Merge pull request #1 from mblackgeo/develop

Implementation of package v0.1.0

.flake8

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+[flake8]
+max-line-length = 88
+exclude = tests/*
+max-complexity = 10

.github/workflows/pipeline.yml

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+name: lint and test
+
+on: [push]
+
+jobs:
+  lint_and_test:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.8
+
+      - name: Install poetry
+        run: pip install poetry==1.1.12
+
+      - name: Install code
+        run: poetry install
+
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          poetry run flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          poetry run flake8 . --count --exit-zero --statistics
+
+      - name: Check formatting with Black
+        run: poetry run black . --check
+
+      - name: Check import order with isort
+        run: poetry run isort -c spatial_kde
+
+      - name: Test with pytest
+        run: poetry run pytest -v --cov spatial_kde

.gitignore

@@ -127,3 +127,7 @@ dmypy.json
 
 # Pyre type checker
 .pyre/
+
+.vscode/
+
+tests/data/**/*.gpkg-*

.pre-commit-config.yaml

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+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v2.3.0
+    hooks:
+      - id: trailing-whitespace
+  - repo: https://github.com/psf/black
+    rev: 21.9b0
+    hooks:
+      - id: black
+  - repo: https://gitlab.com/pycqa/flake8
+    rev: 3.7.9
+    hooks:
+      - id: flake8
+  - repo: https://github.com/pycqa/isort
+    rev: 5.8.0
+    hooks:
+      - id: isort
+        name: isort (python)

Makefile

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+help:
+	@fgrep -h "##" $(MAKEFILE_LIST) | fgrep -v fgrep | sed -e 's/\\$$//' | sed -e 's/##//'
+
+install:  ## Create a new environment with poetry and install with pre-commit hooks
+	poetry install
+	pre-commit install
+
+test:  ## Run the test suite using pytest
+	poetry run pytest --cov spatial_kde
+
+lint:  ## Run linting checks with flake8 and black
+	poetry run flake8 spatial_kde/
+	poetry run black --check spatial_kde/
+
+format:  ## Run black to format the code
+	poetry run black .
+
+test-release:  ## Build the package and releast to test-PyPI
+	poetry config repositories.testpypi https://test.pypi.org/legacy/
+	poetry publish --build -r testpypi
+
+release:  ## Build the package and release to PyPI
+	poetry publish

README.md

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-# spatial-kde
-Spatial Kernel Density / Heatmap creation from point based vector data
+# Spatial Kernel Density Esimation
+Create Spatial Kernel Density / Heatmap raster from point based vector data, à la QGIS / ArcGIS.
+
+![Example showing KDE generated from weighted points](example.png)
+
+Creates a kernel density (heatmap) raster from vector point data using kernel density estimation. The density is calculated based on the number of points in a location, with larger numbers of clustered points resulting in larger values, and points can be optionally weighted. Kernel Density / Heatmaps allow easy for identification of hotspots and clustering of points. This implementation provides an equivalent to [QGIS' Heatmap](https://docs.qgis.org/3.16/en/docs/user_manual/processing_algs/qgis/interpolation.html#heatmap-kernel-density-estimation) and [ArcGIS/ArcMap/ArcPro's Kernel Density spatial analyst](https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/kernel-density.htm) function. Note that any distance calculations are planar, therefore care should be taken when using points over large areas that are in a geographic coordinate system.
+
+The implementation of kernel density uses the Quartic kernel for it's estimates, with the methodology implemented [following QGIS](https://github.com/qgis/QGIS/blob/master/src/analysis/raster/qgskde.cpp) and as described in ArcGIS' documentation explaining [how Kernel Density works](https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-analyst/how-kernel-density-works.htm). There are many alternative kernel density estimate (KDE) functions available in popular python libraries that may offer better performance, for example [scipy](https://docs.scipy.org/doc/scipy/reference/stats.html#univariate-and-multivariate-kernel-density-estimation), [scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KernelDensity.html), [KDEpy](https://kdepy.readthedocs.io/en/latest/index.html) etc., though these alternatives may not implement the Quartic kernel (with optional weights), as is typically found in GIS software. Additionally, performance with this package will be reduced compared to the native C++ implementaion in QGIS.
+
+## Installation
+
+The package can be installed from pip:
+
+```shell
+pip install spatial-kde
+```
+
+## Usage
+
+After installation, the `skde` tool is available from the command line with the following usage:
+
+```shell
+Usage: skde [OPTIONS] VECTOR OUTPUT
+
+  Create a Spatial Kernel Density / Heatmap raster from an input vector.
+
+  The input vector file must be readable by GeoPandas and contain Point type
+  geometry (for non-point geometries the centroid will be used for the KDE).
+
+Arguments:
+  VECTOR  Path to input vector file  [required]
+  OUTPUT  Output path for created raster  [required]
+
+Options:
+  --radius FLOAT                  Radius/Bandwith for the KDE. Same units as
+                                  the CRS of `vector`.  [default: 1]
+  --output-pixel-size FLOAT       Output pixel size (resolution). Same units
+                                  as the CRS of `vector`.  [default: 1]
+  --output-driver TEXT            Output driver (file format) used by rasterio
+                                  (Default = GeoTiff).  [default: GTiff]
+  --weight-field TEXT             Optional field in `vector` containing
+                                  weights of each point.
+  --scaled / --no-scaled          Set to True to scale the KDE values, leave
+                                  false to use raw values.  [default: no-
+                                  scaled]
+```
+
+Alternatively, the [`spatial_kernel_density`](spatial_kde/kde.py) function can be used in python:
+
+```python
+from typing import Optional
+
+import geopandas as gpd
+from spatial_kde import spatial_kernel_density
+
+
+spatial_kernel_density(
+    points: gpd.GeoDataFrame = gdf,
+    radius: float = 1.0,
+    output_path: str = "/output/path.tif",
+    output_pixel_size: float = 1.0,
+    output_driver: str = "GTiff",
+    weight_col: Optional[str] = None,
+    scaled: bool = False,
+)
+
+    """Calculate Kernel Density / heatmap from ``points``
+
+    .. note:: Distance calculations are planar so care should be taken with data
+              that is in geographic coordinate systems
+
+    Parameters
+    ----------
+    points : gpd.GeoDataFrame
+        Input GeoDataFrame of points to generate a KDE from
+    radius : float
+        Radius of KDE, same units as the coordinate reference system of ``points``
+        Sometimes referred to as search radius or bandwidth
+    output_path : str
+        Path to write output raster to
+    output_pixel_size : float
+        Output cell/pixel size of the created array. Same units as the coordinate
+        reference system of ``points``
+    output_driver : str
+        Output format (driver) used to create image. See also
+        https://rasterio.readthedocs.io/en/latest/api/rasterio.drivers.html
+    weight_col : Optional[str], optional
+        A column in ``points`` to weight the kernel density by, any points that
+        are NaN in this field will not contribute to the KDE.
+        If None, the all points will have uniform weight of 1.
+    scaled : bool
+        If True will output mathematically scaled values, else will output raw
+        values.
+    """
+```
+
+## Development
+
+Prequisites:
+
+* [poetry](https://python-poetry.org/)
+* [pre-commit](https://pre-commit.com/)
+
+The Makefile includes helpful commands setting a development environment, get started by installing the package into a new environment and setting up pre-commit by running `make install`. Run `make help` to see additional available commands (e.g. linting, testing and so on).
+
+* [Pytest](https://docs.pytest.org/en/6.2.x/) is used for testing the application (see `/tests`).
+* Code is linted using [flake8](https://flake8.pycqa.org/en/latest/)
+* Code formatting is validated using [Black](https://github.com/psf/black)
+* [pre-commit](https://pre-commit.com/) is used to run these checks locally before files are pushed to git
+* The [Github Actions pipeline](.github/workflows/pipeline.yml) runs these checks and tests
+
+## TODO
+
+- [ ] Github actions pipeline runs on a matrix of python versions
+- [ ] Documentation (e.g. mkdocs, read-the-docs w/ sphinx or similar)
+- [ ] Tooling for managing versioning/releasing (e.g. bump2version)
+- [x] Makefile commands for releasing to (test) pypi
+- [ ] Support geodesic distance calculation
+- [ ] Performance improvements