def draw(self, filename=None, verbose=False):
self._draw_regs()
self._draw_ops(verbose)
_xl = -self._style.margin[0]
_xr = self._cond['xmax'] + self._style.margin[1]
_yb = -self._cond['ymax'] - self._style.margin[2] + 1 - 0.5
_yt = self._style.margin[3] + 0.5
self.ax.set_xlim(_xl, _xr)
self.ax.set_ylim(_yb, _yt)
# update figure size
fig_w = _xr - _xl
fig_h = _yt - _yb
if self._style.figwidth < 0.0:
self._style.figwidth = fig_w * self._scale * self._style.fs / 72 / WID
self.figure.set_size_inches(self._style.figwidth,
self._style.figwidth * fig_h / fig_w)
if get_matplotlib_backend(
) == 'module://ipykernel.pylab.backend_inline':
# returns None when matplotlib is inline mode to prevent Jupyter
# with matplotlib inlining enabled to draw the diagram twice.
im = None
else:
# when matplotlib is not inline mode,
# self.figure.savefig is called twice because...
# ... this is needed to get the in-memory representation
with tempfile.TemporaryDirectory() as tmpdir:
tmpfile = os.path.join(tmpdir, 'circuit.png')
self.figure.savefig(tmpfile,
dpi=self._style.dpi,
bbox_inches='tight')
im = PIL.Image.open(tmpfile)
_utils._trim(im)
os.remove(tmpfile)
# ... and this is needed to delegate in matplotlib the generation of
# the proper format.
if filename:
self.figure.savefig(filename,
dpi=self._style.dpi,
bbox_inches='tight')
return im
def show_stack(self,
indices: Mapping[Indices, Union[int, slice]],
color_map: str = 'gray',
figure_size: Tuple[int, int] = (10, 10),
rescale: bool = False,
p_min: Optional[float] = None,
p_max: Optional[float] = None,
**kwargs):
"""Create an interactive visualization of an image stack
Produces a slider that flips through the selected volume tile-by-tile. Supports manual
adjustment of dynamic range.
Parameters
----------
indices : Mapping[Indices, Union[int, slice]],
Indices to select a volume to visualize. Passed to `Image.get_slice()`.
See `Image.get_slice()` for examples.
color_map : str (default = 'gray')
string id of a matplotlib colormap
figure_size : Tuple[int, int] (default = (10, 10))
size of the figure in inches
rescale : bool (default = False)
if True, rescale the data to exclude high and low-value outliers
(see skimage.exposure.rescale_intensity).
p_min: float
clip values below this intensity percentile. If provided, overrides rescale, above.
(default = None)
p_max: float
clip values above this intensity percentile. If provided, overrides rescale, above.
(default = None)
Raises
------
ValueError :
User must select one of rescale or p_min/p_max to adjust the image dynamic range.
If both are selected, a ValueError is raised.
Notes
-----
For this widget to function interactively in the notebook, after ipywidgets has been
installed, the user must register the widget with jupyter by typing the following command
into the terminal: jupyter nbextension enable --py widgetsnbextension
"""
# infer if %matplotlib inline or notebook
mpl_is_notebook = 'nbAgg' in get_matplotlib_backend()
if not indices:
raise ValueError('indices may not be an empty dict or None')
# get linearized scaled and clipped tiles, along with title names, for plotting
linear_view, labels, n_tiles = self._get_scaled_clipped_linear_view(
indices, rescale, p_min, p_max)
if mpl_is_notebook:
self._show_matplotlib_notebook(linear_view, labels, n_tiles,
figure_size, color_map)
else:
return self._show_matplotlib_inline(linear_view, labels, n_tiles,
figure_size, color_map)