Update notebook based on nbqa

worldbank · Nov 21, 2024 · c23af15 · c23af15
1 parent 886493f
commit c23af15
Showing 1 changed file with 30 additions and 27 deletions.
diff --git a/notebooks/user-docs/s2s_python_lib.ipynb b/notebooks/user-docs/s2s_python_lib.ipynb
@@ -23,17 +23,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from typing import Dict\n",
     "import json\n",
+    "from typing import Dict\n",
     "\n",
+    "import geopandas as gpd\n",
     "import numpy as np\n",
-    "from dotenv import load_dotenv\n",
     "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "from shapely.geometry import shape\n",
-    "from lonboard import Map, ScatterplotLayer\n",
+    "from dotenv import load_dotenv\n",
     "from geojson_pydantic import Feature, Polygon\n",
-    "\n",
+    "from lonboard import Map, ScatterplotLayer\n",
+    "from shapely.geometry import shape\n",
     "from space2stats import StatsTable"
    ]
   },
@@ -155,16 +154,13 @@
     "                [33.78593974945852, -4.725410543134203],\n",
     "                [41.94362577283266, -4.725410543134203],\n",
     "                [41.94362577283266, 5.115816884114494],\n",
-    "                [33.78593974945852, 5.115816884114494]\n",
+    "                [33.78593974945852, 5.115816884114494],\n",
     "            ]\n",
-    "        ]\n",
+    "        ],\n",
     "    },\n",
-    "    \"properties\": {\n",
-    "        \"name\": \"Updated AOI\"\n",
-    "    }\n",
+    "    \"properties\": {\"name\": \"Updated AOI\"},\n",
     "}\n",
-    "          \n",
-    "  \n",
+    "\n",
     "\n",
     "feat = AOIModel(**aoi)"
    ]
@@ -397,7 +393,9 @@
    ],
    "source": [
     "with StatsTable.connect() as stats_table:\n",
-    "    data = stats_table.summaries(aoi=feat, spatial_join_method=\"centroid\", fields=fields, geometry=\"point\")\n",
+    "    data = stats_table.summaries(\n",
+    "        aoi=feat, spatial_join_method=\"centroid\", fields=fields, geometry=\"point\"\n",
+    "    )\n",
     "    df = pd.DataFrame(data)\n",
     "\n",
     "df.head()"
@@ -810,8 +808,8 @@
     }
    ],
    "source": [
-    "df['geometry'] = df['geometry'].apply(lambda geom: shape(json.loads(geom)))\n",
-    "gdf = gpd.GeoDataFrame(df, geometry='geometry', crs='EPSG:4326')\n",
+    "df[\"geometry\"] = df[\"geometry\"].apply(lambda geom: shape(json.loads(geom)))\n",
+    "gdf = gpd.GeoDataFrame(df, geometry=\"geometry\", crs=\"EPSG:4326\")\n",
     "gdf"
    ]
   },
@@ -839,26 +837,31 @@
    "source": [
     "# Define custom breaks and corresponding RGBA colors\n",
     "breaks = [0, 1, 1000, 10000, 50000, 100000, 200000, gdf[\"sum_pop_2020\"].max()]\n",
-    "colors = np.array([\n",
-    "    [211, 211, 211, 255],  # Light gray for 0\n",
-    "    [255, 255, 0, 255],    # Yellow for 1-1000\n",
-    "    [255, 165, 0, 255],    # Orange for 1000-10000\n",
-    "    [255, 0, 0, 255],      # Red for 10000-50000\n",
-    "    [128, 0, 128, 255],    # Purple for 50000-100000\n",
-    "    [0, 0, 255, 255],      # Blue for 100000-200000\n",
-    "    [0, 0, 139, 255],      # Dark blue for 200000+\n",
-    "])\n",
+    "colors = np.array(\n",
+    "    [\n",
+    "        [211, 211, 211, 255],  # Light gray for 0\n",
+    "        [255, 255, 0, 255],  # Yellow for 1-1000\n",
+    "        [255, 165, 0, 255],  # Orange for 1000-10000\n",
+    "        [255, 0, 0, 255],  # Red for 10000-50000\n",
+    "        [128, 0, 128, 255],  # Purple for 50000-100000\n",
+    "        [0, 0, 255, 255],  # Blue for 100000-200000\n",
+    "        [0, 0, 139, 255],  # Dark blue for 200000+\n",
+    "    ]\n",
+    ")\n",
     "\n",
     "# Function to assign colors based on custom bins\n",
+    "\n",
+    "\n",
     "def assign_color(value, breaks, colors):\n",
     "    for i in range(len(breaks) - 1):\n",
     "        if breaks[i] <= value < breaks[i + 1]:\n",
     "            return colors[i]\n",
     "    return colors[-1]  # In case value exceeds all breaks\n",
     "\n",
+    "\n",
     "# Map sum_pop_2020 values to colors using the custom function\n",
-    "gdf['color'] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n",
-    "colors = np.uint8(gdf['color'].tolist())\n",
+    "gdf[\"color\"] = gdf[\"sum_pop_2020\"].apply(lambda x: assign_color(x, breaks, colors))\n",
+    "colors = np.uint8(gdf[\"color\"].tolist())\n",
     "\n",
     "# Create the scatterplot layer with the assigned colors\n",
     "layer = ScatterplotLayer.from_geopandas(gdf, get_radius=2000, get_fill_color=colors)\n",