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• find_positions

find_positions

This module contains tools to find positions for imaging. At the moment it is only used by microscope_automation.

microscope_automation.orchestrator.find_positions.copy_zero_position(sample_object, output_class, image, z_center_background=0)

Create new object with identical zero position.

Input:

sampleObject: object that is searched

output_class: string with name of class of new created output object

image: image of class imageAICS searched

z_center_background: center of background in the z direction

Output:

new_objects: list with objects of class output_class

microscope_automation.orchestrator.find_positions.copy_image_position(sample_object, output_class, image, offset=(0, 0, 0))

Create new object with zero position relative to center of image.

Input:

sample_object: object that is searched

output_class: string with name of class of new created output object

image: image of class imageAICS

offset: offset in um relative to center of image as (x, y, z)

Default: (0, 0, 0) (center of image)

Output:

new_object: object of class output_class

microscope_automation.orchestrator.find_positions.convert_location_list(location_list, image, image_type='czi')

Function to convert the coordinates to microns and relative to the center of the image. First all the locations are converted to microns using the pixel size of the image. Then the locations are converted so that they are relative to the center of the image instead of the bottom right corner. This calculation is done using the size of image.

Input:

location_list: list of coordinates

image: imageAICS object with all the meta data

image_type: tiff or czi: both have different units for physical pixel size

Output:

location_list_relative_to_zero: corrected location list

microscope_automation.orchestrator.find_positions.get_single_slice(image)

Return single image slice.

Input:

image: image of class imageAICS

Output:

image_data: 2D numpy array

microscope_automation.orchestrator.find_positions.location_to_object(sample_object, output_class, image, location_list)

Convert position list to new objects.

Input:

sample_object: object that is searched

output_class: string with name of class of new created output object

image: image of class imageAICS

location_list: list of coordinates

Output:

[new_objects, new_objects_dict]: [list with objects of class output_class, dictionary with name and objects]

microscope_automation.orchestrator.find_positions.find_interactive_position(sample_object, output_class, image, app)

Create new object with zero position based on interactive selection.

Input:

sample_object: object that is searched

output_class: string with name of class of new created output object

image: image of class imageAICS

app: object of class QtGui.QApplication

Output:

new_objects: list with objects of class output_class

microscope_automation.orchestrator.find_positions.segmentation(image_data, segmentation_type='colony', segmentation_settings=None)

Different algorithms to segment sample.

Input:

image_data: 2D numpy array with image data

segmentation_type: possible values:

‘colony’: segment hiPSC colonies (default) ‘otsu’: intensity segmentation based on Ostu algorithm

segmentation_settings: settings from preferences file. Default (None) use default settings

Output:

segmented_position_list: list with (x, y) positions

microscope_automation.orchestrator.find_positions.find_interactive_distance_map(sample_object, output_class, image, segmentation_settings=None, app=None)

Create new object with zero position based on interactive selection.

Input:

sampleObject: object that is searched

output_class: string with name of class of new created output object

image: image of class imageAICS

segmentation_settings: settings from preferences file

app: object of class QtGui.QApplication

Output:

[new_objects, new_objects_dict]: [list with objects of class output_class, dictionary with name and objects]

microscope_automation.orchestrator.find_positions.create_output_objects_from_parent_object(find_type, sample_object, imaging_settings, image, output_class, app, offset=(0, 0, 0))

This function has methods to create output objects (cells, colonies, background) from the parent object(Well, colony)

Input:

find_type: string with name of search algorithm. Possible values:

‘copy_zero_position’: keep position ‘copy_image_position’: use center of image as object zero position ‘CenterMassCellProfiler’ : find cells using cell profiler ‘TwofoldDistanceMap’ : find cells in a colony using two fold distance map ‘Interactive’: select position interactively ‘InteractiveDistanceMap’: shows recommended positions (by segmentation) & can select them interactively

sample_object: object that is searched

imaging_settings: settings from preferences file

image: image of class imageAICS searched

output_class: class of new created output object

app: object of class QtGui.QApplication

offset: offset in um relative to center of image as (x, y, z)

Default: (0, 0, 0) (center of image)

Output:

[new_objects, new_objects_dict]: [list with objects of class output_class, dictionary with name and objects]