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Clipping: don't assume that x-values have uniform spacing

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Do not assume that x-values have uniform spacing -- this can cause problems especially with large datasets and non-uniform spacing (e.g., time-dependent readings from an instrument).

Use `np.searchsorted` instead to find the first and last data index in the view range. This only assumes that x-values are in ascending order. This prevents potentially too strong clipping.
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SamSchott 2019-03-14 22:41:10 +00:00 committed by GitHub
parent 229f650adf
commit 365c13fedc
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1 changed files with 7 additions and 6 deletions
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13
pyqtgraph/graphicsItems/PlotDataItem.py
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@ -540,15 +540,16 @@ class PlotDataItem(GraphicsObject):
if self.opts['clipToView']: if self.opts['clipToView']:
view = self.getViewBox() view = self.getViewBox()
if view is None or not view.autoRangeEnabled()[0]: if view is None or not view.autoRangeEnabled()[0]:
# this option presumes that x-values have uniform spacing # this option presumes that x-values are in increasing order
range = self.viewRect() range = self.viewRect()
if range is not None and len(x) > 1: if range is not None and len(x) > 1:
dx = float(x[-1]-x[0]) / (len(x)-1)
# clip to visible region extended by downsampling value # clip to visible region extended by downsampling value
x0 = np.clip(int((range.left()-x[0])/dx)-1*ds , 0, len(x)-1) idx = np.searchsorted(x, [range.left(), range.right()])
x1 = np.clip(int((range.right()-x[0])/dx)+2*ds , 0, len(x)-1) idx = idx + np.array([-2*ds, 2*ds])
x = x[x0:x1] idx = np.clip(idx, a_min=0, a_max=len(x))
y = y[x0:x1]
x = x[idx[0]:idx[1]]
y = y[idx[0]:idx[1]]
if ds > 1: if ds > 1:
if self.opts['downsampleMethod'] == 'subsample': if self.opts['downsampleMethod'] == 'subsample':