neuro_morpho.reports.stats
==========================

.. py:module:: neuro_morpho.reports.stats

.. autoapi-nested-parse::

   Generates plots and reports for model comparison.



Attributes
----------

.. autoapisummary::

   neuro_morpho.reports.stats.VALID_DISTANCES
   neuro_morpho.reports.stats.ERR_INVALID_DIST
   neuro_morpho.reports.stats.SKELETON_STAT_FN
   neuro_morpho.reports.stats.STAT_N_BRANCHES
   neuro_morpho.reports.stats.STAT_N_TIP_POINTS
   neuro_morpho.reports.stats.STAT_TOTAL_LENGTH
   neuro_morpho.reports.stats.STAT_BRANCH_LENGTHS


Functions
---------

.. autoapisummary::

   neuro_morpho.reports.stats.extract_branch_ids
   neuro_morpho.reports.stats.calculate_n_branches
   neuro_morpho.reports.stats.calculate_n_tip_points
   neuro_morpho.reports.stats.calculate_total_length
   neuro_morpho.reports.stats.calculate_branch_lengths
   neuro_morpho.reports.stats.skeleton_analysis


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

.. py:data:: VALID_DISTANCES

.. py:data:: ERR_INVALID_DIST
   :value: Multiline-String

   .. raw:: html

      <details><summary>Show Value</summary>

   .. code-block:: python

      """Invalid distance type. Must be one of Set.set(elts=[ <Const.str l.10 at 0x78344d9a2990>,
                <Const.str l.10 at 0x78344d9a2a10>])"""

   .. raw:: html

      </details>



.. py:data:: SKELETON_STAT_FN

.. py:data:: STAT_N_BRANCHES
   :value: 'n_branches'


.. py:data:: STAT_N_TIP_POINTS
   :value: 'n_tip_points'


.. py:data:: STAT_TOTAL_LENGTH
   :value: 'total_length'


.. py:data:: STAT_BRANCH_LENGTHS
   :value: 'branch_lengths'


.. py:function:: extract_branch_ids(skan_skel_data: pandas.DataFrame) -> set[int]

   Extract the branch ids from the skan skeleton data.

   :param skan_skel_data: The skan skeleton data.
   :type skan_skel_data: pd.DataFrame

   Returns:



.. py:function:: calculate_n_branches(skan_skel_data: pandas.DataFrame, *, include_isolated_branches: bool = False, include_isolated_cycles: bool = False) -> int

   Calculate the number of branches in the skeleton data.

   :param skan_skel_data: The skan skeleton data.
   :type skan_skel_data: pd.DataFrame

   :returns: The number of branches in the skeleton data.


.. py:function:: calculate_n_tip_points(skan_skel_data: pandas.DataFrame, *, include_isolated_branches: bool = False) -> int

   Calculate the number of tip points in the skeleton data.

   :param skan_skel_data: The skan skeleton data.
   :type skan_skel_data: pd.DataFrame

   :returns: The number of tip points in the skeleton data.


.. py:function:: calculate_total_length(skan_skel_data: pandas.DataFrame, dist_type: str = 'euclidean') -> float

   Calculate the total length of the skeleton data.

   :param skan_skel_data: The skan skeleton data.
   :type skan_skel_data: pd.DataFrame
   :param dist_type: The type of distance to use for the length calculation.
   :type dist_type: str

   :returns: The total length of the skeleton data.


.. py:function:: calculate_branch_lengths(skan_skel_data: pandas.DataFrame, dist_type: str = 'euclidean') -> numpy.ndarray

   Calculate the lengths of each branch in the skeleton data.

   :param skan_skel_data: The skan skeleton data.
   :type skan_skel_data: pd.DataFrame
   :param dist_type: The type of distance to use for the length calculation.
   :type dist_type: str

   :returns: a numpy array of the branch lengths


.. py:function:: skeleton_analysis(skeleton: numpy.ndarray, stat_fns: tuple[list[str], list[SKELETON_STAT_FN]], pixel_size: float = 1, *, assume_single_skeleton: bool = False) -> dict[str, dict[str, float]]

   Generate a summary of the skeleton analysis.

   :param skeleton: The skeleton of the image to analyze, should be 2d.
   :type skeleton: np.ndarray
   :param stat_fns: The list of functions to
                    use for the analysis.
   :type stat_fns: tuple[list[str], list[SKELETON_STAT_FN]]
   :param pixel_size: The size of the pixel in the image.
   :type pixel_size: float