Add skeleton of contained coregistration

clients/python/utils/contained_coreg_xdem.py

@@ -0,0 +1,75 @@
+import xdem
+import geopandas as gpd
+
+# Example files and user input
+fn_epc = ""
+fn_dem = ""
+
+coreg_method_str = "nuthkaab"
+subsample = 500000
+fit_or_bin = "bin_and_fit"
+
+# 1/ INPUTS FROM C++
+####################
+
+# 1A/ Inputs from SlideRule to the Python container
+
+# Elevation point cloud provided by SlideRule
+# If reading file
+epc = gpd.read_file(fn_epc)
+# Otherwise directly a geodataframe
+
+# DEM provided by SlideRule
+# If reading file
+dem = xdem.DEM(fn_dem)
+# Otherwise directly an array+geotransform+CRS+nodata
+# dem_arr =  # NumPy array
+# dem_transform =  # Can be created from a tuple with affine.transform()
+# dem_crs =   # Can be created with pyproj.CRS()
+# dem_nodata =  # Float
+# dem = xdem.DEM.from_array(data=dem_arr, transform=dem_transform, crs=dem_crs, nodata=dem_nodata)
+
+# 1B/ Inputs from user through SlideRule to the Python container
+
+# Mapping coregistration methods
+mapping_coreg_methods = {"nuthkaab": xdem.coreg.NuthKaab, "dhmin": xdem.coreg.DhMinimize,
+                         "icp": xdem.coreg.ICP, "vshift": xdem.coreg.VerticalShift}
+
+# Mapping potential typed inputs, nested typed dictionary: xdem.coreg.base.InputCoregDict
+
+# Randomization is always mapped to init
+init_args = xdem.coreg.base.InRandomDict(subsample=subsample)
+# Other arguments are mapped to fit
+fit_args = xdem.coreg.base.InFitOrBinDict(fit_or_bin=fit_or_bin)
+
+# 2/ RUN COREGISTRATION
+#######################
+
+# Fixed parameters to have defined in the container
+z_name = "h_li"  # Name of z variable in the geodataframe
+ref = "epc"  # Which data is the reference for the user: the point cloud or DEM?
+if ref == "epc":
+    reference_elev = epc
+    to_be_aligned_elev = dem
+else:
+    reference_elev = dem
+    to_be_aligned_elev = epc
+
+# Run the coregistration
+c = mapping_coreg_methods[coreg_method_str](**init_args)
+c.fit(reference_elev=reference_elev, to_be_aligned_elev=to_be_aligned_elev, **fit_args)
+
+# 3/ OUTPUTS TO C++
+###################
+
+# In any case, extract relevant dictionary of outputs: detailed in xdem.coreg.base.OutputCoregDict
+output_random = c.meta["outputs"]["random"]
+output_fitorbin = c.meta["outputs"]["fitorbin"]
+output_affine = c.meta["outputs"]["affine"]
+
+# If prefer to transform the elevation data in Python, run apply as well, otherwise do this in C++ later
+# coreg_tba_elev = c.apply(elev=to_be_aligned_elev)
+# Potentially re-transform DEM object to array+transform+CRS+nodata
+
+# Main outputs for a translation coregistration: shift in X/Y/Z
+print(output_affine["shift_x"], output_affine["shift_y"], output_affine["shift_z"])