def quintic_curve_fitting_and_plotting(ind_filename: str,
dep_filename: str) -> None:
"""Graph a scatter plot and a quintic curve of best fit of the independent and dependent
variables indicated by ind_filename and dep_filename respectively
Preconditions:
- ind_filename != ''
- dep_filename != ''
"""
independent_name = pd_with_pandas.get_name(
ind_filename) # title for the x-axis
lists = pd_with_pandas.filenames_to_lists(ind_filename, dep_filename)
ind_list, dep_list = lists[0], lists[1]
n = len(ind_list)
y_values = []
variables = curve_fit(quintic_function, ind_list, dep_list)
for i in range(n):
y_value = quintic_function(ind_list[i], variables[0][0],
variables[0][1], variables[0][2],
variables[0][3], variables[0][4],
variables[0][5])
y_values.append(y_value)
variables = (ind_list, dep_list, y_values)
plotting_data_with_curve(independent_name, variables)
def exponential_equation_of_best_fit(ind_filename: str, dep_filename: str) -> str:
"""Returns the exponential equation of best fit as a string for the dependent and independent
variables indicated by dep_filename and ind_filename respectively
Preconditions:
- independent_name != ''
- variables != ()
"""
lists = pd_with_pandas.filenames_to_lists(ind_filename, dep_filename)
ind_list, dep_list = lists[0], lists[1]
optimal_values, covariance = curve_fit(exponential_function, ind_list, dep_list)
return 'y = ' + str(optimal_values[0]) + ' ^ x ' + ' + ' + str(optimal_values[1])