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brendancolbrendancol
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Re-execute notebooks and fix rasterize scanline off-by-one
Add half-pixel offset to scanline edge extraction so integer positions correspond to pixel centers instead of edges. Re-execute notebooks 10, 23-31 to capture updated outputs.
1 parent b351db0 commit 32a22e5

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-309
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‎examples/user_guide/10_Mahalanobis_Distance.ipynb‎

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‎examples/user_guide/23_Hydraulic_Erosion.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

‎examples/user_guide/24_Sky_View_Factor.ipynb‎

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},
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"outputs": [],
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

‎examples/user_guide/25_GLCM_Texture.ipynb‎

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},
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"outputs": [],
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",
@@ -359,7 +360,7 @@
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"id": "ec79xdunce9",
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"metadata": {},
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"source": [
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"### Step 1 — Download a Sentinel-2 NIR band\n",
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"### Step 1 \u2014 Download a Sentinel-2 NIR band\n",
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"\n",
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"We read a 500 x 500 pixel window (5 km x 5 km at 10 m resolution) straight from a\n",
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"Cloud-Optimized GeoTIFF hosted on AWS. The scene is\n",
@@ -442,7 +443,7 @@
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"id": "joxz7n8olpc",
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"metadata": {},
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"source": [
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"### Step 2 — Compute GLCM texture features\n",
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"### Step 2 \u2014 Compute GLCM texture features\n",
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"\n",
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"We pick four metrics that tend to separate water (uniform, high energy, high homogeneity) from land (rough, high contrast):\n",
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"\n",

‎examples/user_guide/26_Contour_Lines.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

‎examples/user_guide/27_Stream_Analysis_Dinf_MFD.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

‎examples/user_guide/28_Rasterize.ipynb‎

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‎examples/user_guide/29_Preview.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

‎examples/user_guide/30_Zonal_Crosstab.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Slope range: 0.0° - 0.0°\n"
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"Slope range: 0.0\u00b0 - 0.0\u00b0\n"
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]
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}
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],
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"slope_agg = terrain.xrs.slope()\n",
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"slope_agg.name = \"Slope\"\n",
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"\n",
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"print(f\"Slope range: {float(slope_agg.min()):.1f}° - {float(slope_agg.max()):.1f}°\")"
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"print(f\"Slope range: {float(slope_agg.min()):.1f}\u00b0 - {float(slope_agg.max()):.1f}\u00b0\")"
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]
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},
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{
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" Peaks: 2001m - 2014m\n",
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"\n",
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"Slope classes:\n",
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" Flat: 0.0° - 0.0°\n",
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" Gentle: 0.0° - 0.0°\n",
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" Moderate: 0.0° - 0.0°\n",
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" Steep: 0.0° - 0.0°\n",
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" Very Steep: 0.0° - 0.0°\n"
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" Flat: 0.0\u00b0 - 0.0\u00b0\n",
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" Gentle: 0.0\u00b0 - 0.0\u00b0\n",
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" Moderate: 0.0\u00b0 - 0.0\u00b0\n",
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" Steep: 0.0\u00b0 - 0.0\u00b0\n",
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" Very Steep: 0.0\u00b0 - 0.0\u00b0\n"
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]
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}
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],
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"slope_names = [\"Flat\", \"Gentle\", \"Moderate\", \"Steep\", \"Very Steep\"]\n",
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"\n",
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"elev_labels = bin_ranges(elevation_zones, terrain, unit='m', decimals=0)\n",
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"slope_labels = bin_ranges(slope_classes, slope_agg, unit='°', decimals=1)\n",
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"slope_labels = bin_ranges(slope_classes, slope_agg, unit='\u00b0', decimals=1)\n",
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"\n",
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"print(\"Elevation zones:\")\n",
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"for name, label in zip(zone_names, elev_labels):\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Flat\\n(0.0° - 0.0°)</th>\n",
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" <th>Gentle\\n(0.0° - 0.0°)</th>\n",
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" <th>Moderate\\n(0.0° - 0.0°)</th>\n",
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" <th>Steep\\n(0.0° - 0.0°)</th>\n",
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" <th>Very Steep\\n(0.0° - 0.0°)</th>\n",
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" <th>Flat\\n(0.0\u00b0 - 0.0\u00b0)</th>\n",
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" <th>Gentle\\n(0.0\u00b0 - 0.0\u00b0)</th>\n",
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" <th>Moderate\\n(0.0\u00b0 - 0.0\u00b0)</th>\n",
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" <th>Steep\\n(0.0\u00b0 - 0.0\u00b0)</th>\n",
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" <th>Very Steep\\n(0.0\u00b0 - 0.0\u00b0)</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Elevation Zone</th>\n",
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"</div>"
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],
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"text/plain": [
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" Flat\\n(0.0° - 0.0°) Gentle\\n(0.0° - 0.0°) \\\n",
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" Flat\\n(0.0\u00b0 - 0.0\u00b0) Gentle\\n(0.0\u00b0 - 0.0\u00b0) \\\n",
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"Elevation Zone \n",
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"Valley\\n(1952m - 1998m) 0.160460 2.379629 \n",
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"Lowlands\\n(1998m - 2000m) 4.109002 30.917339 \n",
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"Foothills\\n(2000m - 2000m) 85.650793 14.349207 \n",
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"Mountains\\n(2000m - 2001m) 9.803059 48.124300 \n",
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"Peaks\\n(2001m - 2014m) 0.298084 4.136447 \n",
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"\n",
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" Moderate\\n(0.0° - 0.0°) Steep\\n(0.0° - 0.0°) \\\n",
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" Moderate\\n(0.0\u00b0 - 0.0\u00b0) Steep\\n(0.0\u00b0 - 0.0\u00b0) \\\n",
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"Elevation Zone \n",
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"Valley\\n(1952m - 1998m) 10.866168 28.294407 \n",
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"Lowlands\\n(1998m - 2000m) 39.824184 22.381570 \n",
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"Foothills\\n(2000m - 2000m) 0.000000 0.000000 \n",
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"Mountains\\n(2000m - 2001m) 31.051921 10.701385 \n",
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"Peaks\\n(2001m - 2014m) 18.181055 38.655471 \n",
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"\n",
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" Very Steep\\n(0.0° - 0.0°) \n",
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" Very Steep\\n(0.0\u00b0 - 0.0\u00b0) \n",
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"Elevation Zone \n",
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"Valley\\n(1952m - 1998m) 58.299336 \n",
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"Lowlands\\n(1998m - 2000m) 2.767906 \n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Mean Slope (°)</th>\n",
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" <th>Std Dev (°)</th>\n",
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" <th>Mean Slope (\u00b0)</th>\n",
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" <th>Std Dev (\u00b0)</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Elevation Zone</th>\n",
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"</div>"
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],
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"text/plain": [
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" Mean Slope (°) Std Dev (°)\n",
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" Mean Slope (\u00b0) Std Dev (\u00b0)\n",
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"Elevation Zone \n",
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"Valley 0.000198 0.000183\n",
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"Lowlands 0.000044 0.000032\n",
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"mean_slope_by_elev = slope_agg.xrs.zonal_stats(elevation_zones, stats_funcs=['mean', 'std'])\n",
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"mean_slope_by_elev['Elevation Zone'] = zone_names\n",
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"mean_slope_by_elev = mean_slope_by_elev[['Elevation Zone', 'mean', 'std']]\n",
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"mean_slope_by_elev.columns = ['Elevation Zone', 'Mean Slope (°)', 'Std Dev (°)']\n",
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"mean_slope_by_elev.columns = ['Elevation Zone', 'Mean Slope (\u00b0)', 'Std Dev (\u00b0)']\n",
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"mean_slope_by_elev.set_index('Elevation Zone', inplace=True)\n",
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"\n",
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"mean_slope_by_elev"
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],
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"source": [
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"fig, ax = plt.subplots(figsize=(10, 5))\n",
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"mean_slope_by_elev['Mean Slope (°)'].plot(\n",
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"mean_slope_by_elev['Mean Slope (\u00b0)'].plot(\n",
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" kind=\"bar\", ax=ax, color=\"orangered\",\n",
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" yerr=mean_slope_by_elev['Std Dev (°)'], capsize=5)\n",
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" yerr=mean_slope_by_elev['Std Dev (\u00b0)'], capsize=5)\n",
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"ax.set_xlabel(\"Elevation Zone\")\n",
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"ax.set_ylabel(\"Mean Slope (degrees)\")\n",
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"ax.set_title(\"Mean terrain steepness by elevation zone\")\n",

‎examples/user_guide/31_Viewshed.ipynb‎

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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import xarray as xr\n",

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