style(tests): format test covidcast_row with black

Author: dshemetov

tests/common/test_covidcast_row.py

@@ -7,49 +7,47 @@
 from delphi.epidata.common.covidcast_row import CovidcastRow, set_df_dtypes
 from delphi.epidata.acquisition.covidcast.test_utils import (
     CovidcastTestRow,
-    covidcast_rows_as_api_compatibility_row_df, 
-    covidcast_rows_as_api_row_df, 
-    covidcast_rows_from_args, 
-    transpose_dict, 
+    covidcast_rows_as_api_compatibility_row_df,
+    covidcast_rows_as_api_row_df,
+    covidcast_rows_from_args,
+    transpose_dict,
 )
 from delphi.epidata.acquisition.covidcast.test_utils import MSA
 
 # py3tester coverage target (equivalent to `import *`)
-__test_target__ = 'delphi.epidata.common.covidcast_row'
+__test_target__ = "delphi.epidata.common.covidcast_row"
 
 
 class TestCovidcastRows(unittest.TestCase):
-    expected_df = set_df_dtypes(DataFrame({
-        "source": ["src"] * 10,
-        "signal": ["sig_base"] * 5 + ["sig_other"] * 5,
-        "time_type": ["day"] * 10,
-        "geo_type": ["msa"] * 10,
-        "time_value": [2021_05_01 + i for i in range(5)] * 2,
-        "geo_value": [MSA[0]] * 10,
-        "value": range(10),
-        "stderr": [10.0] * 10,
-        "sample_size": [10.0] * 10,
-        "missing_value": [Nans.NOT_MISSING] * 10,
-        "missing_stderr": [Nans.NOT_MISSING] * 10,
-        "missing_sample_size": [Nans.NOT_MISSING] * 10,
-        "issue": [2021_05_01 + i for i in range(5)] * 2,
-        "lag": [0] * 10,
-        "direction": [None] * 10
-    }), CovidcastRow._pandas_dtypes)
-
-    def test_transpose_dict(self):
-        assert transpose_dict(
+    expected_df = set_df_dtypes(
+        DataFrame(
             {
-                "a": [2, 4, 6],
-                "b": [3, 5, 7],
-                "c": [10, 20, 30]
+                "source": ["src"] * 10,
+                "signal": ["sig_base"] * 5 + ["sig_other"] * 5,
+                "time_type": ["day"] * 10,
+                "geo_type": ["msa"] * 10,
+                "time_value": [2021_05_01 + i for i in range(5)] * 2,
+                "geo_value": [MSA[0]] * 10,
+                "value": range(10),
+                "stderr": [10.0] * 10,
+                "sample_size": [10.0] * 10,
+                "missing_value": [Nans.NOT_MISSING] * 10,
+                "missing_stderr": [Nans.NOT_MISSING] * 10,
+                "missing_sample_size": [Nans.NOT_MISSING] * 10,
+                "issue": [2021_05_01 + i for i in range(5)] * 2,
+                "lag": [0] * 10,
+                "direction": [None] * 10,
             }
-        ) == [
-            {"a": 2, "b": 3, "c": 10}, 
-            {"a": 4, "b": 5, "c": 20}, 
-            {"a": 6, "b": 7, "c": 30}
-        ]
+        ),
+        CovidcastRow._pandas_dtypes,
+    )
 
+    def test_transpose_dict(self):
+        assert transpose_dict({"a": [2, 4, 6], "b": [3, 5, 7], "c": [10, 20, 30]}) == [
+            {"a": 2, "b": 3, "c": 10},
+            {"a": 4, "b": 5, "c": 20},
+            {"a": 6, "b": 7, "c": 30},
+        ]
 
     def test_CovidcastRow(self):
         df = CovidcastTestRow.make_default_row(
@@ -70,13 +68,12 @@ def test_CovidcastRow(self):
         expected_df = self.expected_df.iloc[0:1][df.columns]
         assert_frame_equal(df, expected_df)
 
-
     def test_covidcast_rows(self):
         covidcast_rows = covidcast_rows_from_args(
             signal=["sig_base"] * 5 + ["sig_other"] * 5,
             time_value=[2021_05_01 + i for i in range(5)] * 2,
             value=list(range(10)),
-            sanitize_fields = True
+            sanitize_fields=True,
         )
         df = covidcast_rows_as_api_row_df(covidcast_rows)
         expected_df = self.expected_df
@@ -86,7 +83,7 @@ def test_covidcast_rows(self):
             signal=["sig_base"] * 5 + ["sig_other"] * 5,
             time_value=[2021_05_01 + i for i in range(5)] * 2,
             value=list(range(10)),
-            sanitize_fields = True
+            sanitize_fields=True,
         )
         df = covidcast_rows_as_api_compatibility_row_df(covidcast_rows)
         expected_df = self.expected_df[df.columns]