Applied black formatting.

Author: ZviBaratz

rtanalysis/generate_testdata.py

@@ -19,8 +19,9 @@ def generate_test_df(meanRT, sdRT, meanAcc, n=100):
     # get random accuracy values and threshold for intended proportion
     accuracy_continuous = np.random.rand(n)
     accuracy = pd.Series(
-        accuracy_continuous < scipy.stats.scoreatpercentile(
-            accuracy_continuous, 100 * meanAcc))
+        accuracy_continuous
+        < scipy.stats.scoreatpercentile(accuracy_continuous, 100 * meanAcc)
+    )
 
     # scale the correct RTs only
     rt_correct = rt.mask(~accuracy)
@@ -29,8 +30,7 @@ def generate_test_df(meanRT, sdRT, meanAcc, n=100):
     # NB: .where() replaces values where the condition is False
     rt_scaled_with_inaccurate_rts = rt_scaled.where(accuracy, rt)
 
-    return(pd.DataFrame({'rt': rt_scaled_with_inaccurate_rts,
-                         'accuracy': accuracy}))
+    return pd.DataFrame({"rt": rt_scaled_with_inaccurate_rts, "accuracy": accuracy})
 
 
 def scale_values(values, mean, sd):
@@ -44,4 +44,4 @@ def scale_values(values, mean, sd):
     values = values * (sd / np.std(values))
     values = (values - np.mean(values)) + mean
 
-    return(values)
+    return values

rtanalysis/rtanalysis.py

@@ -8,8 +8,8 @@
 
 # %%
 class RTAnalysis:
-    """[summary]
-    """
+    """[summary]"""
+
     def __init__(self, outlier_cutoff_sd=None):
         """
         RT analysis
@@ -35,30 +35,30 @@ def fit(self, rt, accuracy, verbose=True):
 
         try:
             assert rt.shape[0] == accuracy.shape[0]
-        except AssertionError:
-            raise ValueError('rt and accuracy must be the same length!')
+        except AssertionError as e:
+            raise ValueError("rt and accuracy must be the same length!") from e
 
         # ensure that accuracy values are boolean
-        assert len(set(accuracy.unique()).difference([True, False])) == 0
+        assert not set(accuracy.unique()).difference([True, False])
 
         if self.outlier_cutoff_sd is not None:
             cutoff = rt.std() * self.outlier_cutoff_sd
             if verbose:
-                print(f'outlier rejection excluded {(rt > cutoff).sum()} trials')
+                print(f"outlier rejection excluded {(rt > cutoff).sum()} trials")
             rt = rt.mask(rt > cutoff)
 
         self.meanacc_ = accuracy.mean()
         try:
             assert self.meanacc_ > 0
-        except:
-            raise ValueError('accuracy is zero')
+        except AssertionError as e:
+            raise ValueError("accuracy is zero") from e
 
         rt = rt.mask(~accuracy)
         self.meanrt_ = rt.mean()
 
         if verbose:
-            print(f'mean RT: {self.meanrt_}')
-            print(f'mean accuracy: {self.meanacc_}')
+            print(f"mean RT: {self.meanrt_}")
+            print(f"mean accuracy: {self.meanacc_}")
 
     @staticmethod
     def _ensure_series_type(var):
@@ -74,4 +74,4 @@ def _ensure_series_type(var):
 
         if type(var) is not pd.core.series.Series:
             var = pd.Series(var)
-        return(var)
+        return var

tests/test_1_smoketest.py

@@ -7,4 +7,3 @@
 def test_rtanalysis_smoke():
     rta = RTAnalysis()
     assert rta is not None
-

tests/test_4_fixture.py

@@ -12,26 +12,24 @@
 
 @pytest.fixture
 def params():
-    return({'meanRT': 2.1,
-            'sdRT': 0.9,
-            'meanAcc': 0.8})
+    return {"meanRT": 2.1, "sdRT": 0.9, "meanAcc": 0.8}
 
 
 @pytest.fixture
 def simulated_data(params):
-    return(generate_test_df(
-        params['meanRT'], params['sdRT'], params['meanAcc']))
+    return generate_test_df(params["meanRT"], params["sdRT"], params["meanAcc"])
 
 
 def test_rtanalysis_fit(simulated_data, params):
     rta = RTAnalysis()
     rta.fit(simulated_data.rt, simulated_data.accuracy)
-    assert np.allclose(params['meanRT'], rta.meanrt_)
-    assert np.allclose(params['meanAcc'], rta.meanacc_)
+    assert np.allclose(params["meanRT"], rta.meanrt_)
+    assert np.allclose(params["meanAcc"], rta.meanacc_)
 
 
 def test_rtanalysis_checkfail(simulated_data, params):
     rta = RTAnalysis()
     with pytest.raises(ValueError):
-        rta.fit(simulated_data.rt,
-                simulated_data.accuracy.loc[1:])  # omit first datapoint
+        rta.fit(
+            simulated_data.rt, simulated_data.accuracy.loc[1:]
+        )  # omit first datapoint

tests/test_5_parametric.py

@@ -10,9 +10,9 @@
 from rtanalysis.rtanalysis import RTAnalysis
 
 
-@pytest.mark.parametrize("meanRT, sdRT, meanAcc",
-                         [(1.5, 1.0, 0.9), (1500, 1000, 0.9),
-                         (1.5, 1.0, 0)])
+@pytest.mark.parametrize(
+    "meanRT, sdRT, meanAcc", [(1.5, 1.0, 0.9), (1500, 1000, 0.9), (1.5, 1.0, 0)]
+)
 def test_rtanalysis_parameteric(meanRT, sdRT, meanAcc):
     test_df = generate_test_df(meanRT, sdRT, meanAcc)
     rta = RTAnalysis()