fix merge

.github/workflows/build.yml

@@ -22,9 +22,9 @@ jobs:
         python-version: ["3.6", "3.7", "3.8", "3.9", "3.10", "3.11", "3.12"]
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies
@@ -40,4 +40,6 @@ jobs:
       run: |
         pytest --doctest-modules --ignore=$MODULE_NAME/tests $MODULE_NAME
     - name: Upload coverage to Codecov
-      uses: codecov/codecov-action@v3
+      uses: codecov/codecov-action@v4
+      env:
+        CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}

CITATION.cff

@@ -1,24 +1,46 @@
 cff-version: 1.2.0
-message: "If you use this software, please cite it as below."
+message: >-
+  If you use this software, please cite it using the metadata from this file.
+type: software
+title: 'neurodsp: neuro digital signal processing'
+authors:
+- given-names: 'Thomas'
+  family-names: 'Donoghue'
+  orcid: 'https://orcid.org/0000-0001-5911-0472'
+- given-names: 'Ryan'
+  family-names: 'Hammonds'
+  orcid: 'https://orcid.org/0000-0002-5032-3241'
+- given-names: 'Scott'
+  family-names: 'Cole'
+  orcid: 'https://orcid.org/0000-0002-6168-9951'
+- given-names: 'Richard'
+  family-names: 'Gao'
+  orcid: 'https://orcid.org/0000-0001-5916-6433'
+- given-names: 'Bradley'
+  family-names: 'Voytek'
+  orcid: 'https://orcid.org/0000-0003-1640-2525'
+repository-code: 'https://github.com/neurodsp-tools/neurodsp'
+url: 'https://neurodsp-tools.github.io/'
+license: Apache-2.0
 preferred-citation:
   type: article
   authors:
-  - family-names: "Cole"
-    given-names: "Scott"
-    orcid: "https://orcid.org/0000-0002-6168-9951"
-  - family-names: "Donoghue"
-    given-names: "Thomas"
-    orcid: "https://orcid.org/0000-0001-5911-0472"
-  - family-names: "Gao"
-    given-names: "Richard"
-    orcid: "https://orcid.org/0000-0001-5916-6433"
-  - family-names: "Voytek"
-    given-names: "Bradley"
-    orcid: "https://orcid.org/0000-0003-1640-2525"
-  doi: "10.21105/joss.01272"
-  journal: "Journal of Open Source Software"
+  - given-names: 'Scott'
+    family-names: 'Cole'
+    orcid: 'https://orcid.org/0000-0002-6168-9951'
+  - given-names: 'Thomas'
+    family-names: 'Donoghue'
+    orcid: 'https://orcid.org/0000-0001-5911-0472'
+  - given-names: 'Richard'
+    family-names: 'Gao'
+    orcid: 'https://orcid.org/0000-0001-5916-6433'
+  - given-names: 'Bradley'
+    family-names: 'Voytek'
+    orcid: 'https://orcid.org/0000-0003-1640-2525'
+  doi: '10.21105/joss.01272'
+  journal: 'Journal of Open Source Software'
   month: 4
-  title: "NeuroDSP: A package for neural digital signal processing"
+  title: 'NeuroDSP: A package for neural digital signal processing'
   issue: 36
   volume: 4
   year: 2019

doc/api.rst

@@ -114,6 +114,7 @@ Spectral Power
     compute_spectrum_welch
     compute_spectrum_wavelet
     compute_spectrum_medfilt
+    compute_spectrum_multitaper
 
 Spectral Measures
 ~~~~~~~~~~~~~~~~~

neurodsp/aperiodic/autocorr.py

@@ -3,15 +3,18 @@
 import numpy as np
 from scipy.optimize import curve_fit
 
+from neurodsp.utils.decorators import multidim
+
 ###################################################################################################
 ###################################################################################################
 
+@multidim()
 def compute_autocorr(sig, max_lag=1000, lag_step=1, demean=True):
     """Compute the signal autocorrelation (lagged correlation).
 
     Parameters
     ----------
-    sig : array 1D
+    sig : array
         Time series to compute autocorrelation over.
     max_lag : int, optional, default: 1000
         Maximum delay to compute autocorrelations for, in samples.
@@ -24,7 +27,7 @@ def compute_autocorr(sig, max_lag=1000, lag_step=1, demean=True):
     -------
     timepoints : 1d array
         Time points, in samples, at which autocorrelations are computed.
-    autocorrs : 1d array
+    autocorrs : array
         Autocorrelation values, for across time lags.
 
     Examples

neurodsp/aperiodic/dfa.py

@@ -4,16 +4,18 @@
 
 from neurodsp.utils.data import split_signal
 from neurodsp.utils.checks import check_param_options
+from neurodsp.utils.decorators import multidim
 
 ###################################################################################################
 ###################################################################################################
 
+@multidim(select=[0])
 def compute_fluctuations(sig, fs, n_scales=10, min_scale=0.01, max_scale=1.0, deg=1, method='dfa'):
     """Compute a fluctuation analysis on a signal.
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Time series.
     fs : float
         Sampling rate, in Hz.
@@ -38,9 +40,9 @@ def compute_fluctuations(sig, fs, n_scales=10, min_scale=0.01, max_scale=1.0, de
     -------
     t_scales : 1d array
         Time-scales over which fluctuation measures were computed.
-    fluctuations : 1d array
+    fluctuations : array
         Average fluctuation at each scale.
-    result : float
+    result : float or 1d array
         Slope of line in log-log when plotting time scales against fluctuations.
         This is the alpha value for DFA, or the Hurst exponent for rescaled range.
 
@@ -101,20 +103,20 @@ def compute_fluctuations(sig, fs, n_scales=10, min_scale=0.01, max_scale=1.0, de
 
     return t_scales, fluctuations, result
 
-
+@multidim()
 def compute_rescaled_range(sig, win_len):
     """Compute rescaled range of a given time series at a given scale.
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Time series.
     win_len : int
         Window length for each rescaled range computation, in samples.
 
     Returns
     -------
-    rs : float
+    rs : float or 1d array
         Average rescaled range over windows.
 
     Notes
@@ -140,13 +142,13 @@ def compute_rescaled_range(sig, win_len):
 
     return rs
 
-
+@multidim()
 def compute_detrended_fluctuation(sig, win_len, deg=1):
     """Compute detrended fluctuation of a time series at the given window length.
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Time series.
     win_len : int
         Window length for each detrended fluctuation fit, in samples.
@@ -155,7 +157,7 @@ def compute_detrended_fluctuation(sig, win_len, deg=1):
 
     Returns
     -------
-    det_fluc : float
+    det_fluc : float or 1d array
         Measured detrended fluctuation, as the average error fits of the window.
 
     Notes

neurodsp/aperiodic/irasa.py

@@ -8,16 +8,18 @@
 from scipy.optimize import curve_fit
 
 from neurodsp.spectral import compute_spectrum, trim_spectrum
+from neurodsp.utils.decorators import multidim
 
 ###################################################################################################
 ###################################################################################################
 
+@multidim(select=[0])
 def compute_irasa(sig, fs, f_range=None, hset=None, thresh=None, **spectrum_kwargs):
     """Separate aperiodic and periodic components using IRASA.
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Time series.
     fs : float
         The sampling frequency of sig.
@@ -36,9 +38,9 @@ def compute_irasa(sig, fs, f_range=None, hset=None, thresh=None, **spectrum_kwar
     -------
     freqs : 1d array
         Frequency vector.
-    psd_aperiodic : 1d array
+    psd_aperiodic : array
         The aperiodic component of the power spectrum.
-    psd_periodic : 1d array
+    psd_periodic : array
         The periodic component of the power spectrum.
 
     Notes

neurodsp/burst/dualthresh.py

@@ -19,7 +19,7 @@ def detect_bursts_dual_threshold(sig, fs, dual_thresh, f_range=None,
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Time series.
     fs : float
         Sampling rate, in Hz.
@@ -44,7 +44,7 @@ def detect_bursts_dual_threshold(sig, fs, dual_thresh, f_range=None,
 
     Returns
     -------
-    is_burst : 1d array
+    is_burst : array
         Boolean indication of where bursts are present in the input signal.
         True indicates that a burst was detected at that sample, otherwise False.
 

neurodsp/filt/filter.py

@@ -5,18 +5,20 @@
 from neurodsp.filt.fir import filter_signal_fir
 from neurodsp.filt.iir import filter_signal_iir
 from neurodsp.utils.checks import check_param_options
+from neurodsp.utils.decorators import multidim
 
 ###################################################################################################
 ###################################################################################################
 
+@multidim(pass_2d_input=True)
 def filter_signal(sig, fs, pass_type, f_range, filter_type=None,
                   print_transitions=False, plot_properties=False, return_filter=False,
                   **filter_kwargs):
     """Apply a bandpass, bandstop, highpass, or lowpass filter to a neural signal.
 
     Parameters
     ----------
-    sig : 1d or 2d array
+    sig : array
         Time series to be filtered.
     fs : float
         Sampling rate, in Hz.

neurodsp/filt/utils.py

@@ -217,14 +217,14 @@ def remove_filter_edges(sig, filt_len):
 
     Parameters
     ----------
-    sig : 1d array
+    sig : array
         Filtered signal to have edge artifacts removed from.
     filt_len : int
         Length of the filter that was applied.
 
     Returns
     -------
-    sig : 1d array
+    sig : array
         Filter signal with edge artifacts switched to NaNs.
 
     Examples

neurodsp/plts/settings.py

@@ -29,6 +29,7 @@
 
 ## Define default values for aesthetics
 # These are all custom style arguments
+SUPTITLE_FONTSIZE = 24
 TITLE_FONTSIZE = 20
 LABEL_SIZE = 16
 TICK_LABELSIZE = 16

neurodsp/plts/utils.py

@@ -1,13 +1,45 @@
 """Utility functions for plots."""
 
+from copy import deepcopy
 from functools import wraps
 from os.path import join as pjoin
 
 import matplotlib.pyplot as plt
 
+from neurodsp.plts.settings import SUPTITLE_FONTSIZE
+
 ###################################################################################################
 ###################################################################################################
 
+def subset_kwargs(kwargs, label):
+    """Subset a set of kwargs from a dictionary.
+
+    Parameters
+    ----------
+    kwargs : dict
+        Dictionary of keyword arguments.
+    label : str
+        Label to use to subset.
+        Any entries with label in the key will be subset from the kwargs dict.
+
+    Returns
+    -------
+    kwargs : dict
+        The kwargs dictionary, with subset items removed.
+    subset : dict
+        The collection of subset kwargs.
+    """
+
+    kwargs = deepcopy(kwargs)
+
+    subset = {}
+    for key in list(kwargs.keys()):
+        if label in key:
+            subset[key] = kwargs.pop(key)
+
+    return kwargs, subset
+
+
 def check_ax(ax, figsize=None):
     """Check whether a figure axes object is defined, define if not.
 
@@ -77,3 +109,49 @@ def save_figure(file_name, file_path=None, close=False, **save_kwargs):
 
     if close:
         plt.close()
+
+
+def make_axes(n_rows, n_cols, figsize=None, row_size=4, col_size=3.6,
+              wspace=None, hspace=None, title=None, **plt_kwargs):
+    """Make a subplot with multiple axes.
+
+    Parameters
+    ----------
+    n_rows, n_cols : int
+        The number of rows and columns axes to create in the figure.
+    figsize : tuple of float, optional
+        Size to make the overall figure.
+        If not given, is estimated from the number of axes.
+    row_size, col_size : float, optional
+        The size to use per row / column.
+        Only used if `figsize` is None.
+    wspace, hspace : float, optional
+        Parameters for spacing between subplots.
+        These get passed into `plt.subplots_adjust`.
+    title : str, optional
+        A super title to add to the figure.
+    **plt_kwargs
+        Extra arguments to pass to `plt.subplots`.
+
+    Returns
+    -------
+    axes : 1d array of AxesSubplot
+        Collection of axes objects.
+    """
+
+    if not figsize:
+        figsize = (n_cols * col_size, n_rows * row_size)
+
+    plt_kwargs, title_kwargs = subset_kwargs(plt_kwargs, 'title')
+
+    _, axes = plt.subplots(n_rows, n_cols, figsize=figsize, **plt_kwargs)
+
+    if wspace or hspace:
+        plt.subplots_adjust(wspace=wspace, hspace=hspace)
+
+    if title:
+        plt.suptitle(title,
+                     fontsize=title_kwargs.pop('title_fontsize', SUPTITLE_FONTSIZE),
+                     **title_kwargs)
+
+    return axes