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)