245 lines
7.8 KiB
Python
245 lines
7.8 KiB
Python
# -*- coding: utf-8 -*-
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from pyqtgraph.Qt import QtCore, QtGui
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from ..Node import Node
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from scipy.signal import detrend
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from scipy.ndimage import median_filter, gaussian_filter
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#from pyqtgraph.SignalProxy import SignalProxy
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import functions
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from common import *
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import numpy as np
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try:
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import metaarray
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HAVE_METAARRAY = True
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except:
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HAVE_METAARRAY = False
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class Downsample(CtrlNode):
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"""Downsample by averaging samples together."""
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nodeName = 'Downsample'
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uiTemplate = [
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('n', 'intSpin', {'min': 1, 'max': 1000000})
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]
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def processData(self, data):
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return functions.downsample(data, self.ctrls['n'].value(), axis=0)
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class Subsample(CtrlNode):
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"""Downsample by selecting every Nth sample."""
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nodeName = 'Subsample'
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uiTemplate = [
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('n', 'intSpin', {'min': 1, 'max': 1000000})
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]
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def processData(self, data):
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return data[::self.ctrls['n'].value()]
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class Bessel(CtrlNode):
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"""Bessel filter. Input data must have time values."""
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nodeName = 'BesselFilter'
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uiTemplate = [
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('band', 'combo', {'values': ['lowpass', 'highpass'], 'index': 0}),
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('cutoff', 'spin', {'value': 1000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
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('order', 'intSpin', {'value': 4, 'min': 1, 'max': 16}),
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('bidir', 'check', {'checked': True})
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]
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def processData(self, data):
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s = self.stateGroup.state()
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if s['band'] == 'lowpass':
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mode = 'low'
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else:
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mode = 'high'
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return functions.besselFilter(data, bidir=s['bidir'], btype=mode, cutoff=s['cutoff'], order=s['order'])
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class Butterworth(CtrlNode):
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"""Butterworth filter"""
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nodeName = 'ButterworthFilter'
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uiTemplate = [
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('band', 'combo', {'values': ['lowpass', 'highpass'], 'index': 0}),
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('wPass', 'spin', {'value': 1000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
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('wStop', 'spin', {'value': 2000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
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('gPass', 'spin', {'value': 2.0, 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'dB', 'siPrefix': True}),
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('gStop', 'spin', {'value': 20.0, 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'dB', 'siPrefix': True}),
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('bidir', 'check', {'checked': True})
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]
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def processData(self, data):
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s = self.stateGroup.state()
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if s['band'] == 'lowpass':
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mode = 'low'
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else:
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mode = 'high'
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ret = functions.butterworthFilter(data, bidir=s['bidir'], btype=mode, wPass=s['wPass'], wStop=s['wStop'], gPass=s['gPass'], gStop=s['gStop'])
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return ret
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class Mean(CtrlNode):
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"""Filters data by taking the mean of a sliding window"""
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nodeName = 'MeanFilter'
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uiTemplate = [
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('n', 'intSpin', {'min': 1, 'max': 1000000})
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]
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@metaArrayWrapper
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def processData(self, data):
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n = self.ctrls['n'].value()
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return functions.rollingSum(data, n) / n
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class Median(CtrlNode):
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"""Filters data by taking the median of a sliding window"""
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nodeName = 'MedianFilter'
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uiTemplate = [
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('n', 'intSpin', {'min': 1, 'max': 1000000})
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]
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@metaArrayWrapper
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def processData(self, data):
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return median_filter(data, self.ctrls['n'].value())
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class Mode(CtrlNode):
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"""Filters data by taking the mode (histogram-based) of a sliding window"""
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nodeName = 'ModeFilter'
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uiTemplate = [
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('window', 'intSpin', {'value': 500, 'min': 1, 'max': 1000000}),
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]
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@metaArrayWrapper
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def processData(self, data):
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return functions.modeFilter(data, self.ctrls['window'].value())
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class Denoise(CtrlNode):
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"""Removes anomalous spikes from data, replacing with nearby values"""
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nodeName = 'DenoiseFilter'
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uiTemplate = [
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('radius', 'intSpin', {'value': 2, 'min': 0, 'max': 1000000}),
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('threshold', 'doubleSpin', {'value': 4.0, 'min': 0, 'max': 1000})
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]
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def processData(self, data):
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#print "DENOISE"
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s = self.stateGroup.state()
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return functions.denoise(data, **s)
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class Gaussian(CtrlNode):
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"""Gaussian smoothing filter."""
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nodeName = 'GaussianFilter'
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uiTemplate = [
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('sigma', 'doubleSpin', {'min': 0, 'max': 1000000})
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]
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@metaArrayWrapper
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def processData(self, data):
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return gaussian_filter(data, self.ctrls['sigma'].value())
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class Derivative(CtrlNode):
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"""Returns the pointwise derivative of the input"""
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nodeName = 'DerivativeFilter'
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def processData(self, data):
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if HAVE_METAARRAY and isinstance(data, metaarray.MetaArray):
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info = data.infoCopy()
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if 'values' in info[0]:
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info[0]['values'] = info[0]['values'][:-1]
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return MetaArray(data[1:] - data[:-1], info=info)
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else:
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return data[1:] - data[:-1]
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class Integral(CtrlNode):
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"""Returns the pointwise integral of the input"""
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nodeName = 'IntegralFilter'
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@metaArrayWrapper
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def processData(self, data):
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data[1:] += data[:-1]
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return data
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class Detrend(CtrlNode):
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"""Removes linear trend from the data"""
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nodeName = 'DetrendFilter'
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@metaArrayWrapper
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def processData(self, data):
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return detrend(data)
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class AdaptiveDetrend(CtrlNode):
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"""Removes baseline from data, ignoring anomalous events"""
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nodeName = 'AdaptiveDetrend'
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uiTemplate = [
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('threshold', 'doubleSpin', {'value': 3.0, 'min': 0, 'max': 1000000})
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]
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def processData(self, data):
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return functions.adaptiveDetrend(data, threshold=self.ctrls['threshold'].value())
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class HistogramDetrend(CtrlNode):
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"""Removes baseline from data by computing mode (from histogram) of beginning and end of data."""
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nodeName = 'HistogramDetrend'
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uiTemplate = [
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('windowSize', 'intSpin', {'value': 500, 'min': 10, 'max': 1000000, 'suffix': 'pts'}),
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('numBins', 'intSpin', {'value': 50, 'min': 3, 'max': 1000000})
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]
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def processData(self, data):
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ws = self.ctrls['windowSize'].value()
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bn = self.ctrls['numBins'].value()
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return functions.histogramDetrend(data, window=ws, bins=bn)
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class ExpDeconvolve(CtrlNode):
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"""Exponential deconvolution filter."""
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nodeName = 'ExpDeconvolve'
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uiTemplate = [
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('tau', 'spin', {'value': 10e-3, 'step': 1, 'minStep': 100e-6, 'dec': True, 'range': [0.0, None], 'suffix': 's', 'siPrefix': True})
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]
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def processData(self, data):
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tau = self.ctrls['tau'].value()
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return functions.expDeconvolve(data, tau)
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#dt = 1
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#if isinstance(data, MetaArray):
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#dt = data.xvals(0)[1] - data.xvals(0)[0]
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#d = data[:-1] + (self.ctrls['tau'].value() / dt) * (data[1:] - data[:-1])
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#if isinstance(data, MetaArray):
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#info = data.infoCopy()
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#if 'values' in info[0]:
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#info[0]['values'] = info[0]['values'][:-1]
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#return MetaArray(d, info=info)
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#else:
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#return d
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class ExpReconvolve(CtrlNode):
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"""Exponential reconvolution filter. Only works with MetaArrays that were previously deconvolved."""
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nodeName = 'ExpReconvolve'
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#uiTemplate = [
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#('tau', 'spin', {'value': 10e-3, 'step': 1, 'minStep': 100e-6, 'dec': True, 'range': [0.0, None], 'suffix': 's', 'siPrefix': True})
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#]
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def processData(self, data):
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return functions.expReconvolve(data)
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class Tauiness(CtrlNode):
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"""Sliding-window exponential fit"""
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nodeName = 'Tauiness'
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uiTemplate = [
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('window', 'intSpin', {'value': 100, 'min': 3, 'max': 1000000}),
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('skip', 'intSpin', {'value': 10, 'min': 0, 'max': 10000000})
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]
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def processData(self, data):
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return functions.tauiness(data, self.ctrls['window'].value(), self.ctrls['skip'].value())
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