added a working example

README.md

@@ -4,3 +4,6 @@ A library for projecting solar shadows on digital elevation models.
 ![Raycasting on SRTM3](https://github.com/tomderuijter/python-dem-raycast/blob/master/raycast_shade.gif "Raycasting on a tile of the SRTM3 dataset.")
 
 Most of the code in this library is based on the R insol package, which was published under the GPL-2 license.
+
+## Code test
+Follow example.py

example.py

@@ -0,0 +1,104 @@
+# -*- coding: utf-8 -*-
+"""
+SHADOW CASTING ALGORITHM EXAMPLE USING PYTHON-DEM-SHADOWS
+
+Dec 2023
+Fabio Oriani, fabio.oriani <at> protonmail.com
+
+"""
+#%% DEPENDENCIES
+import python_dem_shadows.shadows as sh
+import python_dem_shadows.solar as so
+import matplotlib.pyplot as plt
+import rasterio 
+import numpy as np
+import datetime as dt
+from osgeo import ogr
+from osgeo import osr
+
+# function for color rescale in a multiband image
+def imrisc(im_in,qmin,qmax):
+    im_out=im_in.copy()
+    for i in range(np.shape(im_in)[2]):
+        band=im_in[:,:,i].copy()
+        band2=band[~np.isnan(band)]
+        vmin=np.percentile(band2,qmin)
+        vmax=np.percentile(band2,qmax)
+        band[band<vmin]=vmin
+        band[band>vmax]=vmax
+        band=(band-vmin)/(vmax-vmin)
+        im_out[:,:,i]=band
+    return im_out
+
+#%% IMPORT TEST DEM IMAGE AND ONE SAT IMAGE (TO CHECK THE CAST SHADOWS)
+
+filename = "data/test_image.tif" # test SAT image
+dataset = rasterio.open(filename)
+im_extent = dataset.bounds # image limits
+im_extent = [im_extent[0],im_extent[2],im_extent[3],im_extent[1]] # good order for plt.imshow
+#im_crs = dataset.crs # CRS (same as dem)
+im = dataset.read([3,2,1]).transpose([1,2,0]) # multiband raster
+im = np.flipud(imrisc(im,3,97))
+dataset = None
+
+filename = "data/test_dem.tif" # test DEM image
+dataset = rasterio.open(filename)
+dem_extent = dataset.bounds # image limits
+dem_extent = [dem_extent[0],dem_extent[2],dem_extent[3],dem_extent[1]] # good order for plt.imshow
+dem_crs = int(dataset.crs.data['init'][5:]) # CRS code
+dem_res = dataset.transform[0] # spatial resolution
+dem_xllc = dataset.transform[2] # lower left corner x coord
+dem_yllc = dataset.transform[5] # lower left corner y coord
+dem_nrows = dataset.height # raster rows
+dem_ncols = dataset.width # taster columns
+dem = dataset.read(1) # raster
+dem = np.flipud(dem)
+dataset = None
+
+# display the images
+plt.figure(figsize=(8,4)) 
+plt.subplot(121)
+plt.imshow(im, extent =  im_extent )
+plt.title('Landscape RGB')
+plt.subplot(122)
+plt.imshow(dem, extent = dem_extent)
+plt.title('DEM')
+plt.tight_layout
+
+#%% COMPUTE THE CAST SHADOW
+
+# a wrapper function to generate the shadow mask
+def shadow_mask(dem,lon,lat,date,tzone,dx): 
+    jd=so.to_juliandate(d) # transform date to julian date
+    sun_vector= so.sun_vector(jd,lat,lon,tzone) # compute the sun position vector
+    shad=sh.project_shadows(dem, sun_vector, dx, dy=dx)  # compute cast shadow
+    shad[shad==1]=np.nan # make a mask (1 = shadow, nan elsewhere) OPTIONAL
+    shad[shad==0]=1
+    return shad
+
+# image center grid coordinates in WGS84 as reference point for the image location    
+cx=dem_xllc+dem_res*int(dem_ncols/2) # center coords 
+cy=dem_yllc+dem_res*int(dem_nrows/2)
+Point = ogr.Geometry(ogr.wkbPoint) # point object
+Point.AddPoint(cx,cy)
+InSR = osr.SpatialReference() # target crs WGS84
+InSR.ImportFromEPSG(dem_crs) 
+Point.AssignSpatialReference(InSR) # assign image crs
+OutSR = osr.SpatialReference() # target crs WGS84
+OutSR.ImportFromEPSG(4326) 
+Point.TransformTo(OutSR)  # transform coords to WGS84
+lon=Point.GetY() # get transformed coords
+lat=Point.GetX()
+
+# time params
+d=dt.datetime(2009,8,6,9,59,30) # acquisition date and time from im metadata
+tzone=0 # time zone of the caquisition date (here Zulu = UTM 0)
+
+# shadow computation
+shad = shadow_mask(dem,lon,lat,d,tzone,dem_res)
+
+# display the shadow over the image
+plt.figure()
+plt.imshow(im)
+plt.imshow(shad,alpha=0.5)
+plt.title('Cast shadow over Landscape')