microscope_automation.orchestrator package

Submodules

microscope_automation.orchestrator.find_positions module

Created on Aug 23, 2018

Tools to find positions for imaging @author: winfriedw

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.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.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.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]

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.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.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.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.microscope_automation module

Main script for Microscope automation Created on Jun 9, 2016

@author: winfriedw

class microscope_automation.orchestrator.microscope_automation.MicroscopeAutomation(prefs, app=None)

Bases: object

calculate_all_wells_correction(prefs, plate_holder_object, _experiment)

Calculate correction factor for well coordinate system for all wells.

Input:

prefs: dictionary with preferences

plate_holder_object: object for plateholder that contains well

_experiment: not used, necessary for compatibility

Output:

none

calculate_plate_correction(prefs, plate_holder_object, _experiment)

Calculate correction factor for plate coordinate system.

Input:

prefs: dictionary with preferences

plate_holder_object: object for plate that contains well

_experiment: not used, necessary for compatibility

Output:

none

control_autofocus(sample_object, imaging_settings)

Switch on/off autofocus.

Input:

sample_object: object of class inherited from ImagingSystem

imaging_settings: settings derived from preferences file

Output:

none

get_barcode_object(well_object, barcode)

Return Barcode object for given well.

Input:

well_object: object for well that contains barcode

barcode: string of name for barcode

Output:

barcode_object: object for barcode

get_objective_offsets(plate_holder_object, magnification)

Function to return the objective offsets

Input:

plate_holder_object: Plate Holder object containing all the information

magnification: integer defining which objective to get offsets for

Output:

x and y offsets for objectives

get_well_object(plate_holder_object, plate_name, well)

Return well object for given well.

Input:

plate_holder_object: object for plateholder that contains well

plate_name: name for plate, typically barcode

well: name of well in form ‘A1’

Output:

well_object: object for well

initialize_microscope(initialize_prefs, plate_holder_object, _experiment)

Update z positions for plate (upper side of coverslip) Set load and work positions for focus drive.

Input:

initialize_prefs: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

_experiment: not used, necessary for compatibility

Output:

none

microscope_automation()

Main script for Microscope automation.

Input:

none

Output:

none

read_barcode(prefs, plate_holder_object, barcode, well=None, barcode_name=None)

Move manually to barcode, read and decipher barcode.

Input:

prefs: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

barcode: barcode for plate, often used as plate name

well: well the barcode is associated with in format ‘A1’. If empty ask user to navigate to barcode.

Output:

barcode: id encoded in barcode

read_first_barcode_from_plateholderobject(plate_holder_object)

Given plate_holder_object that can store one plate, return that plate’s barcode.

Input:

plate_holder_object: object of type PlateHolder from module sample with well information

Output:

barcode: barcode of plate in plate_holder_object

recover_previous_settings(plate_holder_object, plate_object, experiment)

Recover the the objects created in the last experiment and attach them to the right objects.

Input:

plate_holder_object: object of type PlateHolder from module sample with well information

plate_object: object of class plate

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

Output:

none

run_macro(imaging_settings, plate_holder_object, experiment=None, repetition=0, wait_after_image=None)

Input:

imaging_settings: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

experiment: not used, necessary for compatibility

repetition: not used, necessary for compatibility

wait_after_image: not used, necessary for compatibility

Output:

none

save_segmented_image(imaging_settings, image_data, location_list, plate_object, well_object)

A simple function to save the positions that were a result of well segmentation and represent which positions will be imaged in 100X

Input:

imaging_settings: yaml preferences

image_data: pixel data of the image

location_list: list of positions approved by the user

plate_object: plate object

well_object: well object associated with the image

Output:

none

scan_all_objects(imaging_settings, sample_list, plate_object, experiment, repetition=0, wait_after_image=None)

Scan all objects in dictionary.

Input:

imaging_settings: dictionary with preferences

sample_list: list with all objects to scan

plate_object: object sample list belongs to

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

repetition: counter for time lapse experiments

wait_after_image: wait preferences as dictionary to determine whether to wait after execution

Image: Wait after each image

Plate: Reset wait status after each plate

Repetition: Reset wait status after each repetition

Status: Show last image and allow adjustments if True, enter fully automatic mode if False

Output:

none

scan_plate(imaging_settings, plate_holder_object, experiment, repetition=0, wait_after_image=None)

Scan selected wells in plate to create overview.

Input:

imaging_settings: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

repetition: counter for time lapse experiments

wait_after_image: wait preferences as dictionary to determine whether to wait after execution

Image: Wait after each image

Plate: Reset wait status after each plate

Repetition: Reset wait status after each repetition

Status: Show last image and allow adjustments if True, enter fully automatic mode if False

Output:

none

scan_samples(imaging_settings, plate_holder_object, experiment, repetition=0, wait_after_image=None)

Step through all plates and call scan_all_objects.

Input:

imaging_settings: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

repetition: counter for time lapse experiments

wait_after_image: wait preferences as dictionary to determine whether to wait after execution

Image: Wait after each image

Plate: Reset wait status after each plate

Repetition: Reset wait status after each repetition

Status: Show last image and allow adjustments if True, enter fully automatic mode if False

Output:

none

scan_single_ROI(imaging_settings, experiment_dict, sample_object, reference_object, image_path, meta_dict, verbose=True, number_selected_postions=0, repetition=0)

Acquire tiled image as defined in pos_list

Input:

imaging_settings: dictionary with preferences

experiment_dict: The dictionary with full information about the experiment

sample_object: object of a sample type (e.g. cell, colony) to be imaged

reference_object: object used to set parfocality and parcentricity, typically a well in plate

image_path: string for filename with path to save image in original format or tuple with string to directory and list with template for file name. Default=None: no saving

meta_dict: directory with additional meta data, e.g. {‘aics_well’:, ‘A1’}

verbose: if True print debug information (Default = True)

number_selected_postions: number of positions collected so far

repetition: counter for time lapse experiments

Output:

return_dict: dictionary of form {‘Image’: [images], ‘Continue’: True/False}

scan_wells_zero(prefs, plate_holder_object, barcode)

Scan selected wells at center. This method is mainly used to test calibrations.

Input:

prefs: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

barcode: barcode for plate, often used as plate name

Output:

none

segment_wells(imaging_settings, plate_holder_object, experiment, repetition, wait_after_image=None)

Writes a position list of segemented wells to a CSV file.

Input:

imaging_settings: dictionary with preferences

plate_holder_object: Plate Holder object containing all the information

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

repetition: counter for time lapse experiments

wait_after_image: wait preferences as dictionary to determine whether to wait after execution

Image: Wait after each image

Plate: Reset wait status after each plate

Repetition: Reset wait status after each repetition

Status: Show last image and allow adjustments if True, enter fully automatic mode if False

Output:

none

set_objective_offset(offset_prefs, plate_holder_object, _experiment)

Set parfocality and parcentricity for objectives used in experiments.

Input:

offset_prefs: dictionary with preferences based on preferences.yml

plate_holder_object: object that contains all plates and all wells with sample information.

_experiment: not used, necessary for compatibility

Output:

none

set_up_koehler(initialize_prefs, plate_object)

Set up Koehler illumination.

Input:

initialize_prefs: dictionary with preferences based on preferences.yml

plate_object: object of class plate

Output:

none

set_up_objectives(set_up_settings, plate_holder_instance, _experiment)

Retrieve objectives mounted at microscope.

Input:

set_up_settings: dictionary with preferences

plate_holder_instance: object of type PlateHolder from module sample with well information

_experiment: not used, necessary for compatibility

Output:

none

setup_immersion_system(prefs, plate_holder_instance)

Setup system to deliver immersion water

Input:

prefs: dictionary with preferences

plate_holder_instance: instance for plateholder that contains immersion system

Output:

none

update_plate_z_zero(image_settings, plate_holder_object, experiment)

Update z positions for plate (upper side of coverslip) Set load and work positions for focus drive.

Input:

image_settings: dictionary with preferences

plate_holder_object: object of type PlateHolder from module sample with well information

experiment: dictionary with keys ‘Experiment’, Repetitions’, ‘Input’, ‘Output’, ‘WorkflowList’, ‘WorflowType’, ‘ObjectsDict’ and ‘OriginalWorkflow’

Output:

none

validate_experiment(imaging_settings, microscope_object)

If the experiment is not defined in Zen, give the user two tries to create it

Input:

image_settings: preferences from the yaml file

microscope_object: microscope hardware object

Output:

none

microscope_automation.orchestrator.microscope_automation.main()

microscope_automation.orchestrator.write_zen_tiles_experiment module

Export position list for ZEN blue tiles experiment (.czsh) Created on Jul 26, 2019

authors: Brian Kim, Calysta Yan, winfriedw

class microscope_automation.orchestrator.write_zen_tiles_experiment.PositionWriter(zsd, plate, production_path, testing_mode=False)

Bases: object

Converts positions to stage coordinates, saves positions to .czsh file

Requirements:

1) Ensure the dummy_tile_positions.czsh path is specified in preferences under key “PathDummy”

convert_to_stage_coords(offset_x=0, offset_y=0, positions_list=[], header=False)

Converts the distance of points from center of image to x-y coordinates in stage with 10 to 100x objective offsets.

Input:

offset_x: x_offset to account for in stage coordinate conversion

offset_y: y_offset to accoutn for in stage coordinate conversion

positions_list: center of well positions to convert to stage coordinates

header: boolean of whether positions_list includes a header. Skips the first row if True. Default: False

Output:

converted_list: stage coordinates of positions converted from center of well positions

get_next_pos_name(test_mode=False, test_file_names=[])

Find the next available letter label, but dont backfill files.

Input:

test_mode: False by default. Runs in test mode if True.

test_file_names: list of test file names to use if running in test mode

Output:

label: unused alphabetical label for new positions list

iter_all_strings()

label_gen()

write(converted=[], dummy='', name_czsh='positions_output.czsh')

Writes coordinates to a dummy.czsh file, and saves it

Input:

converted: positions to write to the czsh file

dummy: empty (no coordinates) .czsh file to use for writing

name_czsh: name to save written .czsh file as

Output:

none

Module contents

Orchestrator package for Microscope Automation.