def test_weighted_average(): """Test weighted average and distance from weighted average features.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['weighted_average']) weighted_avg = np.average(values, weights=1. / (errors**2)) weighted_var = np.average((values - weighted_avg)**2, weights=1. / (errors**2)) npt.assert_allclose(f['weighted_average'], weighted_avg) dists_from_weighted_avg = values - weighted_avg stds_from_weighted_avg = (dists_from_weighted_avg / np.sqrt(weighted_var)) f = generate_features(times, values, errors, ['percent_beyond_1_std']) npt.assert_equal(f['percent_beyond_1_std'], np.mean(stds_from_weighted_avg > 1.))
def test_lomb_scargle_irregular_multi_freq(): """Test Lomb-Scargle model features on irregularly-sampled periodic data with multiple frequencies, each with a single harmonic. More difficult than regularly-sampled case, so we allow parameter estimates to be slightly noisy. """ frequencies = WAVE_FREQS amplitudes = np.zeros((len(frequencies), 4)) amplitudes[:, 0] = [4, 2, 1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) for i, frequency in enumerate(frequencies): npt.assert_allclose(frequency, all_lomb['freq{}_freq'.format(i + 1)], rtol=1e-2) for (i, j), amplitude in np.ndenumerate(amplitudes): npt.assert_allclose(amplitude, all_lomb['freq{}_amplitude{}'.format(i + 1, j + 1)], rtol=1e-1, atol=1e-1) for i in [2, 3]: npt.assert_allclose(amplitudes[i - 1, 0] / amplitudes[0, 0], all_lomb['freq_amplitude_ratio_{}1'.format(i)], atol=2e-2) npt.assert_allclose(frequencies[i - 1] / frequencies[0], all_lomb['freq_frequency_ratio_{}1'.format(i)], atol=5e-2) npt.assert_array_less(10., all_lomb['freq1_signif'])
def test_lomb_scargle_irregular_single_freq(): """Test Lomb-Scargle model features on irregularly-sampled periodic data with one frequency/multiple harmonics. More difficult than regularly-sampled case, so we allow parameter estimates to be slightly noisy. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS) - 1))) amplitudes = np.zeros((len(WAVE_FREQS), 4)) amplitudes[0, :] = [8, 4, 2, 1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Only test the first (true) frequency; the rest correspond to noise npt.assert_allclose(all_lomb['freq1_freq'], frequencies[0], rtol=1e-2) # Only test first frequency here; noise gives non-zero amplitudes for residuals for j in range(amplitudes.shape[1]): npt.assert_allclose(amplitudes[0, j], all_lomb['freq1_amplitude{}'.format(j + 1)], rtol=5e-2, atol=5e-2) if j >= 1: npt.assert_allclose(phase * j * (-1**j), all_lomb['freq1_rel_phase{}'.format(j + 1)], rtol=1e-1, atol=1e-1) npt.assert_array_less(10., all_lomb['freq1_signif']) # Only one frequency, so this should explain basically all the variance npt.assert_allclose(0., all_lomb['freq_varrat'], atol=5e-3) npt.assert_allclose(-np.mean(values), all_lomb['freq_y_offset'], rtol=5e-2)
def test_lomb_scargle_period_folding(): """Tests for features derived from fitting a Lomb-Scargle periodic model and period-folding the data by the estimated period. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) amplitudes = np.zeros((len(WAVE_FREQS),4)) amplitudes[0,:] = [8,4,2,1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Folding is numerically unstable so we need to use the exact fitted frequency freq_est = all_lomb['freq1_freq'] # Fold by 1*period fold1ed_times = (times-times[0]) % (1./freq_est) sort_indices = np.argsort(fold1ed_times) fold1ed_times = fold1ed_times[sort_indices] fold1ed_values = values[sort_indices] # Fold by 2*period fold2ed_times = (times-times[0]) % (2./freq_est) sort_indices = np.argsort(fold2ed_times) fold2ed_times = fold2ed_times[sort_indices] fold2ed_values = values[sort_indices] npt.assert_allclose(np.sum(np.diff(fold2ed_values)**2) / np.sum(np.diff(values)**2), all_lomb['p2p_scatter_2praw']) npt.assert_allclose(np.sum(np.diff(values)**2) / ((len(values) - 1) * np.var(values)), all_lomb['p2p_ssqr_diff_over_var']) npt.assert_allclose(np.median(np.abs(np.diff(values))) / np.median(np.abs(values-np.median(values))), all_lomb['p2p_scatter_over_mad']) npt.assert_allclose(np.median(np.abs(np.diff(fold1ed_values))) / np.median(np.abs(values-np.median(values))), all_lomb['p2p_scatter_pfold_over_mad'])
def test_percent_close_to_median(): """Test feature which finds the percentage of points near the median value.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['percent_close_to_median']) amplitude = (max(values) - min(values)) / 2. within_buffer = np.abs(values - np.median(values)) < 0.2 * amplitude npt.assert_allclose(f['percent_close_to_median'], np.mean(within_buffer))
def test_lomb_scargle_irregular_multi_freq(): """Test Lomb-Scargle model features on irregularly-sampled periodic data with multiple frequencies, each with a single harmonic. More difficult than regularly-sampled case, so we allow parameter estimates to be slightly noisy. """ frequencies = WAVE_FREQS amplitudes = np.zeros((len(frequencies),4)) amplitudes[:,0] = [4,2,1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) for i, frequency in enumerate(frequencies): npt.assert_allclose(frequency, all_lomb['freq{}_freq'.format(i+1)], rtol=1e-2) for (i,j), amplitude in np.ndenumerate(amplitudes): npt.assert_allclose(amplitude, all_lomb['freq{}_amplitude{}'.format(i+1,j+1)], rtol=1e-1, atol=1e-1) for i in [2,3]: npt.assert_allclose(amplitudes[i-1,0] / amplitudes[0,0], all_lomb['freq_amplitude_ratio_{}1'.format(i)], atol=2e-2) npt.assert_allclose(frequencies[i-1] / frequencies[0], all_lomb['freq_frequency_ratio_{}1'.format(i)], atol=5e-2) npt.assert_array_less(10., all_lomb['freq1_signif'])
def test_lomb_scargle_irregular_single_freq(): """Test Lomb-Scargle model features on irregularly-sampled periodic data with one frequency/multiple harmonics. More difficult than regularly-sampled case, so we allow parameter estimates to be slightly noisy. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) amplitudes = np.zeros((len(WAVE_FREQS),4)) amplitudes[0,:] = [8,4,2,1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Only test the first (true) frequency; the rest correspond to noise npt.assert_allclose(all_lomb['freq1_freq'], frequencies[0], rtol=1e-2) # Only test first frequency here; noise gives non-zero amplitudes for residuals for j in range(amplitudes.shape[1]): npt.assert_allclose(amplitudes[0,j], all_lomb['freq1_amplitude{}'.format(j+1)], rtol=5e-2, atol=5e-2) if j >= 1: npt.assert_allclose(phase*j*(-1**j), all_lomb['freq1_rel_phase{}'.format(j+1)], rtol=1e-1, atol=1e-1) npt.assert_array_less(10., all_lomb['freq1_signif']) # Only one frequency, so this should explain basically all the variance npt.assert_allclose(0., all_lomb['freq_varrat'], atol=5e-3) npt.assert_allclose(-np.mean(values), all_lomb['freq_y_offset'], rtol=5e-2)
def test_feature_generation(): """Compare generated features to reference values.""" this_dir = os.path.join(os.path.dirname(__file__)) test_files = [ os.path.join(this_dir, 'data/257141.dat'), os.path.join(this_dir, 'data/245486.dat'), os.path.join(this_dir, 'data/247327.dat'), ] features_extracted = None values_computed = None for i, ts_data_file_path in enumerate(test_files): t, m, e = data_management.parse_ts_data(ts_data_file_path) features = generate_features(t, m, e, SCIENCE_FEATS) sorted_features = sorted(features.items()) if features_extracted is None: features_extracted = [f[0] for f in sorted_features] values_computed = np.zeros( (len(test_files), len(features_extracted))) values_computed[i, :] = [f[1] for f in sorted_features] def features_from_csv(filename): with open(filename) as f: feature_names = f.readline().strip().split(",") feature_values = np.loadtxt(f, delimiter=',') return feature_names, feature_values this_dir = os.path.join(os.path.dirname(__file__)) features_expected, values_expected = features_from_csv( os.path.join(this_dir, "data/expected_features.csv")) npt.assert_equal(features_extracted, features_expected) npt.assert_array_almost_equal(values_computed, values_expected)
def test_feature_generation(): """Compare generated features to reference values.""" this_dir = os.path.join(os.path.dirname(__file__)) test_files = [ os.path.join(this_dir, 'data/257141.dat'), os.path.join(this_dir, 'data/245486.dat'), os.path.join(this_dir, 'data/247327.dat'), ] features_extracted = None values_computed = None for i, ts_data_file_path in enumerate(test_files): t, m, e = data_management.parse_ts_data(ts_data_file_path) features = generate_features(t, m, e, SCIENCE_FEATS) sorted_features = sorted(features.items()) if features_extracted is None: features_extracted = [f[0] for f in sorted_features] values_computed = np.zeros((len(test_files), len(features_extracted))) values_computed[i,:] = [f[1] for f in sorted_features] def features_from_csv(filename): with open(filename) as f: feature_names = f.readline().strip().split(",") feature_values = np.loadtxt(f, delimiter=',') return feature_names, feature_values this_dir = os.path.join(os.path.dirname(__file__)) features_expected, values_expected = features_from_csv( os.path.join(this_dir, "data/expected_features.csv")) npt.assert_equal(features_extracted, features_expected) npt.assert_array_almost_equal(values_computed, values_expected)
def test_amplitude(): """Test features related to amplitude/magnitude percentiles.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['amplitude']) npt.assert_allclose(f['amplitude'], (max(values) - min(values)) / 2.) f = generate_features(times, values, errors, ['percent_amplitude']) max_scaled = 10**(-0.4 * max(values)) min_scaled = 10**(-0.4 * min(values)) med_scaled = 10**(-0.4 * np.median(values)) peak_from_median = max(abs((max_scaled - med_scaled) / med_scaled), abs((min_scaled - med_scaled)) / med_scaled) npt.assert_allclose(f['percent_amplitude'], peak_from_median, rtol=5e-4) f = generate_features(times, values, errors, ['percent_difference_flux_percentile']) band_offset = 13.72 w_m2 = 10**(-0.4 * (values + band_offset) - 3 ) # 1 erg/s/cm^2 = 10^-3 w/m^2 npt.assert_allclose( f['percent_difference_flux_percentile'], np.diff(np.percentile(w_m2, [5, 95])) / np.median(w_m2)) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid20']) npt.assert_allclose( f['flux_percentile_ratio_mid20'], np.diff(np.percentile(w_m2, [40, 60])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid35']) npt.assert_allclose( f['flux_percentile_ratio_mid35'], np.diff(np.percentile(w_m2, [32.5, 67.5])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid50']) npt.assert_allclose( f['flux_percentile_ratio_mid50'], np.diff(np.percentile(w_m2, [25, 75])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid65']) npt.assert_allclose( f['flux_percentile_ratio_mid65'], np.diff(np.percentile(w_m2, [17.5, 82.5])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid80']) npt.assert_allclose( f['flux_percentile_ratio_mid80'], np.diff(np.percentile(w_m2, [10, 90])) / np.diff(np.percentile(w_m2, [5, 95])))
def test_percent_close_to_median(): """Test feature which finds the percentage of points near the median value.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['percent_close_to_median']) amplitude = (max(values) - min(values)) / 2. within_buffer = np.abs(values - np.median(values)) < 0.2*amplitude npt.assert_allclose(f['percent_close_to_median'], np.mean(within_buffer))
def test_lomb_scargle_regular_single_freq(): """Test Lomb-Scargle model features on regularly-sampled periodic data with one frequency/multiple harmonics. Estimated parameters should be very accurate in this case. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) amplitudes = np.zeros((len(frequencies),4)) amplitudes[0,:] = [8,4,2,1] phase = 0.1 times, values, errors = regular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Only test the first (true) frequency; the rest correspond to noise npt.assert_allclose(all_lomb['freq1_freq'], frequencies[0]) # Hard-coded value from previous solution npt.assert_allclose(0.001996007984, all_lomb['freq1_lambda'], rtol=1e-7) for (i,j), amplitude in np.ndenumerate(amplitudes): npt.assert_allclose(amplitude, all_lomb['freq{}_amplitude{}'.format(i+1,j+1)], rtol=1e-2, atol=1e-2) # Only test the first (true) frequency; the rest correspond to noise for j in range(1, amplitudes.shape[1]): npt.assert_allclose(phase*j*(-1**j), all_lomb['freq1_rel_phase{}'.format(j+1)], rtol=1e-2, atol=1e-2) # Frequency ratio not relevant since there is only; only test amplitude/signif for i in [2,3]: npt.assert_allclose(0., all_lomb['freq_amplitude_ratio_{}1'.format(i)], atol=1e-3) npt.assert_array_less(10., all_lomb['freq1_signif']) # Only one frequency, so this should explain basically all the variance npt.assert_allclose(0., all_lomb['freq_varrat'], atol=5e-3) # Exactly periodic, so the same minima/maxima should reoccur npt.assert_allclose(0., all_lomb['freq_model_max_delta_mags'], atol=1e-6) npt.assert_allclose(0., all_lomb['freq_model_min_delta_mags'], atol=1e-6) # Linear trend should be zero since the signal is exactly sinusoidal npt.assert_allclose(0., all_lomb['linear_trend'], atol=1e-4) folded_times = times % 1./(frequencies[0]/2.) sort_indices = np.argsort(folded_times) folded_times = folded_times[sort_indices] folded_values = values[sort_indices] # Residuals from doubling period should be much higher npt.assert_array_less(10., all_lomb['medperc90_2p_p']) # Slopes should be the same for {un,}folded data; use unfolded for stability slopes = np.diff(values) / np.diff(times) npt.assert_allclose(np.percentile(slopes,10), all_lomb['fold2P_slope_10percentile'], rtol=1e-2) npt.assert_allclose(np.percentile(slopes,90), all_lomb['fold2P_slope_90percentile'], rtol=1e-2)
def test_stetson(): """Test Stetson variability features.""" times, values, errors = irregular_random(size=201) f = generate_features(times, values, errors, ['stetson_j']) # Stetson mean approximately equal to standard mean for large inputs dists = np.sqrt(float(len(values)) / (len(values) - 1.)) * (values - np.mean(values)) / 0.1 npt.assert_allclose(f['stetson_j'], np.mean(np.sign(dists**2-1)*np.sqrt(np.abs(dists**2-1))), rtol=1e-2) # Stetson_j should be somewhat close to (scaled) variance for normal data npt.assert_allclose(f['stetson_j']*0.1, np.var(values), rtol=2e-1) # Hard-coded original value npt.assert_allclose(f['stetson_j'], 7.591347175195703) f = generate_features(times, values, errors, ['stetson_k']) npt.assert_allclose(f['stetson_k'], 1./0.798 * np.mean(np.abs(dists)) / np.sqrt(np.mean(dists**2)), rtol=5e-4) # Hard-coded original value npt.assert_allclose(f['stetson_k'], 1.0087218792719013)
def test_scatter_res_raw(): """Test feature that measures scatter of Lomb-Scargle residuals.""" times, values, errors = irregular_random() lomb_model = lomb_scargle.lomb_scargle_model(times, values, errors) residuals = values - lomb_model['freq_fits'][0]['model'] resid_mad = np.median(np.abs(residuals - np.median(residuals))) value_mad = np.median(np.abs(values - np.median(values))) f = generate_features(times, values, errors, ['scatter_res_raw']) npt.assert_allclose(f['scatter_res_raw'], resid_mad / value_mad, atol=3e-2)
def test_lomb_scargle_linear_trend(): frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS)-1))) amplitudes = np.zeros((len(WAVE_FREQS),4)) amplitudes[0,:] = [8,4,2,1] phase = 0.1 slope = 0.5 # Estimated trend should be almost exact for noiseless data times, values, errors = regular_periodic(frequencies, amplitudes, phase) values += slope * times all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) npt.assert_allclose(slope, all_lomb['linear_trend'], rtol=1e-3) # Should still be close to true trend when noise is present times, values, errors = irregular_periodic(frequencies, amplitudes, phase) values += slope * times values += np.random.normal(scale=1e-3, size=len(times)) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) npt.assert_allclose(slope, all_lomb['linear_trend'], rtol=1e-1)
def test_lomb_scargle_linear_trend(): frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS) - 1))) amplitudes = np.zeros((len(WAVE_FREQS), 4)) amplitudes[0, :] = [8, 4, 2, 1] phase = 0.1 slope = 0.5 # Estimated trend should be almost exact for noiseless data times, values, errors = regular_periodic(frequencies, amplitudes, phase) values += slope * times all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) npt.assert_allclose(slope, all_lomb['linear_trend'], rtol=1e-3) # Should still be close to true trend when noise is present times, values, errors = irregular_periodic(frequencies, amplitudes, phase) values += slope * times values += np.random.normal(scale=1e-3, size=len(times)) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) npt.assert_allclose(slope, all_lomb['linear_trend'], rtol=1e-1)
def test_qso_features(): """Test features which measure fit of QSO model. Reference values are hard-coded values from previous implementation; not sure of examples with a closed-form solution. """ times, values, errors = irregular_random() f = generate_features(times, values, errors, ['qso_log_chi2_qsonu', 'qso_log_chi2nuNULL_chi2nu']) npt.assert_allclose(f['qso_log_chi2_qsonu'], 6.9844064754) npt.assert_allclose(f['qso_log_chi2nuNULL_chi2nu'], -0.456526327522)
def test_stetson(): """Test Stetson variability features.""" times, values, errors = irregular_random(size=201) f = generate_features(times, values, errors, ['stetson_j']) # Stetson mean approximately equal to standard mean for large inputs dists = np.sqrt(float(len(values)) / (len(values) - 1.)) * (values - np.mean(values)) / 0.1 npt.assert_allclose( f['stetson_j'], np.mean(np.sign(dists**2 - 1) * np.sqrt(np.abs(dists**2 - 1))), rtol=1e-2) # Stetson_j should be somewhat close to (scaled) variance for normal data npt.assert_allclose(f['stetson_j'] * 0.1, np.var(values), rtol=2e-1) # Hard-coded original value npt.assert_allclose(f['stetson_j'], 7.591347175195703) f = generate_features(times, values, errors, ['stetson_k']) npt.assert_allclose(f['stetson_k'], 1. / 0.798 * np.mean(np.abs(dists)) / np.sqrt(np.mean(dists**2)), rtol=5e-4) # Hard-coded original value npt.assert_allclose(f['stetson_k'], 1.0087218792719013)
def test_lomb_scargle_fast_irregular(): """Test gatspy's fast Lomb-Scargle period estimate on irregularly-sampled periodic data. Note: this model fits only a single sinusoid with no additional harmonics, so we use only 1 frequency and 1 amplitude to generate test data. """ frequencies = np.array([4]) amplitudes = np.array([[1]]) phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) f = generate_features(times, values, errors, ['period_fast']) npt.assert_allclose(f['period_fast'], 1. / frequencies[0], rtol=3e-2)
def test_amplitude(): """Test features related to amplitude/magnitude percentiles.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['amplitude']) npt.assert_allclose(f['amplitude'], (max(values) - min(values)) /2.) f = generate_features(times, values, errors, ['percent_amplitude']) max_scaled = 10**(-0.4 * max(values)) min_scaled = 10**(-0.4 * min(values)) med_scaled = 10**(-0.4 * np.median(values)) peak_from_median = max(abs((max_scaled - med_scaled) / med_scaled), abs((min_scaled - med_scaled)) / med_scaled) npt.assert_allclose(f['percent_amplitude'], peak_from_median, rtol=5e-4) f = generate_features(times, values, errors, ['percent_difference_flux_percentile']) band_offset = 13.72 w_m2 = 10**(-0.4*(values+band_offset)-3) # 1 erg/s/cm^2 = 10^-3 w/m^2 npt.assert_allclose(f['percent_difference_flux_percentile'], np.diff( np.percentile(w_m2, [5, 95])) / np.median(w_m2)) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid20']) npt.assert_allclose(f['flux_percentile_ratio_mid20'], np.diff(np.percentile(w_m2, [40, 60])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid35']) npt.assert_allclose(f['flux_percentile_ratio_mid35'], np.diff(np.percentile(w_m2, [32.5, 67.5])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid50']) npt.assert_allclose(f['flux_percentile_ratio_mid50'], np.diff(np.percentile(w_m2, [25, 75])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid65']) npt.assert_allclose(f['flux_percentile_ratio_mid65'], np.diff(np.percentile(w_m2, [17.5, 82.5])) / np.diff(np.percentile(w_m2, [5, 95]))) f = generate_features(times, values, errors, ['flux_percentile_ratio_mid80']) npt.assert_allclose(f['flux_percentile_ratio_mid80'], np.diff(np.percentile(w_m2, [10, 90])) / np.diff(np.percentile(w_m2, [5, 95])))
def test_lomb_scargle_period_folding(): """Tests for features derived from fitting a Lomb-Scargle periodic model and period-folding the data by the estimated period. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS) - 1))) amplitudes = np.zeros((len(WAVE_FREQS), 4)) amplitudes[0, :] = [8, 4, 2, 1] phase = 0.1 times, values, errors = irregular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Folding is numerically unstable so we need to use the exact fitted frequency freq_est = all_lomb['freq1_freq'] # Fold by 1*period fold1ed_times = (times - times[0]) % (1. / freq_est) sort_indices = np.argsort(fold1ed_times) fold1ed_times = fold1ed_times[sort_indices] fold1ed_values = values[sort_indices] # Fold by 2*period fold2ed_times = (times - times[0]) % (2. / freq_est) sort_indices = np.argsort(fold2ed_times) fold2ed_times = fold2ed_times[sort_indices] fold2ed_values = values[sort_indices] npt.assert_allclose( np.sum(np.diff(fold2ed_values)**2) / np.sum(np.diff(values)**2), all_lomb['p2p_scatter_2praw']) npt.assert_allclose( np.sum(np.diff(values)**2) / ((len(values) - 1) * np.var(values)), all_lomb['p2p_ssqr_diff_over_var']) npt.assert_allclose( np.median(np.abs(np.diff(values))) / np.median(np.abs(values - np.median(values))), all_lomb['p2p_scatter_over_mad']) npt.assert_allclose( np.median(np.abs(np.diff(fold1ed_values))) / np.median(np.abs(values - np.median(values))), all_lomb['p2p_scatter_pfold_over_mad'])
def test_skew(): """Test statistical skew feature.""" from scipy import stats times, values, errors = irregular_random() f = generate_features(times, values, errors, ['skew']) npt.assert_allclose(f['skew'], stats.skew(values))
def test_lomb_scargle_regular_single_freq(): """Test Lomb-Scargle model features on regularly-sampled periodic data with one frequency/multiple harmonics. Estimated parameters should be very accurate in this case. """ frequencies = np.hstack((WAVE_FREQS[0], np.zeros(len(WAVE_FREQS) - 1))) amplitudes = np.zeros((len(frequencies), 4)) amplitudes[0, :] = [8, 4, 2, 1] phase = 0.1 times, values, errors = regular_periodic(frequencies, amplitudes, phase) all_lomb = generate_features(times, values, errors, LOMB_SCARGLE_FEATS) # Only test the first (true) frequency; the rest correspond to noise npt.assert_allclose(all_lomb['freq1_freq'], frequencies[0]) # Hard-coded value from previous solution npt.assert_allclose(0.001996007984, all_lomb['freq1_lambda'], rtol=1e-7) for (i, j), amplitude in np.ndenumerate(amplitudes): npt.assert_allclose(amplitude, all_lomb['freq{}_amplitude{}'.format(i + 1, j + 1)], rtol=1e-2, atol=1e-2) # Only test the first (true) frequency; the rest correspond to noise for j in range(1, amplitudes.shape[1]): npt.assert_allclose(phase * j * (-1**j), all_lomb['freq1_rel_phase{}'.format(j + 1)], rtol=1e-2, atol=1e-2) # Frequency ratio not relevant since there is only; only test amplitude/signif for i in [2, 3]: npt.assert_allclose(0., all_lomb['freq_amplitude_ratio_{}1'.format(i)], atol=1e-3) npt.assert_array_less(10., all_lomb['freq1_signif']) # Only one frequency, so this should explain basically all the variance npt.assert_allclose(0., all_lomb['freq_varrat'], atol=5e-3) # Exactly periodic, so the same minima/maxima should reoccur npt.assert_allclose(0., all_lomb['freq_model_max_delta_mags'], atol=1e-6) npt.assert_allclose(0., all_lomb['freq_model_min_delta_mags'], atol=1e-6) # Linear trend should be zero since the signal is exactly sinusoidal npt.assert_allclose(0., all_lomb['linear_trend'], atol=1e-4) folded_times = times % 1. / (frequencies[0] / 2.) sort_indices = np.argsort(folded_times) folded_times = folded_times[sort_indices] folded_values = values[sort_indices] # Residuals from doubling period should be much higher npt.assert_array_less(10., all_lomb['medperc90_2p_p']) # Slopes should be the same for {un,}folded data; use unfolded for stability slopes = np.diff(values) / np.diff(times) npt.assert_allclose(np.percentile(slopes, 10), all_lomb['fold2P_slope_10percentile'], rtol=1e-2) npt.assert_allclose(np.percentile(slopes, 90), all_lomb['fold2P_slope_90percentile'], rtol=1e-2)
def test_min(): """Test minimum value feature.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['minimum']) npt.assert_equal(f['minimum'], min(values))
def test_median(): """Test median value feature.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['median']) npt.assert_allclose(f['median'], np.median(values))
def test_median_absolute_deviation(): """Test median absolute deviation (from the median) feature.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['median_absolute_deviation']) npt.assert_allclose(f['median_absolute_deviation'], np.median(np.abs(values - np.median(values))))
def test_std(): """Test standard deviation feature.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['std']) npt.assert_allclose(f['std'], np.std(values))
def test_max_slope(): """Test maximum slope feature, which finds the INDEX of the largest slope.""" times, values, errors = irregular_random() f = generate_features(times, values, errors, ['max_slope']) slopes = np.diff(values) / np.diff(times) npt.assert_allclose(f['max_slope'], np.max(np.abs(slopes)))