Merge pull request #9 from MeteoSwiss/fix/remove_multiprocessing

fix: remove parralelization with multiprocessing

HISTORY.rst

@@ -2,13 +2,14 @@
 History
 =======
 
-0.3.0 (2023-12-20)
+0.3.0 (2024-01-15)
 ------------------
 
 * Move from DataArray to Dataset for DEM object to allow transferring global attributes.
 * Add units as variable attributes.
 * Output slope in units of [degree] instead of [m / pixel].
 * Fix bug in slope calculation.
+* Remove parallelization of scales with multiprocessing for valley and ridge
 
 0.2.1 (2022-10-19)
 ------------------

topo_descriptors/topo.py

@@ -6,7 +6,6 @@
 import dask.array as da
 from numba import njit, prange
 from scipy import ndimage, signal
-from multiprocessing import Pool, cpu_count
 
 import topo_descriptors.helpers as hlp
 from topo_descriptors import CFG
@@ -371,44 +370,20 @@ def compute_valley_ridge(
 
     scales_pxl, _ = hlp.scale_to_pixel(scales, dem_ds)
     sigmas = hlp.get_sigmas(smth_factors, scales_pxl)
+    dem_val = hlp.get_da(dem_ds).values
+    units = "1"
 
-    pool = Pool(processes=min(len(scales_pxl), cpu_count()))
     for idx, scale_pxl in enumerate(scales_pxl):
         logger.info(
             f"Computing scale {scales[idx]} meters with smoothing factor"
             f" {smth_factors[idx]} ..."
         )
         names = _valley_ridge_names(scales[idx], mode, smth_factors[idx])
-        pool.apply_async(
-            _valley_ridge_wrap,
-            args=(
-                dem_ds,
-                scale_pxl,
-                mode,
-                flat_list,
-                sigmas[idx],
-                names,
-                ind_nans,
-                crop,
-                outdir,
-            ),
-        )
-
-    pool.close()
-    pool.join()
-
-
-def _valley_ridge_wrap(
-    dem_ds, size, mode, flat_list, sigma, names, ind_nans, crop, outdir
-):
-    """Wrapper to valley_ridge and hlp.to_netcdf functions to ease the parallelization
-    of the different scales"""
+        arrays = valley_ridge(dem_val, scale_pxl, mode, flat_list, sigmas[idx])
 
-    arrays = valley_ridge(hlp.get_da(dem_ds).values, size, mode, flat_list, sigma)
-    units = "1"
-    for array, name in zip(arrays, names):
-        array[ind_nans] = np.nan
-        hlp.to_netcdf(array, dem_ds, name, crop, outdir, units)
+        for array, name in zip(arrays, names):
+            array[ind_nans] = np.nan
+            hlp.to_netcdf(array, dem_ds, name, crop, outdir, units)
 
 
 @hlp.timer