analyze_stats.py

import numpy as np
import cPickle
import matplotlib.pyplot as plt

def calc_vec_energy(vec):
    isquared = np.power(vec[0],2.0)
    qsquared = np.power(vec[1], 2.0)
    inst_energy = np.sqrt(isquared+qsquared)
    return sum(inst_energy)

def calc_mod_energies(ds):
    for modulation, snr in ds:
        avg_energy = 0
        nvectors = ds[(modulation,snr)].shape[0]
        for vec in ds[(modulation, snr)]:
            avg_energy += calc_vec_energy(vec)
        avg_energy /= nvectors
        print "%s at %i has %i vectors avg energy of %2.1f" % (modulation, snr, nvectors, avg_energy)

def calc_mod_bias(ds):
    for modulation, snr in ds:
        avg_bias_re = 0
        avg_bias_im = 0
        nvectors = ds[(modulation,snr)].shape[0]
        for vec in ds[(modulation, snr)]:
            avg_bias_re += (np.mean(vec[0]))
            avg_bias_im += (np.mean(vec[1]))
        #avg_bias_re /= nvectors
        #avg_bias_im /= nvectors
        print "%s at %i has %i vectors avg bias of %2.1f + %2.1f j" % (modulation, snr, nvectors, avg_bias_re, avg_bias_im)

def calc_mod_stddev(ds):
    for modulation, snr in ds:
        avg_stddev = 0
        nvectors = ds[(modulation,snr)].shape[0]
        for vec in ds[(modulation, snr)]:
            avg_stddev += np.abs(np.std(vec[0]+1j*vec[1]))
        #avg_stddev /= nvectors
        print "%s at %i has %i vectors avg stddev of %2.1f" % (modulation, snr, nvectors, avg_stddev)

def open_ds(location="X_4_dict.dat"):
    f = open(location)
    ds = cPickle.load(f)
    return ds

def main():
    ds = open_ds()
    #plt.plot(ds[('BPSK', 12)][25][0][:])
    #plt.plot(ds[('BPSK', 12)][25][1][:])
    #plt.show()
    #calc_mod_energies(ds)
    #calc_mod_stddev(ds)
    calc_mod_bias(ds)

if __name__ == "__main__":
    main()