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Merge pull request #283 from fooof-tools/time
[ENH] - Add `SpectralTimeModel` and `SpectralTimeEventModel`
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‎.github/workflows/build.yml

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push:
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branches: [ main ]
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pull_request:
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branches: [ main ]
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jobs:
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build:
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# Tag ubuntu version to 20.04, in order to support python 3.6
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# See issue: https://github.com/actions/setup-python/issues/544
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# When ready to drop 3.6, can revert from 'ubuntu-20.04' -> 'ubuntu-latest'
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runs-on: ubuntu-20.04
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runs-on: ubuntu-latest
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env:
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MODULE_NAME: specparam
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strategy:
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matrix:
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python-version: ["3.6", "3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
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python-version: ["3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
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steps:
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- uses: actions/checkout@v3

‎README.rst

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@@ -73,7 +73,7 @@ This documentation includes:
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Dependencies
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------------
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SpecParam is written in Python, and requires Python >= 3.6 to run.
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SpecParam is written in Python, and requires Python >= 3.7 to run.
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It has the following required dependencies:
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‎doc/api.rst

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@@ -40,6 +40,17 @@ The SpectralGroupModel object allows for parameterizing groups of power spectra.
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SpectralGroupModel
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Time & Event Objects
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~~~~~~~~~~~~~~~~~~~~
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The time & event objects allows for parameterizing power spectra organized across time and/or events.
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.. autosummary::
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:toctree: generated/
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SpectralTimeModel
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SpectralTimeEventModel
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Object Utilities
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~~~~~~~~~~~~~~~~
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get_band_peak
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get_band_peak_group
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get_band_peak_event
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**Array Inputs**
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Generate Power Spectra
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~~~~~~~~~~~~~~~~~~~~~~
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Functions for simulating neural power spectra.
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Functions for simulating neural power spectra and spectrograms.
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.. currentmodule:: specparam.sim
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sim_power_spectrum
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sim_group_power_spectra
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sim_spectrogram
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Manage Parameters
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~~~~~~~~~~~~~~~~~
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Plot Power Spectra
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~~~~~~~~~~~~~~~~~~
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Plots for visualizing power spectra.
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Plots for visualizing power spectra and spectrograms.
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.. currentmodule:: specparam.plts
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.. autosummary::
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:toctree: generated/
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plot_spectra
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plot_spectrogram
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Plots for plotting power spectra with shaded regions.
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.. autosummary::
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:toctree: generated/
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plot_group
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plot_group_model
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.. currentmodule:: specparam.plts.time
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.. autosummary::
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:toctree: generated/
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plot_time_model
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.. currentmodule:: specparam.plts.event
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.. autosummary::
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:toctree: generated/
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plot_event_model
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Annotated Plots
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~~~~~~~~~~~~~~~
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:toctree: generated/
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load_model
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load_group
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load_group_model
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load_time_model
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load_event_model
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Methods Reports
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~~~~~~~~~~~~~~~

‎examples/manage/plot_fit_models_3d.py

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from specparam.sim import sim_group_power_spectra
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from specparam.sim.utils import create_freqs
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from specparam.sim.params import param_sampler
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from specparam.utils.io import load_group
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from specparam.utils.io import load_group_model
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###################################################################################################
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# Example Set-Up
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###################################################################################################
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# Reload our list of SpectralGroupModels
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fgs = [load_group(file_name, file_path='results') \
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fgs = [load_group_model(file_name, file_path='results') \
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for file_name in os.listdir('results')]
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###################################################################################################

‎setup.py

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'Operating System :: POSIX',
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'Operating System :: Unix',
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'Programming Language :: Python',
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'Programming Language :: Python :: 3.6',
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'Programming Language :: Python :: 3.7',
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'Programming Language :: Python :: 3.8',
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'Programming Language :: Python :: 3.9',

‎specparam/__init__.py

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from .version import __version__
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from .bands import Bands
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from .objs import SpectralModel, SpectralGroupModel
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from .objs import SpectralModel, SpectralGroupModel, SpectralTimeModel, SpectralTimeEventModel
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from .objs.utils import fit_models_3d

‎specparam/analysis/__init__.py

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"""Analysis sub-module for model parameters and related metrics."""
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from .error import compute_pointwise_error, compute_pointwise_error_group
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from .periodic import get_band_peak, get_band_peak_group
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from .periodic import get_band_peak, get_band_peak_group, get_band_peak_event

‎specparam/analysis/periodic.py

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@@ -30,7 +30,7 @@ def get_band_peak(model, band, select_highest=True, threshold=None,
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Returns
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-------
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1d or 2d array
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peaks : 1d or 2d array
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Peak data. Each row is a peak, as [CF, PW, BW].
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Examples
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Returns
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-------
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2d array
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peaks : 2d array
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Peak data. Each row is a peak, as [CF, PW, BW].
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Each row represents an individual model from the input object.
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threshold, thresh_param)
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def get_band_peak_event(event, band, threshold=None, thresh_param='PW', attribute='peak_params'):
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"""Extract peaks from a band of interest from an event model object.
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Parameters
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----------
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event : SpectralTimeEventModel
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Object to extract peak data from.
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band : tuple of (float, float)
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Frequency range for the band of interest.
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Defined as: (lower_frequency_bound, upper_frequency_bound).
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select_highest : bool, optional, default: True
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Whether to return single peak (if True) or all peaks within the range found (if False).
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If True, returns the highest power peak within the search range.
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threshold : float, optional
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A minimum threshold value to apply.
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thresh_param : {'PW', 'BW'}
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Which parameter to threshold on. 'PW' is power and 'BW' is bandwidth.
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attribute : {'peak_params', 'gaussian_params'}
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Which attribute of peak data to extract data from.
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Returns
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-------
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peaks : 3d array
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Array of peak data, organized as [n_events, n_time_windows, n_peak_params].
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"""
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peaks = np.zeros([event.n_events, event.n_time_windows, 3])
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for ind in range(event.n_events):
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peaks[ind, :, :] = get_band_peak_group(\
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event.get_group(ind, None, 'group'), band, threshold, thresh_param, attribute)
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return peaks
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def get_band_peak_group_arr(peak_params, band, n_fits, threshold=None, thresh_param='PW'):
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"""Extract peaks within a given band of interest, from peaks from a group fit.
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‎specparam/core/io.py

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return full_path
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def get_files(file_path, select=None):
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"""Get a list of files from a directory.
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Parameters
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----------
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file_path : Path or str
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Name of the folder to get the list of files from.
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select : str, optional
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A search string to use to select files.
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Returns
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-------
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list of str
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A list of files.
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"""
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# Get list of available files, and drop hidden files
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files = os.listdir(file_path)
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files = [file for file in files if file[0] != '.']
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if select:
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files = [file for file in files if select in file]
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return files
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def save_model(model, file_name, file_path=None, append=False,
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save_results=False, save_settings=False, save_data=False):
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"""Save out data, results and/or settings from a model object into a JSON file.
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file_name : str or FileObject
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File to save data to.
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file_path : Path or str, optional
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Path to directory to load from. If None, loads from current directory.
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Path to directory to load from. If None, saves to current directory.
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append : bool, optional, default: False
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Whether to append to an existing file, if available.
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This option is only valid (and only used) if 'file_name' is a str.
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raise ValueError("Save file not understood.")
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def save_event(event, file_name, file_path=None, append=False,
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save_results=False, save_settings=False, save_data=False):
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"""Save out results and/or settings from event object. Saves out to a JSON file.
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Parameters
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----------
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event : SpectralTimeEventModel
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Object to save data from.
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file_name : str or FileObject
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File to save data to.
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file_path : str, optional
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Path to directory to load from. If None, saves to current directory.
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append : bool, optional, default: False
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Whether to append to an existing file, if available.
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This option is only valid (and only used) if 'file_name' is a str.
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save_results : bool, optional
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Whether to save out model fit results.
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save_settings : bool, optional
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Whether to save out settings.
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save_data : bool, optional
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Whether to save out power spectra data.
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Raises
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------
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ValueError
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If the data or save file specified are not understood.
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"""
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fg = event.get_group(None, None, 'group')
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if save_settings and not save_results and not save_data:
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fg.save(file_name, file_path, append=append, save_settings=True)
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else:
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ndigits = len(str(len(event)))
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for ind, gres in enumerate(event.event_group_results):
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fg.group_results = gres
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if save_data:
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fg.power_spectra = event.spectrograms[ind, :, :].T
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fg.save(file_name + '_{:0{ndigits}d}'.format(ind, ndigits=ndigits),
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file_path=file_path, append=append, save_results=save_results,
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save_settings=save_settings, save_data=save_data)
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def load_json(file_name, file_path):
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"""Load json file.
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‎specparam/core/modutils.py

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"""Utility functions & decorators for the module."""
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from importlib import import_module
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from copy import deepcopy
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from functools import wraps
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from importlib import import_module
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###################################################################################################
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###################################################################################################
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return front + back
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def docs_replace_param(docstring, replace, new_param):
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"""Replace a parameter description in a docstring.
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Parameters
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----------
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docstring : str
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Docstring to replace parameter description within.
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replace : str
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The name of the parameter to switch out.
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new_param : str
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The new parameter description to replace into the docstring.
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This should be a string structured to be copied directly into the docstring.
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Returns
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-------
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Update docstring, with parameter switched out.
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"""
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# Take a copy to make sure to avoid any potential aliasing
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docstring = deepcopy(docstring)
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# Find the index where the param to replace is
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p_ind = docstring.find(replace)
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# Find the second newline (end of to-replace param)
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ti = docstring[p_ind:].find('\n')
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n_ind = docstring[p_ind + ti + 1:].find('\n')
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end_ind = p_ind + ti + 1 + n_ind
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# Reconstitute docstring, replacing specified parameter
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new_docstring = docstring[:p_ind] + new_param + docstring[end_ind:]
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return new_docstring
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def docs_append_to_section(docstring, section, add):
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"""Append extra information to a specified section of a docstring.
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