neurots.generate.algorithms.tmdgrower¶
Basic class for TreeGrower Algorithms.
Classes
|
TreeGrower of TMD basic growth. |
|
TreeGrower of TMD apical growth. |
|
TreeGrower of TMD apical growth. |
- class neurots.generate.algorithms.tmdgrower.TMDAlgo(input_data, params, start_point, context=None, random_generator=numpy.random, **_)¶
Bases:
AbstractAlgo
TreeGrower of TMD basic growth.
- Parameters:
input_data (dict) – All the data required for the growth.
params (dict) – The parameters required for growth. It should include the
branching_method
selected from:|bio_oriented, symmetric, directional]
.start_point (list[float]) – The first point of the trunk. the “min_bar_length” parameter are validated.
context (Any) – An object containing contextual information.
random_generator (numpy.random.Generator) – The random number generator to use.
- bifurcate(current_section)¶
When the section bifurcates two new sections need to be created.
This method computes from the current state the data required for the generation of two new sections and returns the corresponding dictionaries.
- extend(current_section)¶
Definition of stop criterion for the growth of the current section.
- get_stop_criteria(current_section)¶
Return stop criteria that are the commonly shared by all TMDPath algorithms.
- Returns:
Two dictionaries, each containing one entry whose key is
TMD
and value is aneurots.generate.algorithms.common.TMDStop
object.- Return type:
- initialize()¶
Generates the data to be used for the initialization of the first section to be grown.
Saves the extracted input data into the corresponding structures.
- Returns:
A
neurots.generate.algorithms.common.TMDStop
object and the number of sections.- Return type:
- static metric(section)¶
Return the metric at the current position, here path distance, recorded in section.
- static metric_ref(section)¶
Return the metric reference.
The metric reference is the path distance reference, or zero if no section is provided as input.
- select_persistence(input_data, random_generator=numpy.random)¶
Select the persistence.
Sample one persistence diagram from a list of diagrams and modifies according to input parameters.
- terminate(current_section)¶
Terminate the current section.
When the growth of a section is terminated the “term” must be removed from the TMD grower.
- class neurots.generate.algorithms.tmdgrower.TMDApicalAlgo(*args, **kwargs)¶
Bases:
TMDAlgo
TreeGrower of TMD apical growth.
- bifurcate(current_section)¶
When the section bifurcates two new sections need to be created.
This method computes from the current state the data required for the generation of two new sections and returns the corresponding dictionaries.
- initialize()¶
Initialize the algorithm.
TMD basic grower of an apical tree based on path distance. Initializes the tree grower and computes the apical distance using the input barcode.
- class neurots.generate.algorithms.tmdgrower.TMDGradientAlgo(*args, **kwargs)¶
Bases:
TMDApicalAlgo
TreeGrower of TMD apical growth.
- _majorize_process(section, stop, process, input_dir)¶
Currates the non-major processes to apply a gradient to large components.
- bifurcate(current_section)¶
When the section bifurcates two new sections need to be created.
This method computes from the current state the data required for the generation of two new sections and returns the corresponding dictionaries.