def show(self,
title=None,
method='scatter',
force_show=False,
fig=None,
**kwargs):
"""Display this vector graphically.
Parameters
----------
title : string, optional
Set the title of the figure
method : string, optional
The following plotting methods are available:
'scatter' : point plot
'plot' : graph plot
force_show : bool, optional
Whether the plot should be forced to be shown now or deferred until
later. Note that some backends always displays the plot, regardless
of this value.
fig : `matplotlib.figure.Figure`, optional
Figure to draw into. Expected to be of same "style" as
the figure given by this function. The most common use case
is that ``fig`` is the return value of an earlier call to
this function.
kwargs : {'figsize', 'saveto', ...}, optional
Extra keyword arguments passed on to the display method.
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
Resulting figure. If ``fig`` was given, the returned object
is a reference to it.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
from odl.discr import uniform_grid
grid = uniform_grid(0, self.size - 1, self.size)
return show_discrete_data(self.asarray(),
grid,
title=title,
method=method,
force_show=force_show,
fig=fig,
**kwargs)
def show(self,
title=None,
method='scatter',
show=False,
fig=None,
**kwargs):
"""Display the function graphically.
Parameters
----------
title : `str`, optional
Set the title of the figure
method : `str`, optional
1d methods:
'plot' : graph plot
'scatter' : point plot
show : `bool`, optional
If the plot should be showed now or deferred until later.
fig : `matplotlib.figure.Figure`
The figure to show in. Expected to be of same "style", as
the figure given by this function. The most common use case
is that ``fig`` is the return value from an earlier call to
this function.
kwargs : {'figsize', 'saveto', ...}
Extra keyword arguments passed on to display method
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
The resulting figure. It is also shown to the user.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
from odl.discr import RegularGrid
grid = RegularGrid(0, self.size - 1, self.size)
return show_discrete_data(self.asarray(),
grid,
title=title,
method=method,
show=show,
fig=fig,
**kwargs)
def show(self, title=None, method='scatter', force_show=False, fig=None,
**kwargs):
"""Display this vector graphically.
Parameters
----------
title : string, optional
Set the title of the figure
method : string, optional
The following plotting methods are available:
'scatter' : point plot
'plot' : graph plot
force_show : bool, optional
Whether the plot should be forced to be shown now or deferred until
later. Note that some backends always displays the plot, regardless
of this value.
fig : `matplotlib.figure.Figure`, optional
Figure to draw into. Expected to be of same "style" as
the figure given by this function. The most common use case
is that ``fig`` is the return value of an earlier call to
this function.
kwargs : {'figsize', 'saveto', ...}, optional
Extra keyword arguments passed on to the display method.
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
Resulting figure. If ``fig`` was given, the returned object
is a reference to it.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
from odl.discr import uniform_grid
grid = uniform_grid(0, self.size - 1, self.size)
return show_discrete_data(self.asarray(), grid, title=title,
method=method, force_show=force_show,
fig=fig, **kwargs)
def show(self, title=None, method='scatter', show=False, fig=None,
**kwargs):
"""Display this vector graphically.
Parameters
----------
title : string, optional
Set the title of the figure
method : string, optional
The following plotting methods are available:
'scatter' : point plot
'plot' : graph plot
show : bool, optional
If ``True``, the plot is shown immediately. Otherwise, display is
deferred to a later point in time.
fig : `matplotlib.figure.Figure`, optional
Figure to draw into. Expected to be of same "style" as
the figure given by this function. The most common use case
is that ``fig`` is the return value of an earlier call to
this function.
kwargs : {'figsize', 'saveto', ...}
Extra keyword arguments passed on to the display method.
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
Resulting figure. If ``fig`` was given, the returned object
is a reference to it.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
from odl.discr import RegularGrid
grid = RegularGrid(0, self.size - 1, self.size)
return show_discrete_data(self.asarray(), grid, title=title,
method=method, show=show, fig=fig, **kwargs)
def show(self, title=None, method='scatter', show=False, fig=None,
**kwargs):
"""Display the function graphically.
Parameters
----------
title : `str`, optional
Set the title of the figure
method : `str`, optional
1d methods:
'plot' : graph plot
'scatter' : point plot
show : `bool`, optional
If the plot should be showed now or deferred until later.
fig : `matplotlib.figure.Figure`
The figure to show in. Expected to be of same "style", as
the figure given by this function. The most common use case
is that ``fig`` is the return value from an earlier call to
this function.
kwargs : {'figsize', 'saveto', ...}
Extra keyword arguments passed on to display method
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
The resulting figure. It is also shown to the user.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
from odl.discr import RegularGrid
grid = RegularGrid(0, self.size - 1, self.size)
return show_discrete_data(self.asarray(), grid, title=title,
method=method, show=show, fig=fig, **kwargs)
def show(self,
title=None,
method='',
indices=None,
force_show=False,
fig=None,
**kwargs):
"""Display the function graphically.
Parameters
----------
title : string, optional
Set the title of the figure
method : string, optional
1d methods:
``'plot'`` : graph plot
``'scatter'`` : scattered 2d points (2nd axis <-> value)
2d methods:
``'imshow'`` : image plot with coloring according to
value, including a colorbar.
``'scatter'`` : cloud of scattered 3d points
(3rd axis <-> value)
indices : index expression, optional
Display a slice of the array instead of the full array. The
index expression is most easily created with the `numpy.s_`
constructor, i.e. supply ``np.s_[:, 1, :]`` to display the
first slice along the second axis.
For data with 3 or more dimensions, the 2d slice in the first
two axes at the "middle" along the remaining axes is shown
(semantically ``[:, :, shape[2:] // 2]``).
This option is mutually exclusive to ``coords``.
force_show : bool, optional
Whether the plot should be forced to be shown now or deferred until
later. Note that some backends always displays the plot, regardless
of this value.
fig : `matplotlib.figure.Figure`, optional
The figure to show in. Expected to be of same "style", as
the figure given by this function. The most common use case
is that ``fig`` is the return value of an earlier call to
this function.
kwargs : {'figsize', 'saveto', 'clim', ...}, optional
Extra keyword arguments passed on to the display method.
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
The resulting figure. It is also shown to the user.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.discr import uniform_grid
from odl.util.graphics import show_discrete_data
# Default to showing x-y slice "in the middle"
if indices is None and self.ndim >= 3:
indices = tuple([slice(None)] * 2 +
[n // 2 for n in self.space.shape[2:]])
if isinstance(indices, (Integral, slice)):
indices = (indices, )
elif indices is None or indices == Ellipsis:
indices = (slice(None), ) * self.ndim
else:
indices = tuple(indices)
# Replace None by slice(None)
indices = tuple(slice(None) if idx is None else idx for idx in indices)
if Ellipsis in indices:
# Replace Ellipsis with the correct number of [:] expressions
pos = indices.index(Ellipsis)
indices = (indices[:pos] + (np.s_[:], ) *
(self.ndim - len(indices) + 1) + indices[pos + 1:])
if len(indices) < self.ndim:
raise ValueError('too few axes ({} < {})'.format(
len(indices), self.ndim))
if len(indices) > self.ndim:
raise ValueError('too many axes ({} > {})'.format(
len(indices), self.ndim))
# Squeeze grid and values according to the index expression
full_grid = uniform_grid([0] * self.ndim,
np.array(self.shape) - 1, self.shape)
grid = full_grid[indices].squeeze()
values = self.asarray()[indices].squeeze()
return show_discrete_data(values,
grid,
title=title,
method=method,
force_show=force_show,
fig=fig,
**kwargs)
def show(self, title=None, method='', coords=None, indices=None,
show=False, fig=None, **kwargs):
"""Display the function graphically.
Parameters
----------
title : `str`, optional
Set the title of the figure
method : `str`, optional
1d methods:
'plot' : graph plot
'scatter' : scattered 2d points
(2nd axis <-> value)
2d methods:
'imshow' : image plot with coloring according to value,
including a colorbar.
'scatter' : cloud of scattered 3d points
(3rd axis <-> value)
coords : array-like, optional
Display a slice of the array instead of the full array. The values
are shown accordinging to the given values, `None` represent all
values along that dimension. For example [None, None, 0.5] shows
all values in the first two dimensions, with third coordinate equal
to 0.5.
This option is mutually exclusive to indices.
indices : index expression, optional
Display a slice of the array instead of the full array. The
index expression is most easily created with the `numpy.s_`
constructor, i.e. supply ``np.s_[:, 1, :]`` to display the
first slice along the second axis.
For data with 3 or more dimensions, the 2d slice in the first
two axes at the "middle" along the remaining axes is shown
(semantically ``[:, :, shape[2:] // 2]``).
This option is mutually exclusive to coords.
show : `bool`, optional
If the plot should be showed now or deferred until later.
fig : `matplotlib.figure.Figure`
The figure to show in. Expected to be of same "style", as
the figure given by this function. The most common use case
is that ``fig`` is the return value from an earlier call to
this function.
kwargs : {'figsize', 'saveto', 'clim', ...}
Extra keyword arguments passed on to display method
See the Matplotlib functions for documentation of extra
options.
Returns
-------
fig : `matplotlib.figure.Figure`
The resulting figure. It is also shown to the user.
See Also
--------
odl.util.graphics.show_discrete_data : Underlying implementation
"""
from odl.util.graphics import show_discrete_data
if coords is not None:
if indices is not None:
raise ValueError('Cannot provide both coords and indices')
interv = self.space.uspace.domain
shape = self.shape
indices = []
for begin, end, n, coord in zip(interv.begin, interv.end,
shape, coords):
if coord is None:
indices += [slice(None)]
else:
if not begin <= coord <= end:
raise ValueError('coord {} not in range {}'
''.format(coord, [begin, end]))
if begin == end:
indices += [0]
else:
normalized_pos = (float(coord) - begin) / (end - begin)
indices += [int(n * normalized_pos)]
# Default to showing x-y slice "in the middle"
if indices is None and self.ndim >= 3:
indices = [np.s_[:]] * 2
indices += [n // 2 for n in self.space.shape[2:]]
if isinstance(indices, (Integral, slice)):
indices = [indices]
elif indices is None or indices == Ellipsis:
indices = [np.s_[:]] * self.ndim
else:
indices = list(indices)
if Ellipsis in indices:
# Replace Ellipsis with the correct number of [:] expressions
pos = indices.index(Ellipsis)
indices = (indices[:pos] +
[np.s_[:]] * (self.ndim - len(indices) + 1) +
indices[pos + 1:])
if len(indices) < self.ndim:
raise ValueError('too few axes ({} < {}).'.format(len(indices),
self.ndim))
if len(indices) > self.ndim:
raise ValueError('too many axes ({} > {}).'.format(len(indices),
self.ndim))
if self.ndim <= 3:
axis_labels = ['x', 'y', 'z']
else:
axis_labels = ['x{}'.format(axis) for axis in range(self.ndim)]
squeezed_axes = [axis for axis in range(self.ndim)
if not isinstance(indices[axis], Integral)]
axis_labels = [axis_labels[axis] for axis in squeezed_axes]
# Squeeze grid and values according to the index expression
grid = self.space.grid[indices].squeeze()
values = self.asarray()[indices].squeeze()
return show_discrete_data(values, grid, title=title, method=method,
show=show, fig=fig, axis_labels=axis_labels,
**kwargs)
