:py:mod:`AFQ.tasks.viz`
=======================

.. py:module:: AFQ.tasks.viz


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   AFQ.tasks.viz._viz_prepare_vol
   AFQ.tasks.viz.viz_bundles
   AFQ.tasks.viz.viz_indivBundle
   AFQ.tasks.viz.plot_tract_profiles
   AFQ.tasks.viz.init_viz_backend
   AFQ.tasks.viz.get_viz_plan



Attributes
~~~~~~~~~~

.. autoapisummary::

   AFQ.tasks.viz.logger


.. py:data:: logger

   

.. py:function:: _viz_prepare_vol(vol, xform, mapping, scalar_dict)


.. py:function:: viz_bundles(base_fname, viz_backend, data_imap, mapping_imap, segmentation_imap, tracking_params, segmentation_params, best_scalar, sbv_lims_bundles=[None, None], volume_opacity_bundles=0.3, n_points_bundles=40)

   
   figure for the visualizaion of the recognized
   bundles in the subject's brain.


   :Parameters:

       **sbv_lims_bundles** : ndarray
           Of the form (lower bound, upper bound). Shading based on
           shade_by_volume will only differentiate values within these bounds.
           If lower bound is None, will default to 0.
           If upper bound is None, will default to the maximum value in
           shade_by_volume.
           Default: [None, None]

       **volume_opacity_bundles** : float, optional
           Opacity of volume slices.
           Default: 0.3

       **n_points_bundles** : int or None
           n_points to resample streamlines to before plotting. If None, no
           resampling is done.
           Default: 40

   :Returns:

       List of Figure, String or just the Figure:
           ..

       If file can be generated, returns a tuple including the figure and the
           ..

       path to the file.
           ..

       Otherwise, returns the figure.
           ..













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.. py:function:: viz_indivBundle(base_fname, output_dir, viz_backend, data_imap, mapping_imap, segmentation_imap, tracking_params, segmentation_params, best_scalar, sbv_lims_indiv=[None, None], volume_opacity_indiv=0.3, n_points_indiv=40)

   
   list of full paths to html or gif files
   containing visualizaions of individual bundles


   :Parameters:

       **sbv_lims_indiv** : ndarray
           Of the form (lower bound, upper bound). Shading based on
           shade_by_volume will only differentiate values within these bounds.
           If lower bound is None, will default to 0.
           If upper bound is None, will default to the maximum value in
           shade_by_volume.
           Default: [None, None]

       **volume_opacity_indiv** : float, optional
           Opacity of volume slices.
           Default: 0.3

       **n_points_indiv** : int or None
           n_points to resample streamlines to before plotting. If None, no
           resampling is done.
           Default: 40














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.. py:function:: plot_tract_profiles(base_fname, scalars, tracking_params, segmentation_params, segmentation_imap)

   
   list of full paths to png files,
   where files contain plots of the tract profiles
















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.. py:function:: init_viz_backend(viz_backend_spec='plotly_no_gif', virtual_frame_buffer=False)

   
   An instance of the `AFQ.viz.utils.viz_backend` class.


   :Parameters:

       **virtual_frame_buffer** : bool, optional
           Whether to use a virtual fram buffer. This is neccessary if
           generating GIFs in a headless environment. Default: False

       **viz_backend_spec** : str, optional
           Which visualization backend to use.
           See Visualization Backends page in documentation for details:
           https://tractometry.org/pyAFQ/usage/viz_backend.html
           One of {"fury", "plotly", "plotly_no_gif"}.
           Default: "plotly_no_gif"














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.. py:function:: get_viz_plan(kwargs)