Merge pull request #54 from wri/fix/setup_requires

update setup

city_metrix/layers/smart_surface_lulc.py

@@ -7,6 +7,7 @@
 import psutil
 from exactextract import exact_extract
 import pickle
+import importlib.resources as pkg_resources
 import warnings
 warnings.filterwarnings('ignore',category=UserWarning)
 
@@ -24,7 +25,7 @@ def get_data(self, bbox):
         crs = get_utm_zone_epsg(bbox)
 
         # load building roof slope classifier
-        with open('city_metrix/models/building_classifier/building_classifier.pkl', 'rb') as f:
+        with pkg_resources.files('city_metrix.models.building_classifier').joinpath('building_classifier.pkl').open('rb') as f:
             clf = pickle.load(f)
 
         # ESA world cover

docs/developer.md

@@ -11,7 +11,7 @@ The `city_metrix` library allows users of geospatial data to collect and apply z
 It provides two main functionalities:
 1. Extracting geospatial `layers` based on specific areas of interests (defined as geodataframe)' These data layers are collected from any cloud source (Google Earth Engine, AWS S3 public buckets, Public APIs). Two formats of data layers are handled in `city_metrix`: Rasters and vectors. 
     - Rasters data are collected and transformed into _arrays_ using `xarray` (GEE images collections are converted also into `arrays` using `xee`).
-    - Vectors adata are stored as `GeoDataFrame`.  
+    - Vectors data are stored as `GeoDataFrame`.  
 2. Measuring `indicators` using the extracted `layers` by implementing zonal statistics operations 
 
 The main package source code is located in the `city_metrix` directory.
@@ -81,9 +81,11 @@ Hopefully we already have the layers you need in `city_metrix/layers/` and you c
 
 5. Add a test to [tests/layers.py](../tests/layers.py) to ensure the new layer is working as expected.
 
-6. Add a section to the get layers.ipynb notebook to demonstrate how to use the new layer.
+6. Add new dependencies to [setup.py](../setup.py) and [environment.yml](../environment.yml).
 
-7. Create a PR to merge the new layer into the main branch with these in the PR description:
+7. Add a section to the get layers.ipynb notebook to demonstrate how to use the new layer.
+
+8. Create a PR to merge the new layer into the main branch with these in the PR description:
     - Link to Jira ticket (if any)
     - A brief description of the new layer
     - A link to the Airtable record for the new layer
@@ -101,6 +103,16 @@ Once you have all the data layers you need as inputs, here is the process to cre
 
     Define a function for new indicator with the input of the calculation zones as a `GeoDataFrame` and output of the calculated indicators as a `GeoSeries`.
 
+3. Add a test to [tests/metrics.py](../tests/metrics.py) to ensure the new indicator is working as expected.
+
+4. Add new dependencies to [setup.py](../setup.py) and [environment.yml](../environment.yml).
+
+5. Create a PR to merge the new indicator into the main branch with these in the PR description:
+    - Link to Jira ticket (if any)
+    - A brief description of the new indicator
+    - A link to the Airtable record for the new indicator
+    - Explain how to test the new indicator
+
 
 ## Adding Cities
 You can always have users just provide their own boudary files, but if you are working on a project where you want to provide access to a common set of city boundaries, the best option is to add them to the API

environment.yml

@@ -23,6 +23,5 @@ dependencies:
   - boto3=1.34.124
   - scikit-learn=1.5.0
   - pip:
-    - cartoframes==1.2.5
     - git+https://github.com/isciences/exactextract
     - overturemaps==0.6.0

setup.py

@@ -1,10 +1,16 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 
 setup(
     name="city_metrix",
-    version="0.1.0",
+    version="0.1.1",
     description="Module to calculate various metrics on cities.",
-    packages=["city_metrix", "city_metrix.layers", "city_metrix.metrics"],
+    packages=find_packages(),
+    include_package_data=True,
+    package_data={
+        'city_metrix.models.building_classifier': [
+            'building_classifier.pkl',
+        ],
+    },
     author="Justin Terry",
     license="MIT",
     install_requires=[
@@ -16,10 +22,14 @@
         "xarray-spatial",
         "xee",
         "rioxarray",
-        "cartoframes",
         "utm",
         "osmnx",
         "geopandas",
         "s3fs",
+        "dask>=2023.11.0",
+        "boto3",
+        "exactextract",
+        "overturemaps",
+        "scikit-learn>=1.5.0",
     ],
 )