Merge pull request #255 from neurodsp-tools/pltfix

[MNT] - Update tests & small plot bug fixes

neurodsp/plts/time_series.py

@@ -104,7 +104,9 @@ def plot_instantaneous_measure(times, sigs, measure='phase', ax=None, **kwargs):
 
     if measure == 'phase':
         plot_time_series(times, sigs, ax=ax, ylabel='Phase (rad)', **kwargs)
-        plt.yticks([-np.pi, 0, np.pi], ['-$\pi$', 0, '$\pi$'])
+        ax = ax if ax else plt.gca()
+        ax.set_yticks([-np.pi, 0, np.pi])
+        ax.set_yticklabels([r'-$\pi$', 0, r'$\pi$'])
     elif measure == 'amplitude':
         plot_time_series(times, sigs, ax=ax, ylabel='Amplitude', **kwargs)
     elif measure == 'frequency':
@@ -145,7 +147,5 @@ def plot_bursts(times, sig, bursting, ax=None, **kwargs):
     >>> plot_bursts(times, sig, is_burst, labels=['Raw Data', 'Detected Bursts'])
     """
 
-    ax = check_ax(ax, (15, 3))
-
     bursts = ma.array(sig, mask=np.invert(bursting))
     plot_time_series(times, [sig, bursts], ax=ax, **kwargs)

neurodsp/sim/aperiodic.py

@@ -112,7 +112,7 @@ def sim_synaptic_current(n_seconds, fs, n_neurons=1000, firing_rate=2.,
     sig = sim_poisson_pop((n_seconds + t_ker), fs, n_neurons, firing_rate,
                           mean=None, variance=None)
     ker = sim_synaptic_kernel(t_ker, fs, tau_r, tau_d)
-    sig = np.convolve(sig, ker, 'valid')[:-1]
+    sig = np.convolve(sig, ker, 'valid')[:compute_nsamples(n_seconds, fs)]
 
     return sig
 

neurodsp/tests/aperiodic/test_dfa.py

@@ -6,10 +6,9 @@
 
 from neurodsp.sim import sim_powerlaw
 
-from neurodsp.tests.settings import FS, FS_HIGH
+from neurodsp.tests.settings import N_SECONDS
 
-from neurodsp.aperiodic.dfa import (compute_fluctuations, compute_rescaled_range,
-                                    compute_detrended_fluctuation)
+from neurodsp.aperiodic.dfa import *
 
 ###################################################################################################
 ###################################################################################################
@@ -27,22 +26,31 @@ def test_compute_fluctuations(tsig):
     with raises(ValueError):
         t_scales, flucs, exp = compute_fluctuations(tsig, 500, method='nope.')
 
-def test_compute_fluctuations_dfa(tsig_white, tsig_brown):
-    """Tests fluctuation analysis for the DFA method."""
+def test_compute_fluctuations_dfa():
+
+    # Tests for DFA method
+    #   Note: these tests need a high sampling rate, so we use local simulations
 
     # Test white noise: expected DFA of 0.5
-    t_scales, flucs, exp = compute_fluctuations(tsig_white, FS_HIGH, method='dfa')
+    fs = 1000
+    white = sim_powerlaw(N_SECONDS, fs, exponent=0)
+    t_scales, flucs, exp = compute_fluctuations(white, fs, method='dfa')
     assert np.isclose(exp, 0.5, atol=0.1)
 
     # Test brown noise: expected DFA of 1.5
-    t_scales, flucs, exp = compute_fluctuations(tsig_brown, FS_HIGH, method='dfa')
+    brown = sim_powerlaw(N_SECONDS, fs, exponent=-2)
+    t_scales, flucs, exp = compute_fluctuations(brown, fs, method='dfa')
     assert np.isclose(exp, 1.5, atol=0.1)
 
-def test_compute_fluctuations_rs(tsig_white):
-    """Tests fluctuation analysis for the RS method."""
+def test_compute_fluctuations_rs():
+
+    # Tests for RS method
+    #   Note: these tests need a high sampling rate, so we use local simulations
 
     # Test white noise: expected RS of 0.5
-    t_scales, flucs, exp = compute_fluctuations(tsig_white, FS_HIGH, method='rs')
+    fs = 1000
+    white = sim_powerlaw(N_SECONDS, fs, exponent=0)
+    t_scales, flucs, exp = compute_fluctuations(white, fs, method='rs')
     assert np.isclose(exp, 0.5, atol=0.1)
 
 def test_compute_rescaled_range(tsig):

neurodsp/tests/aperiodic/test_irasa.py

@@ -5,7 +5,7 @@
 from neurodsp.sim import sim_combined
 from neurodsp.spectral import compute_spectrum, trim_spectrum
 
-from neurodsp.tests.settings import FS, N_SECONDS_LONG, EXP1
+from neurodsp.tests.settings import FS, EXP1
 
 from neurodsp.aperiodic.irasa import *
 

neurodsp/tests/conftest.py

@@ -8,8 +8,7 @@
 
 from neurodsp.sim import sim_oscillation, sim_powerlaw, sim_combined
 from neurodsp.utils.sim import set_random_seed
-from neurodsp.tests.settings import (N_SECONDS, N_SECONDS_LONG,
-                                     FS, FS_HIGH, FREQ_SINE, FREQ1, EXP1,
+from neurodsp.tests.settings import (N_SECONDS, FS, FREQ_SINE, FREQ1, EXP1,
                                      BASE_TEST_FILE_PATH, TEST_PLOTS_PATH)
 
 ###################################################################################################
@@ -32,29 +31,22 @@ def tsig2d():
 @pytest.fixture(scope='session')
 def tsig_sine():
 
-	yield sim_oscillation(N_SECONDS, FS, freq=FREQ_SINE, variance=None, mean=None)
-
-@pytest.fixture(scope='session')
-def tsig_sine_long():
-
-	yield sim_oscillation(N_SECONDS_LONG, FS, freq=FREQ_SINE, variance=None, mean=None)
+    yield sim_oscillation(N_SECONDS, FS, freq=FREQ_SINE, variance=None, mean=None)
 
 @pytest.fixture(scope='session')
 def tsig_comb():
 
     components = {'sim_powerlaw': {'exponent' : EXP1},
                   'sim_oscillation': {'freq' : FREQ1}}
-    yield sim_combined(n_seconds=N_SECONDS_LONG, fs=FS, components=components)
+    yield sim_combined(n_seconds=N_SECONDS, fs=FS, components=components)
 
 @pytest.fixture(scope='session')
-def tsig_white():
-
-    yield sim_powerlaw(N_SECONDS_LONG, FS_HIGH, exponent=0)
+def tsig_burst():
 
-@pytest.fixture(scope='session')
-def tsig_brown():
-
-    yield sim_powerlaw(N_SECONDS_LONG, FS_HIGH, exponent=-2)
+    components = {'sim_powerlaw': {'exponent' : EXP1},
+                  'sim_bursty_oscillation': {'freq' : FREQ1}}
+    yield sim_combined(n_seconds=N_SECONDS, fs=FS,
+                       components=components, component_variances=[0.5, 1])
 
 @pytest.fixture(scope='session', autouse=True)
 def check_dir():

neurodsp/tests/filt/test_filter.py

@@ -2,7 +2,7 @@
 
 from pytest import raises, warns
 
-from neurodsp.tests.settings import FS
+from neurodsp.tests.settings import FS, F_RANGE
 
 from neurodsp.filt.filter import *
 from neurodsp.filt.filter import _iir_checks
@@ -12,14 +12,14 @@
 
 def test_filter_signal(tsig):
 
-    out = filter_signal(tsig, FS, 'bandpass', (8, 12), filter_type='fir')
+    out = filter_signal(tsig, FS, 'bandpass', F_RANGE, filter_type='fir')
     assert out.shape == tsig.shape
 
-    out = filter_signal(tsig, FS, 'bandpass', (8, 12), filter_type='iir', butterworth_order=3)
+    out = filter_signal(tsig, FS, 'bandpass', F_RANGE, filter_type='iir', butterworth_order=3)
     assert out.shape == tsig.shape
 
     with raises(ValueError):
-        out = filter_signal(tsig, FS, 'bandpass', (8, 12), filter_type='bad')
+        out = filter_signal(tsig, FS, 'bandpass', F_RANGE, filter_type='bad')
 
 def test_iir_checks():
 

neurodsp/tests/plts/test_filt.py

@@ -18,22 +18,25 @@ def test_plot_filter_properties():
     coefs = design_fir_filter(FS, 'bandpass', (8, 12), 3)
     f_db, db = compute_frequency_response(coefs, a_vals=1, fs=FS)
 
-    plot_filter_properties(f_db, db, FS, coefs, save_fig=True,
-                           file_name='test_plot_filter_properties.png', file_path=TEST_PLOTS_PATH)
+    plot_filter_properties(f_db, db, FS, coefs,
+                           save_fig=True, file_path=TEST_PLOTS_PATH,
+                           file_name='test_plot_filter_properties.png')
 
 @plot_test
 def test_plot_frequency_response():
 
     coefs = design_fir_filter(FS, 'bandpass', (8, 12), 3)
     f_db, db = compute_frequency_response(coefs, a_vals=1, fs=FS)
 
-    plot_frequency_response(f_db, db, save_fig=True, file_name='test_plot_frequency_response.png',
-                            file_path=TEST_PLOTS_PATH)
+    plot_frequency_response(f_db, db,
+                            save_fig=True, file_path=TEST_PLOTS_PATH,
+                            file_name='test_plot_frequency_response.png')
 
 @plot_test
 def test_plot_impulse_response():
 
     coefs = design_fir_filter(FS, 'bandpass', (8, 12), 3)
 
-    plot_impulse_response(FS, coefs, save_fig=True, file_name='test_plot_impulse_response.png',
-                          file_path=TEST_PLOTS_PATH)
+    plot_impulse_response(FS, coefs,
+                          save_fig=True, file_path=TEST_PLOTS_PATH,
+                          file_name='test_plot_impulse_response.png')

neurodsp/tests/plts/test_rhythm.py

@@ -14,12 +14,16 @@
 def test_plot_swm_pattern():
 
     data = np.arange(0, 2, 0.1)
-    plot_swm_pattern(data, save_fig=True, file_name='test_plot_swm_pattern.png',
-                     file_path=TEST_PLOTS_PATH)
+
+    plot_swm_pattern(data,
+                     save_fig=True, file_path=TEST_PLOTS_PATH,
+                     file_name='test_plot_swm_pattern.png')
 
 @plot_test
 def test_plot_lagged_coherence():
 
     data = np.arange(0, 2, 0.1)
-    plot_lagged_coherence(data, data, save_fig=True, file_name='test_plot_lagged_coherence.png',
-                          file_path=TEST_PLOTS_PATH)
+
+    plot_lagged_coherence(data, data,
+                          save_fig=True, file_path=TEST_PLOTS_PATH,
+                          file_name='test_plot_lagged_coherence.png')

neurodsp/tests/plts/test_spectral.py

@@ -2,10 +2,10 @@
 
 import numpy as np
 
-from neurodsp.spectral.variance import compute_spectral_hist
 from neurodsp.spectral.power import compute_spectrum
+from neurodsp.spectral.variance import compute_spectral_hist, compute_scv, compute_scv_rs
 
-from neurodsp.tests.settings import TEST_PLOTS_PATH
+from neurodsp.tests.settings import TEST_PLOTS_PATH, FS
 from neurodsp.tests.tutils import plot_test
 
 from neurodsp.plts.spectral import *
@@ -14,40 +14,57 @@
 ###################################################################################################
 
 @plot_test
-def test_plot_power_spectra():
+def test_plot_power_spectra(tsig_comb, tsig_burst):
 
-    freqs, powers = np.array([1, 2, 3, 4]), np.array([10, 20, 30, 40])
-    plot_power_spectra(freqs, powers)
-    plot_power_spectra([freqs, freqs], [powers, powers], labels=['a', 'b'], colors=['k', 'r'],
+    freqs1, powers1 = compute_spectrum(tsig_comb, FS)
+    freqs2, powers2 = compute_spectrum(tsig_burst, FS)
+
+    plot_power_spectra(freqs1, powers1,
+                       save_fig=True, file_path=TEST_PLOTS_PATH,
+                       file_name='test_plot_power_spectra-1.png')
+
+    plot_power_spectra([freqs1, freqs2], [powers1, powers2],
+                       labels=['first', 'second'], colors=['k', 'r'],
                        save_fig=True, file_path=TEST_PLOTS_PATH,
-                       file_name='test_plot_power_spectra.png')
+                       file_name='test_plot_power_spectra-2.png')
 
 @plot_test
-def test_plot_scv():
+def test_plot_scv(tsig_comb):
+
+    freqs, scv = compute_scv(tsig_comb, FS)
+
+    plot_scv(freqs, scv,
+             save_fig=True, file_path=TEST_PLOTS_PATH,
+             file_name='test_plot_scv.png')
 
-    freqs, scv = np.array([1, 2, 3, 4]), np.array([10, 20, 30, 40])
-    plot_scv(freqs, scv, save_fig=True, file_path=TEST_PLOTS_PATH, file_name='test_plot_scv.png')
+def test_other_scv_plots(tsig_comb):
+
+    freqs, t_inds, scv_rs = compute_scv_rs(tsig_comb, FS, nperseg=FS/2, method='rolling')
+
+    _plot_scv_rs_lines(freqs, scv_rs)
+    _plot_scv_rs_matrix(freqs, t_inds, scv_rs)
 
 @plot_test
-def test_plot_scv_rs_lines():
+def _plot_scv_rs_lines(freqs, scv_rs):
 
-    freqs, scv_rs = np.array([1, 2, 3]), np.array([[2, 3, 4], [2, 3, 4], [3, 4, 5]])
-    plot_scv_rs_lines(freqs, scv_rs, save_fig=True, file_path=TEST_PLOTS_PATH,
+    plot_scv_rs_lines(freqs, scv_rs,
+                      save_fig=True, file_path=TEST_PLOTS_PATH,
                       file_name='test_plot_scv_rs_lines.png')
 
 @plot_test
-def test_plot_scv_rs_matrix():
+def _plot_scv_rs_matrix(freqs, t_inds, scv_rs):
 
-    freqs, times = np.array([1, 2, 3]), np.array([1, 2, 3])
-    scv_rs = np.array([[2, 3, 4], [2, 3, 4], [3, 4, 5]])
-    plot_scv_rs_matrix(freqs, times, scv_rs, save_fig=True, file_path=TEST_PLOTS_PATH,
+    plot_scv_rs_matrix(freqs, t_inds, scv_rs,
+                       save_fig=True, file_path=TEST_PLOTS_PATH,
                        file_name='test_plot_scv_rs_matrix.png')
 
 @plot_test
-def test_plot_spectral_hist(tsig):
+def test_plot_spectral_hist(tsig_comb):
 
-    freqs, power_bins, spectral_hist = compute_spectral_hist(tsig, fs=1000)
-    spectrum_freqs, spectrum = compute_spectrum(tsig, fs=1000)
-    plot_spectral_hist(freqs, power_bins, spectral_hist, spectrum=spectrum,
-                       spectrum_freqs=spectrum_freqs, save_fig=True, file_path=TEST_PLOTS_PATH,
+    freqs, power_bins, spectral_hist = compute_spectral_hist(tsig_comb, fs=FS)
+    spectrum_freqs, spectrum = compute_spectrum(tsig_comb, fs=FS)
+
+    plot_spectral_hist(freqs, power_bins, spectral_hist,
+                       spectrum=spectrum, spectrum_freqs=spectrum_freqs,
+                       save_fig=True, file_path=TEST_PLOTS_PATH,
                        file_name='test_plot_spectral_hist.png')

neurodsp/tests/plts/test_time_series.py

@@ -3,7 +3,11 @@
 from pytest import raises
 import numpy as np
 
-from neurodsp.tests.settings import TEST_PLOTS_PATH
+from neurodsp.utils import create_times
+from neurodsp.burst import detect_bursts_dual_threshold
+from neurodsp.timefrequency import amp_by_time, phase_by_time, freq_by_time
+
+from neurodsp.tests.settings import TEST_PLOTS_PATH, N_SECONDS, FS, F_RANGE
 from neurodsp.tests.tutils import plot_test
 
 from neurodsp.plts.time_series import *
@@ -12,44 +16,53 @@
 ###################################################################################################
 
 @plot_test
-def test_plot_time_series(tsig):
+def test_plot_time_series(tsig_comb, tsig_burst):
 
-    times = np.arange(0, len(tsig), 1)
+    times = create_times(N_SECONDS, FS)
 
     # Check single time series plot
-    plot_time_series(times, tsig)
+    plot_time_series(times, tsig_comb,
+                     save_fig=True, file_path=TEST_PLOTS_PATH,
+                     file_name='test_plot_time_series-1.png')
 
-    # Check multi time series plot, with colors & labels
-    plot_time_series(times, [tsig, tsig[::-1]], labels=['signal', 'signal reversed'],
-                     colors=['k', 'r'], save_fig=True, file_name='test_plot_time_series.png',
-                     file_path=TEST_PLOTS_PATH)
+    # Check multi time series plot, labels
+    plot_time_series(times, [tsig_comb, tsig_comb[::-1]],
+                     labels=['signal', 'signal reversed'],
+                     save_fig=True, file_path=TEST_PLOTS_PATH,
+                     file_name='test_plot_time_series-2.png')
 
     # Test 2D arrays
-    times_2d = np.vstack((times, times))
-    tsig_2d = np.vstack((tsig, tsig))
-    plot_time_series(times_2d, tsig_2d)
+    plot_time_series(times, np.array([tsig_comb, tsig_burst]),
+                     save_fig=True, file_path=TEST_PLOTS_PATH,
+                     file_name='test_plot_time_series-2arr.png')
 
 @plot_test
-def test_plot_instantaneous_measure(tsig):
+def test_plot_instantaneous_measure(tsig_comb):
 
-    times = np.arange(0, len(tsig), 1)
+    times = create_times(N_SECONDS, FS)
 
-    plot_instantaneous_measure(times, tsig, 'phase', save_fig=True, file_path=TEST_PLOTS_PATH,
-                               file_name='test_plot_instantaneous_measure_phase.png')
-    plot_instantaneous_measure(times, tsig, 'amplitude', save_fig=True, file_path=TEST_PLOTS_PATH,
+    plot_instantaneous_measure(times, amp_by_time(tsig_comb, FS, F_RANGE), 'amplitude',
+                               save_fig=True, file_path=TEST_PLOTS_PATH,
                                file_name='test_plot_instantaneous_measure_amplitude.png')
-    plot_instantaneous_measure(times, tsig, 'frequency', save_fig=True, file_path=TEST_PLOTS_PATH,
+
+    plot_instantaneous_measure(times, phase_by_time(tsig_comb, FS, F_RANGE), 'phase',
+                               save_fig=True, file_path=TEST_PLOTS_PATH,
+                               file_name='test_plot_instantaneous_measure_phase.png')
+
+    plot_instantaneous_measure(times, freq_by_time(tsig_comb, FS, F_RANGE), 'frequency',
+                               save_fig=True, file_path=TEST_PLOTS_PATH,
                                file_name='test_plot_instantaneous_measure_frequency.png')
 
     # Check the error for bad measure
     with raises(ValueError):
-        plot_instantaneous_measure(times, tsig, 'BAD')
+        plot_instantaneous_measure(times, tsig_comb, 'BAD')
 
 @plot_test
-def test_plot_bursts(tsig):
+def test_plot_bursts(tsig_burst):
 
-    times = np.arange(0, len(tsig), 1)
-    bursts = np.array([True] * len(tsig))
+    times = create_times(N_SECONDS, FS)
+    bursts = detect_bursts_dual_threshold(tsig_burst, FS, (0.75, 1.5), F_RANGE)
 
-    plot_bursts(times, tsig, bursts, save_fig=True, file_path=TEST_PLOTS_PATH,
+    plot_bursts(times, tsig_burst, bursts,
+                save_fig=True, file_path=TEST_PLOTS_PATH,
                 file_name='test_plot_bursts.png')