GeorgeSeif

#define DIM 9
#define BLANK 0
#define SPACE " "
#define LINE "|"
#define NEW_ROW "-------------------------------------"
#define GRID_FULL std::make_pair(9, 9)

// Prints the Soduko grid
void print_grid(int grid[DIM][DIM])
{

GeorgeSeif

// Returns a boolean which indicates whether any assigned entry
// in the specified row matches the given number. 
bool used_in_row(int grid[DIM][DIM], int row, int num)
{
	for (int col = 0; col < DIM; col++)
		if (grid[row][col] == num)
		{
			return true;
		}
	return false;

GeorgeSeif

// Takes a partially filled-in grid and attempts to assign values to
// all unassigned locations in such a way to meet the requirements
// for Sudoku solution (non-duplication across rows, columns, and boxes) 
bool solve_soduko(int grid[DIM][DIM])
{
	// If the Soduko grid has been filled, we are done
	if (GRID_FULL == get_unassigned_location(grid))
	{
		return true; 
	}

GeorgeSeif

int main()
{
	cout << "********************************\n\n\tSudoku Solver\n\n********************************" << endl << endl;

	int grid[DIM][DIM] = { { 0, 9, 0, 0, 0, 0, 8, 5, 3 },
						   { 0, 0, 0, 8, 0, 0, 0, 0, 4 },
						   { 0, 0, 8, 2, 0, 3, 0, 6, 9 },
						   { 5, 7, 4, 0, 0, 2, 0, 0, 0 },
						   { 0, 0, 0, 0, 0, 0, 0, 0, 0 },
						   { 0, 0, 0, 9, 0, 0, 6, 3, 7 },

GeorgeSeif

import matplotlib.pyplot as plt
import numpy as np

def scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False):

    # Create the plot object
    _, ax = plt.subplots()

    # Plot the data, set the size (s), color and transparency (alpha)
    # of the points

GeorgeSeif

import matplotlib.pyplot as plt
import numpy as np

def scatterplot(x_data, y_data, x_label="", y_label="", title="", color = "r", yscale_log=False):

    # Create the plot object
    _, ax = plt.subplots()

    # Plot the data, set the size (s), color and transparency (alpha)
    # of the points

GeorgeSeif

def lineplot(x_data, y_data, x_label="", y_label="", title=""):
    # Create the plot object
    _, ax = plt.subplots()

    # Plot the best fit line, set the linewidth (lw), color and
    # transparency (alpha) of the line
    ax.plot(x_data, y_data, lw = 2, color = '#539caf', alpha = 1)

    # Label the axes and provide a title
    ax.set_title(title)

GeorgeSeif

def histogram(data, n_bins, cumulative=False, x_label = "", y_label = "", title = ""):
    _, ax = plt.subplots()
    ax.hist(data, n_bins = n_bins, cumulative = cumulative, color = '#539caf')
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)

GeorgeSeif

# Overlay 2 histograms to compare them
def overlaid_histogram(data1, data2, n_bins = 0, data1_name="", data1_color="#539caf", data2_name="", data2_color="#7663b0", x_label="", y_label="", title=""):
    # Set the bounds for the bins so that the two distributions are fairly compared
    max_nbins = 10
    data_range = [min(min(data1), min(data2)), max(max(data1), max(data2))]
    binwidth = (data_range[1] - data_range[0]) / max_nbins


    if n_bins == 0
    	bins = np.arange(data_range[0], data_range[1] + binwidth, binwidth)

GeorgeSeif

def barplot(x_data, y_data, error_data, x_label="", y_label="", title=""):
    _, ax = plt.subplots()
    # Draw bars, position them in the center of the tick mark on the x-axis
    ax.bar(x_data, y_data, color = '#539caf', align = 'center')
    # Draw error bars to show standard deviation, set ls to 'none'
    # to remove line between points
    ax.errorbar(x_data, y_data, yerr = error_data, color = '#297083', ls = 'none', lw = 2, capthick = 2)
    ax.set_ylabel(y_label)
    ax.set_xlabel(x_label)
    ax.set_title(title)