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Fixed handling of the saturate keyword when we multiply be an array. #298

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Fixed handling of the saturate keyword when we multiply be an array. #298

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27 changes: 21 additions & 6 deletions banzai/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -134,20 +134,35 @@ def __getitem__(self, section):
return self.trim(trim_section=section)

def __imul__(self, value):
# TODO: Handle the case where this is an array. Add SATURATE and GAIN handling when array.
self.data *= value
self.uncertainty *= value
self.meta['SATURATE'] *= value
self.meta['GAIN'] /= value
self.meta['MAXLIN'] *= value
# Note: For array inputs, we need the arrays to be normalized around 1 for the saturate and gain keywords to
# make sense.
if isinstance(value, CCDData):
self.uncertainty = np.abs(self.data * value.data) * \
np.sqrt((self.uncertainty / self.data) ** 2 + (value.uncertainty / value.data) ** 2)
self.data *= value.data
self.mask |= value.mask
elif isinstance(value, np.ndarray):
self.uncertainty = np.abs(self.uncertainty * value)
self.data *= value
else:
self.data *= value
self.uncertainty *= value
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Is it intentional that there is no protection for the uncertainties for negative values if value is a scalar, unlike for the array case? (Cf. line 145)

self.meta['SATURATE'] *= value
self.meta['GAIN'] /= value
self.meta['MAXLIN'] *= value
return self

def __itruediv__(self, value):
# Note: For array inputs, we need the arrays to be normalized around 1 for the saturate and gain keywords to
# make sense.
if isinstance(value, CCDData):
self.uncertainty = np.abs(self.data / value.data) * \
np.sqrt((self.uncertainty / self.data) ** 2 + (value.uncertainty / value.data) ** 2)
self.data /= value.data
self.mask |= value.mask
elif isinstance(value, np.ndarray):
self.uncertainty = np.abs(self.uncertainty / value)
self.data /= value
else:
self.__imul__(1.0 / value)
return self
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