PEP8: Fixing best.py file (#1324)

Fixing best.py errors

Author: springcoil

.gitignore

@@ -8,7 +8,7 @@ dist/*
 tmtags
 tags
 .DS_Store
-
+.cache
 # IntelliJ IDE
 .idea
 *.iml

pymc3/examples/best.py

@@ -2,62 +2,87 @@
 Bayesian Estimation Supersedes the T-Test
 
 This model replicates the example used in:
-Kruschke, John. (2012) Bayesian estimation supersedes the t test. Journal of Experimental Psychology: General.
+Kruschke, John. (2012) Bayesian estimation supersedes the t test.
+Journal of Experimental Psychology: General.
 
-The original pymc2 implementation was written by Andrew Straw and can be found here: https://github.com/strawlab/best
+The original pymc2 implementation was written by Andrew Straw
+and can be found here:
+https://github.com/strawlab/best
 
 Ported to PyMC3 by Thomas Wiecki (c) 2015.
 """
 
 import numpy as np
+
 import pymc3 as pm
 
-drug = (101,100,102,104,102,97,105,105,98,101,100,123,105,103,100,95,102,106,
-        109,102,82,102,100,102,102,101,102,102,103,103,97,97,103,101,97,104,
-        96,103,124,101,101,100,101,101,104,100,101)
-placebo = (99,101,100,101,102,100,97,101,104,101,102,102,100,105,88,101,100,
-           104,100,100,100,101,102,103,97,101,101,100,101,99,101,100,100,
-           101,100,99,101,100,102,99,100,99)
+drug = (101, 100, 102, 104, 102, 97, 105, 105, 98, 101, 100,
+        123, 105, 103, 100, 95, 102, 106,
+        109, 102, 82, 102, 100, 102, 102, 101, 102, 102, 103,
+        103, 97, 97, 103, 101, 97, 104,
+        96, 103, 124, 101, 101, 100, 101, 101, 104, 100, 101)
+placebo = (99, 101, 100, 101, 102, 100, 97, 101, 104, 101, 102,
+           102, 100, 105, 88, 101, 100, 104, 100, 100, 100,
+           101, 102, 103, 97, 101, 101,
+           100, 101, 99, 101, 100, 100,
+           101, 100, 99, 101, 100, 102, 99, 100, 99)
 
 y1 = np.array(drug)
 y2 = np.array(placebo)
 y = np.concatenate((y1, y2))
 
-mu_m = np.mean( y )
-mu_p = 0.000001 * 1/np.std(y)**2
+mu_m = np.mean(y)
+mu_p = 0.000001 * 1 / np.std(y) ** 2
 
-sigma_low = np.std(y)/1000
-sigma_high = np.std(y)*1000
+sigma_low = np.std(y) / 1000
+sigma_high = np.std(y) * 1000
 
 with pm.Model() as model:
-    group1_mean = pm.Normal('group1_mean', mu=mu_m, tau=mu_p, testval=y1.mean())
-    group2_mean = pm.Normal('group2_mean', mu=mu_m, tau=mu_p, testval=y2.mean())
-    group1_std = pm.Uniform('group1_std', lower=sigma_low, upper=sigma_high, testval=y1.std())
-    group2_std = pm.Uniform('group2_std', lower=sigma_low, upper=sigma_high, testval=y2.std())
-    nu = pm.Exponential('nu_minus_one', 1/29.) + 1
-
-    lam1 = group1_std**-2
-    lam2 = group2_std**-2
-
-    group1 = pm.StudentT('drug', nu=nu, mu=group1_mean, lam=lam1, observed=y1)
-    group2 = pm.StudentT('placebo', nu=nu, mu=group2_mean, lam=lam2, observed=y2)
-
-    diff_of_means = pm.Deterministic('difference of means', group1_mean - group2_mean)
-    diff_of_stds = pm.Deterministic('difference of stds', group1_std - group2_std)
-    effect_size = pm.Deterministic('effect size', diff_of_means / pm.sqrt((group1_std**2 + group2_std**2) / 2))
+    group1_mean = pm.Normal('group1_mean', mu=mu_m, tau=mu_p,
+                            testval=y1.mean())
+    group2_mean = pm.Normal('group2_mean', mu=mu_m, tau=mu_p,
+                            testval=y2.mean())
+    group1_std = pm.Uniform('group1_std', lower=sigma_low, upper=sigma_high,
+                            testval=y1.std())
+    group2_std = pm.Uniform('group2_std', lower=sigma_low, upper=sigma_high,
+                            testval=y2.std())
+    nu = pm.Exponential('nu_minus_one', 1 / 29.) + 1
+
+    lam1 = group1_std ** -2
+    lam2 = group2_std ** -2
+
+    group1 = pm.StudentT(
+        'drug', nu=nu, mu=group1_mean, lam=lam1, observed=y1)
+    group2 = pm.StudentT(
+        'placebo', nu=nu, mu=group2_mean, lam=lam2,
+        observed=y2)
+
+    diff_of_means = pm.Deterministic(
+        'difference of means', group1_mean -
+                               group2_mean)
+    diff_of_stds = pm.Deterministic(
+        'difference of stds',
+        group1_std - group2_std)
+    effect_size = pm.Deterministic(
+        'effect size',
+        diff_of_means / pm.sqrt(
+            (group1_std **
+             2 + group2_std**2) / 2))
 
     step = pm.NUTS()
 
+
 def run(n=3000):
     if n == "short":
         n = 500
     with model:
         trace = pm.sample(n, step)
 
-    burn = n/10
+    burn = n / 10
 
-    pm.traceplot(trace[burn:]);
+    pm.traceplot(trace[burn:])
     pm.plots.summary(trace[burn:])
 
+
 if __name__ == '__main__':
     run()