Lebovits/issu946 add backup vacant land data

data/docker-compose.yml

@@ -11,6 +11,7 @@ services:
       - CLEAN_GREEN_GOOGLE_KEY
       - PYTHONUNBUFFERED=1
       - GOOGLE_CLOUD_BUCKET_NAME
+      - GOOGLE_CLOUD_PROJECT
       - CAGP_SLACK_API_TOKEN
     volumes:
       - ./src:/usr/src/app

data/src/classes/featurelayer.py

@@ -36,8 +36,9 @@ def google_cloud_bucket() -> Bucket:
 
     os.environ["GOOGLE_APPLICATION_CREDENTIALS"] = credentials_path
     bucket_name = os.getenv("GOOGLE_CLOUD_BUCKET_NAME", "cleanandgreenphl")
+    project_name = os.getenv("GOOGLE_CLOUD_PROJECT", "clean-and-green-philly")
 
-    storage_client = storage.Client(project="clean-and-green-philly")
+    storage_client = storage.Client(project=project_name)
     return storage_client.bucket(bucket_name)
 
 

data/src/data_utils/access_process.py

@@ -1,5 +1,6 @@
 from typing import Any
 
+
 def access_process(dataset: Any) -> Any:
     """
     Process a dataset to determine the access process for each property based on

data/src/data_utils/drug_crimes.py

@@ -1,125 +1,10 @@
-import mapclassify
-import matplotlib.pyplot as plt
-import numpy as np
-import rasterio
-from awkde.awkde import GaussianKDE
-from classes.featurelayer import FeatureLayer
-from config.config import USE_CRS
 from constants.services import DRUGCRIME_SQL_QUERY
-from rasterio.transform import Affine
 
 
-def drug_crimes(primary_featurelayer):
-    # Initialize gun_crimes object
-    drug_crimes = FeatureLayer(
-        name="Drug Crimes", carto_sql_queries=DRUGCRIME_SQL_QUERY
-    )
-
-    # Extract x, y coordinates from geometry
-    x = np.array([])
-    y = np.array([])
-
-    for geom in drug_crimes.gdf.geometry:
-        coords = np.array(geom.xy)
-        x = np.concatenate([x, coords[0]])
-        y = np.concatenate([y, coords[1]])
-
-    # Prepare data for KDE
-    X = np.array(list(zip(x, y)))
-
-    # Generate grid for plotting
-    grid_length = 2500
-
-    x_grid, y_grid = (
-        np.linspace(x.min(), x.max(), grid_length),
-        np.linspace(y.min(), y.max(), grid_length),
-    )
-    xx, yy = np.meshgrid(x_grid, y_grid)
-    grid_points = np.array([xx.ravel(), yy.ravel()]).T
-
-    # Compute adaptive KDE values
-    print("fitting KDE for drug crime data")
-    kde = GaussianKDE(glob_bw=0.1, alpha=0.999, diag_cov=True)
-    kde.fit(X)
-
-    z = kde.predict(grid_points)
-    zz = z.reshape(xx.shape)
-
-    # Calculate resolutions and min values
-    x_res = (x.max() - x.min()) / (len(x_grid) - 1)
-    y_res = (y.max() - y.min()) / (len(y_grid) - 1)
-    min_x, min_y = x.min(), y.min()
-
-    # Save the plot in tmp folder
-    plt.pcolormesh(xx, yy, zz)
-    plt.scatter(x, y, c="red", s=0.005)
-    plt.colorbar()
-    plt.savefig("tmp/kde.png")
-
-    # Define the affine transform
-    transform = Affine.translation(min_x, min_y) * Affine.scale(x_res, y_res)
-
-    # Export as raster
-    with rasterio.open(
-        "tmp/drug_crimes.tif",
-        "w",
-        driver="GTiff",
-        height=zz.shape[0],
-        width=zz.shape[1],
-        count=1,
-        dtype=zz.dtype,
-        crs=USE_CRS,
-        transform=transform,
-    ) as dst:
-        dst.write(zz, 1)
+from data_utils.kde import apply_kde_to_primary
 
-    primary_featurelayer.gdf["centroid"] = primary_featurelayer.gdf.geometry.centroid
 
-    coord_list = [
-        (x, y)
-        for x, y in zip(
-            primary_featurelayer.gdf["centroid"].x,
-            primary_featurelayer.gdf["centroid"].y,
-        )
-    ]
-
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=["centroid"])
-
-    src = rasterio.open("tmp/drug_crimes.tif")
-    sampled_values = [x[0] for x in src.sample(coord_list)]
-
-    primary_featurelayer.gdf["drugcrime_density"] = sampled_values
-
-    percentile_breaks = list(range(101))  # [0, 1, 2, ..., 100]
-
-    drugcrime_classifier = mapclassify.Percentiles(
-        primary_featurelayer.gdf["drugcrime_density"], pct=percentile_breaks
-    )
-
-    primary_featurelayer.gdf["drugcrime_density_percentile"] = primary_featurelayer.gdf[
-        "drugcrime_density"
-    ].apply(drugcrime_classifier)
-
-    def label_percentile(value):
-        if value == 1:
-            return "1st Percentile"
-        elif value == 2:
-            return "2nd Percentile"
-        elif value == 3:
-            return "3rd Percentile"
-        else:
-            return f"{value}th Percentile"
-
-    primary_featurelayer.gdf["drugcrime_density_label"] = primary_featurelayer.gdf[
-        "drugcrime_density_percentile"
-    ].apply(label_percentile)
-
-    primary_featurelayer.gdf["drugcrime_density_percentile"] = primary_featurelayer.gdf[
-        "drugcrime_density_percentile"
-    ].astype(float)
-
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(
-        columns=["drugcrime_density"]
+def drug_crimes(primary_featurelayer):
+    return apply_kde_to_primary(
+        primary_featurelayer, "Drug Crimes", DRUGCRIME_SQL_QUERY
     )
-
-    return primary_featurelayer

data/src/data_utils/gun_crimes.py

@@ -1,123 +1,8 @@
-import mapclassify
-import matplotlib.pyplot as plt
-import numpy as np
-import rasterio
-from awkde.awkde import GaussianKDE
-from classes.featurelayer import FeatureLayer
-from config.config import USE_CRS
 from constants.services import GUNCRIME_SQL_QUERY
-from rasterio.transform import Affine
 
 
-def gun_crimes(primary_featurelayer):
-    # Initialize gun_crimes object
-    gun_crimes = FeatureLayer(name="Gun Crimes", carto_sql_queries=GUNCRIME_SQL_QUERY)
-
-    # Extract x, y coordinates from geometry
-    x = np.array([])
-    y = np.array([])
-
-    for geom in gun_crimes.gdf.geometry:
-        coords = np.array(geom.xy)
-        x = np.concatenate([x, coords[0]])
-        y = np.concatenate([y, coords[1]])
-
-    # Prepare data for KDE
-    X = np.array(list(zip(x, y)))
-
-    # Generate grid for plotting
-    grid_length = 2500
-
-    x_grid, y_grid = (
-        np.linspace(x.min(), x.max(), grid_length),
-        np.linspace(y.min(), y.max(), grid_length),
-    )
-    xx, yy = np.meshgrid(x_grid, y_grid)
-    grid_points = np.array([xx.ravel(), yy.ravel()]).T
-
-    # Compute adaptive KDE values
-    print("fitting KDE for gun crime data")
-    kde = GaussianKDE(glob_bw=0.1, alpha=0.999, diag_cov=True)
-    kde.fit(X)
-
-    z = kde.predict(grid_points)
-    zz = z.reshape(xx.shape)
-
-    # Calculate resolutions and min values
-    x_res = (x.max() - x.min()) / (len(x_grid) - 1)
-    y_res = (y.max() - y.min()) / (len(y_grid) - 1)
-    min_x, min_y = x.min(), y.min()
-
-    # Save the plot in tmp folder
-    plt.pcolormesh(xx, yy, zz)
-    plt.scatter(x, y, c="red", s=0.005)
-    plt.colorbar()
-    plt.savefig("tmp/kde.png")
-
-    # Define the affine transform
-    transform = Affine.translation(min_x, min_y) * Affine.scale(x_res, y_res)
-
-    # Export as raster
-    with rasterio.open(
-        "tmp/gun_crimes.tif",
-        "w",
-        driver="GTiff",
-        height=zz.shape[0],
-        width=zz.shape[1],
-        count=1,
-        dtype=zz.dtype,
-        crs=USE_CRS,
-        transform=transform,
-    ) as dst:
-        dst.write(zz, 1)
+from data_utils.kde import apply_kde_to_primary
 
-    primary_featurelayer.gdf["centroid"] = primary_featurelayer.gdf.geometry.centroid
 
-    coord_list = [
-        (x, y)
-        for x, y in zip(
-            primary_featurelayer.gdf["centroid"].x,
-            primary_featurelayer.gdf["centroid"].y,
-        )
-    ]
-
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(columns=["centroid"])
-
-    src = rasterio.open("tmp/gun_crimes.tif")
-    sampled_values = [x[0] for x in src.sample(coord_list)]
-
-    primary_featurelayer.gdf["guncrime_density"] = sampled_values
-
-    percentile_breaks = list(range(101))  # [0, 1, 2, ..., 100]
-
-    guncrime_classifier = mapclassify.Percentiles(
-        primary_featurelayer.gdf["guncrime_density"], pct=percentile_breaks
-    )
-
-    primary_featurelayer.gdf["guncrime_density_percentile"] = primary_featurelayer.gdf[
-        "guncrime_density"
-    ].apply(guncrime_classifier)
-
-    def label_percentile(value):
-        if value == 1:
-            return "1st Percentile"
-        elif value == 2:
-            return "2nd Percentile"
-        elif value == 3:
-            return "3rd Percentile"
-        else:
-            return f"{value}th Percentile"
-
-    primary_featurelayer.gdf["guncrime_density_label"] = primary_featurelayer.gdf[
-        "guncrime_density_percentile"
-    ].apply(label_percentile)
-
-    primary_featurelayer.gdf["guncrime_density_percentile"] = primary_featurelayer.gdf[
-        "guncrime_density_percentile"
-    ].astype(float)
-
-    primary_featurelayer.gdf = primary_featurelayer.gdf.drop(
-        columns=["guncrime_density"]
-    )
-
-    return primary_featurelayer
+def gun_crimes(primary_featurelayer):
+    return apply_kde_to_primary(primary_featurelayer, "Gun Crimes", GUNCRIME_SQL_QUERY)