mpl_interactions.generic module

mpl_interactions.generic module#

Functions that will be useful irrespective of backend.

heatmap_slicer(X, Y, heatmaps, slices='horizontal', heatmap_names=None, linecolor='k', labels=('X', 'Y'), interaction_type='move', fig=None, figsize=(18, 9), **pcolormesh_kwargs)[source]#

Compare horizontal and/or vertical slices across multiple arrays.

Parameters:

X,Y1D array: The values for the x and y axes.
heatmapsarray_like: must be 2-D or 3-D. If 3-D the last two axes should be (X,Y)
slices{‘horizontal’, ‘vertical’, ‘both’}: Direction to draw slice on heatmap. both will draw horizontal and vertical traces on the same plot, while both_separate will make a line plot for each.
heatmap_names(String, String, …): An iterable with the names of the heatmaps. If provided it must have as many names as there are heatmaps
figsizetuple of number, default: (18, 9): The size of the created figure. Ignored if fig is not None.
linecolorcolorlike, default: ‘k’: The color of the cursor showing the slices. Must be a valid Matplotlib linecolor.
labels(string, string), default: (“X”, “Y”): The labels for the x and y axes.
interaction_typestr: Update on mouse movement or mouse click. Options are {‘move’,’click’}
figmatplotlib figure, optional: The figure to use for the heatmap_slicer. Useful when embedding into a gui. If you are embedding into a gui make sure you set up the gui canvas first and then pass the figure to this function
**pcolormesh_kwargs: kwargs passed to ax.pcolormesh.

Returns:

figmatplotlib.Figure.figure
axtuple of matplotlib.axes.Axes

hyperslicer(arr, alpha=None, vmin=None, vmax=None, vmin_vmax=None, autoscale_cmap=True, ax=None, slider_formats=None, title=None, force_ipywidgets=False, play_buttons=False, is_color_image=False, controls=None, display_controls=True, **kwargs)[source]#

View slices from a hyperstack of images selected by sliders.

Also accepts Xarray.DataArrays in which case the axes names and coordinates will be inferred from the xarray dims and coords.

Parameters:

arrarraylike or xarray: Hyperstack of images. The last 2 or 3 dimensions will be treated as individiual images. If an xarray.DataArray then the dimensions will be automatically inferred.
alphafloat, Callable or widget shorthand, indexed controls, optional: The alpha of the image. If a callable then it will receive parameters be autoupdated. Can also be given a widget shorthand to automatically generate a slider (e.g. alpha = (0, 1, 10)).
vmin, vmaxfloat, callable, shorthand for slider or indexed controls, optional: The vmin, vmax values for the colormap. Can accept a float for a fixed value, or any slider shorthand to control with a slider, or an indexed controls object to use an existing slider, or an arbitrary function of the other parameters.
vmin_vmaxtuple of float: Used to generate a range slider for vmin and vmax. Should be given in range slider notation: ("r", 0, 1).
autoscale_cmapbool: If True rescale the colormap for every function update. Will not update if vmin and vmax are provided or if the returned image is RGB(A) like. forwarded to matplotlib
axmatplotlib axis, optional: if None a new figure and axis will be created
slider_formatsNone, string, or dict: If None a default value of decimal points will be used. Uses the new {} style formatting
titleNone or string: If a string then you can have it update automatically using string formatting of the names of the parameters. i.e. to include the current value of tau: title=’the value of tau is: {tau:.2f}’
force_ipywidgetsboolean: If True ipywidgets will always be used, even if not using the ipympl backend. If False the function will try to detect if it is ok to use ipywidgets If ipywidgets are not used the function will fall back on matplotlib widgets
play_buttonsbool or str or dict, optional: Whether to attach an ipywidgets.Play widget to any sliders that get created. If a boolean it will apply to all kwargs, if a dictionary you choose which sliders you want to attach play buttons too. - None: no sliders - True: sliders on the lft - False: no sliders - ‘left’: sliders on the left - ‘right’: sliders on the right
is_color_imageboolean: If True, will treat the last 3 dimensions as comprising a color images and will only set up sliders for the first arr.ndim - 3 dimensions.
controlsmpl_interactions.controller.Controls: An existing controls object if you want to tie multiple plot elements to the same set of controls
display_controlsboolean: Whether the controls should display on creation. Ignored if controls is specified.
**kwargs: names can be used to set the axes names, axes can be used to set the displayed values of multiple sliders, and axis0, axis1 etc can be used with widget shorthand to set the displayed values of the sliders. All valid imshow kwargs are passed through to imshow.

Returns:

controls

class image_segmenter(img, nclasses=1, mask=None, mask_colors=None, mask_alpha=0.75, lineprops=None, props=None, lasso_mousebutton='left', pan_mousebutton='middle', ax=None, figsize=(10, 10), **kwargs)[source]#

Bases: object

Manually segment an image with the lasso selector.

mask#: See Image Segmentation.

class panhandler(fig, button=3)[source]#

Bases: object

Enable panning a plot with any mouse button.

handler = panhandler(my_figure)

# Let it be disabled and garbage collected
handler = None

Parameters:

buttonint: Determines which button will be used (default right click). Left: 1 Middle: 2 Right: 3

disable()[source]#

Disable the panhandler.

Raises:

RuntimeError: If the panhandler is already disabled.

enable()[source]#

Enable the panhandler.

It should not be necessary to call this function unless it’s used after a call to panhandler.disable().

Raises:

RuntimeError: If the panhandler is already enabled.

property enabled: bool#: Status of the panhandler, whether it’s enabled or disabled.

mpl_interactions.generic module

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mpl_interactions.generic module#