def reset(self, **traits):
"""Creates the dataset afresh or resets existing data source."""
# First set the attributes without really doing anything since
# the notification handlers are not called.
self.trait_set(trait_change_notify=False, **traits)
vectors = self.vectors
scalars = self.scalars
x, y, z = [np.atleast_3d(a) for a in (self.x, self.y, self.z)]
u, v, w = self.u, self.v, self.w
if 'vectors' in traits:
u = vectors[:, 0].ravel()
v = vectors[:, 1].ravel()
w = vectors[:, 2].ravel()
self.trait_set(u=u, v=v, w=w, trait_change_notify=False)
else:
if u is not None and len(u) > 0:
#vectors = np.concatenate([u[..., np.newaxis],
# v[..., np.newaxis],
# w[..., np.newaxis] ],
# axis=3)
vectors = np.c_[u.ravel(), v.ravel(),
w.ravel()].ravel()
vectors.shape = (u.shape[0], u.shape[1], w.shape[2], 3)
self.trait_set(vectors=vectors, trait_change_notify=False)
if vectors is not None and len(vectors) > 0 and scalars is not None:
assert len(scalars) == len(vectors)
if x.shape[0] <= 1:
dx = 1
else:
dx = x[1, 0, 0] - x[0, 0, 0]
if y.shape[1] <= 1:
dy = 1
else:
dy = y[0, 1, 0] - y[0, 0, 0]
if z.shape[2] <= 1:
dz = 1
else:
dz = z[0, 0, 1] - z[0, 0, 0]
if self.m_data is None:
ds = ArraySource(transpose_input_array=True)
else:
ds = self.m_data
old_scalar = ds.scalar_data
ds.trait_set(vector_data=vectors,
origin=[x.min(), y.min(), z.min()],
spacing=[dx, dy, dz],
scalar_data=scalars)
if scalars is old_scalar:
ds._scalar_data_changed(scalars)
self.dataset = ds.image_data
self.m_data = ds
def reset(self, **traits):
"""Creates the dataset afresh or resets existing data source."""
# First set the attributes without really doing anything since
# the notification handlers are not called.
self.trait_set(trait_change_notify=False, **traits)
vectors = self.vectors
scalars = self.scalars
x, y, z = [np.atleast_3d(a) for a in (self.x, self.y, self.z)]
u, v, w = self.u, self.v, self.w
if 'vectors' in traits:
u = vectors[:, 0].ravel()
v = vectors[:, 1].ravel()
w = vectors[:, 2].ravel()
self.trait_set(u=u, v=v, w=w, trait_change_notify=False)
else:
if u is not None and len(u) > 0:
#vectors = np.concatenate([u[..., np.newaxis],
# v[..., np.newaxis],
# w[..., np.newaxis] ],
# axis=3)
vectors = np.c_[u.ravel(), v.ravel(),
w.ravel()].ravel()
vectors.shape = (u.shape[0], u.shape[1], w.shape[2], 3)
self.trait_set(vectors=vectors, trait_change_notify=False)
if vectors is not None and len(vectors) > 0 and scalars is not None:
assert len(scalars) == len(vectors)
if x.shape[0] <= 1:
dx = 1
else:
dx = x[1, 0, 0] - x[0, 0, 0]
if y.shape[1] <= 1:
dy = 1
else:
dy = y[0, 1, 0] - y[0, 0, 0]
if z.shape[2] <= 1:
dz = 1
else:
dz = z[0, 0, 1] - z[0, 0, 0]
if self.m_data is None:
ds = ArraySource(transpose_input_array=True)
else:
ds = self.m_data
old_scalar = ds.scalar_data
ds.trait_set(vector_data=vectors,
origin=[x.min(), y.min(), z.min()],
spacing=[dx, dy, dz],
scalar_data=scalars)
if scalars is old_scalar:
ds._scalar_data_changed(scalars)
self.dataset = ds.image_data
self.m_data = ds
def reset(self, **traits):
"""Creates the dataset afresh or resets existing data source."""
# First set the attributes without really doing anything since
# the notification handlers are not called.
self.trait_set(trait_change_notify=False, **traits)
x, y, mask = self.x, self.y, self.mask
scalars = self.scalars
# We may have used this without specifying x and y at all in
# which case we set them from the shape of scalars.
nx, ny = scalars.shape
#Build X and Y from shape of Scalars if they are none
if x is None and y is None:
x, y = np.mgrid[-nx / 2.:nx / 2, -ny / 2.:ny / 2]
if mask is not None and len(mask) > 0:
scalars[mask.astype('bool')] = np.nan
# The NaN trick only works with floats.
scalars = scalars.astype('float')
self.trait_set(scalars=scalars, trait_change_notify=False)
z = np.array([0])
self.trait_set(x=x, y=y, z=z, trait_change_notify=False)
# Do some magic to extract the first row/column, independently of
# the shape of x and y
x = np.atleast_2d(x.squeeze().T)[0, :].squeeze()
y = np.atleast_2d(y.squeeze())[0, :].squeeze()
if x.ndim == 0:
dx = 1
else:
dx = x[1] - x[0]
if y.ndim == 0:
dy = 1
else:
dy = y[1] - y[0]
if self.m_data is None:
ds = ArraySource(transpose_input_array=True)
else:
ds = self.m_data
old_scalar = ds.scalar_data
ds.trait_set(origin=[x.min(), y.min(), 0],
spacing=[dx, dy, 1],
scalar_data=scalars)
if old_scalar is scalars:
ds._scalar_data_changed(scalars)
self.dataset = ds.get_output_dataset()
self.m_data = ds
def reset(self, **traits):
"""Creates the dataset afresh or resets existing data source."""
# First set the attributes without really doing anything since
# the notification handlers are not called.
self.trait_set(trait_change_notify=False, **traits)
x, y, mask = self.x, self.y, self.mask
scalars = self.scalars
# We may have used this without specifying x and y at all in
# which case we set them from the shape of scalars.
nx, ny = scalars.shape
#Build X and Y from shape of Scalars if they are none
if x is None and y is None:
x, y = np.mgrid[-nx / 2.:nx / 2, -ny / 2.:ny / 2]
if mask is not None and len(mask) > 0:
scalars[mask.astype('bool')] = np.nan
# The NaN trick only works with floats.
scalars = scalars.astype('float')
self.trait_set(scalars=scalars, trait_change_notify=False)
z = np.array([0])
self.trait_set(x=x, y=y, z=z, trait_change_notify=False)
# Do some magic to extract the first row/column, independently of
# the shape of x and y
x = np.atleast_2d(x.squeeze().T)[0, :].squeeze()
y = np.atleast_2d(y.squeeze())[0, :].squeeze()
if x.ndim == 0:
dx = 1
else:
dx = x[1] - x[0]
if y.ndim == 0:
dy = 1
else:
dy = y[1] - y[0]
if self.m_data is None:
ds = ArraySource(transpose_input_array=True)
else:
ds = self.m_data
old_scalar = ds.scalar_data
ds.trait_set(origin=[x.min(), y.min(), 0],
spacing=[dx, dy, 1],
scalar_data=scalars)
if old_scalar is scalars:
ds._scalar_data_changed(scalars)
self.dataset = ds.get_output_dataset()
self.m_data = ds
