Update monte_carlo_integration_univariate.py

maths/monte_carlo_integration_univariate.py

@@ -17,21 +17,21 @@
 
 # function to calculate the sin of a particular value of x
 # define your function
-def function_to_be_integrated(x: int) -> float:
+def function_to_be_integrated(univariate_variable: int) -> float:
 
  # Doctest
  """
- :param x: int
+ :param univariate_variable: int
  :return: float
 
  >>> round(function_to_be_integrated(0))
  0
  """
 
- return np.sin(x) # example function
+ return np.sin(univariate_variable) # example function
 
 
-def monte_carlo(lower_limit: int, upper_limit: int, n: int) -> float:
+def monte_carlo(lower_limit: int, upper_limit: int, number_of_sections: int) -> float:
 
  # Doctest
  """
@@ -48,29 +48,29 @@ def monte_carlo(lower_limit: int, upper_limit: int, n: int) -> float:
  plt_vals = []
 
  # array of zeros of length N
- ar = np.zeros(n)
+ ar = np.zeros(number_of_sections)
 
  # we iterate through all the values to generate
  # multiple results and show whose intensity is
  # the most.
- for i in range(n):
+ for i in range(number_of_sections):
 
  # iterating over each Value of ar and filling it
  # with a random value between the limits a and b
  for i in range(len(ar)):
  ar[i] = random.uniform(lower_limit, upper_limit)
 
  # variable to store sum of the functions of different
- # values of x
+ # values of univariate_variable
  integral = 0.0
 
  # iterates and sums up values of different functions
- # of x
+ # of univariate_variable
  for i in ar:
  integral += function_to_be_integrated(i)
 
  # we get the answer by the formula derived adobe
- ans = (upper_limit - lower_limit) / float(n) * integral
+ ans = (upper_limit - lower_limit) / float(number_of_sections) * integral
  # appends the solution to a list for plotting the graph
  plt_vals.append(ans)
 
@@ -91,7 +91,7 @@ def monte_carlo(lower_limit: int, upper_limit: int, n: int) -> float:
  #-----END OF PLOT SECTION (OPTIONAL)------#
  """
 
- return sum(plt_vals) / N # takinf the average value
+ return sum(plt_vals) / number_of_sections # takinf the average value
 
 
 doctest.testmod()
@@ -103,11 +103,9 @@ def monte_carlo(lower_limit: int, upper_limit: int, n: int) -> float:
 lower_limit = 0
 upper_limit = np.pi # gets the value of pi
 
-n = 1000 # Number of individual ares to be considered
+number_of_sections = 1000 # Number of individual ares to be considered
 
 # function call
 # the final area under the curve(integration) value is considered as the average
 # of all the individual areas calculated
-print(
- f"\nThe value calculated by monte carlo integration is {monte_carlo(lower_limit, upper_limit, n)}."
-)
+print(f"The value calculated by monte carlo integration is {monte_carlo(lower_limit, upper_limit, number_of_sections)}.")