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#!/usr/bin/python3 | ||
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from argparse import ArgumentParser | ||
import os | ||
import sigmf | ||
from sigmf import SigMFFile | ||
from sigmf.utils import get_data_type_str | ||
import numpy as np | ||
import torchsig.transforms.transforms as ST | ||
from torchsig.transforms.functional import ( | ||
to_distribution, | ||
uniform_continuous_distribution, | ||
uniform_discrete_distribution, | ||
) | ||
from torchsig.utils.types import SignalData, SignalDescription | ||
from gamutrf.sample_reader import read_recording | ||
from gamutrf.waterfall_samples import parse_filename | ||
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def make_signal(samples, sample_rate, center_frequency): | ||
num_iq_samples = samples.shape[0] | ||
desc = SignalDescription( | ||
sample_rate=sample_rate, | ||
num_iq_samples=num_iq_samples, | ||
center_frequency=center_frequency, | ||
) | ||
# TODO: subclass SignalData with alternate constructor that can take just numpy array | ||
signal = SignalData(samples.tobytes(), np.float32, np.complex128, desc) | ||
return signal | ||
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def get_nosigmf_file(filename): | ||
meta = parse_filename(filename) | ||
sample_rate = meta["sample_rate"] | ||
sample_dtype = meta["sample_dtype"] | ||
sample_len = meta["sample_len"] | ||
center_frequency = meta["freq_center"] | ||
samples = None | ||
for samples_buffer in read_recording( | ||
filename, sample_rate, sample_dtype, sample_len, max_sample_secs=None | ||
): | ||
if samples is None: | ||
samples = samples_buffer | ||
else: | ||
samples = np.concatenate([samples, samples_buffer]) | ||
signal = make_signal(samples, sample_rate, center_frequency) | ||
return filename, signal | ||
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def get_signal(filename): | ||
if not os.path.exists(filename): | ||
raise FileNotFoundError(filename) | ||
meta_ext = filename.find(".sigmf-meta") | ||
if meta_ext == -1: | ||
return get_nosigmf_file(filename) | ||
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meta = sigmf.sigmffile.fromfile(filename) | ||
data_filename = filename[:meta_ext] | ||
meta.set_data_file(data_filename) | ||
# read_samples() always converts to host cf32. | ||
samples = meta.read_samples() | ||
global_meta = meta.get_global_info() | ||
sample_rate = global_meta["core:sample_rate"] | ||
sample_type = global_meta["core:datatype"] | ||
captures_meta = meta.get_captures() | ||
center_frequency = None | ||
if captures_meta: | ||
center_frequency = captures_meta[0].get("core:frequency", None) | ||
signal = make_signal(samples, sample_rate, center_frequency) | ||
return data_filename, signal | ||
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def write_signal(filename, signal, transforms_text): | ||
first_desc = signal.signal_description[0] | ||
signal.iq_data = signal.iq_data.astype(np.complex64) | ||
signal.iq_data.tofile(filename) | ||
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new_meta = SigMFFile( | ||
data_file=filename, | ||
global_info={ | ||
SigMFFile.DATATYPE_KEY: get_data_type_str(signal.iq_data), | ||
SigMFFile.SAMPLE_RATE_KEY: first_desc.sample_rate, | ||
SigMFFile.VERSION_KEY: sigmf.__version__, | ||
SigMFFile.DESCRIPTION_KEY: transforms_text, | ||
}, | ||
) | ||
new_meta.add_capture( | ||
0, | ||
metadata={ | ||
SigMFFile.FREQUENCY_KEY: first_desc.center_frequency, | ||
}, | ||
) | ||
new_meta.tofile(".".join([filename, "sigmf-meta"])) | ||
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def augment(signal, filename, output_dir, n, transforms_text): | ||
# TODO: sadly, due to Torchsig complexity, literal_eval can't be used. | ||
transforms = eval(transforms_text) # nosec | ||
i = 0 | ||
base_augment_name = os.path.basename(filename) | ||
dot = base_augment_name.find(".") | ||
if dot != "-1": | ||
base_augment_name = base_augment_name[:dot] | ||
for _ in range(n): | ||
while True: | ||
augment_name = os.path.join( | ||
output_dir, f"augmented-{i}-{base_augment_name}" | ||
) | ||
if not os.path.exists(augment_name): | ||
break | ||
i += 1 | ||
new_signal = transforms(signal) | ||
write_signal(augment_name, new_signal, transforms_text) | ||
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def argument_parser(): | ||
parser = ArgumentParser() | ||
parser.add_argument( | ||
"filename", | ||
type=str, | ||
help="sigMF file or gamutRF zst recording", | ||
) | ||
parser.add_argument("outdir", type=str, help="output directory") | ||
parser.add_argument("n", type=int, help="number of augmentation passes") | ||
parser.add_argument( | ||
"transforms", | ||
type=str, | ||
help="transforms to eval, e.g. ST.Compose([ST.AddNoise((-40, -20)),ST.RandomPhaseShift(uniform_continuous_distribution(-1, 1))]) (use quotes)", | ||
) | ||
return parser | ||
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def main(): | ||
options = argument_parser().parse_args() | ||
data_filename, signal = get_signal(options.filename) | ||
augment(signal, data_filename, options.outdir, options.n, options.transforms) | ||
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if __name__ == "__main__": | ||
main() |
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