Merge branch 'main' into feature/routing-refactor

.github/actions/setup_env/action.yml

@@ -60,7 +60,7 @@ runs:
         cat environment.yml
 
     - name: Setup conda environment
-      uses: mamba-org/setup-micromamba@v1
+      uses: mamba-org/setup-micromamba@v2
       with:
         environment-file: environment.yml
         environment-name: env

HISTORY.rst

@@ -54,6 +54,8 @@ Unreleased Changes
     * Fixed an issue on Windows where GDAL fails to find its DLLs due to
       an interfering GDAL installation on the PATH, such as from anaconda.
       https://github.com/natcap/invest/issues/1643
+    * Improved error handling of NA values in raster reclassification to provide
+      a more descriptive message.
 * Workbench
     * Several small updates to the model input form UI to improve usability
       and visual consistency (https://github.com/natcap/invest/issues/912).
@@ -81,6 +83,8 @@ Unreleased Changes
       (https://github.com/natcap/invest/issues/1615).
     * Rarity values are now output in CSV format (as well as in raster format)
       (https://github.com/natcap/invest/issues/721).
+    * Improved error handling when there is a missing LULC value in the
+      sensitivity table (https://github.com/natcap/invest/issues/1671).
 * Pollination
     * Fixed an issue with nodata handling that was causing some outputs to be
       filled either with the float32 value for positive infinity, or else with

requirements-dev.txt

@@ -14,7 +14,7 @@
 
 virtualenv>=12.0.1
 pytest
-pytest-subtests<0.14.0
+pytest-subtests!=0.14.0  # https://github.com/pytest-dev/pytest-subtests/issues/173
 wheel>=0.27.0
 pypiwin32; sys_platform == 'win32'  # pip-only
 

src/natcap/invest/urban_nature_access.py

@@ -59,7 +59,8 @@
             'projection_units': u.meter,
             'about': (
                 "A map of LULC codes. "
-                "All values in this raster must have corresponding entries "
+                "Each land use/land cover type must be assigned a unique integer "
+                "code. All values in this raster must have corresponding entries "
                 "in the LULC attribute table. For this model in particular, "
                 "the urban nature types are of importance.  Non-nature types "
                 "are not required to be uniquely identified. All outputs "

src/natcap/invest/utils.py

@@ -741,14 +741,24 @@ def reclassify_raster(
         None
 
     Raises:
-        ValueError if ``values_required`` is ``True`` and a pixel value from
-        ``raster_path_band`` is not a key in ``value_map``.
+        ValueError:
+            - if ``values_required`` is ``True`` and a pixel value from
+            ``raster_path_band`` is not a key in ``value_map``.
+        TypeError:
+            - if there is a ``None`` or ``NA`` key in ``value_map``.
     """
     # Error early if 'error_details' keys are invalid
     raster_name = error_details['raster_name']
     column_name = error_details['column_name']
     table_name = error_details['table_name']
 
+    # check keys in value map to ensure none are NA or None
+    if any((key is pandas.NA or key is None)
+           for key in value_map):
+        error_message = (f"Missing or NA value in '{column_name}' column"
+                         f" in {table_name} table.")
+        raise TypeError(error_message)
+
     try:
         pygeoprocessing.reclassify_raster(
             raster_path_band, value_map, target_raster_path, target_datatype,

tests/test_habitat_quality.py

@@ -2133,3 +2133,48 @@ def test_habitat_quality_validate_missing_fut_column(self):
                 header='column', header_name='fut_path')
         )]
         self.assertEqual(validate_result, expected)
+
+    def test_habitat_quality_missing_lulc_val_in_sens_table(self):
+        """Habitat Quality: test for empty value in LULC column of
+        sensitivity table. Expects TypeError"""
+        from natcap.invest import habitat_quality
+
+        args = {
+            'half_saturation_constant': '0.5',
+            'workspace_dir': self.workspace_dir,
+            'n_workers': -1,
+        }
+
+        lulc_array = numpy.ones((100, 100), dtype=numpy.int8)
+        args['lulc_cur_path'] = os.path.join(
+            args['workspace_dir'], 'lc_samp_cur_b.tif')
+        make_raster_from_array(
+            lulc_array, args['lulc_cur_path'])
+
+        args['sensitivity_table_path'] = os.path.join(
+            args['workspace_dir'], 'sensitivity_samp.csv')
+        with open(args['sensitivity_table_path'], 'w') as open_table:
+            open_table.write('LULC,NAME,HABITAT,threat_1,threat_2\n')
+            open_table.write('1,"lulc 1",1,1,1\n')
+            open_table.write(',"lulc 2",0.5,0.5,1\n')  # missing LULC value
+            open_table.write('3,"lulc 3",0,0.3,1\n')
+
+        make_threats_raster(
+            args['workspace_dir'], threat_values=[1, 1],
+            dtype=numpy.int8, gdal_type=gdal.GDT_Int32, nodata_val=None)
+
+        args['threats_table_path'] = os.path.join(
+            args['workspace_dir'], 'threats_samp.csv')
+
+        # create the threat CSV table
+        with open(args['threats_table_path'], 'w') as open_table:
+            open_table.write(
+                'MAX_DIST,WEIGHT,THREAT,DECAY,BASE_PATH,CUR_PATH,FUT_PATH\n')
+            open_table.write(
+                '0.04,0.7,threat_1,linear,,threat_1_c.tif,threat_1_f.tif\n')
+            open_table.write(
+                '0.07,1.0,threat_2,exponential,,threat_2_c.tif,'
+                'threat_2_f.tif\n')
+
+        with self.assertRaises(TypeError):
+            habitat_quality.execute(args)