makeRGBA/ImageItem: Applying alpha mask on numpy.nan data values (#406)
* Applying alpha mask on numpy.nan data values * Typesafe, checking for `data.dtype.kind`
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@ -78,7 +78,7 @@ def siScale(x, minVal=1e-25, allowUnicode=True):
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pref = SI_PREFIXES_ASCII[m+8]
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p = .001**m
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return (p, pref)
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return (p, pref)
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def siFormat(x, precision=3, suffix='', space=True, error=None, minVal=1e-25, allowUnicode=True):
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@ -1035,7 +1035,6 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
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============== ==================================================================================
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"""
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profile = debug.Profiler()
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if data.ndim not in (2, 3):
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raise TypeError("data must be 2D or 3D")
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if data.ndim == 3 and data.shape[2] > 4:
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@ -1083,7 +1082,12 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
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dtype = np.ubyte
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else:
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dtype = np.min_scalar_type(lut.shape[0]-1)
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# awkward, but fastest numpy native nan evaluation
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#
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nanMask = None
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if data.dtype.kind == 'f' and np.isnan(data.min()):
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nanMask = np.isnan(data)
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# Apply levels if given
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if levels is not None:
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if isinstance(levels, np.ndarray) and levels.ndim == 2:
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@ -1106,10 +1110,8 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
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if minVal == maxVal:
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maxVal = np.nextafter(maxVal, 2*maxVal)
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data = rescaleData(data, scale/(maxVal-minVal), minVal, dtype=dtype)
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profile()
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# apply LUT if given
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if lut is not None:
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data = applyLookupTable(data, lut)
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@ -1152,7 +1154,12 @@ def makeARGB(data, lut=None, levels=None, scale=None, useRGBA=False):
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imgData[..., 3] = 255
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else:
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alpha = True
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# apply nan mask through alpha channel
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if nanMask is not None:
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alpha = True
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imgData[nanMask, 3] = 0
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profile()
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return imgData, alpha
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