Add jupyter-book config

true-color-image/sentinel_helpers.py

@@ -0,0 +1,71 @@
+import glob
+import urllib.parse
+from pathlib import Path
+import zipfile
+
+import geopandas as gp
+import rasterio as r
+
+from rasterio.warp import calculate_default_transform, reproject, Resampling
+
+def band_paths(p, bands, resolution=None):
+    '''
+    Given a zip file or folder at `p`, returns the paths inside p to the raster files containing
+    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').
+   
+    The returned paths are formatted in the zip scheme as per Apache Commons VFS if necessary
+    and can be directly opened by rasterio.
+    '''
+    if p.endswith('.zip'):
+        with zipfile.ZipFile(p) as f:
+            files = f.namelist()
+            rasters = [f for f in files if f.endswith('.jp2')]
+    else:
+        rasters = glob.glob(Path(p) / '**/*.jp2')
+    rasters = [r for r in rasters for b in bands if b in r]
+    if resolution:
+        rasters = [r for r in rasters if resolution in r]
+
+    rasters = ['zip+file://' + p + '!/' + r for r in rasters]
+    return rasters
+
+ 
+def rgb_paths(zip_file, resolution='10m'):
+    return band_paths(zip_file, ['B02', 'B03', 'B04'], resolution)
+
+
+def search_osm(place):
+    '''
+    Returns a GeoDataFrame with results from OpenStreetMap Nominatim for the given search string.
+    This allows us to fetch detailed geometries for virtually any place on earth.
+    '''
+    urlescaped_place = urllib.parse.quote(place)
+    search_url = 'https://nominatim.openstreetmap.org/search/?q={}&format=geojson&polygon_geojson=1'.format(urlescaped_place)
+    return gp.read_file(search_url)
+
+
+def reproject_raster_image(src, dst, target_crs):
+    '''
+    Reprojects `src` into `dst`, given a coordinate reference system `target_crs`.
+    '''
+    transform, width, height = calculate_default_transform(
+        src.crs, target_crs, src.width, src.height, *src.bounds)
+        
+    kwargs = src.meta.copy()
+    kwargs.update({
+        'crs': target_crs,
+        'transform': transform,
+        'width': width,
+        'height': height
+    })
+
+    for i in range(1, src.count + 1):
+        reproject(
+            source=r.band(src, i),
+            destination=r.band(dst, i),
+            src_transform=src.transform,
+            src_crs=src.crs,
+            dst_transform=transform,
+            dst_crs=target_crs,
+            resampling=Resampling.nearest)