def set_dtype(self, value, check=True):
"""Change data type of the bin contents.
Allowed conversions:
- from integral to float types
- between the same category of type (float/integer)
- from float types to integer if weights are trivial
Parameters
----------
value: np.dtype or something convertible to it.
check: bool
If True (default), all values are checked against the limits
"""
# TODO: Refactor out?
# TODO? Deal with unsigned types
value = np.dtype(value)
if value == self.dtype:
return # No change
if value.kind in "iu":
type_info = np.iinfo(value)
elif value.kind == "f":
type_info = np.finfo(value)
else:
raise RuntimeError("Unsupported dtype. Only integer/floating-point types are supported.")
if np.can_cast(self.dtype, value):
pass # Ok
elif check:
if np.issubdtype(value, np.integer):
if self.dtype.kind == "f":
for array in (self._frequencies, self._errors2):
if np.any(array % 1.0):
raise RuntimeError("Data contain non-integer values.")
for array in (self._frequencies, self._errors2):
if np.any((array > type_info.max) | (array < type_info.min)):
raise RuntimeError("Data contain values outside the specified range.")
self._dtype = value
self._frequencies = self._frequencies.astype(value)
self._errors2 = self._errors2.astype(value)
self._missed = self._missed.astype(value)
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