The purpose of this script is to evaluate how severe the run-time overhead is when specifying a transform size parameter for FFT and DCT. It has been observed that specifying this parameter increases the run-time, even if the specified size is equal to the size of the input. The objective here is to figure out whether this overhead is a constant…

test_transform_calls.py

"""The purpose of this script is to evaluate how severe the run-time
overhead is when specifying a transform size parameter for FFT and
DCT. It has been observed that specifying this parameter increases the
run-time, even if the specified size is equal to the size of the
input. The objective here is to figure out whether this overhead is a
constant set-up cost or it depends on the input size.

"""

import numpy as np
import scipy.fftpack as sfft
import time
import matplotlib.pyplot as plt


sizes = 2**np.arange(1,11)
repetitions = 100

times_dct_padding = []
times_dct_normal = []
times_fft_padding = []
times_fft_normal = []

for size in sizes:
    time_dct_padding_acc = 0
    time_dct_normal_acc = 0
    time_fft_padding_acc = 0
    time_fft_normal_acc = 0
    for count in range(repetitions):
        # DCT
        data = np.random.randn(size**2)
        tic = time.time()
        output = sfft.dct(data, n=size**2)
        toc = time.time()
        time_dct_padding_acc += toc - tic
        tic = time.time()
        output = sfft.dct(data)
        toc = time.time()
        time_dct_normal_acc += toc - tic
        # DFT
        data = np.random.randn(size,size)
        tic = time.time()
        output = np.fft.fft2(data, s=data.shape)
        toc = time.time()
        time_fft_padding_acc += toc - tic
        tic = time.time()
        output = np.fft.fft2(data)
        toc = time.time()
        time_fft_normal_acc += toc - tic
    times_dct_padding.append(time_dct_padding_acc / repetitions)
    times_dct_normal.append(time_dct_normal_acc / repetitions)
    times_fft_padding.append(time_fft_padding_acc / repetitions)
    times_fft_normal.append(time_fft_normal_acc / repetitions)

plt.figure()
plt.title('DCT')
plt.plot(sizes**2, times_dct_normal, label='normal')
plt.plot(sizes**2, times_dct_padding, label='padding')
plt.legend()

plt.figure()
plt.title('FFT')
plt.plot(sizes**2, times_fft_normal, label='normal')
plt.plot(sizes**2, times_fft_padding, label='padding')
plt.legend()