formatting

docs/source/notebooks/AWSDataNotebook.ipynb

@@ -2428,7 +2428,7 @@
  ],
  "source": [
  "# FLNS collection\n",
- "col_flns = ldcpy.collect_datasets(\"cam-fv\", [\"FLNS\"], [ds_flns, aws_flns], [\"original\", \"lossy\"])\n"
+ "col_flns = ldcpy.collect_datasets(\"cam-fv\", [\"FLNS\"], [ds_flns, aws_flns], [\"original\", \"lossy\"])"
  ]
  },
  {
@@ -2789,7 +2789,9 @@
  ],
  "source": [
  "# print statistics about 'original', 'lossy', and diff between the two datasets for TMQ at time slice 365\n",
- "ldcpy.compare_stats(col_tmq.isel(time=365), \"TMQ\", [\"original\", \"lossy\"], aggregate_dims = [\"lat\", \"lon\"])"
+ "ldcpy.compare_stats(\n",
+ " col_tmq.isel(time=365), \"TMQ\", [\"original\", \"lossy\"], aggregate_dims=[\"lat\", \"lon\"]\n",
+ ")"
  ]
  },
  {

docs/source/notebooks/MetricsNotebook.ipynb

@@ -417,7 +417,9 @@
  ],
  "source": [
  "data = ts_col.isel(time=0)\n",
- "ldcpy.compare_stats(data, \"TS\", [\"orig\", \"zfp1.0\", \"zfp1e-1\", \"zfp1e-3\"], aggregate_dims=[\"lat\", \"lon\"])"
+ "ldcpy.compare_stats(\n",
+ " data, \"TS\", [\"orig\", \"zfp1.0\", \"zfp1e-1\", \"zfp1e-3\"], aggregate_dims=[\"lat\", \"lon\"]\n",
+ ")"
  ]
  },
  {
@@ -733,7 +735,13 @@
  "source": [
  "# now using weighted averages\n",
  "data = ts_col.isel(time=0)\n",
- "ldcpy.compare_stats(data, \"TS\", [\"orig\", \"zfp1.0\", \"zfp1e-1\", \"zfp1e-3\"], weighted=True, aggregate_dims=[\"lat\", \"lon\"])"
+ "ldcpy.compare_stats(\n",
+ " data,\n",
+ " \"TS\",\n",
+ " [\"orig\", \"zfp1.0\", \"zfp1e-1\", \"zfp1e-3\"],\n",
+ " weighted=True,\n",
+ " aggregate_dims=[\"lat\", \"lon\"],\n",
+ ")"
  ]
  },
  {

ldcpy/util.py

@@ -106,7 +106,7 @@ def collect_datasets(
  weighted = False
  else:
  weighted = True
- 
+
  # preprocess_vars is here for working on jupyter hub...
  def preprocess_vars(ds, varnames):
  return ds[varnames]
@@ -137,7 +137,7 @@ def preprocess_vars(ds, varnames):
  full_ds.attrs['data_type'] = data_type
  full_ds.attrs['file_size'] = None
  full_ds.attrs['weighted'] = weighted
- 
+
  # file sizes?
  if file_sizes is not None:
  file_size_dict = {}
@@ -154,7 +154,7 @@ def preprocess_vars(ds, varnames):
  new_ds[i].attrs['data_type'] = data_type
  new_ds[i].attrs['set_name'] = label
  new_ds[i].attrs['weighted'] = weighted
- 
+
  # d = xr.combine_by_coords(new_ds)
  d = xr.concat(new_ds, 'collection')
  full_ds[v] = d
@@ -237,7 +237,7 @@ def preprocess_vars(ds):
 
  return ds[varnames]
 
- #check the weights
+ # check the weights
  tmp_ds = xr.open_dataset(list_of_files[0])
  if data_type == 'cam-fv' and weights is True:
  weights_name = 'gw'
@@ -256,7 +256,7 @@ def preprocess_vars(ds):
  weighted = False
  else:
  weighted = True
- 
+
  full_ds = xr.open_mfdataset(
  list_of_files,
  concat_dim='collection',
@@ -286,7 +286,7 @@ def preprocess_vars(ds):
  full_ds.attrs['data_type'] = data_type
  full_ds.attrs['file_size'] = file_size_dict
  full_ds.attrs['weighted'] = weighted
- 
+
  for v in varnames[:-1]:
  new_ds = []
  i = 0
@@ -295,7 +295,7 @@ def preprocess_vars(ds):
  new_ds[i].attrs['data_type'] = data_type
  new_ds[i].attrs['set_name'] = label
  new_ds[i].attrs['weighted'] = weighted
- 
+
  # d = xr.combine_by_coords(new_ds)
  d = xr.concat(new_ds, 'collection')
  full_ds[v] = d
@@ -345,13 +345,15 @@ def compare_stats(
  da = ds[varname]
  data_type = ds.attrs['data_type']
  attr_weighted = ds.attrs['weighted']
- 
+
  # no weights for wrf
  if data_type == 'cam-fv':
  if weighted:
  if not attr_weighted:
- print('Warning - this data does not contain weights, so averages will be unweighted.')
- weighted = False
+ print(
+ 'Warning - this data does not contain weights, so averages will be unweighted.'
+ )
+ weighted = False
  if data_type == 'wrf':
  weighted = False
 
@@ -723,7 +725,7 @@ def check_metrics(
  num_fail = 0
  # Pearson less than pcc_tol means fail
  pcc = diff_calcs.get_diff_calc('pearson_correlation_coefficient')
- #print(type(pcc))
+ # print(type(pcc))
  if pcc < pcc_tol:
 
  print(' *FAILED pearson correlation coefficient test...(pcc = {0:.5f}'.format(pcc), ')')