Fix dims --> sizes for xarray datasets

mpas_analysis/ocean/climatology_map_argo.py

@@ -430,7 +430,7 @@ def build_observational_dataset(self, fileName):
         dsObs.coords['month'] = ('Time', np.array(dsObs['calmonth'], int))
 
         # no meaningful year since this is already a climatology
-        dsObs.coords['year'] = ('Time', np.ones(dsObs.dims['Time'], int))
+        dsObs.coords['year'] = ('Time', np.ones(dsObs.sizes['Time'], int))
         dsObs = dsObs[[self.fieldName, 'month']]
 
         slices = []

mpas_analysis/ocean/climatology_map_mld.py

@@ -235,7 +235,7 @@ def build_observational_dataset(self, fileName):
         dsObs.coords['month'] = ('Time', np.array(dsObs['calmonth'], int))
 
         # no meaningful year since this is already a climatology
-        dsObs.coords['year'] = ('Time', np.ones(dsObs.dims['Time'], int))
+        dsObs.coords['year'] = ('Time', np.ones(dsObs.sizes['Time'], int))
 
         dsObs = dsObs[['mld', 'month']]
         return dsObs

mpas_analysis/ocean/climatology_map_woa.py

@@ -372,7 +372,7 @@ def build_observational_dataset(self, fileName):
             # need to add a dummy year coordinate
             dsObs = dsObs.rename({'month': 'Time'})
             dsObs.coords['month'] = dsObs['Time']
-            dsObs.coords['year'] = ('Time', np.ones(dsObs.dims['Time'], int))
+            dsObs.coords['year'] = ('Time', np.ones(dsObs.sizes['Time'], int))
 
         data_vars = {}
         for fieldName in self.fieldNames:

mpas_analysis/ocean/ocean_regional_profiles.py

@@ -303,7 +303,7 @@ def run_task(self):
                                    startDate=startDate,
                                    endDate=endDate) as dsIn:
 
-                for inIndex in range(dsIn.dims['Time']):
+                for inIndex in range(dsIn.sizes['Time']):
 
                     mask = np.logical_and(
                         dsIn.year[inIndex].values == years,

mpas_analysis/ocean/remap_depth_slices_subtask.py

@@ -130,7 +130,7 @@ def run_task(self):
         zBot = zMid.where(zMid.verticalIndex == self.maxLevelCell).sum(
             dim='nVertLevels')
 
-        verticalIndices = np.zeros((len(self.depths), ds.dims['nCells']), int)
+        verticalIndices = np.zeros((len(self.depths), ds.sizes['nCells']), int)
 
         mask = np.zeros(verticalIndices.shape, bool)
 
@@ -145,7 +145,7 @@ def run_task(self):
                 verticalIndices[depthIndex, :] = self.maxLevelCell.values
                 mask[depthIndex, :] = self.maxLevelCell.values >= 0
             else:
-                
+
                 diff = np.abs(zMid - depth).where(ocean_mask, drop=True)
                 verticalIndex = diff.argmin(dim='nVertLevels')
 
@@ -199,7 +199,7 @@ def customize_masked_climatology(self, climatology, season):
 
         climatology.coords['verticalIndex'] = \
             ('nVertLevels',
-             np.arange(climatology.dims['nVertLevels']))
+             np.arange(climatology.sizes['nVertLevels']))
 
         depthNames = [str(depth) for depth in self.depths]
 

mpas_analysis/ocean/streamfunction_moc.py

@@ -1043,7 +1043,7 @@ def _compute_moc_time_series_analysismember(self):
                     refTopDepth = dsMOCIn.depth.values
 
                 # first, copy all computed data
-                for inIndex in range(dsMOCIn.dims['Time']):
+                for inIndex in range(dsMOCIn.sizes['Time']):
 
                     mask = np.logical_and(
                         dsMOCIn.year[inIndex].values == years,
@@ -1219,7 +1219,7 @@ def _compute_moc_time_series_postprocess(self):
                 dsMOCIn.load()
 
                 # first, copy all computed data
-                for inIndex in range(dsMOCIn.dims['Time']):
+                for inIndex in range(dsMOCIn.sizes['Time']):
 
                     mask = np.logical_and(
                         dsMOCIn.year[inIndex].values == years,

mpas_analysis/ocean/time_series_ocean_regions.py

@@ -540,7 +540,7 @@ def run_task(self):
                                    startDate=startDate,
                                    endDate=endDate) as dsOut:
 
-                for inIndex in range(dsOut.dims['Time']):
+                for inIndex in range(dsOut.sizes['Time']):
 
                     mask = numpy.logical_and(
                         dsOut.year[inIndex].values == years,

mpas_analysis/ocean/time_series_transport.py

@@ -270,7 +270,7 @@ def run_task(self):
                                    startDate=startDate,
                                    endDate=endDate) as dsOut:
 
-                for inIndex in range(dsOut.dims['Time']):
+                for inIndex in range(dsOut.sizes['Time']):
 
                     mask = numpy.logical_and(
                         dsOut.year[inIndex].values == years,

mpas_analysis/shared/climatology/climatology.py

@@ -715,7 +715,7 @@ def _setup_climatology_caching(ds, startYearClimo, endYearClimo,
 
     cacheInfo = []
 
-    cacheIndices = -1 * numpy.ones(ds.dims['Time'], int)
+    cacheIndices = -1 * numpy.ones(ds.sizes['Time'], int)
     monthsInDs = ds.month.values
     yearsInDs = ds.year.values
 
@@ -787,7 +787,7 @@ def _cache_individual_climatologies(ds, cacheInfo, printProgress,
 
         totalDays = dsYear.daysInMonth.sum(dim='Time').values
 
-        monthCount = dsYear.dims['Time']
+        monthCount = dsYear.sizes['Time']
 
         climatology = compute_climatology(dsYear, monthValues, calendar,
                                           maskVaries=False)

mpas_analysis/shared/generalized_reader/generalized_reader.py

@@ -157,7 +157,7 @@ def open_multifile_dataset(fileNames, calendar, config,
     # select only the data in the specified range of dates
     ds = ds.sel(Time=slice(startDate, endDate))
 
-    if ds.dims['Time'] == 0:
+    if ds.sizes['Time'] == 0:
         raise ValueError('The data set contains no Time entries between '
                          'dates {} and {}.'.format(
                              days_to_datetime(startDate, calendar=calendar),

mpas_analysis/shared/io/mpas_reader.py

@@ -84,7 +84,7 @@ def open_mpas_dataset(fileName, calendar,
         # select only the data in the specified range of dates
         ds = ds.sel(Time=slice(startDate, endDate))
 
-    if ds.dims['Time'] == 0:
+    if ds.sizes['Time'] == 0:
         raise ValueError('The data set contains no Time entries between '
                          'dates {} and {}.'.format(
                              days_to_datetime(startDate, calendar=calendar),

suite/run_dev_suite.bash

@@ -6,14 +6,13 @@ env_name=mpas_dev
 
 conda_base=$(dirname $(dirname $CONDA_EXE))
 source $conda_base/etc/profile.d/conda.sh
-source $conda_base/etc/profile.d/mamba.sh
 
 export HDF5_USE_FILE_LOCKING=FALSE
 
 branch=$(git symbolic-ref --short HEAD)
 
 # test building the docs
-mamba activate ${env_name}
+conda activate ${env_name}
 cd docs
 make clean
 make html

suite/run_suite.bash

@@ -12,14 +12,14 @@ export HDF5_USE_FILE_LOCKING=FALSE
 
 branch=$(git symbolic-ref --short HEAD)
 
-conda update -y conda conda-build mamba boa
-conda mambabuild ci/recipe
+conda update -y conda conda-build
+conda build ci/recipe
 
 # create the test conda envs
 for py in ${main_py} ${alt_py}
 do
     env=test_mpas_analysis_py${py}
-    mamba create -y -n ${env} --use-local python=${py} mpas-analysis sphinx \
+    conda create -y -n ${env} --use-local python=${py} mpas-analysis sphinx \
         mock sphinx_rtd_theme "tabulate>=0.8.2" "m2r2>=0.3.3" "mistune<2" \
         pytest "mache>=1.11.0" "esmf=*=mpi_mpich_*" jinja2
     conda activate ${env}
@@ -30,7 +30,7 @@ done
 # create another env for testing xarray main branch
 py=${main_py}
 env=test_mpas_analysis_xarray_main
-mamba create --yes --quiet --name ${env} --use-local python=${py} \
+conda create --yes --quiet --name ${env} --use-local python=${py} \
     mpas-analysis pytest
 conda activate ${env}
 pip install git+https://github.com/pydata/xarray.git