temporally changed testing strategy

pyrasa/utils/irasa_utils.py

@@ -10,7 +10,6 @@
 from pyrasa.utils.types import IrasaFun
 
 
-# TODO: Port to Cython
 def _gen_irasa(
     data: np.ndarray,
     orig_spectrum: np.ndarray,
@@ -73,30 +72,6 @@ def _crop_data(
     return freqs, psd_aperiodic, psd_periodic, psd
 
 
-def _gen_time_from_sft(SFT: type[dsp.ShortTimeFFT], sgramm: np.ndarray) -> np.ndarray:  # noqa N803
-    """Generates time from SFT object"""
-
-    tmin, tmax = SFT.extent(sgramm.shape[-1])[:2]
-    delta_t = SFT.delta_t
-
-    time = np.arange(tmin, tmax, delta_t)
-    return time
-
-
-def _find_nearest(sgramm_ud: np.ndarray, time_array: np.ndarray, time_value: float) -> np.ndarray:
-    """Find the nearest time point in an up/downsampled spectrogram"""
-
-    idx = (np.abs(time_array - time_value)).argmin()
-
-    if idx < sgramm_ud.shape[2]:
-        sgramm_sel = sgramm_ud[:, :, idx]
-
-    elif idx == sgramm_ud.shape[2]:
-        sgramm_sel = sgramm_ud[:, :, idx - 1]
-
-    return sgramm_sel
-
-
 def _get_windows(
     nperseg: int, dpss_settings: dict, win_func: Callable, win_func_kwargs: dict
 ) -> tuple[np.ndarray, np.ndarray]:

tests/conftest.py

@@ -28,19 +28,29 @@ def knee_aperiodic_signal(exponent, fs, knee_freq):
 
 
 @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)
+def load_knee_aperiodic_signal(exponent, fs, knee_freq):
+    # % generate and save knee
+    knee_sim = sim_knee(n_seconds=N_SECONDS, fs=fs, exponent1=0, exponent2=exponent, knee=knee_freq)
+    yield knee_sim
+    # 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)
+def load_knee_cmb_signal(exponent, fs, knee_freq, osc_freq):
+    knee = knee_freq ** np.abs(exponent)
+    components = {
+        'sim_knee': {'exponent1': 0, 'exponent2': exponent, 'knee': knee},
+        'sim_oscillation': {'freq': osc_freq},
+    }
+    cmb_sim = sim_combined(n_seconds=N_SECONDS, fs=fs, components=components)
+    yield cmb_sim
+    # 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
 
 

tests/settings.py

@@ -13,19 +13,20 @@
 # If you want to play around with this check: simulations/notebooks/sim_peak_tests.py
 EXPONENT = [-1, -1.5, -2.0]
 KNEE_FREQ = 15
+KNEE_FREQS = [10, 15, 25]
 # 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),
+    (-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
 HIGH_TOLERANCE = 0.5

tests/test_irasa_knee.py

@@ -11,9 +11,9 @@
 
 
 # knee model
-@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session')
+@pytest.mark.parametrize('exponent, knee_freq', EXP_KNEE_COMBO, scope='session')
 @pytest.mark.parametrize('fs', FS, scope='session')
-def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee):
+def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee_freq):
     f_range = [0.1, 100]
     irasa_out = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs})
     # test the shape of the output
@@ -33,18 +33,18 @@ def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee):
     knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** (
         1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0])
     )
-    knee_real = knee ** (1 / np.abs(exponent))
+    knee_real = knee_freq ** (1 / np.abs(exponent))
     assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE))
     # test bic/aic -> should be better for knee
     assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0]
     assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0]
 
 
 # knee model
-@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session')
+@pytest.mark.parametrize('exponent, knee_freq', 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):
+def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee_freq, osc_freq):
     f_range = [0.1, 100]
     irasa_out = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs})
     # test the shape of the output
@@ -64,7 +64,7 @@ def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq):
     knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** (
         1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0])
     )
-    knee_real = knee ** (1 / np.abs(exponent))
+    knee_real = knee_freq ** (1 / np.abs(exponent))
     assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE))
     # test bic/aic -> should be better for knee
     assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0]