Decouple datashader from xarray-spatial

docs/source/getting_started/installation.rst

@@ -88,8 +88,9 @@ needed:
      - Additional ``polygonize`` return types and the ray-traced
        ``gpu_rtx`` functions (rtxpy also needs cupy).
    * - ``examples``
-     - datashader
-     - Used by some of the example notebooks.
+     - matplotlib, geopandas, shapely
+     - Used by the example notebooks for rendering and vector
+       rasterization. datashader is no longer required.
    * - ``doc``, ``tests``
      - sphinx, pytest, ...
      - Building this documentation and running the test suite.

docs/source/user_guide/fire.ipynb

@@ -35,16 +35,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import xarray as xr\n",
     "\n",
-    "from datashader.transfer_functions import shade, stack, Images\n",
-    "\n",
     "from xrspatial.fire import (\n",
     "    dnbr, rdnbr, burn_severity_class,\n",
     "    fireline_intensity, flame_length,\n",
     "    rate_of_spread, kbdi,\n",
-    ")"
+    ")\n"
    ]
   },
   {
@@ -60,8 +59,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
    "metadata": {},
+   "execution_count": null,
    "outputs": [],
    "source": [
     "H, W = 200, 200\n",
@@ -89,13 +88,18 @@
     "pre_nbr_agg = make_da(pre_nbr, 'pre_nbr')\n",
     "post_nbr_agg = make_da(post_nbr, 'post_nbr')\n",
     "\n",
-    "pre_img = shade(pre_nbr_agg, cmap=['brown', 'yellow', 'green'], how='linear')\n",
-    "pre_img.name = 'Pre-fire NBR'\n",
-    "post_img = shade(post_nbr_agg, cmap=['brown', 'yellow', 'green'], how='linear')\n",
-    "post_img.name = 'Post-fire NBR'\n",
-    "imgs = Images(pre_img, post_img)\n",
-    "imgs.num_cols = 2\n",
-    "imgs"
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "nbr_cmap = LinearSegmentedColormap.from_list('nbr', ['brown', 'yellow', 'green'], N=256)\n",
+    "\n",
+    "fig, axes = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "im0 = axes[0].imshow(pre_nbr_agg, cmap=nbr_cmap)\n",
+    "axes[0].set_title('Pre-fire NBR')\n",
+    "fig.colorbar(im0, ax=axes[0])\n",
+    "im1 = axes[1].imshow(post_nbr_agg, cmap=nbr_cmap)\n",
+    "axes[1].set_title('Post-fire NBR')\n",
+    "fig.colorbar(im1, ax=axes[1])\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -116,7 +120,12 @@
     "dnbr_agg = dnbr(pre_nbr_agg, post_nbr_agg)\n",
     "\n",
     "print(f\"dNBR range: {float(dnbr_agg.min()):.3f} to {float(dnbr_agg.max()):.3f}\")\n",
-    "shade(dnbr_agg, cmap=['green', 'lightyellow', 'orange', 'red', 'darkred'], how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['green', 'lightyellow', 'orange', 'red', 'darkred'], N=256)\n",
+    "plt.imshow(dnbr_agg, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -138,7 +147,12 @@
     "\n",
     "print(f\"RdNBR range: {float(np.nanmin(rdnbr_agg.data)):.3f} to \"\n",
     "      f\"{float(np.nanmax(rdnbr_agg.data)):.3f}\")\n",
-    "shade(rdnbr_agg, cmap=['green', 'lightyellow', 'orange', 'red', 'darkred'], how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['green', 'lightyellow', 'orange', 'red', 'darkred'], N=256)\n",
+    "plt.imshow(rdnbr_agg, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -170,22 +184,39 @@
     "\n",
     "severity_float = severity.astype(np.float32)\n",
     "severity_float.values = np.where(severity_float.values == 0, np.nan, severity_float.values)\n",
-    "shade(severity_float,\n",
-    "      cmap=['darkgreen', 'green', 'lightgreen', 'yellow', 'orange', 'red', 'darkred'],\n",
-    "      how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['darkgreen', 'green', 'lightgreen', 'yellow', 'orange', 'red', 'darkred'], N=256)\n",
+    "plt.imshow(severity_float, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
-   "source": "### Fireline Intensity\n\nByram's fireline intensity: `I = H * w * R` where *H* is heat content (kJ/kg), *w* is fuel consumed (kg/m²), and *R* is spread rate (m/s). Output is kW/m. Fires below ~350 kW/m can be attacked by hand crews; above ~4,000 kW/m they typically need indirect attack or aerial resources.\n\n`spread_rate_units` defaults to `'m/min'` so the output of `rate_of_spread` (below) can be passed straight in. The synthetic `spread` array here is already in m/s, so we pass `spread_rate_units='m/s'`."
+   "source": "### Fireline Intensity\n\nByram's fireline intensity: `I = H * w * R` where *H* is heat content (kJ/kg), *w* is fuel consumed (kg/m\u00b2), and *R* is spread rate (m/s). Output is kW/m. Fires below ~350 kW/m can be attacked by hand crews; above ~4,000 kW/m they typically need indirect attack or aerial resources.\n\n`spread_rate_units` defaults to `'m/min'` so the output of `rate_of_spread` (below) can be passed straight in. The synthetic `spread` array here is already in m/s, so we pass `spread_rate_units='m/s'`."
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": "fuel = make_da((veg * 3.0 + rng.uniform(0, 0.5, (H, W))).astype(np.float32), 'fuel')\nspread = make_da((0.02 + 0.03 * rng.uniform(0, 1, (H, W))).astype(np.float32), 'spread')\n\nintensity_agg = fireline_intensity(fuel, spread, heat_content=18000,\n                                   spread_rate_units='m/s')\n\nprint(f\"Intensity range: {float(intensity_agg.min()):.1f} to {float(intensity_agg.max()):.1f} kW/m\")\nshade(intensity_agg, cmap=['lightyellow', 'orange', 'red', 'darkred'], how='linear')"
+   "source": [
+    "fuel = make_da((veg * 3.0 + rng.uniform(0, 0.5, (H, W))).astype(np.float32), 'fuel')\n",
+    "spread = make_da((0.02 + 0.03 * rng.uniform(0, 1, (H, W))).astype(np.float32), 'spread')\n",
+    "\n",
+    "intensity_agg = fireline_intensity(fuel, spread, heat_content=18000,\n",
+    "                                   spread_rate_units='m/s')\n",
+    "\n",
+    "print(f\"Intensity range: {float(intensity_agg.min()):.1f} to {float(intensity_agg.max()):.1f} kW/m\")\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['lightyellow', 'orange', 'red', 'darkred'], N=256)\n",
+    "plt.imshow(intensity_agg, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -205,7 +236,12 @@
     "fl_agg = flame_length(intensity_agg)\n",
     "\n",
     "print(f\"Flame length range: {float(fl_agg.min()):.2f} to {float(fl_agg.max()):.2f} m\")\n",
-    "shade(fl_agg, cmap=['lightyellow', 'orange', 'red'], how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['lightyellow', 'orange', 'red'], N=256)\n",
+    "plt.imshow(fl_agg, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -232,7 +268,12 @@
     "ros_agg = rate_of_spread(slope_agg, wind_agg, moisture_agg, fuel_model=1)\n",
     "\n",
     "print(f\"Rate of spread: {float(ros_agg.min()):.2f} to {float(ros_agg.max()):.2f} m/min\")\n",
-    "shade(ros_agg, cmap=['lightyellow', 'orange', 'red', 'darkred'], how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['lightyellow', 'orange', 'red', 'darkred'], N=256)\n",
+    "plt.imshow(ros_agg, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -250,7 +291,7 @@
    "source": [
     "for fm, name in [(1, 'Short grass'), (3, 'Tall grass'), (4, 'Chaparral'), (8, 'Timber litter')]:\n",
     "    r = rate_of_spread(slope_agg, wind_agg, moisture_agg, fuel_model=fm)\n",
-    "    print(f\"  Model {fm:2d} ({name:15s}): {float(r.min()):8.2f} to {float(r.max()):8.2f} m/min\")"
+    "    print(f\"  Model {fm:2d} ({name:15s}): {float(r.min()):8.2f} to {float(r.max()):8.2f} m/min\")\n"
    ]
   },
   {
@@ -292,7 +333,12 @@
     "print(f\"Day 31: {history[31]:.1f}  (post-rain)\")\n",
     "print(f\"Day 36: {history[-1]:.1f}  (5 days after rain)\")\n",
     "\n",
-    "shade(current, cmap=['green', 'yellow', 'orange', 'red'], how='linear')"
+    "plt.figure(figsize=(8, 5))\n",
+    "from matplotlib.colors import LinearSegmentedColormap\n",
+    "_cmap = LinearSegmentedColormap.from_list('c', ['green', 'yellow', 'orange', 'red'], N=256)\n",
+    "plt.imshow(current, cmap=_cmap)\n",
+    "plt.colorbar()\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -322,4 +368,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}