pyfibre.addons.shg_pl_trans.shg_analyser module

class pyfibre.addons.shg_pl_trans.shg_analyser.SHGAnalyser[source]

Bases: pyfibre.core.base_multi_image_analyser.BaseMultiImageAnalyser

create_figures()[source]

Create and save figures

create_metrics(sigma)[source]

Perform metric analysis on segmented image

Parameters

sigma (float) –

property data_path

Path for analysis data files

database_names = ['global', 'fibre', 'network', 'cell']
property fig_path

Path for figures generated by analysis

fire_parameters = None

Parameters used for FIRE algorithm

get_analysis_options(runner)[source]

Get image-specific options for analysis

image_analysis(runner)[source]

Analyse input image by calculating metrics and segmenting via FIRE algorithm

Parameters

runner (PyFibreRunner) – Instructions for all image analysis algorithms

Returns

databases – Metrics returned by this analysis for a single image

Return type

list of pd.DataFrame

load_databases()[source]

Load pandas DataFrame instances created during the analysis

make_directories()[source]

Creates additional directories for analysis

multi_image = None

Reference to multi image under analysis

network_analysis(sigma, alpha, scale, p_denoise)[source]

Perform FIRE algorithm on image and save networkx objects for further analysis

Parameters

  • sigma (float) – Gaussian standard deviation to filter distance image

  • alpha (float) – Alpha metric to use in hysteresis threshold algorithm

  • scale (float) – Scaling factor to apply to image before performing algorithm

  • p_denoise (tuple (float); shape=(2,)) – Parameters for non-linear means denoise algorithm (used to remove noise)

save_databases(databases)[source]

Save pandas DataFrame instances created during the analysis

segment_parameters = None

Parameters used for segmentation

segmentation_analysis(scale)[source]

Segment image into regions

Parameters

  • scale (float) –

  • segment_parameters (Dict) –

pyfibre.addons.shg_pl_trans.shg_analyser.load_cell_segments(file_name, *, klass=<class 'pyfibre.model.objects.segments.CellSegment'>, mode='array', file_type='cell_segments', **kwargs)

Load a list of ABCPyFibreObject subclass

pyfibre.addons.shg_pl_trans.shg_analyser.load_fibre_networks(file_name, *, klass=<class 'pyfibre.model.objects.fibre_network.FibreNetwork'>, mode='json', file_type='fibre_networks', **kwargs)

Load a list of ABCPyFibreObject subclass

pyfibre.addons.shg_pl_trans.shg_analyser.load_fibre_segments(file_name, *, klass=<class 'pyfibre.model.objects.segments.FibreSegment'>, mode='array', file_type='fibre_segments', **kwargs)

Load a list of ABCPyFibreObject subclass

pyfibre.addons.shg_pl_trans.shg_analyser.save_cell_segments(pyfibre_objects, file_name, *, mode='array', file_type='cell_segments', **kwargs)

Save a list of ABCPyFibreObject subclass

pyfibre.addons.shg_pl_trans.shg_analyser.save_fibre_networks(pyfibre_objects, file_name, *, mode='json', file_type='fibre_networks', **kwargs)

Save a list of ABCPyFibreObject subclass

pyfibre.addons.shg_pl_trans.shg_analyser.save_fibre_segments(pyfibre_objects, file_name, *, mode='array', file_type='fibre_segments', **kwargs)

Save a list of ABCPyFibreObject subclass