Add jupyter-book config

True-Color Image/sentinel_helpers.py

@@ -1,71 +0,0 @@
-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)