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data_generate.py
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data_generate.py
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## @file
# Creates part of flow graph
from __future__ import division, print_function, absolute_import
from multiprocessing import Process, Queue
import numpy as np
from gnuradio import filter
from gnuradio import gr
from gnuradio import analog
from gnuradio import digital
from gnuradio import blocks
from gnuradio import channels
## Whether to use channel model or not
channel_model = False
## String list of SNRs
SNR = ["20", "15", "10", "5", "0", "-5", "-10", "-15", "-20"]
## Amplitude of signal and noise, to create SNRs
SNRV = [[1, 0.32],
[1, 0.435],
[1, 0.56],
[1, 0.75],
[1, 1],
[0.75, 1],
[0.56, 1],
[0.435, 1],
[0.32, 1]]
## List of modulation schemes to use
MOD = ["fsk", "qam16", "qam64", "2psk","gmsk", "wbfm", "nfm"] #"4psk", "8psk",
## \brief Shuffles 2 arrays using same order for both
def shuffle_in_unison_inplace(a, b):
assert len(a) == len(b)
p = np.random.permutation(len(a))
return a[p], b[p]
## \brief Reseeds the RNG
def reseed():
np.random.seed()
## \brief Generate training data using multiple flow graphs running simultaneously
## \param sn List of SNRs
## \param syms List of symbol rates
## \param process Process to be called to create flow graph
## \param train Whether we are generating training data or testing data
def getdata(sn, syms, process, train=False):
mcount = 0
if train:
inp = []
out = []
else:
inp = [[] for k in range(0, len(SNR))]
out = [[] for k in range(0, len(SNR))]
flow = [None for k in range(len(MOD))]
for m in MOD:
z = np.zeros((len(MOD),))
z[mcount] = 1
print("MOD ", z)
q = Queue() # create a queue object
plist = []
if m == "allpsk":
for md in [ "2psk","4psk","8psk" ] :
print(md)
for s in sn: # iterate through SNRs
for sy in syms: # iterate through symbol rates
p = Process(target=process, args=(train, md, s, z, q, sy))
plist.append(p)
p.start()
else:
for s in sn: # iterate through SNRs
for sy in syms: # iterate through symbol rates
p = Process(target=process, args=(train, m, s, z, q, sy))
plist.append(p)
p.start()
for p in plist:
job = q.get()
if train:
inp += job[0]
out += job[1]
else:
for i in range(len(inp)):
inp[i] += job[0][i]
out[i] += job[1][i]
for p in plist:
p.join()
mcount += 1
if not train:
for k in range(0,len(SNR)):
inp[k] = np.array(inp[k])
out[k] = np.array(out[k])
return np.array(inp), np.array(out)
## \brief Initialise blocks for flow graph
## \param modulation Modulation scheme to use
## \param sym List of symbol rates
## \param sn List of SNRs
## \param train Whether we are generating training data or testing data
def create_blocks(self, modulation, sym, sn, train):
self.rational_resampler_xxx_0 = filter.rational_resampler_ccc(
interpolation=1,
decimation=1,
taps=None,
fractional_bw=None,
)
if modulation == "2psk":
self.digital_mod = digital.psk.psk_mod(
constellation_points=2,
mod_code="gray",
differential=True,
samples_per_symbol=sym,
excess_bw=0.35,
verbose=False,
log=False,
)
elif modulation == "4psk":
self.digital_mod = digital.psk.psk_mod(
constellation_points=4,
mod_code="gray",
differential=True,
samples_per_symbol=sym,
excess_bw=0.35,
verbose=False,
log=False,
)
elif modulation == "8psk":
self.digital_mod = digital.psk.psk_mod(
constellation_points=8,
mod_code="gray",
differential=True,
samples_per_symbol=sym,
excess_bw=0.35,
verbose=False,
log=False,
)
elif modulation == "fsk":
self.digital_mod = digital.gfsk_mod(
samples_per_symbol=sym,
sensitivity=1.0,
bt=0.35,
verbose=False,
log=False,
)
elif modulation == "qam16":
self.digital_mod = digital.qam.qam_mod(
constellation_points=16,
mod_code="gray",
differential=True,
samples_per_symbol=sym,
excess_bw=0.35,
verbose=False,
log=False,
)
elif modulation == "qam64":
self.digital_mod = digital.qam.qam_mod(
constellation_points=64,
mod_code="gray",
differential=True,
samples_per_symbol=sym,
excess_bw=0.35,
verbose=False,
log=False,
)
elif modulation == "gmsk":
self.digital_mod = digital.gmsk_mod(
samples_per_symbol=sym,
bt=0.35,
verbose=False,
log=False,
)
if train:
self.blocks_wavfile_source_0 = blocks.wavfile_source(
"music.wav", False)
else:
self.blocks_wavfile_source_0 = blocks.wavfile_source(
"music2.wav", False)
self.analog_wfm_tx_0 = analog.wfm_tx(
audio_rate=44100,
quad_rate=44100 * 5,
tau=75e-6,
max_dev=75e3,
fh=-1.0,
)
self.analog_nfm_tx_0 = analog.nbfm_tx(
audio_rate=44100,
quad_rate=44100 * 2,
tau=75e-6,
max_dev=5e3,
fh=-1.0,
)
self.channels_channel_model_0 = channels.channel_model(
noise_voltage=SNRV[sn][1],
frequency_offset=100.0,
epsilon=1.0,
taps=(1.0 + 1.0j, ),
noise_seed=np.random.randint(np.iinfo(np.int32).max),
block_tags=False
)
#self.channels_channel_model_0 = channels.fading_model( 8, 10.0/samp_rate, False, 4.0, 0 )