neuro_morpho.reports.stats

Generates plots and reports for model comparison.

Attributes

`VALID_DISTANCES`
`ERR_INVALID_DIST`
`SKELETON_STAT_FN`
`STAT_N_BRANCHES`
`STAT_N_TIP_POINTS`
`STAT_TOTAL_LENGTH`
`STAT_BRANCH_LENGTHS`

Functions

`extract_branch_ids`(→ set[int])	Extract the branch ids from the skan skeleton data.
`calculate_n_branches`(→ int)	Calculate the number of branches in the skeleton data.
`calculate_n_tip_points`(→ int)	Calculate the number of tip points in the skeleton data.
`calculate_total_length`(→ float)	Calculate the total length of the skeleton data.
`calculate_branch_lengths`(→ numpy.ndarray)	Calculate the lengths of each branch in the skeleton data.
`skeleton_analysis`(→ dict[str, dict[str, float]])	Generate a summary of the skeleton analysis.

Module Contents

neuro_morpho.reports.stats.VALID_DISTANCES

neuro_morpho.reports.stats.ERR_INVALID_DIST = Multiline-String

Show Value

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

neuro_morpho.reports.stats.SKELETON_STAT_FN

neuro_morpho.reports.stats.STAT_N_BRANCHES = 'n_branches'

neuro_morpho.reports.stats.STAT_N_TIP_POINTS = 'n_tip_points'

neuro_morpho.reports.stats.STAT_TOTAL_LENGTH = 'total_length'

neuro_morpho.reports.stats.STAT_BRANCH_LENGTHS = 'branch_lengths'

neuro_morpho.reports.stats.extract_branch_ids(skan_skel_data: pandas.DataFrame) → set[int][source]

Extract the branch ids from the skan skeleton data.

Parameters:: skan_skel_data (pd.DataFrame) – The skan skeleton data.

Returns:

neuro_morpho.reports.stats.calculate_n_branches(skan_skel_data: pandas.DataFrame, *, include_isolated_branches: bool = False, include_isolated_cycles: bool = False) → int[source]

Calculate the number of branches in the skeleton data.

Parameters:: skan_skel_data (pd.DataFrame) – The skan skeleton data.
Returns:: The number of branches in the skeleton data.

neuro_morpho.reports.stats.calculate_n_tip_points(skan_skel_data: pandas.DataFrame, *, include_isolated_branches: bool = False) → int[source]

Calculate the number of tip points in the skeleton data.

Parameters:: skan_skel_data (pd.DataFrame) – The skan skeleton data.
Returns:: The number of tip points in the skeleton data.

neuro_morpho.reports.stats.calculate_total_length(skan_skel_data: pandas.DataFrame, dist_type: str = 'euclidean') → float[source]

Calculate the total length of the skeleton data.

Parameters:

skan_skel_data (pd.DataFrame) – The skan skeleton data.
dist_type (str) – The type of distance to use for the length calculation.

Returns:

The total length of the skeleton data.

neuro_morpho.reports.stats.calculate_branch_lengths(skan_skel_data: pandas.DataFrame, dist_type: str = 'euclidean') → numpy.ndarray[source]

Calculate the lengths of each branch in the skeleton data.

Parameters:

skan_skel_data (pd.DataFrame) – The skan skeleton data.
dist_type (str) – The type of distance to use for the length calculation.

Returns:

a numpy array of the branch lengths

neuro_morpho.reports.stats.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]][source]

Generate a summary of the skeleton analysis.

Parameters:

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