Add a sample notebook. Refactor regression out.

.gitignore

 build/
 dist/
 xlap.egg-info/
+.ipynb_checkpoints/
\ No newline at end of file

xlap/analyse/__init__.py

 import math
 import numpy as np
 import matplotlib.pyplot as plt
-from sklearn import linear_model
 
 plt.rcParams["figure.figsize"] = (16, 9)
 plt.rcParams.update({'figure.autolayout': True})
@@ -9,16 +8,6 @@ plt.rcParams.update({'figure.autolayout': True})
 
 # TODO: Refactor.
 
-def regress(df, column):
-    x = df.index.values.reshape(-1, 1)
-    y = df[column].values
-
-    model = linear_model.LinearRegression()
-    model.fit(x, y)
-    print("R-Score:", model.score(x, y))
-    plt.scatter(x, y)
-    plt.grid()
-    plt.plot(x, model.predict(x), color="red", linewidth=3)
 
 
 def trace(df, title, export=False):

xlap/analyse/regress.py

+from sklearn import linear_model
+import matplotlib.pyplot as plt
+
+
+def linear(data_frame, column):
+    """
+    Execute a simple linear regression on the given column for the passed data_frame.
+    :param data_frame:
+    :param column:
+    :return:
+    """
+    x = data_frame.index.values.reshape(-1, 1)
+    y = data_frame[column].values
+
+    model = linear_model.LinearRegression()
+    model.fit(x, y)
+    print("R-Score:", model.score(x, y))
+    plt.scatter(x, y)
+    plt.grid()
+    plt.plot(x, model.predict(x), color="red", linewidth=3)
+    plt.show()