"pplt" → "plt"

sources/01a Download from Scihub.ipynb

@@ -460,7 +460,7 @@
    "outputs": [],
    "source": [
     "from sentinel_helpers import plot_all\n",
-    "import matplotlib.pyplot as pplt"
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -496,7 +496,7 @@
     "         [{'color': 'none', 'edgecolor': 'blue', 'alpha': 0.1, 'figsize': (16, 9)},\n",
     "          {'color': 'none', 'edgecolor': 'red'},\n",
     "          {'color': 'none', 'edgecolor': 'grey'}])\n",
-    "pplt.title('Area of interest, its convex hull and products returned from the API')"
+    "plt.title('Area of interest, its convex hull and products returned from the API')"
    ]
   },
   {

sources/01d Brandenburg mosaic.ipynb

@@ -3484,16 +3484,16 @@
    "source": [
     "import rasterio as r\n",
     "import rasterio.plot as rplt\n",
-    "import matplotlib.pyplot as pplt\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "# preview the first downloaded image\n",
     "with r.open(sentinel_helpers.scihub_band_paths(downloaded_paths[3], ['TCI'], '20m')[0]) as true_color:\n",
     "    # we do not need\n",
-    "    fig, (axr, axg, axb) = pplt.subplots(1,3, figsize=(21,7))\n",
+    "    fig, (axr, axg, axb) = plt.subplots(1,3, figsize=(21,7))\n",
     "    rplt.show((true_color, 1), ax=axr, cmap='Reds', title='red channel')\n",
     "    rplt.show((true_color, 2), ax=axg, cmap='Greens', title='green channel')\n",
     "    rplt.show((true_color, 3), ax=axb, cmap='Blues', title='blue channel')\n",
-    "    pplt.show()"
+    "    plt.show()"
    ]
   },
   {
@@ -3505,9 +3505,9 @@
     "# plotting the included true-colo image\n",
     "with r.open(sentinel_helpers.scihub_band_paths(downloaded_paths[3], ['TCI'], '20m')[0]) as true_color:\n",
     "    # note that in order to get the real colors, we need to reverse the bands into \"rasterio band order\"\n",
-    "    pplt.figure(figsize=(20,20))\n",
+    "    plt.figure(figsize=(20,20))\n",
     "    rplt.show(true_color.read(), transform=true_color.transform)\n",
-    "    pplt.show()"
+    "    plt.show()"
    ]
   },
   {
@@ -3650,10 +3650,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, (ax) = pplt.subplots(1, 1, figsize=(8,16))\n",
+    "fig, (ax) = plt.subplots(1, 1, figsize=(8,16))\n",
     "rplt.show(mosaic, transform=mosaic_transform, ax=ax)\n",
     "brandenburg.to_crs(target_crs).plot(ax=ax, facecolor='none', edgecolor='white')\n",
-    "pplt.show()"
+    "plt.show()"
    ]
   },
   {

sources/01e True-color pipeline.ipynb

@@ -288,7 +288,7 @@
     "plot_all([gdf, footprint],\n",
     "        [{'color': 'none', 'edgecolor': 'blue', 'alpha': 0.1, 'figsize': (16, 9)},\n",
     "         {'color': 'none', 'edgecolor': 'red'}])\n",
-    "pplt.title('Region of interest superimposed on all product tiles matching search criteria')"
+    "plt.title('Region of interest superimposed on all product tiles matching search criteria')"
    ]
   },
   {
@@ -382,7 +382,7 @@
     "plot_all([gdf.loc[:idx], footprint],\n",
     "        [{'facecolor': 'none', 'edgecolor': 'blue', 'alpha': 0.1},\n",
     "         {'facecolor': 'none', 'edgecolor': 'red'}])\n",
-    "pplt.title('Region of interest superimposed on selected products')"
+    "plt.title('Region of interest superimposed on selected products')"
    ]
   },
   {
@@ -619,7 +619,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as pplt\n",
+    "import matplotlib.pyplot as plt\n",
     "import rasterio as r\n",
     "from rasterio import plot as rplot\n",
     "from rasterio.warp import calculate_default_transform, reproject, Resampling\n",
@@ -862,7 +862,7 @@
     "    _, height, width = masked.shape\n",
     "    \n",
     "    # show the result\n",
-    "    pplt.figure(figsize=(20,20))\n",
+    "    plt.figure(figsize=(20,20))\n",
     "    rplot.show(masked,\n",
     "               transform=masked_transform,\n",
     "               title=f'Plot of {region_of_interest} using data between {start_date.strftime(\"%Y-%m-%d\")} and {end_date.strftime(\"%Y-%m-%d\")}')\n",

sources/02e Spectral Indices.ipynb

@@ -516,7 +516,7 @@
    "source": [
     "from sentinel_helpers import scihub_cloud_mask\n",
     "import rasterio as r\n",
-    "import matplotlib.pyplot as pplt\n",
+    "import matplotlib.pyplot as plt\n",
     "\n",
     "highres_raster_path = [band_path for band_path in highest_resolution_band_paths if resolution(band_path) == target_resolution][0]\n",
     "highres_raster_path"
@@ -583,7 +583,7 @@
     "                                      target_shape=target_shape,\n",
     "                                      target_transform=target_transform)\n",
     "\n",
-    "pplt.imshow(raster_cloud_mask)"
+    "plt.imshow(raster_cloud_mask)"
    ]
   },
   {
@@ -744,7 +744,7 @@
    "outputs": [],
    "source": [
     "# to debug the cloud / tile mask, uncomment these lines\n",
-    "# pplt.imshow(tile_mask)"
+    "# plt.imshow(tile_mask)"
    ]
   },
   {

sources/03c dNBR.ipynb

@@ -214,7 +214,7 @@
    "outputs": [],
    "source": [
     "import numpy as np\n",
-    "import matplotlib.pyplot as pplt\n",
+    "import matplotlib.pyplot as plt\n",
     "from sentinel_helpers import scihub_band_paths, scihub_band_date, RasterReaderList\n",
     "import rasterio as r\n",
     "import rasterio.plot as rplt\n",
@@ -224,7 +224,7 @@
     "\n",
     "def plot_nbrs(products, geom):\n",
     "    with RasterReaderList(products) as readers:\n",
-    "        fig, axes = pplt.subplots(nrows=1, ncols=3, figsize=(20, 20))\n",
+    "        fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(20, 20))\n",
     "\n",
     "        # we need to reproject from WGS84 so the geometry can be correctly plotted on the map\n",
     "        _geom = geom.to_crs(readers[0].crs)\n",
@@ -252,7 +252,7 @@
     "            _geom.plot(ax=ax, facecolor='none', edgecolor='red')\n",
     "            \n",
     "        # increase horizontal whitespace between subplots\n",
-    "        pplt.subplots_adjust(wspace=0.32)\n",
+    "        plt.subplots_adjust(wspace=0.32)\n",
     "        \n",
     "        # add colorbar using the last image\n",
     "        img = axes[-1].get_images()[0]\n",
@@ -385,7 +385,7 @@
     "dnbr_norm = BoundaryNorm(boundaries, dnbr_cmap.N, clip=True)\n",
     "\n",
     "def plot_dnbr(dnbr, dnbr_crs, dnbr_transform, geom):\n",
-    "    fig, (ax1, ax2) = pplt.subplots(ncols=2, figsize=(16, 8))\n",
+    "    fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(16, 8))\n",
     "    _geom = geom.to_crs(dnbr_crs)\n",
     "\n",
     "    # plot black and white image\n",
@@ -451,8 +451,8 @@
     "\n",
     "potsdam_mittelmark_extract = dnbr[0,500:800,420:810]\n",
     "\n",
-    "img = pplt.imshow(potsdam_mittelmark_extract, cmap='Greys')\n",
-    "pplt.colorbar(img)"
+    "img = plt.imshow(potsdam_mittelmark_extract, cmap='Greys')\n",
+    "plt.colorbar(img)"
    ]
   },
   {
@@ -622,8 +622,8 @@
     "                                     out_shape=(window.height, window.width),\n",
     "                                     transform=window_transform)\n",
     "\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(nature_reserve_mask)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(nature_reserve_mask)"
    ]
   },
   {
@@ -635,8 +635,8 @@
     "burned_in_nature_reserve = dnbr.copy()\n",
     "burned_in_nature_reserve.mask = burned_in_nature_reserve.mask | nature_reserve_mask\n",
     "\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(burned_in_nature_reserve[0], cmap=dnbr_cmap, norm=dnbr_norm)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(burned_in_nature_reserve[0], cmap=dnbr_cmap, norm=dnbr_norm)"
    ]
   },
   {
@@ -653,8 +653,8 @@
    "outputs": [],
    "source": [
     "burned_in_nature_reserve.mask = burned_in_nature_reserve.mask | (burned_in_nature_reserve.data < 0.1)\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(burned_in_nature_reserve[0], cmap=dnbr_cmap, norm=dnbr_norm)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(burned_in_nature_reserve[0], cmap=dnbr_cmap, norm=dnbr_norm)"
    ]
   },
   {
@@ -743,7 +743,7 @@
     "    tci_paths = map(lambda p: scihub_band_paths(p, ['TCI'], '60m')[0], products)\n",
     "    \n",
     "    with RasterReaderList(tci_paths) as readers:\n",
-    "        fig, axes = pplt.subplots(ncols=len(readers), figsize=(20,10))\n",
+    "        fig, axes = plt.subplots(ncols=len(readers), figsize=(20,10))\n",
     "        _geom = geometry.to_crs(readers[0].crs)\n",
     "        \n",
     "        window = geometry_window(readers[0], _geom.buffer(5000))\n",
@@ -869,8 +869,8 @@
     "                                              out_shape=(window.height, window.width),\n",
     "                                              transform=window_transform)\n",
     "\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(luebtheen_military_site_mask)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(luebtheen_military_site_mask)"
    ]
   },
   {
@@ -882,8 +882,8 @@
     "burned_in_military_site = dnbr.copy()\n",
     "burned_in_military_site.mask = burned_in_military_site.mask | luebtheen_military_site_mask\n",
     "\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(burned_in_military_site[0], cmap=dnbr_cmap, norm=dnbr_norm)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(burned_in_military_site[0], cmap=dnbr_cmap, norm=dnbr_norm)"
    ]
   },
   {
@@ -895,8 +895,8 @@
     "# keep only the pixels with a burn serverity of at least 0.1\n",
     "burned_in_military_site.mask = burned_in_military_site.mask | (burned_in_military_site.data < 0.1)\n",
     "\n",
-    "pplt.figure(figsize=(10, 10))\n",
-    "pplt.imshow(burned_in_military_site[0], cmap=dnbr_cmap, norm=dnbr_norm)"
+    "plt.figure(figsize=(10, 10))\n",
+    "plt.imshow(burned_in_military_site[0], cmap=dnbr_cmap, norm=dnbr_norm)"
    ]
   },
   {

sources/sentinel_helpers.py

@@ -69,8 +69,8 @@ def scihub_band_paths(p, bands, resolution=None):
     information for the given bands. Because some bands are available in more than one
     resolution, this can be filtered by prodiding a third parameter (e.g. resolution='10m').
     
-    `p` can be a string or a pathlib.Path.
-    `bands` can be a list of bands or a single band.
+    - `p` can be a string or a pathlib.Path.
+    - `bands` can be a list of bands or a single band.
    
     The returned paths are formatted in the zip scheme as per Apache Commons VFS if necessary
     and can be directly opened by rasterio.
@@ -85,20 +85,16 @@ def scihub_band_paths(p, bands, resolution=None):
         # archive first
         with ZipFile(p) as f:
             files = f.namelist()
-            rasters = [f for f in files if f.endswith('.jp2')]
+            rasters = [Path(f'zip+file://{p.absolute()}!/{f}') for f in files if f.endswith('.jp2')]
     else:
         rasters = p.glob('**/*.jp2')
     
     # take only the paths that contain one of the given bands
-    rasters = [raster for band in bands for raster in rasters if band in raster]
+    rasters = [raster for band in bands for raster in rasters if band in raster.name]
     
     # if a resolution is given, further discard the bands we don't need
     if resolution:
-        rasters = [raster for raster in rasters if resolution in raster]
-
-    if p.suffix == '.zip':
-        # we have to reformat the paths to point inside the zip archive
-        rasters = [f'zip+file://{p}!/{r}' for r in rasters]
+        rasters = [raster for raster in rasters if resolution in raster.name]
     
     return rasters