Fixed W292 and E128

Jacks0nJ · Jan 16, 2024 · fa2698c · fa2698c
1 parent f07e370
commit fa2698c
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Showing 3 changed files with 10 additions and 10 deletions.
diff --git a/pyfpt/analytics/edgeworth_pdf.py b/pyfpt/analytics/edgeworth_pdf.py
@@ -85,4 +85,4 @@ def hermite_poly4(y):
 # "physicist's Hermite polynomials" used by SciPy
 def hermite_poly6(y):
     hermite_poly6 = y**6 - 15*y**4 + 45*y**2 - 15
-    return hermite_poly6
+    return hermite_poly6
diff --git a/tests/test_edgeworth_pdf.py b/tests/test_edgeworth_pdf.py
@@ -40,4 +40,4 @@ def V_ddif(phi):
 
 # We need the following to execute the tests when we run the file in python
 if __name__ == '__main__':
-    unittest.main()
+    unittest.main()
diff --git a/tests/test_importance_sampling_cython.py b/tests/test_importance_sampling_cython.py
@@ -36,8 +36,8 @@ def expected_mean_FPT(x, x_r, x_end):
 
         ts, ws =\
             importance_sampling_simulations_1dim(x_in, x_r, x_end, 0, t_f, dt,
-                                            bias_amp, num_runs, drift_func,
-                                            diffusion_func)
+                                                 bias_amp, num_runs,
+                                                 drift_func, diffusion_func)
         # The ws should all be 1
         self.assertTrue(all(np.array(ws) == 1))
         # There is also an analytical expectation for the mean.
@@ -52,8 +52,8 @@ def expected_mean_FPT(x, x_r, x_end):
         x_end = -2
         ts, ws =\
             importance_sampling_simulations_1dim(x_in, x_r, x_end, 0, t_f, dt,
-                                            bias_amp, num_runs, drift_func,
-                                            diffusion_func)
+                                                 bias_amp, num_runs,
+                                                 drift_func, diffusion_func)
         # The ws should all be 1
         self.assertTrue(all(np.array(ws) == 1))
         # There is also an analytical expectation for the mean.
@@ -67,8 +67,8 @@ def expected_mean_FPT(x, x_r, x_end):
         num_runs = 10**1
         ts, ws =\
             importance_sampling_simulations_1dim(x_in, x_r, x_end, 0, t_f, dt,
-                                            bias_amp, num_runs, drift_func,
-                                            diffusion_func)
+                                                 bias_amp, num_runs,
+                                                 drift_func, diffusion_func)
         # All of these simulations should take the classical amount of time
         expectation = np.abs(np.diff((x_in, x_end))/drift_amp)
         differance = np.abs((expectation-ts))
@@ -88,8 +88,8 @@ def expected_mean_FPT(x, x_r, x_end):
         bias_amp = 1.0
         ts, ws =\
             importance_sampling_simulations_1dim(x_in, x_r, x_end, 0, t_f, dt,
-                                            bias_amp, num_runs, drift_func,
-                                            diffusion_func)
+                                                 bias_amp, num_runs,
+                                                 drift_func, diffusion_func)
         # The mean of the sample distrbution should be a few std from the
         # target mean.
         expectation_mean = np.abs(np.diff((x_in, x_end))/drift_amp)