def __array_wrap__(self, out_arr, context=None):
ret = super(YTArray, self).__array_wrap__(out_arr, context)
if isinstance(ret, YTQuantity) and ret.shape != ():
ret = ret.view(YTArray)
if context is None:
if ret.shape == ():
return ret[()]
else:
return ret
elif context[0] in unary_operators:
u = getattr(context[1][0], "units", None)
if u is None:
u = NULL_UNIT
unit = self._ufunc_registry[context[0]](u)
ret_class = type(self)
elif context[0] in binary_operators:
oper1 = coerce_iterable_units(context[1][0])
oper2 = coerce_iterable_units(context[1][1])
cls1 = type(oper1)
cls2 = type(oper2)
unit1 = getattr(oper1, "units", None)
unit2 = getattr(oper2, "units", None)
ret_class = get_binary_op_return_class(cls1, cls2)
if unit1 is None:
unit1 = Unit(registry=getattr(unit2, "registry", None))
if unit2 is None and context[0] is not power:
unit2 = Unit(registry=getattr(unit1, "registry", None))
elif context[0] is power:
unit2 = oper2
if isinstance(unit2, np.ndarray):
if isinstance(unit2, YTArray):
if unit2.units.is_dimensionless:
pass
else:
raise YTUnitOperationError(context[0], unit1, unit2)
unit2 = 1.0
unit_operator = self._ufunc_registry[context[0]]
if unit_operator in (preserve_units, comparison_unit, arctan2_unit):
if unit1 != unit2:
if not unit1.same_dimensions_as(unit2):
raise YTUnitOperationError(context[0], unit1, unit2)
else:
raise YTUfuncUnitError(context[0], unit1, unit2)
unit = self._ufunc_registry[context[0]](unit1, unit2)
if unit_operator in (multiply_units, divide_units):
if unit.is_dimensionless and unit.base_value != 1.0:
if not unit1.is_dimensionless:
if unit1.dimensions == unit2.dimensions:
np.multiply(out_arr.view(np.ndarray), unit.base_value, out=out_arr)
unit = Unit(registry=unit.registry)
else:
raise RuntimeError("Support for the %s ufunc has not been added " "to YTArray." % str(context[0]))
if unit is None:
out_arr = np.array(out_arr, copy=False)
return out_arr
out_arr.units = unit
if out_arr.size == 1:
return YTQuantity(np.array(out_arr), unit)
else:
if ret_class is YTQuantity:
# This happens if you do ndarray * YTQuantity. Explicitly
# casting to YTArray avoids creating a YTQuantity with size > 1
return YTArray(np.array(out_arr), unit)
return ret_class(np.array(out_arr, copy=False), unit)
def __array_wrap__(self, out_arr, context=None):
ret = super(YTArray, self).__array_wrap__(out_arr, context)
if isinstance(ret, YTQuantity) and ret.shape != ():
ret = ret.view(YTArray)
if context is None:
if ret.shape == ():
return ret[()]
else:
return ret
elif context[0] in unary_operators:
u = getattr(context[1][0], 'units', None)
if u is None:
u = NULL_UNIT
unit = self._ufunc_registry[context[0]](u)
ret_class = type(self)
elif context[0] in binary_operators:
oper1 = coerce_iterable_units(context[1][0])
oper2 = coerce_iterable_units(context[1][1])
cls1 = type(oper1)
cls2 = type(oper2)
unit1 = getattr(oper1, 'units', None)
unit2 = getattr(oper2, 'units', None)
ret_class = get_binary_op_return_class(cls1, cls2)
if unit1 is None:
unit1 = Unit(registry=getattr(unit2, 'registry', None))
if unit2 is None and context[0] is not power:
unit2 = Unit(registry=getattr(unit1, 'registry', None))
elif context[0] is power:
unit2 = oper2
if isinstance(unit2, np.ndarray):
if isinstance(unit2, YTArray):
if unit2.units.is_dimensionless:
pass
else:
raise YTUnitOperationError(context[0], unit1,
unit2)
unit2 = 1.0
unit_operator = self._ufunc_registry[context[0]]
if unit_operator in (preserve_units, comparison_unit,
arctan2_unit):
if unit1 != unit2:
if not unit1.same_dimensions_as(unit2):
raise YTUnitOperationError(context[0], unit1, unit2)
else:
raise YTUfuncUnitError(context[0], unit1, unit2)
unit = self._ufunc_registry[context[0]](unit1, unit2)
if unit_operator in (multiply_units, divide_units):
if unit.is_dimensionless and unit.base_value != 1.0:
if not unit1.is_dimensionless:
if unit1.dimensions == unit2.dimensions:
np.multiply(out_arr.view(np.ndarray),
unit.base_value,
out=out_arr)
unit = Unit(registry=unit.registry)
else:
raise RuntimeError("Support for the %s ufunc has not been added "
"to YTArray." % str(context[0]))
if unit is None:
out_arr = np.array(out_arr, copy=False)
return out_arr
out_arr.units = unit
if out_arr.size == 1:
return YTQuantity(np.array(out_arr), unit)
else:
if ret_class is YTQuantity:
# This happens if you do ndarray * YTQuantity. Explicitly
# casting to YTArray avoids creating a YTQuantity with size > 1
return YTArray(np.array(out_arr), unit)
return ret_class(np.array(out_arr, copy=False), unit)
def __array_wrap__(self, out_arr, context=None):
ret = super(YTArray, self).__array_wrap__(out_arr, context)
if isinstance(ret, YTQuantity) and ret.shape != ():
ret = ret.view(YTArray)
if context is None:
if ret.shape == ():
return ret[()]
else:
return ret
elif context[0] in unary_operators:
u = getattr(context[1][0], 'units', None)
if u is None:
u = Unit()
try:
unit = self._ufunc_registry[context[0]](u)
# Catch the RuntimeError raised inside of ensure_same_dimensions
# Raise YTUnitOperationError up here since we know the context now
except RuntimeError:
raise YTUnitOperationError(context[0], u)
ret_class = type(self)
elif context[0] in binary_operators:
oper1 = coerce_iterable_units(context[1][0])
oper2 = coerce_iterable_units(context[1][1])
cls1 = type(oper1)
cls2 = type(oper2)
unit1 = getattr(oper1, 'units', None)
unit2 = getattr(oper2, 'units', None)
ret_class = get_binary_op_return_class(cls1, cls2)
if unit1 is None:
unit1 = Unit(registry=getattr(unit2, 'registry', None))
if unit2 is None and context[0] is not power:
unit2 = Unit(registry=getattr(unit1, 'registry', None))
elif context[0] is power:
unit2 = oper2
if isinstance(unit2, np.ndarray):
if isinstance(unit2, YTArray):
if unit2.units.is_dimensionless:
pass
else:
raise YTUnitOperationError(context[0], unit1,
unit2)
unit2 = 1.0
if self._ufunc_registry[context[0]] in \
(preserve_units, comparison_unit, arctan2_unit):
if unit1 != unit2:
if not unit1.same_dimensions_as(unit2):
raise YTUnitOperationError(context[0], unit1, unit2)
else:
raise YTUfuncUnitError(context[0], unit1, unit2)
try:
unit = self._ufunc_registry[context[0]](unit1, unit2)
# Catch the RuntimeError raised inside of ensure_same_dimensions
# Raise YTUnitOperationError up here since we know the context now
except RuntimeError:
raise YTUnitOperationError(context[0], unit1, unit2)
else:
raise RuntimeError("Operation is not defined.")
if unit is None:
out_arr = np.array(out_arr, copy=False)
return out_arr
out_arr.units = unit
if out_arr.size == 1:
return YTQuantity(np.array(out_arr), unit)
else:
if ret_class is YTQuantity:
# This happens if you do ndarray * YTQuantity. Explicitly
# casting to YTArray avoids creating a YTQuantity with size > 1
return YTArray(np.array(out_arr, unit))
return ret_class(np.array(out_arr, copy=False), unit)
