Adding changes based on C Vernon's review

JGCRI · Jan 8, 2023 · 0ef1c0c · 0ef1c0c
1 parent ee9585f
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Showing 5 changed files with 28 additions and 135 deletions.
diff --git a/demeter/ncdf_conversion.py b/demeter/ncdf_conversion.py
@@ -259,17 +259,3 @@ def process_output(self,
         return ds
 
 
-if __name__ == "__main__":
-
-    input_file_directory = "/Users/d3y010/Downloads"
-    output_file_directory = "/Users/d3y010/Desktop"
-    target_year = 2005
-
-    # instantiate class
-    x = DemeterToNetcdf(scenario_name="rcp85cooler",
-                        project_name="im3")
-
-    # convert demeter output to a NetCDF file
-    x.process_output(input_file_directory=input_file_directory,
-                     output_file_directory=output_file_directory,
-                     target_year=target_year)
diff --git a/demeter/process.py b/demeter/process.py
@@ -58,8 +58,7 @@ def prep_step(self):
                                                            self.s.lat, self.s.lon, self.step, self.s.kernel_vector,
                                                            self.s.weights, self.s.spat_ludataharm)
 
-        # create transition array to store data
-        #self.transitions = np.zeros(shape=(self.l_spat_region, self.l_order_rules, self.l_order_rules))
+
 
     def intense_pass(self, pass_num):
         """Conduct the first pass of intensification."""
@@ -128,12 +127,7 @@ def outputs(self):
         # convert land cover from sqkm per grid cell per land class to fraction (n_grids, n_landclasses)
         fraction_lu = self.s.spat_ludataharm / np.tile(self.s.cellarea * self.s.celltrunk, (self.l_fcs, 1)).T
 
-        # optionally save land cover transitions as a CSV
-        #if (self.config.save_transitions == 1) and (self.step in self.config.target_years_output):
-
-         #   self.config.logger.info("Saving land cover transition files for time step {0}...".format(self.step))
 
-          #  wdr.write_transitions(self.s, self.config, self.step, self.transitions)
 
         # create a NetCDF file of land cover fraction for each year by grid cell containing each land class
 
@@ -151,7 +145,7 @@ def outputs(self):
                 write_ncdf =True
             return wdr.lc_timestep_csv(self.config, self.step, self.s.final_landclasses, self.s.spat_coords, orig_spat_aez,
                                 self.s.spat_region, self.s.spat_water, self.s.cellarea, self.s.spat_ludataharm,
-                                self.config.metric, self.config.tabular_units, self.write_outputs,write_ncdf,self.sce,self.res,write_csv,self.regrid_res)
+                                self.config.metric, self.config.tabular_units, self.write_outputs, write_ncdf, self.sce, self.res, write_csv, self.regrid_res)
 
     def process(self):
         """

diff --git a/demeter/weight/kernel_density.py b/demeter/weight/kernel_density.py
@@ -18,19 +18,30 @@
 
 
 def handle_single_pft(pft_order, order_rules, final_landclasses, pft_maps, cellindexresin,
-                      spat_ludataharm, kernel_maps, kernel_vector,weights):
-    """
-    Helper function to handle pft convultion filters. This is used to parallelize the convultion filter operation to speed up processing.
+                      spat_ludataharm, kernel_maps, kernel_vector, weights):
 
+    """Helper function to handle pft convultion filters. This is passed to apply_convultiion This is used to parallelize the convultion filter operation to speed up processing.
 
-    """
+       Parameters:
+           pft_order (List[int]): The order of pfts
+           order_rules (List[str]): The order rules defined by user
+           final_landclasses (List[int]): A list of land classes
+           pft_maps (dict): A map of pfts
+           cellindexresin (Union[None, int]): Input resolution
+           spat_ludataharm (pd.Dataframe): Dataframe of land use
+
+
+       Returns:
+           list: processed convultion filters for each LT.
+
+       """
     pft = np.where(order_rules == pft_order)[0][0]
     # get final land class name
     flc = final_landclasses[pft]
-    # print(pft)
+
     # populate pft_maps array with base land use layer data
     pft_maps[np.int_(cellindexresin[0, :]), np.int_(cellindexresin[1, :]), pft] = spat_ludataharm[:, pft]
-    # print(pft_maps.shape)
+
     # apply image filter
 
     kernel_maps[:, :, pft] = ndimage.filters.convolve(pft_maps[:, :, pft], weights, output=None, mode='wrap')
@@ -40,8 +51,8 @@ def handle_single_pft(pft_order, order_rules, final_landclasses, pft_maps, celli
 
     min_seed = 0.0000000001
     kernel_maps[:, :, pft][
-        kernel_maps[:, :, pft] == 0] = min_seed  # np.nanmin(kernel_maps[:, :, pft], [kernel_maps[:, :, pft] > 0])
-    # print(kernel_maps.shape)
+    kernel_maps[:, :, pft] == 0] = min_seed  # np.nanmin(kernel_maps[:, :, pft], [kernel_maps[:, :, pft] > 0])
+
     # reshaping to the spatial grid-cell data (vector)
     kernel_vector[:, pft] = kernel_maps[np.int_(cellindexresin[0, :]), np.int_(cellindexresin[1, :]), pft]
 
@@ -184,13 +195,12 @@ def apply_convolution(self, cellindexresin, pft_maps, kernel_maps, lat, lon, yr,
         order_rules= self.order_rules
         final_landclasses= self.final_landclasses
 
-
-
-        pool.starmap(handle_single_pft, zip(aux_val,repeat(order_rules),repeat(final_landclasses),
+        pool.starmap(handle_single_pft, zip(aux_val,repeat(order_rules), repeat(final_landclasses),
                                                                   repeat(pft_maps),repeat(cellindexresin),
                                                                   repeat(spat_ludataharm),
                                                                   repeat(kernel_maps), repeat(kernel_vector),repeat(weights)))
         pool.terminate()
+
         return kernel_vector
 
 

diff --git a/processing_scripts/convert_csv_col_to_ncdf_subdata.py b/processing_scripts/convert_csv_col_to_ncdf_subdata.py
diff --git a/setup.py b/setup.py
@@ -19,11 +19,11 @@ def readme():
     description='A land use land cover change disaggregation model',
     long_description=readme(),
     long_description_content_type="text/markdown",
-    install_requires=['configobj~=5.0.6',
-                      'numpy~=1.20.3',
-                      'pandas~=1.2.4',
-                      'scipy~=1.6.3',
-                      'requests~=2.20.0',
+    install_requires=['configobj>=5.0.6',
+                      'numpy >=1.20.3',
+                      'pandas >=1.2.4',
+                      'scipy >=1.6.3',
+                      'requests>=2.20.0',
                       'gcamreader>=1.2.5',
                       'xarray >= 0.20.2'],
     include_package_data=True