format code...

dsmts/dsmts.py

@@ -10,31 +10,31 @@ def test_model(N, file_stem):
     model_file = file_stem + ".mod"
     mean_file = file_stem + "-mean.csv"
     sd_file = file_stem + "-sd.csv"
-    mean = pd.read_csv(mean_file).to_numpy()[:,1:]
-    sd = pd.read_csv(sd_file).to_numpy()[:,1:]
+    mean = pd.read_csv(mean_file).to_numpy()[:, 1:]
+    sd = pd.read_csv(sd_file).to_numpy()[:, 1:]
     spn = smfsb.mod_to_spn(model_file)
     u = len(spn.n)
     sx = np.zeros((51, u))
     sxx = np.zeros((51, u))
-    step = spn.step_gillespie() # testing the exact simulator
+    step = spn.step_gillespie()  # testing the exact simulator
     for i in range(N):
         out = smfsb.sim_time_series(spn.m, 0, 50, 1, step)
         sx = sx + out
         si = out - mean
         sxx = sxx + (si * si)
-    sample_mean = sx/N
-    z_scores = np.sqrt(N)*(sample_mean - mean)/sd
-    sts = sxx/N
-    y_scores = (sts/(sd*sd) - 1)*np.sqrt(N/2)
+    sample_mean = sx / N
+    z_scores = np.sqrt(N) * (sample_mean - mean) / sd
+    sts = sxx / N
+    y_scores = (sts / (sd * sd) - 1) * np.sqrt(N / 2)
     fails = np.array([np.sum(abs(z_scores) > 3), np.sum(abs(y_scores) > 5)])
-    if (np.sum(fails) > 0):
+    if np.sum(fails) > 0:
         print(str(fails) + " for " + file_stem)
     return fails
 
 
 # Run a demo test if run as a script
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     print("A demo test run. Use pytest to run the full suite properly.")
     N = 10000
     print(test_model(N, "stochastic/00001/dsmts-001-01"))
@@ -44,4 +44,3 @@ def test_model(N, file_stem):
 
 
 # eof
-