Merge pull request #1650 from NilsNemitz/add_clip_scalar_function
Do not use clip_array on scalars (use clip_scalar instead)
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@ -998,6 +998,9 @@ def solveBilinearTransform(points1, points2):
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return matrix
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def clip_scalar(val, vmin, vmax):
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""" convenience function to avoid using np.clip for scalar values """
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return vmin if val < vmin else vmax if val > vmax else val
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def clip_array(arr, vmin, vmax, out=None):
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# replacement for np.clip due to regression in
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@ -679,8 +679,8 @@ class PlotDataItem(GraphicsObject):
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# workaround for slowdown from numpy deprecation issues in 1.17 to 1.20+
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# x0 = np.clip(int((range.left()-x[0])/dx) - 1*ds, 0, len(x)-1)
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# x1 = np.clip(int((range.right()-x[0])/dx) + 2*ds, 0, len(x)-1)
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x0 = fn.clip_array(int((range.left()-x[0])/dx) - 1*ds, 0, len(x)-1)
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x1 = fn.clip_array(int((range.right()-x[0])/dx) + 2*ds, 0, len(x)-1)
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x0 = fn.clip_scalar(int((range.left()-x[0])/dx) - 1*ds, 0, len(x)-1)
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x1 = fn.clip_scalar(int((range.right()-x[0])/dx) + 2*ds, 0, len(x)-1)
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# if data has been clipped too strongly (in case of non-uniform
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# spacing of x-values), refine the clipping region as required
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@ -688,11 +688,11 @@ class PlotDataItem(GraphicsObject):
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# best case performance: O(1)
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if x[x0] > range.left():
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x0 = np.searchsorted(x, range.left()) - 1*ds
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x0 = fn.clip_array(x0, 0, len(x)) # workaround
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x0 = fn.clip_scalar(x0, 0, len(x)) # workaround
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# x0 = np.clip(x0, 0, len(x))
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if x[x1] < range.right():
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x1 = np.searchsorted(x, range.right()) + 2*ds
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x1 = fn.clip_array(x1, 0, len(x))
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x1 = fn.clip_scalar(x1, 0, len(x))
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# x1 = np.clip(x1, 0, len(x))
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x = x[x0:x1]
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y = y[x0:x1]
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