/usr/lib/python2.7/dist-packages/pyqtgraph/flowchart/library/Filters.py is in python-pyqtgraph 0.9.10-5.
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from ...Qt import QtCore, QtGui
from ..Node import Node
from . import functions
from ... import functions as pgfn
from .common import *
import numpy as np
from ... import PolyLineROI
from ... import Point
from ... import metaarray as metaarray
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 ButterworthNotch(CtrlNode):
"""Butterworth notch filter"""
nodeName = 'ButterworthNotchFilter'
uiTemplate = [
('low_wPass', 'spin', {'value': 1000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
('low_wStop', 'spin', {'value': 2000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
('low_gPass', 'spin', {'value': 2.0, 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'dB', 'siPrefix': True}),
('low_gStop', 'spin', {'value': 20.0, 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'dB', 'siPrefix': True}),
('high_wPass', 'spin', {'value': 3000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
('high_wStop', 'spin', {'value': 4000., 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'Hz', 'siPrefix': True}),
('high_gPass', 'spin', {'value': 2.0, 'step': 1, 'dec': True, 'range': [0.0, None], 'suffix': 'dB', 'siPrefix': True}),
('high_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()
low = functions.butterworthFilter(data, bidir=s['bidir'], btype='low', wPass=s['low_wPass'], wStop=s['low_wStop'], gPass=s['low_gPass'], gStop=s['low_gStop'])
high = functions.butterworthFilter(data, bidir=s['bidir'], btype='high', wPass=s['high_wPass'], wStop=s['high_wStop'], gPass=s['high_gPass'], gStop=s['high_gStop'])
return low + high
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):
try:
import scipy.ndimage
except ImportError:
raise Exception("MedianFilter node requires the package scipy.ndimage.")
return scipy.ndimage.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):
try:
import scipy.ndimage
except ImportError:
raise Exception("GaussianFilter node requires the package scipy.ndimage.")
return pgfn.gaussianFilter(data, self.ctrls['sigma'].value())
class Derivative(CtrlNode):
"""Returns the pointwise derivative of the input"""
nodeName = 'DerivativeFilter'
def processData(self, data):
if hasattr(data, 'implements') and data.implements('MetaArray'):
info = data.infoCopy()
if 'values' in info[0]:
info[0]['values'] = info[0]['values'][:-1]
return metaarray.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):
try:
from scipy.signal import detrend
except ImportError:
raise Exception("DetrendFilter node requires the package scipy.signal.")
return detrend(data)
class RemoveBaseline(PlottingCtrlNode):
"""Remove an arbitrary, graphically defined baseline from the data."""
nodeName = 'RemoveBaseline'
def __init__(self, name):
## define inputs and outputs (one output needs to be a plot)
PlottingCtrlNode.__init__(self, name)
self.line = PolyLineROI([[0,0],[1,0]])
self.line.sigRegionChanged.connect(self.changed)
## create a PolyLineROI, add it to a plot -- actually, I think we want to do this after the node is connected to a plot (look at EventDetection.ThresholdEvents node for ideas), and possible after there is data. We will need to update the end positions of the line each time the input data changes
#self.line = None ## will become a PolyLineROI
def connectToPlot(self, node):
"""Define what happens when the node is connected to a plot"""
if node.plot is None:
return
node.getPlot().addItem(self.line)
def disconnectFromPlot(self, plot):
"""Define what happens when the node is disconnected from a plot"""
plot.removeItem(self.line)
def processData(self, data):
## get array of baseline (from PolyLineROI)
h0 = self.line.getHandles()[0]
h1 = self.line.getHandles()[-1]
timeVals = data.xvals(0)
h0.setPos(timeVals[0], h0.pos()[1])
h1.setPos(timeVals[-1], h1.pos()[1])
pts = self.line.listPoints() ## lists line handles in same coordinates as data
pts, indices = self.adjustXPositions(pts, timeVals) ## maxe sure x positions match x positions of data points
## construct an array that represents the baseline
arr = np.zeros(len(data), dtype=float)
n = 1
arr[0] = pts[0].y()
for i in range(len(pts)-1):
x1 = pts[i].x()
x2 = pts[i+1].x()
y1 = pts[i].y()
y2 = pts[i+1].y()
m = (y2-y1)/(x2-x1)
b = y1
times = timeVals[(timeVals > x1)*(timeVals <= x2)]
arr[n:n+len(times)] = (m*(times-times[0]))+b
n += len(times)
return data - arr ## subract baseline from data
def adjustXPositions(self, pts, data):
"""Return a list of Point() where the x position is set to the nearest x value in *data* for each point in *pts*."""
points = []
timeIndices = []
for p in pts:
x = np.argwhere(abs(data - p.x()) == abs(data - p.x()).min())
points.append(Point(data[x], p.y()))
timeIndices.append(x)
return points, timeIndices
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, 'suffix': 'pts'}),
('numBins', 'intSpin', {'value': 50, 'min': 3, 'max': 1000000}),
('offsetOnly', 'check', {'checked': False}),
]
def processData(self, data):
s = self.stateGroup.state()
#ws = self.ctrls['windowSize'].value()
#bn = self.ctrls['numBins'].value()
#offset = self.ctrls['offsetOnly'].checked()
return functions.histogramDetrend(data, window=s['windowSize'], bins=s['numBins'], offsetOnly=s['offsetOnly'])
class RemovePeriodic(CtrlNode):
nodeName = 'RemovePeriodic'
uiTemplate = [
#('windowSize', 'intSpin', {'value': 500, 'min': 10, 'max': 1000000, 'suffix': 'pts'}),
#('numBins', 'intSpin', {'value': 50, 'min': 3, 'max': 1000000})
('f0', 'spin', {'value': 60, 'suffix': 'Hz', 'siPrefix': True, 'min': 0, 'max': None}),
('harmonics', 'intSpin', {'value': 30, 'min': 0}),
('samples', 'intSpin', {'value': 1, 'min': 1}),
]
def processData(self, data):
times = data.xvals('Time')
dt = times[1]-times[0]
data1 = data.asarray()
ft = np.fft.fft(data1)
## determine frequencies in fft data
df = 1.0 / (len(data1) * dt)
freqs = np.linspace(0.0, (len(ft)-1) * df, len(ft))
## flatten spikes at f0 and harmonics
f0 = self.ctrls['f0'].value()
for i in xrange(1, self.ctrls['harmonics'].value()+2):
f = f0 * i # target frequency
## determine index range to check for this frequency
ind1 = int(np.floor(f / df))
ind2 = int(np.ceil(f / df)) + (self.ctrls['samples'].value()-1)
if ind1 > len(ft)/2.:
break
mag = (abs(ft[ind1-1]) + abs(ft[ind2+1])) * 0.5
for j in range(ind1, ind2+1):
phase = np.angle(ft[j]) ## Must preserve the phase of each point, otherwise any transients in the trace might lead to large artifacts.
re = mag * np.cos(phase)
im = mag * np.sin(phase)
ft[j] = re + im*1j
ft[len(ft)-j] = re - im*1j
data2 = np.fft.ifft(ft).real
ma = metaarray.MetaArray(data2, info=data.infoCopy())
return ma
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