feat: minor config update with massive module management update to dynamically import based on arguments

src/modules/essential_math/probability.py

@@ -1,6 +1,6 @@
-## This module represents the second chapter of the book 
+## This module represents the second chapter of the book
 ##  "Essential Math for Data Science" - Thomas Nield
-##  Chapter 2 - Probability 
+##  Chapter 2 - Probability
 
 from scipy.stats import binom, beta
 from math import factorial
@@ -31,7 +31,7 @@ class BinomialDistribution:
 		# For each number calc the probability of that exact number of outcomes (no order)
 		for k in range(n + 1):
 			# 1. Simple combinatory with the binomial coeficient (combinations of k elements out of a pool of n without repetition without order)
-			combinatory = BinomialDistribution.binomial_coeficient(n, k)	
+			combinatory = BinomialDistribution.binomial_coeficient(n, k)
 			# 2. Probability of success, the probability of making it k times
 			probability_of_success = p ** k
 			# 3. Probability of failure, inverse of the success
@@ -44,14 +44,14 @@ class BinomialDistribution:
 # > Returns a continuous function, so the final probability of X or better must be calculated using integrals (the area under the curve)
 class BetaDistribution:
 	@staticmethod
-	def calc(probability, success_count, failure_count):	
+	def calc(probability, success_count, failure_count):
 		# Only calcs the rpbability to the left
 		return beta.cdf(probability, success_count, failure_count)
 
 	@staticmethod
 	def calc_right(probability, success_count, failure_count):
 		return 1.0 - BetaDistribution.calc(probability, success_count, failure_count)
-	
+
 	@staticmethod
 	def calc_region(init_probability, end_probability, success_count, failure_count):
 		return BetaDistribution.calc(end_probability, success_count, failure_count) - BetaDistribution.calc(init_probability, success_count, failure_count)
@@ -63,7 +63,7 @@ class Exercises:
 	def one():
 		# 30% change of rain and 40% change your umbrella order will arrive. P(R AND U)
 		return p_r * p_u
-	
+
 	@staticmethod
 	def two():
 		# Same Ps as previous. P(!R OR U)
@@ -81,7 +81,7 @@ class Exercises:
 		p_bail = 0.4
 		p_at_least_50_bail = 0.0
 		for x in range(50, n + 1):
-			p_at_least_50_bail += binom.pmf(x, n, p_bail) 
+			p_at_least_50_bail += binom.pmf(x, n, p_bail)
 		return p_at_least_50_bail
 
 	@staticmethod
@@ -91,7 +91,7 @@ class Exercises:
 		t = 2
 		return  1.0 - BetaDistribution.calc(0.5, 8, 2)
 
-	
+
 	@staticmethod
 	def test():
 		print("P(R AND U) = {0}".format(Exercises.one()))
@@ -101,7 +101,7 @@ class Exercises:
 		print("P(fair) = {0}".format(Exercises.five()))
 
 
-def test_probability_module():
+def run():
 	print("=== Probability module ===")
 	print(">> Binomial distribution")
 	BinomialDistribution.example()