#!/usr/bin/env python
#Copyright (c) 2017 James Gibbard
#Plot an IQ data file based on provided parameters
#Tested with python 2.7 and 3.6
#Requires numpy and matplotlib
import argparse
from sys import byteorder
import numpy as np
import matplotlib.pyplot as plt
def plotIQ(data, Fs):
if Fs == None:
plt.plot(np.real(data), label='I')
plt.plot(np.imag(data), label='Q')
else:
plt.plot(np.real(data), label='I')
plt.plot(np.imag(data), label='Q')
plt.grid(True)
plt.legend(loc='upper right', frameon=True)
plt.show()
def plotPSD(data,fftWindow, Fs):
assert fftWindow in ['rectangular', 'bartlett', 'blackman',
'hamming', 'hanning']
N = len(data)
#Generate the selected window
if fftWindow == "rectangular":
window = np.ones(N)
elif fftWindow == "bartlett":
window = np.bartlett(N)
elif args.fftWindow == "blackman":
window = np.blackman(N)
elif fftWindow == "hamming":
window = np.hamming(N)
elif fftWindow == "hanning":
window = np.hanning(N)
dft = np.fft.fft(data*window)
if Fs == None:
#If the sample rate is known then plot PSD as
#Power/Freq in (dB/Hz)
plt.psd(data*window, NFFT=N)
else:
#If sample rate is not known then plot PSD as
#Power/Freq as (dB/rad/sample)
plt.psd(data*window, NFFT=N, Fs=Fs)
plt.show()
def plotSpectrogram(data, fftWindow, fftSize, Fs):
if fftSize == None:
N = len(data)
else:
N = fftSize
if Fs == None:
Fs = 2
if fftWindow == "rectangular":
plt.specgram(data, NFFT=N, Fs=Fs,
window=lambda data: data*np.ones(len(data)), noverlap=int(N/10))
elif fftWindow == "bartlett":
plt.specgram(data, NFFT=N, Fs=Fs,
window=lambda data: data*np.bartlett(len(data)), noverlap=int(N/10))
elif args.fftWindow == "blackman":
plt.specgram(data, NFFT=N, Fs=Fs,
window=lambda data: data*np.blackman(len(data)), noverlap=int(N/10))
elif fftWindow == "hamming":
plt.specgram(data, NFFT=N, Fs=Fs,
window=lambda data: data*np.hamming(len(data)), noverlap=int(N/10))
elif fftWindow == "hanning":
plt.specgram(data, NFFT=N, Fs=Fs,
window=lambda data: data*np.hanning(len(data)), noverlap=int(N/10))
plt.show()
if __name__ == '__main__':
#Generate command line parser to parse inputs
cliParser = argparse.ArgumentParser(description='Plots quadrature IQ signals')
#Get the filename of the input file
cliParser.add_argument('filename', type=str, help='input filename')
cliParser.add_argument('-s', '--startSample', type=int,
help='sample to begin plot from (default=0)', default=0)
cliParser.add_argument('-o', '--offset', type=int,
help='offset in bytes from begining of file (default=0)', default=0)
cliParser.add_argument('-n', '--numberOfSamples', type=int,
help='number of samples to plot', default=0)
cliParser.add_argument('-fs', '--sampleRate', type=float,
help='sets the sample rate [sps] (default=1e6)')
cliParser.add_argument('-f', '--format', type=str,
help='Output format (default=int16)',
choices=["int8", "int16", "int32", "uint8", "uint16", "uint32",
"float16", "float32", "float64"],
default='int16')
cliParser.add_argument('-be', '--bigendian', action='store_true',
help='output data in big endian format (default=False)')
cliParser.add_argument('-qi', '--orderQI', action='store_true',
help='store output data as Q then I (Default = I then Q)')
cliParser.add_argument('-p', '--plotType', type=str,
help='Plot Type (default=iq)', choices=['iq', 'psd', 'spec'],
default='iq')
cliParser.add_argument('-w', '--fftWindow', type=str,
help='FFT window type (default=rectangular)',
choices=['rectangular', 'bartlett', 'blackman', 'hamming', 'hanning'],
default='rectangular')
cliParser.add_argument('-fw', '--fftWidth', type=int,
help='FFT width for spectrogram')
args = cliParser.parse_args()
#By default the file is read from an offset of 0 bytes
fileOffset = 0
#Set initial offset in bytes
#Useful if the file has a header that should be ignored
if args.offset != 0:
fileOffset = args.offset
#Convert sample offset to offset in bytes depending on datatype
if args.startSample != 0:
if args.format[-1:] == "8":
fileOffset += 2 * 1 * args.startSample
elif args.format[-2:] == "16":
fileOffset += 2 * 2 * args.startSample
elif args.format[-2:] == "32":
fileOffset += 2 * 4 * args.startSample
elif args.format[-2:] == "64":
fileOffset += 2 * 8 * args.startSample
#Open the file in binary read mode
with open(args.filename, "rb") as f:
#Seek to the absolue offset set by offset and startSample arguments
f.seek(fileOffset, 0)
if args.numberOfSamples != 0:
#Read twice the number of samples as each sample has a
#real and imaginary part
data = np.fromfile(f, dtype=args.format,
count=args.numberOfSamples*2)
else:
#If number of samples is not specified read to the end of the file
data = np.fromfile(f, dtype=args.format)
#If system byteorder is different to desired input byte order
#Then swich the endianness
if byteorder == 'little':
if args.bigendian == True:
data = data.byteswap()
elif byteorder == 'big':
if args.bigendian == False:
data = data.byteswap()
#Convert to complex data to complex128 data type
#This is two double precision (64bit) floating point numbers
if args.orderQI:
data = data[1::2] + 1j * data[0::2]
else:
data = data[0::2] + 1j * data[1::2]
#Select plotting function
if args.plotType == 'iq':
plotIQ(data, args.sampleRate)
elif args.plotType == 'psd':
plotPSD(data, args.fftWindow, args.sampleRate)
elif args.plotType == 'spec':
plotSpectrogram(data, args.fftWindow, args.fftWidth, args.sampleRate)
