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()