def snr_db_to_noise_variance(snr, n, k):
return float(k) / float(2 * helpers.db_to_ratio(snr) * n)
time_percentile_high = 95
topk_l1_error_percentile_low = 0
topk_l1_error_percentile_high = 95
relative_l2_l2_error_percentile_low = 0
relative_l2_l2_error_percentile_high = 95
random.seed(14389295)
plot = False
sys.stdout = Tee(script_output_filename(tmpdir))
#algs = ['fftw', 'sfft2-mit', 'sfft1-mit', 'aafft', 'sfft1-eth', 'sfft2-eth']
algs = ['fftw', 'sfft2-mit', 'sfft1-mit', 'aafft']
#algs = ['sfft2-mit']
for snr in snr_db_vals:
print 'snr = {} db ({:.6e})'.format(snr, db_to_ratio(snr))
print ' generating input data ...'
input_filename = []
for instance in range(1, num_instances + 1):
print ' instance {}'.format(instance)
dataf = data_filename_snr(tmpdir, n, k, snr, instance)
gen_input(n, k, dataf, seed=random.randint(0, 2000000000),
stats_file=data_stats_filename_snr(tmpdir, n, k, snr, instance),
noise_variance=snr_db_to_noise_variance(snr, n, k),
randomize_phase=True)
input_filename.append(dataf)
print ' writing index file ...'
indexf = index_filename_snr(tmpdir, n, k, snr)
write_index_file(indexf, input_filename)
for alg in algs:
resultsf = results_filename_snr(tmpdir, alg, n, k, snr)
def snr_db_to_noise_variance(snr, n, k): return float(k) / float(2 * helpers.db_to_ratio(snr) * n)
