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Add unit test covering plotdataitem fft

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This commit is contained in:
Luke Campagnola 2017-02-05 23:13:00 -08:00
parent 154b6bacb1
commit f632b02985
2 changed files with 25 additions and 27 deletions
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27
pyqtgraph/graphicsItems/PlotDataItem.py
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@ -500,27 +500,10 @@ class PlotDataItem(GraphicsObject):
if self.xData is None:
return (None, None)
#if self.xClean is None:
#nanMask = np.isnan(self.xData) | np.isnan(self.yData) | np.isinf(self.xData) | np.isinf(self.yData)
#if nanMask.any():
#self.dataMask = ~nanMask
#self.xClean = self.xData[self.dataMask]
#self.yClean = self.yData[self.dataMask]
#else:
#self.dataMask = None
#self.xClean = self.xData
#self.yClean = self.yData
if self.xDisp is None:
x = self.xData
y = self.yData
#ds = self.opts['downsample']
#if isinstance(ds, int) and ds > 1:
#x = x[::ds]
##y = resample(y[:len(x)*ds], len(x)) ## scipy.signal.resample causes nasty ringing
#y = y[::ds]
if self.opts['fftMode']:
x,y = self._fourierTransform(x, y)
# Ignore the first bin for fft data if we have a logx scale
@ -531,14 +514,6 @@ class PlotDataItem(GraphicsObject):
x = np.log10(x)
if self.opts['logMode'][1]:
y = np.log10(y)
#if any(self.opts['logMode']): ## re-check for NANs after log
#nanMask = np.isinf(x) | np.isinf(y) | np.isnan(x) | np.isnan(y)
#if any(nanMask):
#self.dataMask = ~nanMask
#x = x[self.dataMask]
#y = y[self.dataMask]
#else:
#self.dataMask = None
ds = self.opts['downsample']
if not isinstance(ds, int):
@ -591,8 +566,6 @@ class PlotDataItem(GraphicsObject):
self.xDisp = x
self.yDisp = y
#print self.yDisp.shape, self.yDisp.min(), self.yDisp.max()
#print self.xDisp.shape, self.xDisp.min(), self.xDisp.max()
return self.xDisp, self.yDisp
def dataBounds(self, ax, frac=1.0, orthoRange=None):

25
pyqtgraph/graphicsItems/tests/test_PlotDataItem.py Normal file
View File

@ -0,0 +1,25 @@
import numpy as np
import pyqtgraph as pg
pg.mkQApp()
def test_fft():
f = 20.
x = np.linspace(0, 1, 1000)
y = np.sin(2 * np.pi * f * x)
pd = pg.PlotDataItem(x, y)
pd.setFftMode(True)
x, y = pd.getData()
assert abs(x[np.argmax(y)] - f) < 0.03
x = np.linspace(0, 1, 1001)
y = np.sin(2 * np.pi * f * x)
pd.setData(x, y)
x, y = pd.getData()
assert abs(x[np.argmax(y)]- f) < 0.03
pd.setLogMode(True, False)
x, y = pd.getData()
assert abs(x[np.argmax(y)] - np.log10(f)) < 0.01