View

This notebook provides the possibility to use the methodes implemented by entering a function and a degree of approximation. Regarding the quadrature the option

• Newton-Cotes,

• Trapezoidal,

• Monte-Carlo (nested and not nested) and

• Quasi-Monte Carlo

exist. An other way is calling the View function.

# The packages needed here are Tkinter, because the View is implemented using it
# and of course the packages of the projekt
import tkinter as tk

import os
os.chdir("..")
import Methodes.Smolyak_one as Smolyak_one

With this code the view of the project can be called. The view can be used by following these steps.

1.) Write a function that should be approximated (text box in the top).

2.) Wrint the variables of the function to prevent confusion (text box below the first text box)

3.) Choose a degree of application. (text box in the lower middle)

4.) Choose quadrature. (dropdown menu on the lower right side)

Smolyak_one.View()

Overview over methods defined for project

Furthermore, an overview should be given over the methods defined in the project. These methods are written in alphabetic order.

def write_functions():
    functions= [function for function in  dir(Smolyak_one) if
                ((not function.startswith('__') and function.find("_") > 0 and function != "parse_expr") or function == 'smolyak')]
    for k_1 in range(len(functions)):
        print('\033[1m'+functions[k_1])
        print("")
    print('\033[0m')
write_functions()

calculate_stat_data

controller_smolyak

cost_smolyak

epsilon_cost

error_smolyak

fast_eps_cost

find_all_sensible_combinations

find_next_tuples_index

find_right_position

implicit_multiplication

load_stat_data

modified_scatter_plot

monte_carlo_quad

newton_cotes

next_step

one_dim_newton_cotes

one_dim_trapezoidal

qmc_quad

rewrite_function

slice_find

smolyak

sort_tuples

standard_transformations

sum_tuple_shape