PYME.recipes.localisations module

class PYME.recipes.localisations.AddShellMappedCoordinates(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Maps x,y,z co-ordinates into the co-ordinate space of spherical harmonic shell. Notably, a normalized radius is provided, which can be used to determine which localizations are within the structure.

Parameters:None

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.AutocorrelationDriftCorrection(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Perform drift correction using autocorrelation between subsets of the point data

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

calcCorrDrift(x, y, t)
execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
calcCorrDrift(x, y, t)
execute(namespace)
class PYME.recipes.localisations.ClusterCountVsImagingTime(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

WARNING: This module will likely move, dissapear, or be refactored

ClusterCountVsImagingTime iteratively filters a dictionary-like object on t, and at each step counts the number of labeled objects (e.g. DBSCAN clusters) which contain at least N-points. It does this for two N-points, so one can be set according to density with all frames included, and the other can be set for one of the earlier frame-counts.

args:
stepSize: number of frames to add in on each iteration labelsKey: key containing labels for each localization lowerMinPtsPerCluster: higherMinPtsPerCluster:
returns:
dictionary-like object with the following keys:
t: upper bound on frame number included in calculations on each iteration. N_labelsWithLowMinPoints: N_labelsWithHighMinPoints:

From wikipedia: “While minPts intuitively is the minimum cluster size, in some cases DBSCAN can produce smaller clusters. A DBSCAN cluster consists of at least one core point. As other points may be border points to more than one cluster, there is no guarantee that at least minPts points are included in every cluster.”

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.DBSCANClustering(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Performs DBSCAN clustering on input dictionary

Parameters:

searchRadius: search radius for clustering

minPtsForCore: number of points within SearchRadius required for a given point to be considered a core point

Notes

See sklearn.cluster.dbscan for more details about the underlying algorithm and parameter meanings.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
hide_in_overview
class PYME.recipes.localisations.DensityMapping(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Use density estimation methods to generate an image from localizations

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.ExtractTableChannel(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Create and return a ColourFilter which has filtered out one colour channel from a table of localizations.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
default_view
execute(namespace)
pipeline_view
class PYME.recipes.localisations.FiducialCorrection(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Maps each point in the input table to a pixel in a labelled image, and extracts the pixel value at that location to use as a label for the point data.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.IDTransientFrames(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Adds an ‘isTransient’ column to the input datasource so that one can filter localizations that are from frames acquired during z-translation

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.LabelsFromImage(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Maps each point in the input table to a pixel in a labelled image, and extracts the pixel value at that location to use as a label for the point data.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MapAstigZ(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Create a new mapping object which derives mapped keys from original ones

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MeasureClusters3D(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Measures the 3D morphology of clusters of points

Parameters:labelKey: name of column to use as a label identifying clusters

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MergeClumps(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Create a new mapping object which derives mapped keys from original ones

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MultiviewFindClumps(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Create a new mapping object which derives mapped keys from original ones

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MultiviewFold(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Fold localizations from images which have been taken with an image splitting device but analysed without channel awareness.

Images taken in this fashion will have the channels side by side. This module folds the x co-ordinate to overlay the different channels, using the image metadata to determine the appropriate ROI boundaries. The current implementation is somewhat limited as it only handles folding along the x axis, and assumes that ROI sizes and spacings are completely uniform.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MultiviewMergeClumps(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Create a new mapping object which derives mapped keys from original ones

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.MultiviewShiftCorrect(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Applies chromatic shift correction to folded localization data that was acquired with an image splitting device, but localized without splitter awareness.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.Pipelineify(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)
class PYME.recipes.localisations.SphericalHarmonicShell(parent=None, **kwargs)

Bases: PYME.recipes.base.ModuleBase

Fits a shell represented by a series of spherical harmonic co-ordinates to a 3D set of points. The points should represent a hollow, fairly round structure (e.g. the surface of a cell nucleus). The object should NOT be filled (i.e. points should only be on the surface).

Parameters:

max_m_mode: Maximum order to calculate to.

zscale: Factor to scale z by when projecting onto spherical harmonics. It is helpful to scale z such that the

x, y, and z extents are roughly equal.

Attributes

default_view
hide_in_overview
inputs
outputs
pipeline_view
pipeline_view_min

Methods

execute(namespace)
trait_items_event(event_trait,name,items_event)
trait_property_changed(...)
traits_init()
traits_inited([True])
execute(namespace)