def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)
self.tag.unique_value = None
if isinstance(data, np.ndarray) and data.ndim > 0:
flat_data = data.ravel()
if flat_data.shape[0]:
if (flat_data == flat_data[0]).all():
self.tag.unique_value = flat_data[0]
def __init__(self, type, data, name=None):
data_shape = np.shape(data)
if len(data_shape) != type.ndim or any(
ds != ts for ds, ts in zip(np.shape(data), type.shape)
if ts is not None):
raise ValueError(
f"Shape of data ({data_shape}) does not match shape of type ({type.shape})"
)
# We want all the shape information from `data`
new_type = type.clone(shape=data_shape)
assert not any(s is None for s in new_type.shape)
Constant.__init__(self, new_type, data, name)
def __init__(self, type, data, name=None):
assert isinstance(data, slice)
# Numpy ndarray aren't hashable, so get rid of them.
if isinstance(data.start, np.ndarray):
assert data.start.ndim == 0
assert str(data.start.dtype) in integer_dtypes
data = slice(int(data.start), data.stop, data.step)
elif isinstance(data.stop, np.ndarray):
assert data.stop.ndim == 0
assert str(data.stop.dtype) in integer_dtypes
data = slice(data.start, int(data.stop), data.step)
elif isinstance(data.step, np.ndarray):
assert data.step.ndim == 0
assert str(data.step.dtype) in integer_dtypes
data = slice(data.start, int(data.stop), data.step)
Constant.__init__(self, type, data, name)
def __init__(self, type, data, name=None):
Constant.__init__(self, type, data, name)