microscope_automation.samples package

Submodules

microscope_automation.samples.correct_background module

Correct background of microscope images. For information see: http://imagejdocu.tudor.lu/doku.php?id=howto:working:how_to_correct_background_illumination_in_brightfield_microscopy https://clouard.users.greyc.fr/Pantheon/experiments/illumination-correction/index-en.html https://en.wikipedia.org/wiki/Flat-field_correction

Created on 18 Jan 2017

@author: winfriedw

microscope_automation.samples.correct_background.fixed_pattern_correction(image, black_reference)

Correct for fixed pattern with black reference image.

Cameras have faulty pixels that show up as a fixed pattern. To correct the image we use a reference image acquired under the same conditions as the image but with light blocked to reach the camera. We subtract the reference image from the image

Input:

image: one channel image to be corrected

black_reference: correction image acquired with identical exposure settings but camera blocked

Output:

corrected_image: image after correction

microscope_automation.samples.correct_background.illumination_correction(image, black_reference, illumination_reference)

Correct for uneven illumination.

Input:

image: one channel image to be corrected

black_reference: correction image acquired with identical exposure settings but camera blocked

illumination_reference: correction image acquired without sample (brightfield) or dye solution (fluorescence)

Output:

corrected_image: image after correction

microscope_automation.samples.draw_plate module

Draw 96 Well plate. Colorcode wells based on content. Get information from yml file.

Created on Jun 28, 2016

@author: winfriedw

microscope_automation.samples.draw_plate.draw_column_labels(n_row=8, n_col=12, pitch=9)

Draw plate wells based on content

Input:

n_col: number of columns, labeled from 1 to n_col

pitch: distance between individual wells in mm

Output:

none

microscope_automation.samples.draw_plate.draw_label(x=0, y=0, text='Hallo World!', align='center')

microscope_automation.samples.draw_plate.draw_legend(ax, n_row, n_col, pitch, prefs)

Draw legend

Input:

n_row: number of rows, labeled alphabetically

n_col: number of columns

pitch: distance between individual wells in mm

prefs: preferences

Output:

none

microscope_automation.samples.draw_plate.draw_plate(n_col=12, n_row=8, pitch=9, diameter=6.94, prefs=None)

Draw plate and label wells based on content

Input:

n_col: number of columns, labeled from 1 to n_col

n_row: number of rows, labeled alphabetically

pitch: distance between individual wells in mm

diameter: diameter of Well in mm

Output:

none

microscope_automation.samples.draw_plate.draw_row_labels(n_row=8, pitch=9)

Draw plate wells based on content

Input:

n_row: number of rows, labeled alphabetically

pitch: distance between individual wells in mm

Output:

none

microscope_automation.samples.draw_plate.draw_well(pos, diameter, color)

Draws Well

Input:

pos: [x,y] position of Well center in mm

diameter: diameter of Well in mm

Output:

none

microscope_automation.samples.draw_plate.draw_wells(ax, n_col=12, n_row=8, pitch=9, diameter=6.94, prefs=None)

Draw plate wells based on content

Input:

n_col: number of columns, labeled from 1 to n_col

n_row: number of rows, labeled alphabetically

pitch: distance between individual wells in mm

diameter: diameter of Well in mm

Output:

none

microscope_automation.samples.find_cells module

class microscope_automation.samples.find_cells.CalibrationViewer

Bases: object

This class is intended to be a bookkeeper for image viewing threads. Scipy’s imshow doesn’t play nice with

close()

display_image(image, cmap='Greys_r')

class microscope_automation.samples.find_cells.CellFinder(image, prefs, parent_colony_object)

Bases: object

adjust_image()

Adjust contrast, gamma, etc. Will have additional parameters for adjustment preferences.

Input:

none

Output:

seg: 2D numpy array of image data

choose_imaging_location()

Choose a location (or locations) to image. General outline:

• if looking for smooth locations, use twofold distance map algorithm

• if looking for rough locations (TBD)

• convert image locations to stage coordinates

Input:

none

Output:

none

export_cells()

Convert the cell list into Cell objects. Add operation metadata to original image

filter_objects()

Filter objects in image to identify best colonies.

Input:

none

Output:

seg: 2D numpy array of image data

final_edge_detection()

Find improved edges.

Input:

none

Output:

seg: 2D numpy array of image data

find_cells()

Find cells in a given image. General outline:

• segment colony

• choose location to image

• return cell information

Input:

none

Output:

cell_dict: dictionary of the form {‘name’: cell_object}

get_original_data()

Retrieve data from original image. Currently ImageAICS type. Will need to be converted to float and normalized between 0 and 1

Input:

image: ImageAICS image

Output:

seg: 2D numpy array of image data

improve_initial_edges()

Improve initial edge detection with dilation, hole filling, etc.

Input:

none

Output:

seg: 2D numpy array of image data

initial_edge_detection()

Initial edge detection.

Input:

none

Output:

seg: 2D numpy array of image data

static is_outlier(points, thresh=3.5)

Returns a boolean array with True if points are outliers and False otherwise.

Input:

points: An num_observations by num_dimensions array of observations

thresh: The modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers.

Output:

mask: A num_observations-length boolean array.

References:

Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.

segment_image()

Segment a given image. General outline:

• get original data

• adjust image for edge detection

• initial edge detection

• dilation and hole filling

• filtering

• final edge detection

Input:

none

Output:

seg: segmented image

set_calibration(value)

twofold_distance_map()

Compute the entire distance map for a segmented colony

validate(form_title, form_comment, pref_data, default=False)

Function to calibrate steps of the cell finding process

Input:

form_title: title of form to be displayed

form_comment: comment for form to be displayed

pref_data: (list) contains form fields, preference names, preference values, and (in that order)

default: checked or unchecked as default for continuing. (Default = False)

Output:

boolean for whether to continue recursion

microscope_automation.samples.find_cells.display_and_save(image, name, location='/home/mattb/git/microscopeautomation/data/test_data_matthew/segmentationtest/', display=False)

microscope_automation.samples.find_cells.segment(image, img_dir)

FOR TESTING PURPOSES ONLY

Input:

image:

img_dir:

Output:

none

microscope_automation.samples.find_cells.segment_dir(directory, multi=True)

microscope_automation.samples.find_well_center module

Find center of well based on ImageAICS of well segment. We will generate an ImageAICS of the whole well and convolve it with the ImageAICS of the segment. All dimensions in pixels Created on Jun 18, 2016

@author: winfriedw

microscope_automation.samples.find_well_center.create_edge_image(diameter, length, r, phi, add_noise=False)

Create ImageAICS for well edge used for alignment and test purposes.

Input:

diameter: Well diameter in pixels

length: size of image in pixels

r: distance of image from circle center in pixels.

phi: direction of image

addNoise: add noise for test purposes. Default is False.

Output:

im: well edge image to be used for alignment

microscope_automation.samples.find_well_center.create_well_image(diameter)

Create mask for whole well. Using the whole well for alignment will improve accuracy on the cost of speed. We recommend to use create_edge_image if possible

Input:

diameter: well diameter in pixels

Return:

well_image: numpy array for well with well set to 1 and background set to 0

microscope_automation.samples.find_well_center.find_well_center(image, well_diameter, percentage, phi)

Find center of well based on edge ImageAICS.

Input:

image: image with well edge

well_diameter: diameter of well in pixels

percentage: percentage of whole well used for convolution. Large numbers increase accuracy, smaller speed. well_diameter*percentage/100 has to be larger than size of ImageAICS

phi: direction of image from center

Return:

x_center, y_center: expected center of well

microscope_automation.samples.find_well_center.find_well_center_fine(image, direction, test=False)

Find edge of well in image in selected direction.

Input:

image: image with well edge

direction: direction to search for edge. String of form ‘-x’, ‘x’, ‘-y’, or ‘y’

test: returns 1 if True since test images may not have data (Default: False)

Return:

edge_pos: coordinate of well edge in selected direction in pixels with origin in center of image

microscope_automation.samples.find_well_center.show_debug_image(image)

Show ImageAICS when in debug mode.

Input:

image: ImageAICS object to display if debug mode is turned on

Return:

none

microscope_automation.samples.find_well_center.show_debug_summary(image, well_image, correlation, t_image, corr_x, corr_y)

Show all images to calculate new center.

Input:

image: microscope image of well center

well_image: synthetic image of well used for alignment

correlation: image of crosscorrelation

t_image: image after thresholding used for crosscorrelation

corr_x, corr_y: correlation peak

Output:

none

microscope_automation.samples.find_well_center.show_hist(image)

Plot histogram.

Input:

image

Output:

none

We have problems with the pyplot.hist function.

microscope_automation.samples.interactive_location_picker_pyqtgraph module

class microscope_automation.samples.interactive_location_picker_pyqtgraph.ImageLocationPicker(image=None, location_list=[], app=None, spot_size=10, spot_brush='r', spot_symbol='+', spot_pen='r')

Bases: object

failed_image()

flip_coordinates(location_list)

ZEN has the image origin in the upper left corner, QTgraph in the lower left corner. This methods transforms coordinates between the two systems.

Input:

location_list: List of tuples (or single tuple) in the form of (x,y) coordinates to be transformed

Output:

flipped_location_list: list with (x, y) tuples after transformation

plot_points(window_title)

Plots a given list of location on the image, gives the user ability to edit them using add and delete modes

Input:

window_title: name to give the GUI window

Output:

none

round_locations(pos_x, pos_y)

class microscope_automation.samples.interactive_location_picker_pyqtgraph.KeyPressWindow(*args, **kwargs)

Bases: pyqtgraph.graphicsWindows.GraphicsWindow

Creating a class to define a custom key press signal for selecting modes.

keyPressEvent(self, QKeyEvent)

sigKeyPress

microscope_automation.samples.interactive_location_picker_pyqtgraph.test_ImageLocationPicker(prefs, image_save_path, app, verbose=False)

Test location picker on ZEN blue hardware with slide.

Input:

prefs: path to experiment preferences image_save_path: path to directory to save test images app: object of class QtGui.QApplication

Output:

None

microscope_automation.samples.interactive_location_picker_pyqtgraph.test_offline()

microscope_automation.samples.interactive_location_picker_pyqtgraph.test_online()

microscope_automation.samples.positions_list module

Class to create position lists for tiling and multi-position imaging. Created on Aug 14, 2017

@author: winfriedw

class microscope_automation.samples.positions_list.CreateTilePositions(tile_type='none', tile_number=(2, 2), tile_size=(1, 1), degrees=0)

Bases: object

Create position lists for tiling and multi-position imaging.

create_ellipse(nx, ny)

Return positions for ellipse with step size of one.

Input:

nx, ny: number of tiles in x and y

Output:

ellipse_list: list for rectangle positions, centered around zero and with step size of one

create_rectangle(nx, ny)

Return positions for rectangle with step size of one.

Input:

nx, ny: number of tiles in x and y

Output:

rect_list: list for rectangle positions, centered around zero and with step size of one

get_field_rotation()

Set size of tiles in x and y.

Input:

none

Output:

degrees: Angle the tile field is rotated counterclockwise in degrees

get_pos_list(center=(0, 0, 0))

Return list with positions.

Input:

center: Center position for tiles as tuple (x, y, z)

Output:

pos_list: list with tuples (x,y) for tile centers.

get_tile_number()

Return number of tiles in x and y.

Input:

none

Output:

tile_number: tuple with number of tiles in x and y direction

get_tile_size()

Retrieve size of tiles in x and y.

Input:

none

Output:

tile_size: tuple with size of tiles in x and y direction

get_tile_type()

Return type of tiling.

Input:

none

Output:

tile_type: string to describe type of tiling. Allowed values:

‘none’: do not tile

‘rectangle’: tiles are arranged to form a rectangle

‘ellipse’: tiles are arranged to form an ellipse

rotate_pos_list(pos_list, degrees)

Rotate all coordinates in pos_list counterclockwise by angle degree around center = (0, 0, 0).

Input:

pos_list: list with (x, y, z) coordinates

degrees: amount of counterclock rotation around origin in degree

Output:

rot_list: list with (x, y, z) coordinates after rotation.

set_field_rotation(degrees=0)

Set size of tiles in x and y.

Input:

degrees: Angle the tile field is rotated counterclockwise in degrees

Output:

none

set_tile_number(tile_number=(2, 2))

Set number of tiles in x and y.

Input:

tile_number: tuple with number of tiles in x and y direction

Output:

none

set_tile_size(tile_size=(1, 1))

Set size of tiles in x and y.

Input:

tile_size: tuple with size of tiles in x and y direction

Output:

none

set_tile_type(tile_type='none')

Set type of tiling.

Input:

tile_type: string to describe type of tiling. Allowed values:

‘none’: do not tile

‘rectangle’: tiles are arranged to form a rectangle

‘ellipse’: tiles are arranged to form an ellipse

Output:

none

show(center=(0, 0, 0))

Display positions.

Input:

center: Center position for tiles

Output:

none

microscope_automation.samples.samples module

Classes to describe and manipulate samples Created on Jul 11, 2016

@author: winfriedw

class microscope_automation.samples.samples.Background(name='Background', center=[0, 0, 0], well_object=None, image=True, ellipse=[0, 0, 0], meta=None, x_flip=1, y_flip=- 1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class for the background object associated with each plate. It will be used to do background correction.

class microscope_automation.samples.samples.Barcode(name='Barcode', well_object=None, center=[0, 0, 0], x_flip=1, y_flip=- 1, z_flip=1, x_correction=0, y_correction=0, z_correction=0, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to image and read barcode.

read_barcode(experiment, camera_id, file_path, verbose=True)

Take image of barcode and return code.

Input:

experiment: string with imaging conditions as defined within microscope sofware

camera_id: string with name of camera

file_path: filename with path to store images. If None, image is not saved

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

Output:

code: string encoded in barcode

read_barcode_data_acquisition(experiment, camera_id, file_path, verbose=True)

Take image of barcode.

Input:

experiment: string with name for experiment used in microscope software

camera_id: unique ID for camera used to acquire image

file_path: filename with path to store images. If None, image is not saved

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

Output:

image: Image of barcode. Images will be used to decode barcode.

class microscope_automation.samples.samples.Cell(name='Cell', center=[0, 0, 0], colony_object=None, x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and manipulate cells within a colony.

execute_experiment(experiment, camera_id, reference_object=None, file_path=None, meta_dict={}, verbose=True)

Acquire single image using settings defined in microscope software and optionally save.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

experiment: string with experiment name as defined within microscope software

camera_id: string with unique camera ID

reference_object: object of type sample (ImagingSystem) used to correct for xyz offset between different objectives

file_path: filename with path to save image in original format. Default=None: no saving

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

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

Output:

image: ImageAICS object. At this moment they do not include the pixel data. Get pixel data with load_image.

get_cell_line()

Get name of cell line.

Input:

none

Output:

cell_line: string with name of cell line

get_clone()

Get name of clone.

Input:

none

Output:

clone: string with name of clone

set_cell_line(cell_line)

Set name of cell line.

Input:

cell_line: string with name of cell line

Output:

none

set_clone(clone)

Set name of clone.

Input:

clone: string with name of clone

Output:

none

set_interactive_positions(image_data, location_list=[])

Opens up the interactive mode and lets user select objects and return the list of coordinates selected

Input: image_data: The pixel data of the image of the cell - numpy array

Output: location_list: Returns the list of objects selected by the user

class microscope_automation.samples.samples.Colony(name='Colony', center=[0, 0, 0], well_object=None, image=True, ellipse=[0, 0, 0], meta=None, x_flip=1, y_flip=- 1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and manipulate colonies within a Well.

add_cells(cell_objects_dict)

Adds cells to colony.

This method will update cell line and clone information foe cells based on clone information (if available)

Input:

cell_objects_dict: dictionary of form {‘name’: cellObject}

Output:

none

execute_experiment(experiment, camera_id, reference_object=None, file_path=None, meta_dict=None, verbose=True)

Acquire single image using settings defined in microscope software and optionally save.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

experiment: string with experiment name as defined within microscope software

camera_id: string with unique camera ID

reference_object: object of type sample (ImagingSystem) used to correct for xyz offset between different objectives

file_path: filename with path to save image in original format. Default=None: no saving

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

focus: use autofocus (default = False)

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

Output:

image: ImageAICS object. At this moment they do not include the pixel data. Get pixel data with load_image.

find_cell_interactive_distance_map(location)

Find locations of cells to be imaged in colony and add them to colony.

Input:

location: center of the cell in the form (x, y)

Output:

cell_to_add: object of class Cell

find_cells_cell_profiler()

Find locations of cells to be imaged in colony and add them to colony.

Input:

none

Output:

none

find_cells_distance_map(prefs, image)

Find locations of cells to be imaged in colony and add them to colony.

Input:

prefs: preferences read with module preferences with criteria for cells

image: ImageAICS object with colony

Output:

none

get_cell_line()

Get name of cell line.

Input:

none

Output:

cell_line: string with name of cell line

get_cells()

Get all cells in colony.

Input:

none

Output:

cells: dictionary of form {‘name’: cellObject}

get_clone()

Get name of clone.

Input:

none

Output:

clone: string with name of clone

number_cells()

set_cell_line(cell_line)

Set name of cell line.

Input:

cell_line: string with name of cell line

Output:

none

set_clone(clone)

Set name of clone.

Input:

clone: string with name of clone

Output:

none

set_interactive_positions(image_data, location_list=None, app=None)

Opens up the interactive mode and lets user select cells and return the list of coordinates selected

Input:

image_data: The pixel data of the image of the colony - numpy array

location_list: Coordinates to be preplotted on the image

app: pyqt application object

Output:

location_list: Returns the list of cells selected by the user

update_zero(images, verbose=True)

Update zero position of colony in well coordinates.

Input:

images: list with image objects of class ImageAICS

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

Output:

x, y, z: new zero position z after drift correction (as if no drift had happended)

class microscope_automation.samples.samples.ImagingSystem(container=None, name='', image=True, x_zero=0, y_zero=0, z_zero=0, x_flip=1, y_flip=1, z_flip=1, x_correction=0, y_correction=0, z_correction=0, z_correction_x_slope=0, z_correction_y_slope=0, z_correction_z_slope=0, x_safe_position=None, y_safe_position=None, z_safe_position=None, reference_object=None, x_ref=None, y_ref=None, z_ref=None, microscope_object=None, stage_id=None, focus_id=None, auto_focus_id=None, objective_changer_id=None, safety_id=None, camera_ids=[])

Bases: object

acquire_images(experiment, camera_id, reference_object=None, file_path=None, pos_list=None, load=True, use_reference=True, use_auto_focus=False, meta_dict={}, verbose=True)

Acquire image or set of images using settings defined in microscope software and optionally save.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

experiment: string with experiment name as defined within microscope software

camera_id: string with unique camera ID

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

file_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

pos_list: coordinates if multiple images (e.g. tile) should be acquired. The coordinates are absolute stage positions in mum not corrected for objective offset.

load: Move focus in load position before move. Default: True

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

use_autofocus: use autofocus (default = False)

use_reference: use reference object (default = True)

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

Output:

images: list with ImageAICS objects. Does not include the pixel data. Get pixel data with load_image.

add_attached_image(key, image)

Attach image to sample object.

Input:

key: string with name of image (e.g. ‘backgroundGreen_10x’)

image: image object of class ImageAICS

Output:

none

add_camera_id(camera_id)

Add ID to camera_ids without removing the existing IDs.

Input:

camera_id: ID to add to the list of IDs

Output:

none

add_meta(meta_dict)

Update dictionary with meta data.

Input:

meta_dict: dictionary with meta data

Output:

updated_meta_dict: dictionary with additional meta data

add_meta_data_file(meta_data_file_object)

Add object that handles saving of meta data to disk.

Input:

meta_data_file_object: object of type meta_data_file

Output:

none

add_samples(sample_objects_dict)

Adds samples to imaging system.

Input:

sample_objects_dict: dictionary of form {‘name’: sample_object}

Output:

none

add_to_image_dir(list_name, sample_object=None, position=None)

Add sample object to list with name list_name of objects to be imaged.

Input:

list_name: string with name of list (e.g. ‘ColoniesPreScan’)

sample_object: object to be imaged. Can be list. List will always added at end

position: position of object in list. Position will determine order of imaging.

Default: None = Append to end. Has no effect if object is list.

Output:

none

background_correction(uncorrected_image, settings)

Correct background using background images attached to object or one of it’s superclasses.

Input:

uncorrected_image: object of class ImageAICS

settings: object of class Preferences which holds image settings

Output:

corrected: object of class ImageAICS after background correction.

calculate_slope_correction(x, y, verbose=True)

Calculate offset in z because of tilted sample.

Input:

x, y: x and y positions in object coordinates in um the correction is to be calculated

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

Output:

z_slope_correction: offset in z in um

execute_experiment(experiment, camera_id, reference_object=None, file_path=None, meta_dict={}, verbose=True)

Acquire single image using settings defined in microscope software and optionally save.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

experiment: string with experiment name as defined within microscope software

camera_id: string with unique camera ID

reference_object: object of type sample (ImagingSystem) used to correct for xyz offset between different objectives

file_path: filename with path to save image in original format. Default=None: no saving

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

focus: use autofocus (default = False)

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

Output:

image: ImageAICS object. At this moment they do not include the pixel data. Get pixel data with load_image.

find_surface(trials=3, verbose=True)

Find cover slip using Definite Focus 2.

Input:

trials: number of trials before initialization is aborted

verbose: if True, print debug messages (Default: True)

Output:

z: position of focus drive after find surface

get_abs_pos_from_obj_pos(x_object, y_object, z_object=None, verbose=True)

Convert object coordinates into stage coordinates.

Input:

x_object, y_object, z_object: Object coordinates in mum

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

Output:

x, y, z: coordinates in absolute stage coordinates in mum

get_abs_position(stage_id=None, focus_id=None)

Return current stage position. Positions are corrected for centricity and parfocality.

Input:

stage_id: string id to identify stage information is collected from

focus_id: string id to identify focus drive information is collected from

Output:

abs_pos: absolute (x, y, z) position of stage in mum

get_abs_zero(verbose=True)

Return center of object in stage coordinates.

Input:

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

Output:

x_zero, y_zero, z_zero: center of object in mum in stage coordinates

get_attached_image(key)

Retrieve attached image.

Input:

key: string with name of image (e.g. ‘backgroundGreen_10x’)

Output:

image: image object of class ImageAICS

get_auto_focus_id()

Return id for auto-focus used with this sample.

Input:

none

Output:

auto_focus_id: id for auto-focus used with this sample

Searches through all containers until id is found

get_barcode()

Get barcode for plate.

Input:

none

Output:

barcode: string with barcode

get_camera_ids()

Return ids for cameras used with this sample. Searches through all containers until id is found.

Input:

none

Output:

camera_ids: list with ids for cameras used with this sample

get_container()

Return container object that encloses object.

Input:

none

Output:

container: container object

get_container_pos_from_obj_pos(x_object, y_object, z_object, verbose=True)

Calculate container coordinates for given object coordinates.

Input:

x_object, y_object, z_object: Object coordinates in mum

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

Output:

x_container, y_container, z_container: Container coordinates in mum for object coordinates

get_correction()

Get correction term if scaling for object coordinate system is slightly off relative to container coordinate system.

Input:

none

Output:

x_correction, y_correction, z_correction: Correction terms

get_flip()

Return if object coordinate system is flipped relative to container coordinate system.

Input:

none

Output:

x_flip, y_flip: 1 if system is not flipped, otherwise -1

get_focus_id()

Return id for focus used with this sample.

Input:

none

Output:

focus_id: id for focus used with this sample

Searches through all containers until id is found

get_from_image_dir(list_name)

Get list with name list_name of objects to be imaged.

Input:

list_name: string with name of list (e.g. ‘ColoniesPreScan’)

Output:

sample_objects: list of name list_name with objects to be imaged

get_image()

Return image property that defines if sample is included in imaging.

Input:

none

Output:

image: if True, include in imaging

get_images(load=True, get_meta=False)

Retrieve dictionary with images.

Input:

load: if true, will load image data before returning dictionary

get_meta: if true, load meta data

Output:

images: list of images.

get_immersion_delivery_system()

Return the object that describes immersion water delivery system

Input:

none

Output:

immersion_delivery_system: object of class ImmersionDelivery

get_meta()

Return dictionary with meta data.

Input:

none

Output:

meta_dict: dictionary with meta data

get_meta_data_file()

Return object that handles saving of meta data to disk.

Input:

none

Output:

meta_data_file_object: object of type meta_data_file. None if no meta data file exists

get_microscope()

Return object that describes connection to hardware.

Input:

none

Output:

microscope_object: object of class Microscope from module hardware

get_name()

Return name of object.

Input:

none

Output:

name: string name of object

get_obj_pos_from_container_pos(x_container, y_container, z_container, verbose=True)

Calculate object coordinates from container coordinates.

Input:

x_container, y_container, z_container: container coordinates in mum

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

Output:

x_object, y_object, z_object: object coordinates in mum for container coordinates

get_objective_changer_id()

Return id for objective changer used with this sample.

Input:

none

Output:

objective_changer_id: id for objective changer used with this sample

Searches through all containers until id is found

get_pos_from_abs_pos(x=None, y=None, z=None, verbose=True)

Return current position in object coordinates in mum. or transforms (x,y,z) from stage coordinates into object coordinates. This method is based on focus coordinates after drift correction.

Input:

x, y, z: Absolute stage coordinates in mum. If not given or None retrieve current stage position and express in object coordinates.

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

Output:

x_pos, y_pos, z_pos: current or position passed in stage coordinate returned in object coordinates

get_reference_object()

Get reference object to correct for xyz offset between different objectives.

Input:

none

Output:

reference_object: any object of type sample

Searches through all containers until id is found

get_reference_position()

Return position used as reference to correct for xyz offset between different objectives.

Get position from reference object if available. If none is available use zero postion

Input:

none

Output:

x, y, z: position of reference structure in object coordinates

get_safe()

Get safe stage position to start any movement without danger . of collision in sample coordinates.

Input:

None

Output:

x, y, z: safe position in sample coordinates.

get_safety_id()

Return id for safety object. Safety object describes travel safe areas for stage and objectives.

Input:

none

Output:

saftey_id: id for safety object used with this sample

Searches through all containers until id is found

get_sample_type()

Return sample type.

Input:

none

Output:

sample_type: string with name of object type

get_stage_id()

Return id for stage used with this sample.

Input:

none

Output:

stage_id: id for stage used with this sample

Searches through all containers until id is found

get_tile_positions_list(prefs, tile_object='NoTiling', verbose=True)

Get positions for tiles in absolute coordinates. Subclasses have additional tile_objects (e.g. ColonySize, Well).

Input:

prefs: Preferences object with preferences for tiling

tile_object: tile object passed in by preferences. Possible options:

‘NoTiling’: do not tile

‘Fixed’: calculate tiling to image a rectangular area

‘Well’: cover a well with tiles using an elliptical area

‘ColonySize’: cover a colony with tiles using an ellipse

verbose: print debugging information

Output:

tile_position_list: list with absolute positions for tiling

get_use_autofocus()

Return flag about autofocus usage

Input:

none

Output:

use_autofocus: boolean varaible indicating if autofocus should be used

get_well_object()

Get well object for subclass.

Input:

none

Output:

well_object: object for well

get_zero()

Return center of object in container coordinates.

Input:

none

Output:

x_zero, y_zero, z_zero: center of object in mum in container coordinates

live_mode_start(camera_id, experiment)

Start live mode in microscope software.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

camera_id: string with unique camera ID

experiment: string with experiment name as defined within microscope software If None use actual experiment.

Output:

none

live_mode_stop(camera_id, experiment=None)

Stop live mode in microscope software.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

camera_id: string with unique camera ID

experiment: string with experiment name as defined within microscope software If None use actual experiment.

Output:

none

load_image(image, get_meta)

Load image and meta data in object of type ImageAICS.

Methods calls method of container instance until container has method implemented that actually performs action.

Input:

image: image object of class ImageAICS. Holds meta data at this moment, no image data.

get_meta: if true, retrieve meta data from file.

Output:

image: image with data and meta data as ImageAICS class

microscope_is_ready(experiment, reference_object=None, load=True, use_reference=True, use_auto_focus=True, make_ready=True, trials=3, verbose=True)

Check if microscope is ready and setup up for data acquisition.

Input:

experiment: string with name of experiment as defined in microscope software

reference_object: object used to set parfocality and parcentricity

load: move objective into load position before moving stage

use_reference: initialize reference position if not set

make_ready: if True, make attempt to ready microscope, e.g. setup autofocus (Default: True)

trials: maximum number of attempt to initialize microscope. Will allow user to make adjustments on microscope. (Default: 3)

verbose: print debug messages (Default: True)

Output:

ready: True if microscope is ready for use, False if not

move_delta_xyz(x, y, z=0, load=True, verbose=True)

Move in direction x,y,z in micrometers from current position.

Input:

x, y, z: step size in micrometers

load: Move focus in load position before move. Default: True

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

Output:

x_stage, y_stage: x, y position on stage in mum

move_to_abs_position(x=None, y=None, z=None, reference_object=None, load=True, verbose=True)

Move stage to position x, y, z.

Input:

x, y: Position stage should move to in stage coordinates in mum. If None, stage will not move

z: Focus position in mum. If not provided focus will not be changed, but autofocus might engage

reference_object: object of type sample (ImagingSystem) used to correct for xyz offset between different objectives

load: Move focus in load position before move. Default: True

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

Output:

x, y: New stage position

If use_autofocus is set, correct z value according to new autofocus position.

move_to_r_phi(r, phi, load=True, verbose=True)

moves to position r [mum], phi [degree] in radial coordinates. (0,0) is the center of unit (e.g. well). 0 degrees is in direction of x axis.

Input:

r: radius in mum for radial coordinates (center = 0)

phi: angle in degree for radial coordinates (right = 0 degree)

load: Move focus in load position before move. Default: True

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

Output:

x_stage, y_stage: x, y position on stage in mum

move_to_safe(load=True, verbose=True)

Move to safe position for object.

Input:

load: Move focus in load position before move. Default: True

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

Output:

x, y, z: new position in stage coordinates in mum

move_to_xyz(x, y, z=None, reference_object=None, load=True, verbose=True)

Move to position in object coordinates in mum.

If use_autofocus is set, correct z value according to new autofocus position.

Input:

x, y, z; Position in object coordinates in mum. If z == None do not change z position

reference_object: object of type sample (ImagingSystem) used to correct for xyz offset between different objectives

load: Move focus in load position before move. Default: True

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

Output:

x_abs, y_abs, z_abs: new position in absolute stage coordinates in mum

move_to_zero(load=True, verbose=True)

Move to center of object.

Input:

load: Move focus in load position before move. Default: True

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

Output:

x, y, z: new position in stage coordinates in mum

recall_focus(auto_focus_id, pre_set_focus=True)

Find difference between stored focus position and actual autofocus position. Recall focus will move the focus drive to it’s stored position.

Input:

auto_focus_id: string of ID for autofocus to use

pre_set_focus: Move focus to previous auto-focus position. This makes definite focus more robust

Output:

delta_z: difference between stored z position of focus drive and position after recall focus

remove_images()

Remove all images from microscope software display.

Input:

none

Output:

none

set_barcode(barcode)

Set barcode for plate.

Input:

barcode: string with barcode

Output:

none

set_container(container=None)

Object is part of container object (e.g. Plate is container for Well).

Input:

container: object for container

Output:

none

set_correction(x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0, z_correction_z_slope=0, z_correction_offset=0)

Set correction term if scaling for object coordinate system is slightly off relative to container coordinate system.

Input:

x_correction, y_correction, z_correction: Correction terms

Output:

none

set_flip(x_flip=1, y_flip=1, z_flip=1)

Set if object coordinate system is flipped relative to container coordinate system.

Input:

x_flip, y_flip, z_flip: 1 if system is not flipped, otherwise -1

Output:

none

set_hardware(microscope_object=None, stage_id=None, focus_id=None, auto_focus_id=None, objective_changer_id=None, safety_id=None, camera_ids=[])

Store object that describes connection to hardware.

Input:

microscope_object: object of class Microscope from module hardware

stage_id: id string for stage.

focus_id: id string with name for focus drive

auto_focus_id: id string with name for auto-focus

objective_changer_id: id string with name for objective changer

safety_id: id string for safety area that prevents objective damage during stage movement

camera_ids: list with ids for cameras used with this sample

Output:

none

set_image(image=True)

Define if sample should be included in imaging.

Input:

image: if True, include in imaging

Output:

none

set_interactive_positions(tile_image_data, location_list=[], app=None)

Opens up the interactive mode and lets user select colonies and return the list of coordinates selected

Input:

tile_image_data: The pixel data of the image of the well - numpy array

location_list: The list of coordinates to be pre plotted on the image.

app: pyqt application object initialized in microscope_automation.py

Output:

location_list: Returns the list of colonies selected by the user

set_name(name='')

Set name of object

Input:

name: string with name of object

Output:

none

set_reference_object(reference_object)

Set reference object to correct for xyz offset between different objectives. Avoid setting reference positions and connect reference object to same sample.

Input:

reference_object: any object of type sample

Output:

none

set_reference_position(x, y, z)

Set position used as reference to correct for xyz offset between different objectives. Avoid setting reference positions and connect reference object to the same sample.

Input:

x, y, z: position of reference structure in object coordinates. Can be None

Output:

none

set_safe(x, y, z)

Set safe stage position to start any movement without danger of collision in sample coordinates.

Input:

x, y, z: safe position in sample coordinates.

Output:

x_safe, y_safe, z_safe: safe position in sample coordinates.

set_use_autofocus(flag)

Set flag to enable the use of autofocus.

Input:

flag: if true, use autofocus

Output:

use_autofocus: status of use_autofocus

set_zero(x=None, y=None, z=None, verbose=True)

Set center position of object in container coordinates.

Input:

x, y, z: position of object center in mum in coordinate system of enclosing container. If None, use current position

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

Output:

x_zero, y_zero, z_zero: new center position in container coordinates

store_focus(focus_reference_obj=None, trials=3, verbose=True)

Store actual focus position as offset from coverslip.

Input:

focus_reference_obj: Sample object used as reference for autofocus

trials: number of trials before initialization is aborted

verbose: if True, print debug messages (Default: True)

Output:

z: position of focus drive after store focus

tile_images(images, settings)

Create tile of all images associated with object.

Input:

images: list with image objects of class ImageAICS

settings: object of class Preferences which holds image settings

Output:

tile: ImageAICS object with tile

update_correction(x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=1, z_correction_y_slope=1, z_correction_z_slope=1, z_correction_offset=1)

Multiply existing correction terms if scaling for object coordinate system is slightly off relative to container coordinate system with additional correction term.

Input:

x_correction, y_correction, z_correction z_correction_x_slope, z_correction_y_slope, z_correction_z_slope, z_correction_offset: Additional multiplicative correction terms

Output:

x_correction, y_correction, z_correction: updated parameters

update_flip(x_flip=1, y_flip=1, z_flip=1)

Set if object coordinate system should be flippedcompared to current settings.

Input:

x_flip, y_flip, z_flip: 1 if coordinate system flip should stay the same, otherwise -1

Output:

x_flip, y_flip, z_flip: updated parameters

update_zero(x=None, y=None, z=None, verbose=True)

Update center position of object in container coordinates.

Input:

x, y, z: position of object center in mum in coordinate system of inclosing container. If None, use current position

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

Output:

x_zero, y_zero, z_zero: new center position in container coordinates

class microscope_automation.samples.samples.ImmersionDelivery(name='ImmersionDelivery', plate_holder_object=None, safety_id=None, pump_id=None, center=[0, 0, 0], x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0, microscope_object=None)

Bases: microscope_automation.samples.samples.ImagingSystem

Class for pump and tubing to deliver immersion water to objective.

add_counter(increment=1)

Increment counter.

Input:

increment: integer to increment counter value.

default = 1

Output:

count: counter setting after increment

count_and_get_water(objective_magnification=None, pump_id=None, increment=1, verbose=True, automatic=False)

Move objective to water outlet and add drop of water to objective.

Input:

objective_magnification: add water to specific objective with given magnification as float number. Keep current objective if set to None.

pump_id: id of pump to trigger. None by default, which selects the pump_id already associated with this ImmersionDelivery object

increment: integer to increment counter value. Default = 1

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

automatic: if True, trigger pump, else show dialog (Default = False)

Output:

counter: counter value after increment

get_counter()

Get current counter value.

Input:

none

Output:

count: current counter value

get_counter_stop_value()

Get value for counter to stop.

Input:

none

Output:

counter_stop_value: value for counter to stop

get_pump_id()

Return id for pump used with this ImmersionDelivery object.

Input:

none

Output:

pump_id: id for objective changer used with this sample

get_water(objective_magnification=None, pump_id=None, verbose=True, automatic=False)

Move objective to water outlet and add drop of water to objective.

Input:

objective_magnification: add water to specific objective with given magnification as float number. Keep current objective if set to None.

pump_id: id of pump to trigger. None by default, which selects the pump_id already associated with this ImmersionDelivery object

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

automatic: if True, trigger pump, else show dialog

Output:

none

reset_counter()

Reset counter value to 0.

Input:

none

Output:

count: current counter value

set_counter_stop_value(counter_stop_value)

Set value to stop counter and trigger pumping of immersion water.

Input:

none

Output:

counter_stop_value: current counter stop value

set_pump_id(pump_id=None)

Store id of pump associated with this ImmersionDelivery object.

Input:

stage_id: id string for pump

Output:

none

trigger_pump(pump_id=None)

Trigger pump to deliver immersion water.

Input:

pump_id: id of pump to trigger. None by default, which selects the pump_id already associated with this ImmersionDelivery object

Output:

none

class microscope_automation.samples.samples.Plate(name='Plate', plate_holder_object=None, center=[0, 0, 0], x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and navigate Plate.

add_wells(well_objects_dict)

Adds well to plate.

Input:

well_objects_dict: dictionary of form {‘name’: instance of Well class}

Output:

none

get_barcode()

Get barcode for plate.

Input:

none

Output:

barcode: string with barcode

get_well(well_name)

Return wellOjbect for well with name wellName.

Input:

well_name: name of well in form ‘A1’

Output:

well_object: object for one well with name wellName. None if no Well object with well_name exists

get_wells()

Return list with all instancess of class Well associated with plate.

Input:

none

Output:

wells: dict with well objects

get_wells_by_type(sample_type)

Return dictionary with all Well Objects associated with plate that contain samples of given type.

Input:

sample_type: string or set with sample type(s) (e.g. {‘Sample’, ‘Colony’, ‘Barcode’})

Output:

well_objects_of_type: dict with well objects

move_to_well(well)

Moves stage to center of well

Input:

well: name of well in format ‘A1’

Output:

x_stage, y_stage, z_stage: x, y, z position on Stage in mum

set_barcode(barcode)

Set barcode for plate.

Input:

barcode: string with barcode

Output:

none

show(n_col=4, n_row=3, pitch=26, diameter=22.05)

show ImageAICS of plate layout

Input:

n_col: number of columns, labeled from 1 to n_col

n_row: number of rows, labeled alphabetically

pitch: distance between individual wells in mm

diameter: diameter of Well in mm

Output:

none

class microscope_automation.samples.samples.PlateHolder(name='PlateHolder', microscope_object=None, stage_id=None, focus_id=None, auto_focus_id=None, objective_changer_id=None, safety_id=None, immersion_delivery=None, camera_ids=[], center=[0, 0, 0], x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0, x_safe_position=55600, y_safe_position=31800, z_safe_position=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and navigate Stage.

A PlateHolder is the superclass for everything that can be imaged on a microscope (e.g. plates, wells, colonies, cells).

A Stage has it’s own coordinate system measured in mum and nows it’s position in stage coordinates.

A Stage can be moved to a position and acquire images. It will take track of the last image. To keep the image the user has to save it.

add_plates(plate_object_dict)

Adds Plate to Stage.

Input:

plate_object_dict: dictionary of the form {“name”: plate_object}

Output:

none

add_slides(slide_object_dict)

Adds Slide to PlateHolder.

Input:

slide_object_dict:dictionary of the form {“name”: slide_object}

Output:

none

execute_experiment(experiment, camera_id, reference_object=None, file_path=None, meta_dict={'aics_barcode': '', 'aics_well': '', 'aics_xTile': '', 'aics_yTile': ''}, verbose=True)

Acquire image using settings defined in microscope software and optionally save.

Input:

experiment: string with experiment name as defined within microscope software

camera_id: string with unique camera ID

reference_object: object of type sample (ImagingSystem) used to correct for . xyz offset between different objectives

file_path: filename with path to save image in original format. Default=None: no saving

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

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

Output:

image: image of class ImageAICS

Method adds camera_id to image.

find_surface(reference_object=None, trials=3, verbose=True)

Find cover slip using Definite Focus 2 and store position focus_drive object

Input:

reference_object: Used for setting up of autofocus

trials: number of trials before initialization is aborted

verbose: if True, print debug messages (Default: True)

Output:

positions_dict: dictionary {‘absolute’: z_abs, ‘focality_corrected’: z_cor} with focus position in mum

get_abs_stage_position(stage_id=None, focus_drive_id=None)

Get current position in stage coordinates and focus position in mum corrected for parcentricity.

Input:

stage_id: id string for stage.

focus_id: id string with name for focus drive

Output:

x, y, z: real focus position not corrected for drift.

get_camera_ids()

Return a list of camera objects associated with this sample

Input:

none

Output:

camera_ids: list of objects of class Camera

get_corrected_stage_position(verbose=False)

Get current position in stage coordinates and focus position in mum.

Input:

none

Output:

x, y, z: Stage position after centricity correction

get_immersion_delivery_system()

Return object that describes immersion water delivery system for this plate holder.

Input:

none

Output:

immersion_object: object of class ImmersionDelivery

get_microscope()

Return object that describes connection to hardware.

Input:

none

Output:

microscope_object: object of class Microscope from module hardware

get_plates()

Return dictionary of all plate_objects associated with plateholder.

Input:

none

Output:

plate_objects: dictionary with plate objects

get_slides()

Return Slide object attached to PlateHolder

Input:

none

Output:

slide_object: dictionary with slide objects

get_use_autofocus()

Return flag about autofocus usage

Input:

none

Output:

use_autofocus: boolean variable indicating if autofocus should be used

live_mode_start(camera_id, experiment=None)

Start live mode in microscope software.

Input:

camera_id: string with unique camera ID

experiment: string with experiment name as defined within microscope software If None use actual experiment.

Output:

none

live_mode_stop(camera_id, experiment=None)

Start live mode in microscopy software.

Input:

camera_id: string with unique camera ID

experiment: string with experiment name as defined within microscope software If None use actual experiment.

Output:

None

load_image(image, get_meta)

load image and meta data in object of type ImageAICS

Input:

image: meta data as ImageAICS object. No image data loaded so far (if image data exists, it will be replaced)

get_meta: boolean of whether to obtain meta data for the image

Output:

image: image and meta data (if requested) as ImageAICS object

recall_focus(auto_focus_id, pre_set_focus=True)

Find difference between stored focus position and actual autofocus position. Recall focus will move the focus drive to it’s stored position.

Input:

auto_focus_id: string of ID for autofocus to use

pre_set_focus: Move focus to previous auto-focus position. This makes definite focus more robust

Output:

delta_z: difference between stored z position of focus drive and position after recall focus

remove_images()

Remove all images from microscope software display.

Input:

none

Output:

none

save_image(file_path, camera_id, image)

save original image acquired with given camera using microscope software

Input:

file_path: string with filename with path for saved image in original format or tuple with path to directory and template for file name

camera_id: name of camera used for data acquisition

image: image of class ImageAICS

Output:

image: image of class ImageAICS

set_plate_holder_pos_to_zero(x=None, y=None, z=None)

Set current stage position as zero position for PlateHolder.

Input:

x, y, z: optional position in stage coordinates to set as zero position for PlateHolder. If omitted, actual stage position will be used.

Output:

x, y, z: new PlateHolder zero position

set_stage_position(x_stage, y_stage, z_stage=None, reference_object=None, load=True, verbose=True)

Move stage to position in stage coordinates in mum.

Input:

x_stage, y_stage, z_stage: stage position in mum

reference_object: object of type sample (ImagingSystem). Used to correct for xyz offset between different objectives

load: Move focus in load position before move. Default: True

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

Output:

x, y, z: actual stage position

If use_autofocus is set, correct z value according to new autofocus position.

set_use_autofocus(flag)

Set flag to enable the use of autofocus.

Input:

flag: if true, use autofocus

Output:

use_autofocus: status of use_autofocus

store_focus(focus_reference_obj=None, trials=3, verbose=True)

Store actual focus position as offset from coverslip.

Input:

focus_reference_obj: Sample object used as reference for autofocus

trials: number of trials before initialization is aborted

verbose: if True, print debug messages (Default: True)

Output:

positions_dict: dictionary {‘absolute’: z_abs, ‘focality_corrected’: z_cor} with focus position in mum

class microscope_automation.samples.samples.Sample(name='Sample', well_object=None, center=[0, 0, 0], experiment=None, x_flip=1, y_flip=- 1, z_flip=1, x_correction=0, y_correction=0, z_correction=0, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and manipulate samples within a Well. Input:

Output:

None

class microscope_automation.samples.samples.Slide(name='Slide', plate_holder_object=None, center=[0, 0, 0], x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and navigate slide.

class microscope_automation.samples.samples.Well(name='Well', center=[0, 0, 0], diameter=1, plate_object=None, well_position_numeric=(0, 0), well_position_string=('A', '1'), x_flip=1, y_flip=1, z_flip=1, x_correction=1, y_correction=1, z_correction=1, z_correction_x_slope=0, z_correction_y_slope=0)

Bases: microscope_automation.samples.samples.ImagingSystem

Class to describe and navigate single Well

add_barcode(barcode_objects_dict)

Adds barcode to well. Raises TypeError if non-barcode object is added.

Input:

barcode_objects_dict: dictionary of form {‘name’: barcodeObject}

Output:

none

add_colonies(colony_objects_dict)

Adds colonies to well. Raises TypeError if non-colony object is added.

Input:

colony_objects_dict: dictionary of form {‘name’: colony_object}

Output:

none

add_samples(sample_objects_dict)

Adds samples to well.

Input:

sample_objects_dict: dictionary of form {‘name’: sample_object}

Output:

none

calculate_well_correction(update=True)

Calculate correction factor for well coordinate system.

We find position within the well (e.g. colonies) based on their distance from the center in mum. For some experiments we use coordinates measured on other systems. Their might be a slight difference in calibration for different systems. We will use the well diameter to calculate a compensation factor for these differences.

Input:

update: if True update correction factor and do not replace to keep earlier corrections in place

Output:

none

find_colonies(location_list)

Find locations of colonies to be imaged in well and add them to well.

Input:

location_list: list of the colony locations relative to the center of the well

Output:

colony_list: List of Colony Objects

find_well_center_fine(experiment, well_diameter, camera_id, settings, verbose=True, test=False)

Find center of well with higher precision.

Method takes four images of well edges and calculates center. Will set origin of well coordinate system to this value.

Input:

experiment: string with imaging conditions defined within microscope software

well_diameter: diameter of reference well in mum

camera_id: string with name of camera

settings: object of class Preferences which holds image settings

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

test: if True will call find_well_center.find_well_center_fine in test mode. (Default = False)

Output:

x_center, y_center, z_center: Center of well in absolute stage coordinates in mum. z after drift correction (as if no drift had occured)

get_assigned_diameter()

Get well diameter for well as measured by external means.

Input:

none

Output

assigned_diameter: predefined diameter in mum (e.g from plate specifications or other instrument)

get_colonies()

Get all colonies in well.

Input:

none

Output:

colonies: dict with colony objects

get_diameter()

Get well diameter for well. If measured diameter is available it will used, otherwise the set_diameter from specifications is used.

Input:

none

Output

diameter: diameter in mum

get_failed_image()

get_measured_diameter()

Get well diameter for well as measured in find_well_center_fine.

Input:

none

Output

measured_diameter: diameter in mum as measured in find_well_center_fine

get_name()

Return name of object.

Input:

none

Output:

name: string name of object

get_samples(sample_type={'Barcode', 'Colony', 'Sample'})

Get all samples in well.

Input:

sample_type: list with types of samples to retrieve. At this moment {‘Sample’, ‘Barcode’, ‘Colony’} are supported

Output:

samples: dict with sample objects

get_tile_positions_list(prefs, tile_object='Well', verbose=True)

Get positions for tiles in absolute coordinates. Other classes have additional tile_objects (e.g. ColonySize).

Input:

prefs: Preferences object with preferences for tiling

tile_object: tile object passed in by preferences. Possible options:

‘NoTiling’: do not tile

‘Fixed’: calculate tiling to image a rectangular area

‘Well’: cover a well with tiles using an elliptical area

‘ColonySize’: cover a colony with tiles using an ellipse

verbose: print debugging information

Output:

tile_position_list: list with absolute positions for tiling

get_well_object()

Get well object for subclass.

Input:

none

Output:

well_object: object for well

get_well_position_numeric()

Get row and column number.

Input:

none

Output:

well_position_numeric: (column, row) as integer tuple (e.g. (0,0) for well A1

get_well_position_string()

Get row and column as string.

Input:

none

Output:

well_position_string: (row, column) as string tuple (e.g. (‘A’,’1’) for well A1)

set_assigned_diameter(assigned_diameter)

Set diameter from specifications or measured externally for well.

Input:

assigned_diameter: predefined diameter in mum (e.g from plate specifications or other instrument)

Output:

none

set_diameter(diameter)

Set diameter for well. If measured diameter is available it will used, otherwise the set_diameter from specifications is used.

Input:

diameter: diameter in mum

Output:

none

set_interactive_positions(tile_image_data, location_list=None, app=None)

Opens up the interactive mode and lets user select colonies and return the list of coordinates selected

Input:

tile_image_data: The pixel data of the image of the well - numpy array

location_list: The list of coordinates to be pre plotted on the image.

app: pyqt application object initialized in microscope_automation.py

Output:

location_list: Returns the list of colonies selected by the user

set_measured_diameter(measured_diameter)

Set diameter measured during experiment for well.

Input:

measured_diameter: diameter in mum as measured in find_well_center_fine

Output:

none

set_well_position_numeric(position)

Set row and column number.

Input:

position: (column, row) as integer tuple (e.g. (0,0) for well A1

Output:

none

set_well_position_string(position)

Set row and column as strings.

Input:

position: (row, column) as string tuple (e.g. (‘A’,’1’) for well A1)

Output:

none

microscope_automation.samples.samples.create_plate(plate_format)

Set up coordinates for different plate layouts for standard plates to be used with class Plate.

Input:

plate_format: sting for format of plate (‘12’, ‘24’, ‘96’)

Output:

plate_layout: dictionary to describe plate with entries

name: name of plate

well_diameter: diameter of well in mum

well_names: (x,y) coordinates of well center in plate coordinates in mum. The center of well A1 = (0,0)

microscope_automation.samples.samples.create_plate_holder_manually(m, prefs)

Create plate holder manually instead of using setup_samples.

microscope_automation.samples.samples.create_rect_tile(n_col, n_row, x_pitch, y_pitch, z_pos=0)

Create coordinates for rectangular tile scan.. The tiles will be centered around the current stage position.

Input:

n_col, n_row: number of tiles in x and y

x_pitch, y_pitch: distance between tile centers in x and y

z_pos: offset in z in mum

Output:

pos_list: list with tuples (x,y) for tile centers.

microscope_automation.samples.segmentation_filters module

microscope_automation.samples.segmentation_filters.apply_filter(filter_name, input_image, filter_values)

microscope_automation.samples.segmentation_filters.filter_by_distance(input_image, filter_values)

Function to filter segmented objects by their distance from the center of the image

Input:

input_image: The image (numpy array) to the filter to be applied on

filter_values: (list) [max distance from the center, number of objects requested]

Output:

filtered_image: Image (numpy array) after applying the filter mask

microscope_automation.samples.segmentation_filters.filter_by_size(input_image, filter_values)

Function to filter segmented objects by their area.

Input:

input_image: The image (numpy array) to the filter to be applied on

filter_values: (list) [minimum area, maximum area (optional)]

Output:

filtered_image: Image (numpy array) after applying the filter mask

microscope_automation.samples.setup_samples module

Function to setup hardware and samples based on reference files Created on Aug 1, 2016 Split into it’s own module: 05/21/2020

@author: winfriedw

microscope_automation.samples.setup_samples.add_barcode(name, well_object, layout)

Add barcode to well.

Input:

name: string with name for barcode

well_object: instance of Well class from module samples

layout: preferences for plate layout

Output:

none

microscope_automation.samples.setup_samples.add_colonies(well_object, colonies, hardware_settings)

Add colonies from Celigo scan to well.

Input:

well_object: instance of well_object from module samples

colonies: pandas frame with colony information. This information was extracted with CellProfiler from plate-scanner images.

hardware_settings: preferences with description of microscope components, here coordinate transformation between colonies and well

Output:

colony_list: list with all colony objects

microscope_automation.samples.setup_samples.filter_colonies(prefs, colonies, well_dict)

Select colonies to image based on settings in preferences file.

Input:

prefs: preferences with selection criteria

colonies: table with colony data

well_dict: list with wells that should be considered. Well names in format ‘A1’.

Output:

slected_colonies: subset of colonies to be imaged

microscope_automation.samples.setup_samples.get_colony_data(prefs, colony_file)

Get data and positions of colonies.

Input:

prefs: preferences with information about colony file

colony_file: path to .csv file with colony data.

Output:

colonies: pandas frame with content of .csv file

microscope_automation.samples.setup_samples.setup_plate(prefs, colony_file=None, microscope_object=None, barcode=None)

Create object of class plateholder from module sample that holds information about all colonies scanned with plate reader.

Input:

prefs: preferences file for experiment

colony_file: path to .csv file with colony data.

microscope_object: object create by setup_microscope describing hardware components

barcode: string barcode for plate. If not provided can be read from colony file or will be requested by pop-up window.

Output:

plate_holder_object: object that contains all wells with sample information.

microscope_automation.samples.setup_samples.setup_slide(prefs, microscope_object=None)

Create basic object of class slide from module sample that consists of plate holder and slide.

Input:

prefs: preferences file for experiment

Output:

plate_holder_object: object that contains one slide.

microscope_automation.samples.tile_images module

microscope_automation.samples.tile_images.tile_images(images, method='stack', output_image=True, image_output_path=None)

Restitch tiled images based off of location

Input:

images: (list, ImageAICS) images to tile

method: (string) tiling method to use, currently only method is “stack”

output_image: (bool) flag to output image

image_output_path: (string) specified image output path

Output:

img: tiled image of type ImageAICS

microscope_automation.samples.well_overview_segmentation module

class microscope_automation.samples.well_overview_segmentation.WellSegmentation(image_data, colony_filters_dict=None, mode='A', canny_sigma=0.01, canny_low_threshold=0.025, remove_small_holes_area_threshold=1000)

Bases: object

downscale_filter_dictionary(colony_filters_dict)

get_mode_c_points(dist_mask, threshold_min, threshold_max)

Function to find a point at the center, edge & ridge in a colony

Input:

dist_mask: The image with two fold distance map applied

threshold_min: The minimum percentage threshold for the distance from the edge

threshold_max: The maximum percentage threshold for the distance from the edge

Output:

smooth_point_corrected: the location of the point

property point_locations

segment_and_find_positions()

Function segments out the colonies, applied filters and find the smooth points based on the mode

Input:

none

Output:

none

microscope_automation.samples.well_segmentation_refined module

class microscope_automation.samples.well_segmentation_refined.WellSegmentation(image_data, colony_filters_dict=None, mode='A', canny_sigma=0.01, canny_low_threshold=0.025, remove_small_holes_area_threshold=1000)

Bases: object

create_circular_mask(center=None, radius=None)

To create a circular mask over an image, masking out edges of a well

Input:

center: user-defined center of circular mask

radius: radius of circular mask

Output:

mask: a mask with masked-out area being 0, and in-mask area being 1

downscale_filter_dictionary(colony_filters_dict)

To downscale filters from original image to processing image scale

Input:

colony_filters_dict: dictionary of filters with colony_filters_dict[‘distFromCenter’][0] as distance from center and colony_filters_dict[‘distFromCenter’][1] as number of positions

Output:

colony_filters_dict_corrected: corrected colony filteres dictionary with downscaling factor

filter_small_objects(bw_img, area)

To filter small objects from image

Input:

bw_img: A binary image with objects

area: Objects smaller than this area will be filtered out

Output:

int_img: binary image with objects smaller than specified area be filtered out

find_center_position(mask, distance, smoothed_well)

find_edge_position(colony_mask)

Function to find edge position from a colony mask

Parameters

colony_mask – a [0, 1] image showing the segmentation of 1 colony

Returns

edge_position: a tuple (y, x) of the selected edge position in the downsampled well overview image

find_edge_ridge_pair(colony_mask, center_pos, dist_well=30.0)

A method to find a pair of edge and ridge point in a colony that is far away from each other and the center position :param colony_mask: a binary colony mask :param center_pos: a tuple (y, x) of the center position :param dist_well: a float > 1 to indicate the size of the well that should be masked out :return:

find_positions(mode='A')

To find a position in a colony that passes the size filter, and is positioned 40% from the edge of colony, maximum in distance map that indicates preferred smoothness in region, and is close to the center of the well for good imaging practice.

Input:

none

Output:

none

find_ridge_position(colony_mask, edge_position)

Function to find ridge position, optimized by selecting a position in a colony furthest away from the edge position :param colony_mask: a [0, 1] image showing the segmentation of 1 colony :param edge_position: a tuple (y, x) of the selected edge position in the downsampled well overview image :return:

property point_locations

preprocessing_image()

To pre-process input image with correction for uneven illumination and rescaling intensity to enhance contrast

Input:

none

Output:

img_rescale_2: rescaled image ready for segmentation

process_colonies(binary_colony_mask)

To partition binary colony mask into separate, distinct, labelled colonies using distance map to find markers of colonies and separate connected colonies with watershed segmentation.

Input:

binary_colony_mask: A binary image of colonies(1) and background (0)

Output:

none

segment_and_find_positions()

Function segments out the colonies, applies filters, and finds the smooth points based on the mode

Input:

none

Output:

none

segment_colonies(rescaled_image)

To segment colonies from grayscale image using edge detection method to approximate location of colony edges and watershed segmentation to fill in the colonies

Input:

rescaled_image: The rescaled image from preprocessing with enhanced contrast

Output:

binary_colony_mask: binary image of mask of colonies(1) and background(0)

Module contents

Samples package for Microscope Automation.