created hilbert.pyx. working on doc testing.

C_Optimized_Functions.egg-info/PKG-INFO

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+Metadata-Version: 2.1
+Name: C-Optimized-Functions
+Version: 0.0.0
+License-File: LICENSE

C_Optimized_Functions.egg-info/SOURCES.txt

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+LICENSE
+README.md
+pyproject.toml
+setup.py
+C_Optimized_Functions.egg-info/PKG-INFO
+C_Optimized_Functions.egg-info/SOURCES.txt
+C_Optimized_Functions.egg-info/dependency_links.txt
+C_Optimized_Functions.egg-info/top_level.txt
+ieeg/calc/concat.c
+ieeg/calc/mixup.c
+ieeg/timefreq/hilbert.c
+ieeg/calc/concat.c
+ieeg/calc/mixup.c
+ieeg/timefreq/hilbert.c

C_Optimized_Functions.egg-info/dependency_links.txt

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+

C_Optimized_Functions.egg-info/top_level.txt

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+concat
+hilbert
+mixup

build/lib.win-amd64-cpython-311/ieeg/__init__.py

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+from os import PathLike as PL
+
+import mne.io
+from mne.epochs import BaseEpochs
+from mne.evoked import Evoked
+from mne.io import base
+from numba import njit
+from numpy import ndarray, random
+
+PathLike = str | PL
+RunDict = dict[int, mne.io.Raw]
+SubDict = dict[str, RunDict]
+Doubles = tuple[float, float] | list[float, float] | ndarray[(2,), float]
+Signal = base.BaseRaw | BaseEpochs | Evoked
+ListNum = int | float | ndarray | list | tuple
+
+
+@njit(cache=True)
+def _rand_seed(seed: int = 42):
+    random.seed(seed)

build/lib.win-amd64-cpython-311/ieeg/_tests/test_ieeg.py

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+import os
+
+import mne
+import numpy as np
+import pytest
+from bids import BIDSLayout
+from mne.io import BaseRaw
+from mne_bids import BIDSPath
+
+from ieeg.io import raw_from_layout
+
+bids_root = mne.datasets.epilepsy_ecog.data_path()
+seeg = mne.io.read_raw(mne.datasets.misc.data_path() /
+                       'seeg' / 'sample_seeg_ieeg.fif')
+layout = BIDSLayout(bids_root)
+
+
+def test_bids():
+    assert "pt1" in layout.get_subjects()
+
+
+def test_bidspath_from_layout():
+    from ieeg.io import bidspath_from_layout
+    expected = "sub-pt1_ses-presurgery_task-ictal_ieeg.eeg"
+    bidspath = bidspath_from_layout(layout, subject="pt1",
+                                    extension=".eeg")
+    assert isinstance(bidspath, BIDSPath)
+    assert bidspath.basename == expected
+
+
+def test_raw_from_layout():
+    raw = raw_from_layout(layout, subject="pt1", extension=".vhdr")
+    assert isinstance(raw, BaseRaw)
+
+
+@pytest.mark.parametrize("n_jobs", [1, 8])
+def test_line_filter(n_jobs):
+    from ieeg.mt_filter import line_filter
+    mne.set_log_file("output.log",
+                     "%(levelname)s: %(message)s - %(asctime)s",
+                     overwrite=True)
+    mne.set_log_level("DEBUG")
+    raw = raw_from_layout(layout, subject="pt1", preload=True,
+                          extension=".vhdr")
+    filt = line_filter(raw, raw.info['sfreq'], [60])
+    raw_dat = raw._data
+    filt_dat = filt._data
+    assert filt_dat.shape == raw_dat.shape
+    params = dict(method='multitaper', tmax=20, fmin=55, fmax=65,
+                  bandwidth=0.5, n_jobs=n_jobs)
+    rpsd = raw.compute_psd(**params)
+    fpsd = filt.compute_psd(**params)
+    assert np.mean(np.abs(rpsd.get_data() - fpsd.get_data())) > 1e-10
+
+
+if os.path.isfile("spec.npy"):
+    spec_check = np.load("spec.npy")
+else:
+    spec_check = np.load("ieeg/_tests/spec.npy")
+
+
+def test_spect_1():
+    from ieeg.timefreq.multitaper import spectrogram
+    raw = raw_from_layout(layout, subject="pt1", preload=True,
+                          extension=".vhdr")
+    freqs = np.arange(10, 20, 2)
+    spectra = spectrogram(raw, freqs, 'onset', -0.5, 0.5, 'onset', -1, -0.5,
+                          n_jobs=-1, picks=[0, 1], decim=10, pad="0.5s")
+    out = spectra._data
+    assert np.allclose(out, spec_check)
+
+
+def test_spect_2():
+    from ieeg.timefreq.multitaper import spectrogram
+    from ieeg.navigate import trial_ieeg
+    raw = raw_from_layout(layout, subject="pt1", preload=True,
+                          extension=".vhdr")
+    freqs = np.arange(10, 20, 2)
+    on1 = trial_ieeg(raw, 'onset', (-1, 1))
+    on2 = trial_ieeg(raw, 'onset', (-1.5, 0))
+    spectra = spectrogram(on1, freqs, on2, n_jobs=-1, picks=[0, 1], decim=10,
+                          pad="0.5s")
+    out = spectra._data
+    assert np.allclose(out, spec_check)
+
+
+@pytest.mark.parametrize("input1, input2, expected", [
+    (4, np.inf, ['LAMY 7', 'RAHP 3']),
+    (3, 2, ['LAMY 7', 'LPHG 6', 'LBRI 3', 'RAHP 3', 'LENT 3', 'LPIT 5'])
+])
+def test_outlier(input1, input2, expected):
+    from ieeg.navigate import channel_outlier_marker
+    outs = channel_outlier_marker(seeg, input1, input2)
+    assert outs == expected
+
+
+@pytest.mark.parametrize("outliers, n_out", [
+    (4, 89955),
+    (5, 25987)
+])
+def test_trial_outlier(outliers, n_out):
+    from ieeg.navigate import trial_ieeg, outliers_to_nan
+    trials = trial_ieeg(seeg, 'Response', (-1, 1))
+    outs = outliers_to_nan(trials, outliers)
+    assert np.isnan(outs._data).sum() == n_out