Remove code with side effects from main (#1577)

* Remove code with side effects from main

When running tests withy pytest, some modules execute code in main scope
and open plot or browser windows.

Moves such code under `if __name__ == "__main__"`.

* fixup! Format Python code with psf/black push

fuzzy_logic/fuzzy_operations.py

@@ -6,14 +6,15 @@ Requirements:
 Python:
   - 3.5
 """
-# Create universe of discourse in python using linspace ()
 import numpy as np
+import skfuzzy as fuzz
 
+
+if __name__ == "__main__":
+    # Create universe of discourse in python using linspace ()
     X = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
 
     # Create two fuzzy sets by defining any membership function (trapmf(), gbellmf(),gaussmf(), etc).
-import skfuzzy as fuzz
-
     abc1 = [0, 25, 50]
     abc2 = [25, 50, 75]
     young = fuzz.membership.trimf(X, abc1)
@@ -42,7 +43,6 @@ bdd_difference = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
     # max-min composition
     # max-product composition
 
-
     # Plot each set A, set B and each operation result using plot() and subplot().
     import matplotlib.pyplot as plt
 

machine_learning/polymonial_regression.py

@@ -36,6 +36,7 @@ def viz_polymonial():
     return
 
 
+if __name__ == "__main__":
     viz_polymonial()
 
     # Predicting a new result with Polymonial Regression

neural_network/gan.py

@@ -59,6 +59,7 @@ def plot(samples):
     return fig
 
 
+if __name__ == "__main__":
     # 1. Load Data and declare hyper
     print("--------- Load Data ----------")
     mnist = input_data.read_data_sets("MNIST_data", one_hot=False)
@@ -71,25 +72,28 @@ G_input = 100
     hidden_input, hidden_input2, hidden_input3 = 128, 256, 346
     hidden_input4, hidden_input5, hidden_input6 = 480, 560, 686
 
-
     print("--------- Declare Hyper Parameters ----------")
     # 2. Declare Weights
     D_W1 = (
-    np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0))) * 0.002
+        np.random.normal(size=(784, hidden_input), scale=(1.0 / np.sqrt(784 / 2.0)))
+        * 0.002
     )
     # D_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))       *0.002
     D_b1 = np.zeros(hidden_input)
 
     D_W2 = (
-    np.random.normal(size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0)))
+        np.random.normal(
+            size=(hidden_input, 1), scale=(1.0 / np.sqrt(hidden_input / 2.0))
+        )
         * 0.002
     )
     # D_b2 = np.random.normal(size=(1),scale=(1. / np.sqrt(1 / 2.)))           *0.002
     D_b2 = np.zeros(1)
 
-
     G_W1 = (
-    np.random.normal(size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0)))
+        np.random.normal(
+            size=(G_input, hidden_input), scale=(1.0 / np.sqrt(G_input / 2.0))
+        )
         * 0.002
     )
     # G_b1 = np.random.normal(size=(128),scale=(1. / np.sqrt(128 / 2.)))      *0.002
@@ -97,7 +101,8 @@ G_b1 = np.zeros(hidden_input)
 
     G_W2 = (
         np.random.normal(
-        size=(hidden_input, hidden_input2), scale=(1.0 / np.sqrt(hidden_input / 2.0))
+            size=(hidden_input, hidden_input2),
+            scale=(1.0 / np.sqrt(hidden_input / 2.0)),
         )
         * 0.002
     )
@@ -106,7 +111,8 @@ G_b2 = np.zeros(hidden_input2)
 
     G_W3 = (
         np.random.normal(
-        size=(hidden_input2, hidden_input3), scale=(1.0 / np.sqrt(hidden_input2 / 2.0))
+            size=(hidden_input2, hidden_input3),
+            scale=(1.0 / np.sqrt(hidden_input2 / 2.0)),
         )
         * 0.002
     )
@@ -115,7 +121,8 @@ G_b3 = np.zeros(hidden_input3)
 
     G_W4 = (
         np.random.normal(
-        size=(hidden_input3, hidden_input4), scale=(1.0 / np.sqrt(hidden_input3 / 2.0))
+            size=(hidden_input3, hidden_input4),
+            scale=(1.0 / np.sqrt(hidden_input3 / 2.0)),
         )
         * 0.002
     )
@@ -124,7 +131,8 @@ G_b4 = np.zeros(hidden_input4)
 
     G_W5 = (
         np.random.normal(
-        size=(hidden_input4, hidden_input5), scale=(1.0 / np.sqrt(hidden_input4 / 2.0))
+            size=(hidden_input4, hidden_input5),
+            scale=(1.0 / np.sqrt(hidden_input4 / 2.0)),
         )
         * 0.002
     )
@@ -133,7 +141,8 @@ G_b5 = np.zeros(hidden_input5)
 
     G_W6 = (
         np.random.normal(
-        size=(hidden_input5, hidden_input6), scale=(1.0 / np.sqrt(hidden_input5 / 2.0))
+            size=(hidden_input5, hidden_input6),
+            scale=(1.0 / np.sqrt(hidden_input5 / 2.0)),
         )
         * 0.002
     )
@@ -173,7 +182,6 @@ v16, m16 = 0, 0
     v17, m17 = 0, 0
     v18, m18 = 0, 0
 
-
     beta_1, beta_2, eps = 0.9, 0.999, 0.00000001
 
     print("--------- Started Training ----------")

web_programming/crawl_google_results.py

@@ -5,6 +5,8 @@ from bs4 import BeautifulSoup
 from fake_useragent import UserAgent
 import requests
 
+
+if __name__ == "__main__":
     print("Googling.....")
     url = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
     res = requests.get(url, headers={"UserAgent": UserAgent().random})