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#!/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()
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