pyqtgraph/flowchart/library/Filters.py
2012-03-01 21:55:32 -05:00

245 lines
7.8 KiB
Python

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