Subversion Repositories tcp_socket

#!/usr/bin/env python

import subprocess

import atexit

from math import pi

try:

    import scipy as s

except ImportError:

    print "You need scipy to run this script!"

    exit(0)

try:

    import numpy as n

except ImportError:

    print "You need numpy to run this script!"

    exit(0)

try:

    from matplotlib import pyplot

except ImportError:

    print "You need matplotlib to run this script!"

    exit(0)

children = []

def cleanup():

    for child in children:

        print "Terminating child process"

        child.terminate()

atexit.register(cleanup)

def run_c(file_name):

    process = subprocess.Popen(["../c2verilog", "iverilog", "run", str(file_name)])

    children.append(process)

    process.wait()

    children.remove(process)

def test():

    run_c("fft.c")

    x_re = [float(i) for i in open("x_re")]

    x_im = [float(i) for i in open("x_im")]

    fft_x_re = [float(i) for i in open("fft_x_re")]

    fft_x_im = [float(i) for i in open("fft_x_im")]

    time_complex = [i + (j*1.0) for i, j in zip(x_re, x_im)]

    numpy_complex = s.fft(time_complex)

    numpy_magnitude = n.abs(numpy_complex)

    chips_complex = [i + (j*1.0j) for i, j in zip(fft_x_re, fft_x_im)]

    chips_magnitude = n.abs(chips_complex)

    f, subplot = pyplot.subplots(3, sharex=True)

    pyplot.subplots_adjust(hspace=1.0)

    subplot[0].plot(x_re, 'g')

    subplot[1].plot(numpy_magnitude, 'r')

    subplot[2].plot(chips_magnitude, 'b')

    pyplot.xlim(0, 1023)

    subplot[0].set_title("Time Domain Signal (64 point sine)")

    subplot[1].set_title("Frequency Spectrum - Numpy")

    subplot[2].set_title("Frequency Spectrum - Chips")

    subplot[0].set_xlabel("Sample")

    subplot[1].set_xlabel("Sample")

    subplot[2].set_xlabel("Sample")

    pyplot.savefig("../docs/source/examples/images/example_5.png")

    pyplot.show()

def indent(lines):

    return "\n    ".join(lines.splitlines())

def generate_docs():

    documentation = """

Fast Fourier Transform

----------------------

This example builds on the Taylor series example. We assume that the sin and

cos routines have been placed into a library of math functions math.h, along

with the definitions of :math:`\\pi`, M_PI.

The `Fast Fourier Transform (FFT) <http://en.wikipedia.org/wiki/Fast_Fourier_transform>`_

is an efficient method of decomposing discretely sampled signals into a frequency spectrum, it

is one of the most important algorithms in Digital Signal Processing (DSP).

`The Scientist and Engineer's Guide to Digital Signal Processing <http://www.dspguide.com/>`_

gives a straight forward introduction, and can be viewed on-line for free.

The example shows a practical method of calculating the FFT using the

`Cooley-Tukey algorithm <http://en.wikipedia.org/wiki/Fast_Fourier_transform#Cooley.E2.80.93Tukey_algorithm>`_.

.. code-block:: c

    %s

The C code includes a simple test routine that calculates the frequency spectrum of a 64 point sine wave.

.. image:: images/example_5.png

"""%indent(open("fft.c").read())

    document = open("../docs/source/examples/example_5.rst", "w").write(documentation)

test()

generate_docs()