diff --git a/docs/source/notebooks/AWSDataNotebook.ipynb b/docs/source/notebooks/AWSDataNotebook.ipynb
index 0406652..82e7825 100644
--- a/docs/source/notebooks/AWSDataNotebook.ipynb
+++ b/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",
+    ")"
    ]
   },
   {
diff --git a/docs/source/notebooks/MetricsNotebook.ipynb b/docs/source/notebooks/MetricsNotebook.ipynb
index af92e16..7e3d81a 100644
--- a/docs/source/notebooks/MetricsNotebook.ipynb
+++ b/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",
+    ")"
    ]
   },
   {
diff --git a/ldcpy/util.py b/ldcpy/util.py
index e56093d..a0380a9 100644
--- a/ldcpy/util.py
+++ b/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), ')')