Source code for netpyne.plotting.plotLFPPSD

# Generate plots of LFP (local field potentials) and related analyses

import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import math
from ..analysis.utils import exception #, loadData
from import loadData
from .plotter import LinesPlotter
from .plotter import ImagePlotter
from .plotter import MetaFigure
import numpy as np

[docs]@exception def plotLFPPSD( PSDData=None, axis=None, timeRange=None, electrodes=['avg', 'all'], pop=None, separation=1.0, roundOffset=True, NFFT=256, noverlap=128, nperseg=256, minFreq=1, maxFreq=100, stepFreq=1, smooth=0, logy=False, normSignal=False, normPSD=False, filtFreq=False, filtOrder=3, detrend=False, transformMethod='morlet', orderInverse=True, legend=True, colorList=None, returnPlotter=False, **kwargs): """Function to produce a line plot of LFP electrode signals NetPyNE Options --------------- sim : NetPyNE sim object The *sim object* from which to get data. *Default:* ``None`` uses the current NetPyNE sim object Parameters ---------- PSDData : dict, str The data necessary to plot the LFP PSD. *Default:* ``None`` uses ``analysis.preparePSD`` to produce ``PSDData`` using the current NetPyNE sim object. If a *str* it must represent a file path to previously saved data. axis : matplotlib axis The axis to plot into, allowing overlaying of plots. *Default:* ``None`` produces a new figure and axis. timeRange : list Time range to include in the raster: ``[min, max]``. *Default:* ``None`` uses the entire simulation electrodes : list A *list* of the electrodes to plot from. *Default:* ``['avg', 'all']`` plots each electrode as well as their average pop : str A population name to calculate PSD from. *Default:* ``None`` uses all populations. separation : float Use to increase or decrease distance between signals on the plot. *Default:* ``1.0`` roundOffset : bool Attempts to line up PSD signals with gridlines *Default:* ``True`` NFFT : int (power of 2) Number of data points used in each block for the PSD and time-freq FFT. **Default:** ``256`` noverlap : int (<nperseg) Number of points of overlap between segments for PSD and time-freq. **Default:** ``128`` nperseg : int Length of each segment for time-freq. **Default:** ``256`` minFreq : float Minimum frequency shown in plot for PSD and time-freq. **Default:** ``1`` maxFreq : float Maximum frequency shown in plot for PSD and time-freq. **Default:** ``100`` stepFreq : float Step frequency. **Default:** ``1`` smooth : int Window size for smoothing LFP; no smoothing if ``0`` **Default:** ``0`` logy : bool Whether to use a log axis. *Default:* ``False`` normSignal : bool Whether to normalize the LFP data. *Default:* ``False`` normPSD : bool Whether to normalize the PSD data. *Default:* ``False`` filtFreq : int or list Frequency for low-pass filter (int) or frequencies for bandpass filter in a list: [low, high] *Default:* ``None`` does not filter the data filtOrder : int Order of the filter defined by `filtFreq`. *Default:* ``3`` detrend : bool Whether to detrend the data. *Default:* ``False`` transformMethod : str The transformation method to use, either 'morlet' or 'fft'. *Default:* ``'morlet'`` orderInverse : bool Whether to invert the order of plotting. *Default:* ``True`` legend : bool Whether or not to add a legend to the plot. *Default:* ``True`` adds a legend. colorList : list A *list* of colors to draw from when plotting. *Default:* ``None`` uses the default NetPyNE colorList. returnPlotter : bool Whether to return the figure or the NetPyNE MetaFig object. *Default:* ``False`` returns the figure. Plot Options ------------ showFig : bool Whether to show the figure. *Default:* ``False`` saveFig : bool Whether to save the figure. *Default:* ``False`` overwrite : bool whether to overwrite existing figure files. *Default:* ``True`` overwrites the figure file *Options:* ``False`` adds a number to the file name to prevent overwriting legendKwargs : dict a *dict* containing any or all legend kwargs. These include ``'title'``, ``'loc'``, ``'fontsize'``, ``'bbox_to_anchor'``, ``'borderaxespad'``, and ``'handlelength'``. rcParams : dict a *dict* containing any or all matplotlib rcParams. To see all options, execute ``import matplotlib; print(matplotlib.rcParams)`` in Python. Any options in this *dict* will be used for this current figure and then returned to their prior settings. title : str the axis title xlabel : str label for x-axis ylabel : str label for y-axis linewidth : int line width alpha : float line opacity (0-1) Returns ------- LFPPSDPlot : *matplotlib figure* By default, returns the *figure*. If ``returnPlotter`` is ``True``, instead returns the NetPyNE MetaFig. """ # If there is no input data, get the data from the NetPyNE sim object if PSDData is None: if 'sim' not in kwargs: from .. import sim else: sim = kwargs['sim'] PSDData = sim.analysis.preparePSD( PSDData=PSDData, sim=sim, timeRange=timeRange, electrodes=electrodes, pop=pop, NFFT=NFFT, noverlap=noverlap, nperseg=nperseg, minFreq=minFreq, maxFreq=maxFreq, stepFreq=stepFreq, smooth=smooth, logy=logy, normSignal=normSignal, normPSD=normPSD, filtFreq=filtFreq, filtOrder=filtOrder, detrend=detrend, transformMethod=transformMethod, **kwargs ) print('Plotting LFP power spectral density (PSD)...') # If input is a dictionary, pull the data out of it if type(PSDData) == dict: freqs = PSDData['psdFreqs'] freq = freqs[0] psds = PSDData['psdSignal'] names = PSDData['psdNames'] colors = PSDData.get('colors') linewidths = PSDData.get('linewidths') alphas = PSDData.get('alphas') axisArgs = PSDData.get('axisArgs') # Set up colors, linewidths, and alphas for the plots if not colors: if not colorList: from .plotter import colorList colors = colorList[0:len(psds)] if not linewidths: linewidths = [1.0 for name in names] if not alphas: alphas = [1.0 for name in names] # Create a dictionary to hold axis inputs if not axisArgs: axisArgs = {} title = 'LFP Power Spectral Density' if pop: title += ' - Population: ' + pop axisArgs['title'] = title axisArgs['xlabel'] = 'Frequency (Hz)' axisArgs['ylabel'] = 'Power (mVˆ2 / Hz )' axisArgs['grid'] = {'which': 'both'} # Link colors to traces, make avg plot black, add separation to traces plotColors = [] legendLabels = [] colorIndex = 0 offset = np.absolute(psds).max() * separation if roundOffset: sigfigs = 1 if type(roundOffset) == int: sigfigs = roundOffset offset = round(offset, sigfigs - int(math.floor(math.log10(abs(offset)))) - 1) for index, (name, psd) in enumerate(zip(names, psds)): legendLabels.append(name) if orderInverse: psds[index] = index * offset - psd axisArgs['invert_yaxis'] = True else: psds[index] = index * offset + psd if name == 'avg': plotColors.append('black') else: plotColors.append(colors[colorIndex]) colorIndex += 1 # Create a dictionary with the inputs for a line plot linesData = {} linesData['x'] = freq linesData['y'] = psds linesData['color'] = plotColors linesData['marker'] = None linesData['markersize'] = None linesData['linewidth'] = None linesData['alpha'] = None linesData['label'] = legendLabels # If a kwarg matches a lines input key, use the kwarg value instead of the default for kwarg in list(kwargs.keys()): if kwarg in linesData: linesData[kwarg] = kwargs[kwarg] kwargs.pop(kwarg) # create Plotter object linesPlotter = LinesPlotter(data=linesData, kind='LFPPSD', axis=axis, **axisArgs, **kwargs) metaFig = linesPlotter.metafig # remove the y-axis tick labels # linesPlotter.axis.get_yaxis().set_ticklabels([]) # Set up the default legend settings legendKwargs = {} legendKwargs['title'] = 'Electrodes' #legendKwargs['bbox_to_anchor'] = (1.025, 1) legendKwargs['loc'] = 'upper right' #legendKwargs['borderaxespad'] = 0.0 #legendKwargs['handlelength'] = 0.5 legendKwargs['fontsize'] = 'small' legendKwargs['title_fontsize'] = 'small' # add the legend if legend: axisArgs['legend'] = legendKwargs # Generate the figure PSDPlot = linesPlotter.plot(**axisArgs, **kwargs) # Default is to return the figure, but you can also return the plotter if returnPlotter: return metaFig else: return PSDPlot