#!/usr/bin/env python3
# -*- python -*-


# Test of linear interpolation.
# Intended as a test of the test and
# as a reference implemention of a test.


# Load matplotlib.pyplot,
# it can take some time so
# print information about it.
print('Loading matplotlib.pyplot...')
import matplotlib.pyplot as plot
print('Done loading matplotlib.pyplot')

# Modules used for input data generation
from math import *
from random import *


def main():
    # Create a page with graphs
    fig = plot.figure()
    
    # Add graphs
    add_graph(fig, 221, [i / 15 for i in range(16)])
    add_graph(fig, 222, [sin(6 * i / 15) for i in range(16)])
    add_graph(fig, 223, [(i / 15) ** 0.5 for i in range(16)])
    add_graph(fig, 224, [random() for i in range(16)])
    
    # Show graphs
    plot.show()

def add_graph(fig, graph_pos, input_values):
    '''
    Add a graph
    
    @param  fig:Figure                The page to which to add the graph
    @param  graph_pos:int             Where to place the graph
    @param  input_values:list<float>  The input values for each point
    '''
    # Interpolate data
    output_values = interpolate(input_values)
    # Number of input points
    n = len(input_values)
    # Number of output points
    m = len(output_values)
    # Create graph
    graph = fig.add_subplot(graph_pos)
    # Plot interpolated data
    graph.plot([i / (m - 1) for i in range(m)], output_values, 'b-')
    # Plot input data
    graph.plot([i / (n - 1) for i in range(n)], input_values, 'ro')


def interpolate(small):
    '''
    Interpolate data
    
    @param   small:list<float>  The input values for each point
    @return  :list<float>       The values for each point in a scaled up version
    '''
    large = [None] * len(small) ** 2
    small_, large_ = len(small) - 1, len(large) - 1
    for i in range(len(large)):
        # Scaling
        j = i * small_ / large_
        # Floor, weight, ceiling
        j, w, k = int(j), j % 1, min(int(j) + 1, small_)
        # Interpolation
        large[i] = small[j] * (1 - w) + small[k] * w
    return large

# Plot interpolation
main()