextjs.org/docs).
+
+### React
+
+React is the frontend, Javascript library that Next.js itself is built around. It is built around the philosophy of separating portions of a web application's visual design and functionality into separate chunks known as **components**, which can combined and reuse in modular ways across the application. You can read more about React on its [docs](https://react.dev/reference/react).
+
+### Tailwind
+
+We use Tailwind as our CSS framework, which is an increasing popular for styling in modern web applications. You can read more on their [docs](https://v3.tailwindcss.com/docs/installation). Clean & Green Philly currently uses Tailwind v3 as opposed to the most recently released v4, which includes some major design differences, so be sure you are referencing the correct portion of the docs.
+
+### Eslint and Prettier
+
+For linting and formatting, we use the standard solutions of ESLint and Prettier.
+
+ESLint - https://eslint.org/docs/latest/
+
+Prettier - https://prettier.io/docs/
+
+Because the overlap in roles for linting and formatters can be quite close, be sure to reference the integration docs about the interaction between the two technologies [here](https://prettier.io/docs/integrating-with-linters).
+
+### Maplibre
+
+The primary functionality of our web application is the interactive map to view vacant properties across Philadelphia. We achieve that using the Maplibre GL library, which is an open source Typescript library for rendering maps based on vector tile information in the browser environment. We supply the library with the primary feature information for visualization from our data pipeline and Maplibre GL creates an interactive map with zoom, panning, selection, and other capabilities.
+
+There are two documentation sources to note - the original library which includes all of the original Typescript classes and objects for interacting with the map and `react-map-gl`, a library of React component that wrap that functionality and allow them to be included in React-based applications with declarative style.
+
+Maplibre GL - https://maplibre.org/maplibre-gl-js/docs/
+
+React Maplibre - https://visgl.github.io/react-map-gl/docs/api-reference/maplibre/map
+
+## ETL Pipeline
+
+### Python
+
+All our data sourcing and pipeline code is written in Python. It's likely already installed on any particular contributors computer, but you can find documentation for installation and other resources on the main site [here](https://www.python.org/doc/). Keep in mind we use specific Python versioning within the project that you will need to adhere to using other tooling mentioned later in this document and the setup details.
+
+You can find a lightweight refresher of basic syntax and concetps across the language at [w3schools](https://www.w3schools.com/python/) or more in-depth and comprehensive discussion on the main [docs](https://docs.python.org/3/).
+
+### Geopandas
+
+As we are working with geospatial data and sourcing and transforming it in various ways, we use the main Python-based, open source library for working with such data - `geopandas`. It is an extension on the popular `pandas` framework that allwos you to organize data into flexible `DataFrame` objects for easy manipulation and transformation as well as adding additional support for geographic objects in the `GeoSeries` and `GeoDataFrame` types. The documentation is available [here](https://geopandas.org/en/stable/docs.html).
+
+### File Formatting - Geoparquet, PMTiles, tippiecanoe
+
+We use a variety of file types for storage of the end dataset constructed by the ETL pipeline. The most common format for encoding geospatial data is GeoJSON, which stores the geometry and other information in a familiar JSON format. However, because this is inefficient for loading and fetching client-side in our web application, we convert to several other file formats for better performance.
+
+The first are PMtiles, which is a single-file vector tile format for geospatial data. It's main benefit is that it, as a single file, encompasses all vector tiles are each zoom level for our data, and so it can be hosted in our Google Cloud Bucket and simply fetched from by Maplibre rather than needing a more complex backend or querying solution as an intermediary. More information can be found [here](https://docs.protomaps.com/pmtiles/).
+
+The second are GeoParquet files, which are a geospatial addition to Apache Parquet files. Their benefit is they are a highly performant way to compress and store columnar data, and they have useful mechanisms for self-describing and partitioning the data within the file itself for faster retrieval and querying in the future. We are in the process of shifting the majority of our storage into this format and phasing out the Postgres-based service we have previously been using, which is mentioned below. You can find out more [here](https://geoparquet.org/releases/v0.2.0/).
+
+### Python version and dependency management - Pipenv - Pyenv
+
+We manage our Python dependencies using `pipenv` which mantains them through the `Pipfile` and `Pipfile.lock`. Our Python versioning is managed through Docker when running the main script, but if you were interested in doing local development, you can also pin your python version to 3.11.4 using `pyenv`.
+
+`pipenv` - https://pipenv.pypa.io/en/latest/
+
+`pyenv` - https://github.com/pyenv/pyenv
+
+### Vulture
+
+Vulture is a code checking utility used for identifying dead and unused Python code within our repo. It is included as a check in our precommit hook for PRs. The Github repo for the tool can be found [here](https://github.com/jendrikseipp/vulture).
+
+###
+
+## Devops - CI/CD - Infrastructure
+
+### Docker - Docker Compose
+
+Docker is a container tool that is used for creating an isolated and controllable environment to run our code that is replicable across different machines. Our ETL pipeline runs inside what is referred to as a **container** in Docker, which manages the versioning and dependencies of everything we use in our repo as well as ensuring it interfaces with a standardized operating system within the container's virtualized environment.
+
+You can read more about in the Docker docs [here](https://docs.docker.com/), and the easiest way to get setup with using Docker is Docker Desktop with installation instructions [here](https://docs.docker.com/desktop/).
+
+### Google Cloud
+
+All of the data produced by our pipeline needs to be stored somewhere for access from our web application, and we do so with a Google Cloud bucket that holds the geospatial data as well as street view images to be served up request from the site. In addition, we have a Google Cloud Compute Engine (VM) setup to run our pipeline on a regularly scheduled interval to update our data.
+
+Google Cloud is a broad platform and subject with a lot of details, but those interested in learning more or taking on any infrastructure tasks that crop up in the future can learn more on their [docs](https://cloud.google.com/docs).
+
+### Github Actions
+
+For our CI/CD, we use Github Action for a number of workflows to manage incoming pull requests, labelling issues and assigning reviews, and generating automated releases. Some of those workflows need additional correction and revision in their current form, which we are getting to, but they can be found in the `.github` directory of the repo.
+
+Documentation for possible tasks accomplished by Github actions and writing them is [here](https://docs.github.com/en/actions).
+
+### Other Useful Links
+
+https://advena.hashnode.dev/heavy-map-visualizations-fundamentals-for-web-developers
+
+Useful blog discussing approaches to architectural approaches and serving geospatial data to frontend applications.
diff --git a/src/components/FilterView.tsx b/src/components/FilterView.tsx
index 4c768fb2..7d903d02 100644
--- a/src/components/FilterView.tsx
+++ b/src/components/FilterView.tsx
@@ -1,32 +1,31 @@
 'use client';
 
+import { useFilter } from '@/context/FilterContext';
 import { FC } from 'react';
-import { PiX } from 'react-icons/pi';
-import { ThemeButton } from './ThemeButton';
-import { BarClickOptions } from '@/app/find-properties/[[...opa_id]]/page';
-import { rcos, neighborhoods, zoning } from './Filters/filterOptions';
-import FilterDescription from './Filters/FilterDescription';
 import ButtonGroup from './Filters/ButtonGroup';
+import FilterDescription from './Filters/FilterDescription';
+import { neighborhoods, rcos, zoning } from './Filters/filterOptions';
 import MultiSelect from './Filters/MultiSelect';
 import Panels from './Filters/Panels';
+import { ThemeButton } from './ThemeButton';
 
-interface FilterViewProps {
-  updateCurrentView: (view: BarClickOptions) => void;
-}
-
-const FilterView: FC<FilterViewProps> = ({ updateCurrentView }) => {
+const FilterView: FC = () => {
+  const { dispatch } = useFilter();
   return (
     <div className="relative p-6">
-      {/* Add ID to the close button */}
       <ThemeButton
         color="secondary"
-        className="right-4 lg:right-[24px] absolute top-8 min-w-[3rem]"
+        className="right-4 lg:right-[24px] absolute top-8 min-w-[4rem] font-medium"
         aria-label="Close filter panel"
-        startContent={<PiX />}
-        id="close-filter-button" // Add an ID to this button
-        onPress={() => {
-          updateCurrentView('filter');
-        }}
+        label="Reset"
+        id="close-filter-button"
+        onPress={() =>
+          dispatch({
+            type: 'CLEAR_DIMENSIONS',
+            property: '',
+            dimensions: [],
+          })
+        }
       />
       <div className="pt-3 pb-6">
         <FilterDescription
@@ -66,7 +65,6 @@ const FilterView: FC<FilterViewProps> = ({ updateCurrentView }) => {
           useIndexOfFilter={true}
         />
       </div>
-
       <div className="pt-3 pb-6">
         <FilterDescription title="Zoning" />
         <MultiSelect