Bump docutils from 0.17.1 to 0.21.2

docs/_toc.yml

@@ -15,4 +15,3 @@ parts:
     chapters:
       - file: notebooks/Tutorials/UrbanAreas_tutorials.ipynb
       - file: notebooks/Tutorials/LEI_Example.ipynb
-

docs/conf.py

@@ -24,7 +24,7 @@
     "sphinx_jupyterbook_latex",
     "sphinx.ext.napoleon",
     "sphinxcontrib.apidoc",
-    #"nbsphinx"
+    # "nbsphinx"
 ]
 external_toc_exclude_missing = True
 external_toc_path = "_toc.yml"

notebooks/Replications/URB_NovelUrbanization.py

@@ -1,39 +1,43 @@
-import sys, os, shutil, requests
+import sys
+import os
+import shutil
+import requests
 import rasterio
 
-import pandas as pd
-import geopandas as gpd
-import numpy as np
 
 import GOSTurban.UrbanRaster as urban
 
+
 def download_pop_file(url, filename):
     # Open the url
     r = requests.get(url)
     # Set decode_content value to True, otherwise the downloaded image file's size will be zero.
     r.raw.decode_content = True
     # Open a local file with wb ( write binary ) permission.
-    with open(filename,'wb') as f:
+    with open(filename, "wb") as f:
         shutil.copyfileobj(r.raw, f)
 
+
 def main(iso3, out_folder):
     # download the population data
-    wp_url = f'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/{iso3.upper()}/{iso3.lower()}_ppp_2020_1km_Aggregated.tif'
-    print (wp_url)
+    wp_url = f"https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/{iso3.upper()}/{iso3.lower()}_ppp_2020_1km_Aggregated.tif"
+    print(wp_url)
     if not os.path.exists(out_folder):
         os.makedirs(out_folder)
-    out_file = os.path.join(out_folder, f'{iso3}_ppp_2020_1km_Aggregated.tif')
+    out_file = os.path.join(out_folder, f"{iso3}_ppp_2020_1km_Aggregated.tif")
     out_urban = os.path.join(out_folder, "urban_extents.geojson")
     out_hd_urban = os.path.join(out_folder, "hd_urban_extents.geojson")
-    
+
     try:
         if not os.path.exists(out_file):
             download_pop_file(wp_url, out_file)
     except:
         print(f"Could not download national population data for {iso3} from {wp_url}")
-        print("If you can manually download to the defined out_folder, the script will run")
+        print(
+            "If you can manually download to the defined out_folder, the script will run"
+        )
     if os.path.exists(out_file):
-        inR = rasterio.open(out_file)   
+        inR = rasterio.open(out_file)
         urban_calculator = urban.urbanGriddedPop(inR)
         urban_extents = urban_calculator.calculateUrban(
             densVal=300, totalPopThresh=5000, smooth=False, queen=False
@@ -43,14 +47,14 @@ def main(iso3, out_folder):
             densVal=1500,
             totalPopThresh=50000,
             smooth=True,
-            queen=True,  # high density extents use queen's case contiguity, and are smoothed            
+            queen=True,  # high density extents use queen's case contiguity, and are smoothed
         )
 
         urban_extents.to_file(out_urban, driver="GeoJSON")
         hd_urban_extents.to_file(out_hd_urban, driver="GeoJSON")
 
+
 if __name__ == "__main__":
     iso3 = sys.argv[1]
     out_folder = sys.argv[2]
     main(iso3, out_folder)
-

notebooks/Tutorials/LEI_Example.ipynb

@@ -24,20 +24,16 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import sys\n",
-    "import importlib\n",
     "import rasterio\n",
     "import rasterio.features\n",
     "\n",
     "import geopandas as gpd\n",
     "import pandas as pd\n",
-    "import numpy as np\n",
     "\n",
     "import GOSTrocks.rasterMisc as rMisc\n",
     "import GOSTrocks.ghslMisc as ghslMisc\n",
     "import GOSTurban.LEI as lei\n",
     "import GOSTrocks.mapMisc as mapMisc\n",
-    "import GOSTrocks.rasterMisc as rMisc\n",
     "\n",
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -71,18 +67,22 @@
     "    temp_folder = \"C:/Temp\"\n",
     "    # clip from global GHSL file\n",
     "    ghsl_folder = \"J:/Data/GLOBAL/GHSL/built\"\n",
-    "    ghsl_files = [os.path.join(ghsl_folder, x) for x in os.listdir(ghsl_folder) if x.endswith(\".tif\")]    \n",
+    "    ghsl_files = [\n",
+    "        os.path.join(ghsl_folder, x)\n",
+    "        for x in os.listdir(ghsl_folder)\n",
+    "        if x.endswith(\".tif\")\n",
+    "    ]\n",
     "    inA = gpd.read_file(aoi_file)\n",
-    "    \n",
+    "\n",
     "    temp_ghsl_files = []\n",
     "    for ghsl_file in ghsl_files:\n",
     "        temp_file = os.path.join(temp_folder, os.path.basename(ghsl_file))\n",
     "        temp_ghsl_files.append(temp_file)\n",
     "        if not os.path.exists(temp_file):\n",
     "            rMisc.clipRaster(rasterio.open(ghsl_file), inA, temp_file)\n",
-    "            \n",
+    "\n",
     "    ghsl_res, ghsl_profile = ghslMisc.combine_ghsl_annual(temp_ghsl_files)\n",
-    "    with rasterio.open(input_ghsl, 'w', **ghsl_profile) as outR:\n",
+    "    with rasterio.open(input_ghsl, \"w\", **ghsl_profile) as outR:\n",
     "        outR.write_band(1, ghsl_res)"
    ]
   },
@@ -116,7 +116,7 @@
    "source": [
     "ghsl_r = rasterio.open(input_ghsl)\n",
     "ghsl_d = ghsl_r.read()\n",
-    "ghsl_d[ghsl_d == ghsl_r.meta['nodata']] = 0\n",
+    "ghsl_d[ghsl_d == ghsl_r.meta[\"nodata\"]] = 0\n",
     "\n",
     "thresh = list(range(1975, 2031, 5))\n",
     "with rMisc.create_rasterio_inmemory(ghsl_r.profile, ghsl_d) as temp_ghsl:\n",
@@ -218,8 +218,14 @@
    ],
    "source": [
     "# This calculates the change from 1990 and 2000\n",
-    "lei_raw = lei.calculate_LEI(input_ghsl, old_list=list(range(1975,1991,5)), new_list=list(range(1995,2001,5)))\n",
-    "lei_90_00 = gpd.GeoDataFrame(pd.DataFrame(lei_raw, columns=[\"geometry\", \"old\", \"total\"]), geometry='geometry', crs=ghsl_r.crs)\n",
+    "lei_raw = lei.calculate_LEI(\n",
+    "    input_ghsl, old_list=list(range(1975, 1991, 5)), new_list=list(range(1995, 2001, 5))\n",
+    ")\n",
+    "lei_90_00 = gpd.GeoDataFrame(\n",
+    "    pd.DataFrame(lei_raw, columns=[\"geometry\", \"old\", \"total\"]),\n",
+    "    geometry=\"geometry\",\n",
+    "    crs=ghsl_r.crs,\n",
+    ")\n",
     "lei_90_00[\"LEI\"] = lei_90_00[\"old\"] / lei_90_00[\"total\"]\n",
     "\n",
     "lei_90_00.head()"
@@ -260,19 +266,27 @@
     }
    ],
    "source": [
-    "#Map LEI results\n",
-    "leap_val=0.30\n",
-    "exp_val=0.70\n",
-    "lei_90_00['area'] = lei_90_00['geometry'].apply(lambda x: x.area)\n",
+    "# Map LEI results\n",
+    "leap_val = 0.30\n",
+    "exp_val = 0.70\n",
+    "lei_90_00[\"area\"] = lei_90_00[\"geometry\"].apply(lambda x: x.area)\n",
+    "\n",
+    "\n",
     "def calculate_LEI(val, leap_val, exp_val):\n",
     "    if val <= leap_val:\n",
     "        return 3\n",
     "    elif val < exp_val:\n",
     "        return 2\n",
     "    else:\n",
     "        return 1\n",
-    "lei_90_00[\"class\"] = lei_90_00[\"LEI\"].apply(lambda x: calculate_LEI(x, leap_val, exp_val))\n",
-    "mapMisc.static_map_vector(lei_90_00, \"class\", edgecolor='match', colormap=\"Dark2\")#, basemap=ctx.providers.CartoDB.Voyager)"
+    "\n",
+    "\n",
+    "lei_90_00[\"class\"] = lei_90_00[\"LEI\"].apply(\n",
+    "    lambda x: calculate_LEI(x, leap_val, exp_val)\n",
+    ")\n",
+    "mapMisc.static_map_vector(\n",
+    "    lei_90_00, \"class\", edgecolor=\"match\", colormap=\"Dark2\"\n",
+    ")  # , basemap=ctx.providers.CartoDB.Voyager)"
    ]
   },
   {
@@ -395,7 +409,9 @@
    ],
    "source": [
     "# This calculates the change from 2000 and 2014\n",
-    "lei_raw = lei.calculate_LEI(input_ghsl, old_list=list(range(1975,2011,5)), new_list=list(range(2015,2030,5)))\n",
+    "lei_raw = lei.calculate_LEI(\n",
+    "    input_ghsl, old_list=list(range(1975, 2011, 5)), new_list=list(range(2015, 2030, 5))\n",
+    ")\n",
     "lei_00_14 = pd.DataFrame(lei_raw, columns=[\"geometry\", \"old\", \"total\"])\n",
     "lei_00_14[\"LEI\"] = lei_00_14[\"old\"] / lei_00_14[\"total\"]\n",
     "lei_00_14.head()"
@@ -436,19 +452,27 @@
     }
    ],
    "source": [
-    "#Map LEI results\n",
-    "leap_val=0.30\n",
-    "exp_val=0.70\n",
-    "lei_90_00['area'] = lei_90_00['geometry'].apply(lambda x: x.area)\n",
+    "# Map LEI results\n",
+    "leap_val = 0.30\n",
+    "exp_val = 0.70\n",
+    "lei_90_00[\"area\"] = lei_90_00[\"geometry\"].apply(lambda x: x.area)\n",
+    "\n",
+    "\n",
     "def calculate_LEI(val, leap_val, exp_val):\n",
     "    if val <= leap_val:\n",
     "        return 3\n",
     "    elif val < exp_val:\n",
     "        return 2\n",
     "    else:\n",
     "        return 1\n",
-    "lei_90_00[\"class\"] = lei_90_00[\"LEI\"].apply(lambda x: calculate_LEI(x, leap_val, exp_val))\n",
-    "mapMisc.static_map_vector(lei_90_00, \"class\", edgecolor='match', colormap=\"Dark2\")#, basemap=ctx.providers.CartoDB.Voyager)"
+    "\n",
+    "\n",
+    "lei_90_00[\"class\"] = lei_90_00[\"LEI\"].apply(\n",
+    "    lambda x: calculate_LEI(x, leap_val, exp_val)\n",
+    ")\n",
+    "mapMisc.static_map_vector(\n",
+    "    lei_90_00, \"class\", edgecolor=\"match\", colormap=\"Dark2\"\n",
+    ")  # , basemap=ctx.providers.CartoDB.Voyager)"
    ]
   },
   {

notebooks/Tutorials/UrbanAreas_tutorials.ipynb

@@ -100,7 +100,7 @@
     }
    ],
    "source": [
-    "mapMisc.static_map_raster(inR, thresh=[1,5,50,100,300,1000,3000])"
+    "mapMisc.static_map_raster(inR, thresh=[1, 5, 50, 100, 300, 1000, 3000])"
    ]
   },
   {
@@ -223,7 +223,7 @@
     "urban_extents = urban_calculator.calculateUrban(\n",
     "    densVal=300, totalPopThresh=5000, smooth=False, queen=False, verbose=True\n",
     ")\n",
-    "urban_extents['Type'] = 1\n",
+    "urban_extents[\"Type\"] = 1\n",
     "urban_extents.head()"
    ]
   },
@@ -406,7 +406,7 @@
     "    queen=True,  # high density extents use queen's case contiguity, and\n",
     "    verbose=True,\n",
     ")  # High density extents have hole smoothing applied.\n",
-    "hd_urban_extents['Type'] = 2\n",
+    "hd_urban_extents[\"Type\"] = 2\n",
     "hd_urban_extents.head()"
    ]
   },
@@ -491,7 +491,7 @@
    ],
    "source": [
     "combo_extents = pd.concat([urban_extents, hd_urban_extents])\n",
-    "mapMisc.static_map_vector(combo_extents, \"Type\", colormap='magma')"
+    "mapMisc.static_map_vector(combo_extents, \"Type\", colormap=\"magma\")"
    ]
   },
   {

pyproject.toml

@@ -36,7 +36,7 @@ dependencies = [
 [project.optional-dependencies]
 dev = ["pytest", "pytest-cov"]
 docs = [
-	"docutils==0.17.1",
+	"docutils==0.21.2",
 	"jupyter-book>=1,<2",
 	"sphinxcontrib-apidoc>=0.4.0,<1",
 ]

requirements.txt

@@ -10,5 +10,3 @@ elevation
 geojson
 hatch
 git
-
-

src/GOSTurban/LEI.py

@@ -195,4 +195,4 @@ def calculate_LEI(val, leap_val, exp_val):
     res["class"] = res["LEI"].apply(lambda x: calculate_LEI(x, leap_val, exp_val))
     xx = res.groupby("class")
 
-    return xx['area'].sum()
+    return xx["area"].sum()