core package

Submodules

core.dask_cluster module

Classes and functions for file management

class core.dask_cluster.DaskCluster(requested_nb_nodes, maximum_load=0.6, memory_per_worker=12000)

Bases: object

Used to manage parallel run thanks the Dask package

create_distributed_client()

Instance workers

initialize_cluster()

Defines the number of threads allocated

core.dask_cluster.try_get_client()

Check if client is alive

Returns

Client instance or None

Return type

Dask.Client

core.data_file module

Data files module

Manage files operations, depending of DataManager.

class core.data_file.BlockAlignmentFile(relative_shifts, rms_image, contour)

Bases: core.data_file.DataFile

delete_data()
save(folder_path, basename)
class core.data_file.BothImgRbgFile(image1_uncorrected, image2_corrected_raw)

Bases: core.data_file.DataFile

delete_data()
save(folder_path, basename)

Overlays two images as R and B and saves them to output file

class core.data_file.DataFile(data=None)

Bases: object

delete_data()
save(folder_path: str, basename: str)
class core.data_file.EcsvFile(data)

Bases: core.data_file.DataFile

save(folder_path: str, basename: str)
class core.data_file.EqualizationHistogramsFile(i_histogram, lower_threshold)

Bases: core.data_file.DataFile

delete_data()
save(folder_path, basename)

Overlays two images as R and B and saves them to output file

class core.data_file.FocalPlaneMatrixFile(data, title)

Bases: core.data_file.DataFile

save(folder_path: str, basename: str)
class core.data_file.JsonFile(data)

Bases: core.data_file.DataFile

save(folder_path: str, basename: str)
class core.data_file.NpyFile(npy_data, status: str, cycle='', path='', basename='', label='')

Bases: core.data_file.DataFile

get_root()
load()
save(folder_path: str, basename: str)
class core.data_file.Png2DFile(data)

Bases: core.data_file.DataFile

save(folder_path: str, basename: str)
class core.data_file.RefDiff3DSlicesFile(reference_3d, target_uncorrected_3d, target_corrected_3d, reference_cycle=None, target_cycle=None, n_xz_slices=5, n_yz_slices=5)

Bases: core.data_file.DataFile

delete_data()
save(folder_path, basename)
class core.data_file.RefDiffFile(preprocessed_ref, shifted_img, preprocessed_img, reference_cycle=None, target_cycle=None)

Bases: core.data_file.DataFile

delete_data()
save(folder_path, basename)

Overlays two images as R and B and saves them to output file

class core.data_file.TifFile(path, basename, ext, label, cycle)

Bases: object

get_root()
load()
core.data_file.save_json(data, file_name)

Save a python dict as a JSON file

Parameters

  • data (dict) – Data to save

  • file_name (str) – Output JSON file name

core.data_manager module

core.function_caller module

core.parameters module

core.pyhim_logging module

Classes and functions for pyHiM logging

class core.pyhim_logging.Logger(root_folder, parallel=False, session_name='HiM_analysis', init_msg='')

Bases: object

setup_logger()
setup_md_file(session_name: str = 'HiM_analysis')
core.pyhim_logging.print_analyzing_label(text: str)
core.pyhim_logging.print_dashes()
core.pyhim_logging.print_framed_text(text: str, frame: str)

Example: ================= text =================

Parameters

  • text (str) – Text to print in the middle

  • frame (str) – Template of frame to put in right and left Example: “=================”

core.pyhim_logging.print_log(message, status='INFO')

Shows message to terminal and logs it to file. Compatible with dask workers. Used the dask logger that used itself logging logger instance before.

Parameters

  • message (str) – message.

  • status (str, optional) – either DEBUG, INFO or WARN. The default is ‘INFO’.

Returns

Return type

None.

core.pyhim_logging.print_section(section: str)
core.pyhim_logging.print_session_name(name: str)
core.pyhim_logging.print_title(title: str)
core.pyhim_logging.print_unknown_params(unknown_params: dict)
core.pyhim_logging.write_string_to_file(file_name, text_to_output, attribute='a')

write a line of text into a file

Parameters

  • file_name (str) – log file

  • text_to_output (str) – text to write in file

  • attribute (str, optional) – Open file mode option, by default “a”

core.run_args module

pyHiM argument parser module

class core.run_args.RunArgs(command_line_arguments)

Bases: object

Store and check run arguments

args_to_str()

Print parameters in your shell terminal

Parameters

dictionary (dict) – Parameters dictionary

static get_2d_commands()

Default commands for 2D pipeline

Returns

Set of 2D commands

Return type

frozenset

static get_3d_commands()

Default commands for 3D pipeline

Returns

Set of 3D commands

Return type

frozenset

static get_available_commands()

Available commands for pyHiM

Returns

Set of available commands

Return type

frozenset

classmethod parse_cmd(cmd)

Parse the input command list give by the user as a string (comma-separated)

Parameters

cmd (str) – A comma-separated human-list of commands

Returns

A Python list of commands

Return type

List[str]

core.saving module

Functions for common image processing

core.saving.annotate_heatmap(im, data=None, valfmt='{x:.1f}', textcolors=('black', 'white'), threshold=None, **textkw)

A function to annotate a heatmap.

Parameters

  • im – The AxesImage to be labeled.

  • data – Data used to annotate. If None, the image’s data is used. Optional.

  • valfmt – The format of the annotations inside the heatmap. This should either use the string format method, e.g. “$ {x:.2f}”, or be a matplotlib.ticker.Formatter. Optional.

  • textcolors – A pair of colors. The first is used for values below a threshold, the second for those above. Optional.

  • threshold – Value in data units according to which the colors from textcolors are applied. If None (the default) uses the middle of the colormap as separation. Optional.

  • **kwargs – All other arguments are forwarded to each call to text used to create the text labels.

core.saving.display_3d_assembled(images, localizations=None, plotting_range=None, normalize_b=True, masks=None)
core.saving.heatmap(data, row_labels, col_labels, ax=None, cbar_kw=None, cbarlabel='', fontsize=12, **kwargs)

Create a heatmap from a numpy array and two lists of labels.

Parameters

  • data – A 2D numpy array of shape (N, M).

  • row_labels – A list or array of length N with the labels for the rows.

  • col_labels – A list or array of length M with the labels for the columns.

  • ax – A matplotlib.axes.Axes instance to which the heatmap is plotted. If not provided, use current axes or create a new one. Optional.

  • cbar_kw – A dictionary with arguments to matplotlib.Figure.colorbar. Optional.

  • cbarlabel – The label for the colorbar. Optional.

  • **kwargs – All other arguments are forwarded to imshow.

core.saving.image_show_with_values(matrices, output_name: str = 'tmp.png', cbarlabels: list = None, fontsize=6, verbose: bool = False, title='')

Plots a list of matrices with their values in each pixel.

Parameters

  • matrices (list) – matrices to plot. Should be 2D numpy arrays

  • output_name (TYPE, optional) – DESCRIPTION. The default is “tmp.png”.

  • cbarlabels (list, optional) – titles of subplots. The default is [“focalPlane”].

  • fontsize (float, optional) – fontsize. The default is 6.

  • verbose (Boolean, optional) – self explanatory. The default is False.

  • title (str, optional) – figure title. The default is “”.

Returns

Return type

None.

core.saving.image_show_with_values_single(ax, matrix, cbarlabel, fontsize, cbar_kw, valfmt='{x:.0f}', cmap='YlGn')
core.saving.plot_3d_shift_matrices(shift_matrices, fontsize=8, log=False, valfmt='{x:.1f}')
core.saving.plot_4_images(allimages, titles=None)
core.saving.plot_raw_images_and_labels(image, label)
Parameters

  • image (List of numpy ndarray (N-dimensional array)) – 3D raw image of format .tif

  • label (List of numpy ndarray (N-dimensional array)) – 3D labeled image of format .tif

core.saving.save_image_2d_cmd(image, file_name)
core.saving.save_image_as_blocks(img, full_filename, block_size_xy=256, label='raw_image')

Module contents