[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
NCAR · Dec 18, 2023 · c87d18f · c87d18f
1 parent f6f2379
commit c87d18f
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Showing 10 changed files with 13 additions and 46 deletions.
diff --git a/.github/dependabot.yml b/.github/dependabot.yml
@@ -4,8 +4,8 @@ updates:
   #   directory: "/"
   #   schedule:
   #     interval: daily
-  - package-ecosystem: 'github-actions'
-    directory: '/'
+  - package-ecosystem: "github-actions"
+    directory: "/"
     schedule:
       # Check for updates once a week
-      interval: 'weekly'
+      interval: "weekly"
diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml
@@ -2,12 +2,12 @@ name: CI
 on:
   push:
     branches:
-      - '*'
+      - "*"
   pull_request:
     branches:
-      - '*'
+      - "*"
   schedule:
-    - cron: '0 0 * * *' # Daily “At 00:00” UTC
+    - cron: "0 0 * * *" # Daily “At 00:00” UTC
   workflow_dispatch: # allows you to trigger the workflow run manually
 
 jobs:
@@ -22,8 +22,8 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        os: ['ubuntu-latest']
-        python-version: ['3.7', '3.8', '3.9']
+        os: ["ubuntu-latest"]
+        python-version: ["3.7", "3.8", "3.9"]
     steps:
       - name: Cancel previous runs
         uses: styfle/cancel-workflow-action@0.11.0
@@ -34,7 +34,7 @@ jobs:
         with:
           channels: conda-forge
           channel-priority: strict
-          mamba-version: '*'
+          mamba-version: "*"
           activate-environment: ldcpy # Defined in ci/environment.yml
           auto-update-conda: false
           python-version: ${{ matrix.python-version }}
@@ -76,7 +76,7 @@ jobs:
         with:
           channels: conda-forge
           channel-priority: strict
-          mamba-version: '*'
+          mamba-version: "*"
           activate-environment: ldcpy # Defined in ci/environment.yml
           auto-update-conda: false
           python-version: 3.9

diff --git a/.github/workflows/linting.yaml b/.github/workflows/linting.yaml
@@ -2,9 +2,9 @@ name: code-style
 
 on:
   push:
-    branches: '*'
+    branches: "*"
   pull_request:
-    branches: '*'
+    branches: "*"
 
 jobs:
   linting:

diff --git a/.github/workflows/pypi.yaml b/.github/workflows/pypi.yaml
@@ -14,7 +14,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: '3.x'
+          python-version: "3.x"
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip

diff --git a/docs/source/notebooks/MutualInformation_V2.ipynb b/docs/source/notebooks/MutualInformation_V2.ipynb
@@ -299,7 +299,6 @@
     "\n",
     "    I = []\n",
     "    for bcount, bit_pos_dict in enumerate(dict_list_H):\n",
-    "\n",
     "        p00 = np.divide(bit_pos_dict[\"00\"], num - 1, dtype=np.float64)\n",
     "        p01 = np.divide(bit_pos_dict[\"01\"], num - 1, dtype=np.float64)\n",
     "        p10 = np.divide(bit_pos_dict[\"10\"], num - 1, dtype=np.float64)\n",

diff --git a/ldcpy/calcs.py b/ldcpy/calcs.py
@@ -169,7 +169,6 @@ def _is_memoized(self, calc_name: str) -> bool:
         return hasattr(self, calc_name) and (self.__getattribute__(calc_name) is not None)
 
     def _con_var(self, dir, dataset) -> xr.DataArray:
-
         if dir == 'ns':
             tt = dataset.diff(self._lat_dim_name, 1)
 
@@ -417,7 +416,6 @@ def entropy(self) -> xr.DataArray:
         # lower is better (1.0 means random - no compression possible)
         """
         if not self._is_memoized('_entropy'):
-
             a1 = self._ds.data
             if dask.is_dask_collection(a1):
                 a1 = a1.compute()
@@ -1052,7 +1050,6 @@ def vfftmax(self) -> xr.DataArray:
     @property
     def stft(self) -> xr.DataArray:
         if not self._is_memoized('_stft'):
-
             f, t, self._stft = np.abs(
                 stft(
                     self.mean.isel({'lat': 100}).squeeze(), nperseg=1, nfft=382, detrend='constant'
@@ -1552,7 +1549,6 @@ def covariance(self) -> xr.DataArray:
         The covariance between the two datasets
         """
         if not self._is_memoized('_covariance'):
-
             # need to use unweighted means
             c1_mean = self._calcs1.get_calc('ds').mean(skipna=True)
             c2_mean = self._calcs2.get_calc('ds').mean(skipna=True)
@@ -1582,7 +1578,6 @@ def pearson_correlation_coefficient(self):
         returns the pearson correlation coefficient between the two datasets
         """
         if not self._is_memoized('_pearson_correlation_coefficient'):
-
             # we need to do this with  unweighted data
             c1_std = float(self._calcs1.get_calc('ds').std(skipna=True))
             c2_std = float(self._calcs2.get_calc('ds').std(skipna=True))
@@ -1659,7 +1654,6 @@ def spatial_rel_error(self):
             self._spatial_rel_error = 0
             self._max_spatial_rel_error = 0
         else:
-
             if z.size > 0:
                 m_t1_denom = np.ma.masked_invalid(t1_denom).compressed()
             else:
@@ -1799,7 +1793,6 @@ def ssim_value(self):
             ssim_mats_array = []
 
             for this_lev in range(nlevels):
-
                 with tempfile.TemporaryDirectory() as tmpdirname:
                     filename_1, filename_2 = (
                         f'{tmpdirname}/t_ssim1.png',
@@ -1908,7 +1901,6 @@ def ssim_value_fp_slow(self):
         """
 
         if not self._is_memoized('_ssim_value_fp_slow'):
-
             # if this is a 3D variable, we will do each level seperately
             if self._calcs1._vert_dim_name is not None:
                 vname = self._calcs1._vert_dim_name
@@ -1977,13 +1969,11 @@ def ssim_value_fp_slow(self):
                 # go through 2D arrays - each grid point x0, y0  has
                 # a 2D window [x0 - k, x0+k]  [y0 - k, y0 + k]
                 for i in range(X):
-
                     # don't go over boundaries
                     imin = max(0, i - k)
                     imax = min(X - 1, i + k)
 
                     for j in range(Y):
-
                         if np.isnan(sc_a1[i, j]):
                             # SKIP IF gridpoint is nan
                             ssim_mat[i, j] = np.nan
@@ -2079,7 +2069,6 @@ def ssim_value_fp_fast(self):
         from astropy.convolution import Gaussian2DKernel, convolve, interpolate_replace_nans
 
         if not self._is_memoized('_ssim_value_fp_fast'):
-
             # if this is a 3D variable, we will do each level separately
             if self._calcs1._vert_dim_name is not None:
                 vname = self._calcs1._vert_dim_name

diff --git a/ldcpy/comp_checker.py b/ldcpy/comp_checker.py
@@ -51,7 +51,6 @@ def __init__(
         comp_mode='p',
         accept_first=False,
     ):
-
         self._calc_type = calc_type
         self._calc_tol = calc_tol
         self._tol_greater_than = True
@@ -73,7 +72,6 @@ def reset_checker(self):  # call before doing the next timestep
         self._opt_level = None
 
     def eval_comp_level(self, orig_da, comp_da, comp_level):
-
         dc = lm.Diffcalcs(orig_da, comp_da)
         val = dc.get_diff_calc(self._calc_type)
 
@@ -166,7 +164,6 @@ def _comp_rules(self, comp_level, level_passed):
         return new_level
 
     def _zfp_rules(self, comp_level, level_passed):
-
         if self._comp_mode == 'p':  # precision
             pmax = 28
             pmin = 6

diff --git a/ldcpy/derived_vars.py b/ldcpy/derived_vars.py
@@ -10,7 +10,6 @@
 
 
 def _preprocess(set_labels, list_of_cols):
-
     contU = True
     num_sets = len(set_labels)
 
@@ -26,7 +25,6 @@ def _preprocess(set_labels, list_of_cols):
 
 # top of the model radiation budget
 def cam_restom(all_col, sets):
-
     col = []
 
     fsnt = all_col['FSNT']
@@ -64,7 +62,6 @@ def cam_restom(all_col, sets):
 
 # global precipitation
 def cam_precip(all_col, sets):
-
     col = []
 
     precc = all_col['PRECC']
@@ -101,7 +98,6 @@ def cam_precip(all_col, sets):
 
 # evaporation-precipitation
 def cam_ep(all_col, sets):
-
     # QFLX is "kg/m2/s or mm/s
     # PRECC and PRECL are m/s
     # 1 kg/m2/s = 86400 mm/day.
@@ -148,7 +144,6 @@ def cam_ep(all_col, sets):
 
 # surface energy balance
 def cam_ressurf(all_col, sets):
-
     col = []
 
     # all in W/m^2

diff --git a/ldcpy/plot.py b/ldcpy/plot.py
@@ -80,7 +80,6 @@ def __init__(
         cmax=None,
         cmin=None,
     ):
-
         self._ds = ds
 
         self._cmax = cmax
@@ -209,7 +208,6 @@ def get_plot_data(self, raw_data_1, raw_data_2=None):
         return plot_data
 
     def get_title(self, calc_name, c_name=None):
-
         if c_name is not None:
             das = f'{c_name}'
         else:
@@ -238,7 +236,6 @@ def get_title(self, calc_name, c_name=None):
             title = f'{title} {self._calc_type}'
 
         if self._group_by is not None:
-
             title = f'{title} by {self._group_by}'
 
         if self.title_lat is not None:
@@ -282,7 +279,6 @@ def update_label(event_axes):
         return
 
     def spatial_plot(self, da_sets, titles, data_type):
-
         if self.vert_plot:
             nrows = int((da_sets.sets.size))
         else:
@@ -326,7 +322,6 @@ def spatial_plot(self, da_sets, titles, data_type):
             central = 300.0
 
         for i in range(da_sets.sets.size):
-
             if self.vert_plot:
                 axs[i] = plt.subplot(
                     nrows, 1, i + 1, projection=ccrs.Robinson(central_longitude=central)
@@ -563,7 +558,6 @@ def time_series_plot(
         if da_sets.size / da_sets.sets.size == 1:
             tick_interval = 1
         if self._group_by == 'time.dayofyear':
-
             group_string = 'dayofyear'
             xlabel = 'Day of Year'
         elif self._group_by == 'time.month':
@@ -704,7 +698,6 @@ def get_calc_label(self, calc, data, data_type):
                     calc_name = f'{calc}: cutoff {zscore_cutoff[0]:.2e}, % sig: {percent_sig:.2f}'
 
             elif calc == 'mean' and self._plot_type == 'spatial' and self._calc_type == 'raw':
-
                 if self._weighted:
                     a1_data = (
                         lm.Datasetcalcs(data, data_type, ['time'], weighted=self._weighted)

diff --git a/ldcpy/util.py b/ldcpy/util.py
@@ -526,7 +526,6 @@ def check_metrics(
     # Pearson less than pcc_tol means fail
     pcc = diff_calcs.get_diff_calc('pearson_correlation_coefficient')
     if pcc < pcc_tol:
-
         print('     *FAILED pearson correlation coefficient test...(pcc = {0:.5f}'.format(pcc), ')')
         num_fail = num_fail + 1
     else:
@@ -581,7 +580,6 @@ def subset_data(
     if lon_coord_name is None:
         lon_coord_name = ds.cf.coordinates['longitude'][0]
     if lat_coord_name is None:
-
         lat_coord_name = ds.cf.coordinates['latitude'][0]
     if vertical_dim_name is None:
         try:
@@ -624,19 +622,16 @@ def subset_data(
             ds_subset = ds_subset.isel({vertical_dim_name: lev})
 
     if latdim == 1:
-
         if lat is not None:
             ds_subset = ds_subset.sel(**{lat_coord_name: [lat], 'method': 'nearest'})
         if lon is not None:
             ds_subset = ds_subset.sel(**{lon_coord_name: [lon + 180], 'method': 'nearest'})
 
     elif latdim == 2:
-
         # print(ds_subset)
 
         if lat is not None:
             if lon is not None:
-
                 # lat is -90 to 90
                 # lon should be 0- 360
                 ad_lon = lon
@@ -665,7 +660,6 @@ def subset_data(
 
 
 def var_and_wt_coords(varname, ds_col):
-
     ca_coord = ds_col.coords['cell_area']
     if dask.is_dask_collection(ca_coord):
         ca_coord = ca_coord.compute()