diff --git a/.gitattributes b/.gitattributes index 07fe41c..cd5b36d 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1,2 +1,4 @@ # GitHub syntax highlighting pixi.lock linguist-language=YAML linguist-generated=true +tests/test_data/knee_data/*.npy filter=lfs diff=lfs merge=lfs -text +tests/test_data/knee_osc_data/*.npy filter=lfs diff=lfs merge=lfs -text diff --git a/pixi.lock b/pixi.lock index 1f007db..f9729fc 100644 --- a/pixi.lock +++ b/pixi.lock @@ -28539,7 +28539,7 @@ packages: name: pyrasa version: 0.1.0.dev0 path: . - sha256: 4654050ff12a55c80a925ef9847396f3fc6b4ed766c259d48f724409bff7679f + sha256: be3bb8dc71703f61f5ebe96399791305ea95ca48b8f56dfdb4eb24655e185172 requires_dist: - numpy - pandas diff --git a/simulations/sim4tests/sim_knees.py b/simulations/sim4tests/sim_knees.py new file mode 100644 index 0000000..29d6359 --- /dev/null +++ b/simulations/sim4tests/sim_knees.py @@ -0,0 +1,35 @@ +#%% +from neurodsp.sim import sim_combined, sim_knee +import numpy as np +from os.path import join + +n_secs=60 +fs_list=[500, 750, 1000] +exp1=0 +exp2_list=[-1., -1.5, -2.0] +knee_freq_list=[10, 15, 25] +osc_list = [5, 10, 20] + +base_folder = '/Users/fabian.schmidt/git/pyrasa/tests/test_data/' + +for fs in fs_list: + for exp2 in exp2_list: + for osc_freq in osc_list: + for knee_freq in knee_freq_list: + #% generate and save knee osc + knee = knee_freq ** np.abs(exp2) + components = {'sim_knee': {'exponent1': exp1, 'exponent2': exp2, 'knee': knee}, + 'sim_oscillation': {'freq': osc_freq} + } + cmb_sim = sim_combined(n_seconds=n_secs, fs=fs, components=components) + + fname = f'cmb_sim__fs_{fs}__exp1_{np.abs(exp1)}__exp2_{np.abs(exp2)}_knee_{np.round(knee, 0)}__osc_freq_{osc_freq}_.npy' + np.save(join(base_folder + 'knee_osc_data', fname), cmb_sim, allow_pickle=False) + + #% generate and save knee + knee_sim = sim_knee(n_seconds=n_secs, fs=fs, exponent1=exp1, exponent2=exp2, knee=knee) + + fname = f'knee_sim__fs_{fs}__exp1_{np.abs(exp1)}__exp2_{np.abs(exp2)}_knee_{np.round(knee, 0)}_.npy' + np.save(join(base_folder + 'knee_data', fname), knee_sim, allow_pickle=False) + +# %% diff --git a/tests/conftest.py b/tests/conftest.py index 28c46ed..80a1df9 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -24,7 +24,24 @@ def fixed_aperiodic_signal(exponent, fs): @pytest.fixture(scope='session') def knee_aperiodic_signal(exponent, fs, knee_freq): - yield sim_knee(n_seconds=N_SECONDS, fs=fs, exponent1=0, exponent2=exponent, knee=knee_freq ** np.abs(exponent)) + yield sim_knee(n_seconds=N_SECONDS, fs=fs, exponent1=0, exponent2=exponent, knee=knee_freq ** np.abs(exponent)) # noqa: E501 + + +@pytest.fixture(scope='session') +def load_knee_aperiodic_signal(exponent, fs, knee): + base_dir = 'tests/test_data/knee_data/' + yield np.load( + base_dir + f'knee_sim__fs_{fs}__exp1_0__exp2_{exponent}_knee_{knee}_.npy', + ) # allow_pickle=True) + + +@pytest.fixture(scope='session') +def load_knee_cmb_signal(exponent, fs, knee, osc_freq): + base_dir = 'tests/test_data/knee_osc_data/' + yield np.load( + base_dir + f'cmb_sim__fs_{fs}__exp1_0__exp2_{exponent}_knee_{knee}__osc_freq_{osc_freq}_.npy', + ) # allow_pickle=True) + # noqa: E501 @pytest.fixture(scope='session') @@ -66,20 +83,8 @@ def gen_mne_data_raw(): meg_path = data_path / 'MEG' / 'sample' raw_fname = meg_path / 'sample_audvis_raw.fif' - - raw = mne.io.read_raw_fif(raw_fname) - picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=False, eog=False, exclude='bads') - raw.pick(picks) - - yield raw - - -@pytest.fixture(scope='session') -def gen_mne_data_epoched(): - data_path = sample.data_path() - - meg_path = data_path / 'MEG' / 'sample' - raw_fname = meg_path / 'sample_audvis_raw.fif' + event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' + events = mne.read_events(event_fname) raw = mne.io.read_raw_fif(raw_fname) picks = mne.pick_types(raw.info, meg='mag', eeg=False, stim=False, eog=False, exclude='bads') @@ -96,19 +101,15 @@ def gen_mne_data_epoched(): tmax = 0.5 # Load real data as the template - event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif' - events = mne.read_events(event_fname) - epochs = mne.Epochs( raw, events, event_id, tmin, tmax, - # picks=picks, baseline=None, preload=True, verbose=False, ) - yield epochs + yield raw, epochs diff --git a/tests/settings.py b/tests/settings.py index a29d159..e6d9e30 100644 --- a/tests/settings.py +++ b/tests/settings.py @@ -6,10 +6,25 @@ FS = [500, 750, 1000] OSC_FREQ = [5, 10, 20] MANY_OSC_FREQ = np.arange(2, 30, 1) -EXPONENT = [-0.5, -1.0, -2.0] +EXPONENT = [-1, -1.5, -2.0] KNEE_FREQ = 15 -TOLERANCE = 0.2 -KNEE_TOLERANCE = 3 +# There seems to be a higher error in knee fits for knee and exponent estimates when +# the difference in pre and post knee exponent is low. This kinda makes sense +# TODO: Test this systematically -> see whether this is an issue with irasa or slope fitting in general + +EXP_KNEE_COMBO = [ + (1.0, 10.0), + (1.0, 15.0), + (1.0, 25.0), + (1.5, 125.0), + (1.5, 32.0), + (1.5, 58.0), + (2.0, 100.0), + (2.0, 225.0), + (2.0, 625.0), +] # we test exp + knee combined as both relate to each other +TOLERANCE = 0.3 # 0.15 +KNEE_TOLERANCE = 5 MIN_R2 = 0.8 # seems like a sensible minimum MIN_R2_SPRINT = 0.7 MIN_CORR_PSD_CMB = 0.99 diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index db0e4b1..02c3f3f 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -4,7 +4,7 @@ from pyrasa.utils.aperiodic_utils import compute_slope -from .settings import EXPONENT, FS, KNEE_FREQ, KNEE_TOLERANCE, MIN_R2, TOLERANCE +from .settings import EXPONENT, FS, MIN_R2, TOLERANCE # Test slope fitting functionality @@ -67,35 +67,22 @@ def test_slope_fitting_settings( # Takes too long need to pregenerate -@pytest.mark.parametrize('exponent, fs, knee_freq', [(-1, 500, 15)], scope='session') -def test_slope_fitting_knee(knee_aperiodic_signal, fs, exponent): - f_range = [1, 200] - # test whether recombining periodic and aperiodic spectrum is equivalent to the original spectrum - freqs, psd = dsp.welch(knee_aperiodic_signal, fs, nperseg=int(4 * fs)) - freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1]) - freqs, psd = freqs[freq_logical], psd[freq_logical] - # test whether we can reconstruct the exponent correctly - ap_params_k, gof_k = compute_slope(psd, freqs, fit_func='knee') - ap_params_f, gof_f = compute_slope(psd, freqs, fit_func='fixed') - # assert pytest.approx(ap_params_k['Exponent_1'][0], abs=TOLERANCE) == 0 - assert bool(np.isclose(ap_params_k['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) - assert bool(np.isclose(ap_params_k['Knee Frequency (Hz)'][0], KNEE_FREQ, atol=KNEE_TOLERANCE)) - # test goodness of fit - assert gof_k['r_squared'][0] > MIN_R2 - assert gof_k['r_squared'][0] > gof_f['r_squared'][0] # r2 for knee model should be higher than knee if knee - # bic and aic for knee model should be better if knee - assert gof_k['AIC'][0] < gof_f['AIC'][0] - assert gof_k['BIC'][0] < gof_f['BIC'][0] - - -# #pytest.mark.usefixtures(['exponent', 'fs'], [EXPONENT[0], FS[0]], scope='session') -# @pytest.mark.parametrize('exponent', EXPONENT, scope='session') -# @pytest.mark.parametrize('fs', FS, scope='session') -# def test_slope_fitting_settings(fixed_aperiodic_signal, fs, exponent): -# f_range = [0, 100] +# @pytest.mark.parametrize('exponent, fs, knee_freq', [(-1, 500, 15)], scope='session') +# def test_slope_fitting_knee(knee_aperiodic_signal, fs, exponent): +# f_range = [1, 200] # # test whether recombining periodic and aperiodic spectrum is equivalent to the original spectrum -# freqs, psd = dsp.welch(fixed_aperiodic_signal, 500, nperseg=int(4 * 500)) +# freqs, psd = dsp.welch(knee_aperiodic_signal, fs, nperseg=int(4 * fs)) # freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1]) -# ap_params, gof = compute_slope(psd, freqs, fit_func='fixed') -# ap_params, gof = compute_slope(psd, freqs, fit_func='fixed', fit_bound=(0, 40)) -# ap_params, gof = compute_slope(psd, freqs, fit_func='fixed', fit_bound=(-1, 500)) +# freqs, psd = freqs[freq_logical], psd[freq_logical] +# # test whether we can reconstruct the exponent correctly +# ap_params_k, gof_k = compute_slope(psd, freqs, fit_func='knee') +# ap_params_f, gof_f = compute_slope(psd, freqs, fit_func='fixed') +# # assert pytest.approx(ap_params_k['Exponent_1'][0], abs=TOLERANCE) == 0 +# assert bool(np.isclose(ap_params_k['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) +# assert bool(np.isclose(ap_params_k['Knee Frequency (Hz)'][0], KNEE_FREQ, atol=KNEE_TOLERANCE)) +# # test goodness of fit +# assert gof_k['r_squared'][0] > MIN_R2 +# assert gof_k['r_squared'][0] > gof_f['r_squared'][0] # r2 for knee model should be higher than knee if knee +# 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@@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8a9b67b937b9812481f756e7d33da0c1304ebba589854ced10ce4c2f29b8655c +size 360128 diff --git a/tests/test_irasa_knee.py b/tests/test_irasa_knee.py new file mode 100644 index 0000000..90c24ea --- /dev/null +++ b/tests/test_irasa_knee.py @@ -0,0 +1,72 @@ +import numpy as np +import pytest +import scipy.signal as dsp + +from pyrasa import irasa +from pyrasa.utils.aperiodic_utils import compute_slope +from pyrasa.utils.peak_utils import get_peak_params + +from .settings import EXP_KNEE_COMBO, FS, KNEE_TOLERANCE, MIN_CORR_PSD_CMB, OSC_FREQ, TOLERANCE + +# Estimate periodic and aperiodic components with IRASA +# These tests should cover the basic functionality of the IRASA workflow + + +# knee model +@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +@pytest.mark.parametrize('fs', FS, scope='session') +def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): + f_range = [0.1, 100] + freqs_rasa, psd_ap, psd_pe = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) + # test the shape of the output + assert freqs_rasa.shape[0] == psd_ap.shape[1] == psd_pe.shape[1] + freqs_psd, psd = dsp.welch(load_knee_aperiodic_signal, fs, nperseg=int(4 * fs)) + psd_cmb = psd_ap[0, :] + psd_pe[0, :] + freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) + r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] + assert r > MIN_CORR_PSD_CMB + ap_params_k, gof_k = compute_slope(psd_ap, freqs_rasa, fit_func='knee') + ap_params_f, gof_f = compute_slope(psd_ap, freqs_rasa, fit_func='fixed') + # test whether we can get the first exponent correctly + assert bool(np.isclose(ap_params_k['Exponent_1'][0], 0, atol=TOLERANCE)) + # test whether we can get the second exponent correctly + assert bool(np.isclose(ap_params_k['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) + # test whether we can get the knee correctly + knee_hat = ap_params_k['Knee'][0] ** (1 / (2 * ap_params_k['Exponent_1'][0] + ap_params_k['Exponent_2'][0])) + knee_real = knee ** (1 / np.abs(exponent)) + assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) + # test bic/aic -> should be better for knee + assert gof_k['AIC'][0] < gof_f['AIC'][0] + assert gof_k['BIC'][0] < gof_f['BIC'][0] + + +# knee model +@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +@pytest.mark.parametrize('fs', FS, scope='session') +@pytest.mark.parametrize('osc_freq', OSC_FREQ, scope='session') +def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): + f_range = [0.1, 100] + freqs_rasa, psd_ap, psd_pe = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) + # test the shape of the output + assert freqs_rasa.shape[0] == psd_ap.shape[1] == psd_pe.shape[1] + freqs_psd, psd = dsp.welch(load_knee_cmb_signal, fs, nperseg=int(4 * fs)) + psd_cmb = psd_ap[0, :] + psd_pe[0, :] + freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) + r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] + assert r > MIN_CORR_PSD_CMB + ap_params_k, gof_k = compute_slope(psd_ap, freqs_rasa, fit_func='knee') + ap_params_f, gof_f = compute_slope(psd_ap, freqs_rasa, fit_func='fixed') + # test whether we can get the first exponent correctly + assert bool(np.isclose(ap_params_k['Exponent_1'][0], 0, atol=TOLERANCE)) + # test whether we can get the second exponent correctly + assert bool(np.isclose(ap_params_k['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) + # test whether we can get the knee correctly + knee_hat = ap_params_k['Knee'][0] ** (1 / (2 * ap_params_k['Exponent_1'][0] + ap_params_k['Exponent_2'][0])) + knee_real = knee ** (1 / np.abs(exponent)) + assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) + # test bic/aic -> should be better for knee + assert gof_k['AIC'][0] < gof_f['AIC'][0] + assert gof_k['BIC'][0] < gof_f['BIC'][0] + # test whether we can reconstruct the peak frequency correctly + pe_params = get_peak_params(psd_pe, freqs_rasa) + assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) diff --git a/tests/test_mne.py b/tests/test_mne.py index d8508da..aad426c 100644 --- a/tests/test_mne.py +++ b/tests/test_mne.py @@ -8,21 +8,17 @@ @pytest.mark.filterwarnings('ignore:RuntimeWarning') @pytest.mark.filterwarnings('ignore:UserWarning') -def test_mne_raw(gen_mne_data_raw): +def test_mne(gen_mne_data_raw): + mne_data, epochs = gen_mne_data_raw + + # test raw aperiodic_mne, periodic_mne = irasa_raw( - gen_mne_data_raw, band=(0.25, 50), duration=2, hset_info=(1.0, 2.0, 0.05), as_array=False + mne_data, band=(0.25, 50), duration=2, hset_info=(1.0, 2.0, 0.05), as_array=False ) aperiodic_mne.get_slopes(fit_func='fixed') - # aperiodic_mne.get_slopes(fit_func='knee', scale=True) periodic_mne.get_peaks(smoothing_window=2) - -@pytest.mark.filterwarnings('ignore:RuntimeWarning') -@pytest.mark.filterwarnings('ignore:UserWarning') -def test_mne_epoched(gen_mne_data_epoched): - aperiodic, periodic = irasa_epochs(gen_mne_data_epoched, band=(0.5, 50), hset_info=(1.0, 2.0, 0.05), as_array=False) - - # aperiodic.get_slopes(fit_func='knee', scale=True) + # test epochs + aperiodic, periodic = irasa_epochs(epochs, band=(0.5, 50), hset_info=(1.0, 2.0, 0.05), as_array=False) aperiodic.get_slopes(fit_func='fixed', scale=True) - periodic.get_peaks(smoothing_window=2)