4.1.1.1.1.2. lib.data_handling.data_converter#

Module that provides functionality in order to post-process data, here construct experiment object from input data.

@author: Thomas Arne Hensel, 2023

4.1.1.1.1.2.1. Module Contents#

4.1.1.1.1.2.1.1. Classes#

Constructor

Class that provides a constructor object in order to create experiments from loaded data. Can be full experiments (from simulations) or extracted data from real experiments.

4.1.1.1.1.2.1.2. API#

class lib.data_handling.data_converter.Constructor(obj, collect_artefacts=True)#

Class that provides a constructor object in order to create experiments from loaded data. Can be full experiments (from simulations) or extracted data from real experiments.

Initialization

Initialize the Constructor object.

Parameters:

  • obj (Experiment or str) – Experiment object or path to data (tif, tiff, yaml).

  • collect_artefacts (bool) – Whether to collect artefacts during extraction (default is True).

get_experiments(batchsize=1, agnostic=True, fast_return=False)#

Construct experiments from a file or experiment object, segmented at bleaching steps. Currently functional for phase scans!!!

Parameters:

  • batchsize (int, optional) – Number of lines to be averaged with moving average.

  • agnostic (bool, optional) – Boolean, default=True. Forget about all information from the experiment.

  • fast_return (bool, optional) – Optional direct return of the experiment object. The routine effectively only reshapes the record.

Returns:

List of experiments.

Return type:

list

_get_bgr_estimate(avg_scan_seg)#

Retrieve the average number of background photons from 0M experiment.

Parameters:

avg_scan_seg (list) – List containing x and y segments.

Returns:

Average number of background photons in each axis.

Return type:

numpy.ndarray

_get_beta_estimate(N_bgr, brightness)#

Calculate the beta estimate.

Parameters:

  • N_bgr (numpy.ndarray) – Average number of background photons in each axis.

  • brightness (list) – Estimated brightness.

Returns:

Beta estimate.

Return type:

numpy.float64

_get_brightness_estimate(avg_scan_seg, N_bgr)#

Calculate the brightness estimate.

Parameters:

  • avg_scan_seg (list) – List containing x and y segments.

  • N_bgr (numpy.ndarray) – Average number of background photons in each axis.

Returns:

Estimated brightness.

Return type:

list

_construct_record(seg, exp, brightness, restore_line_order=False, agnostic=True)#

Construct a measurement record from experimental or simulated data, e.g., after averaging.

Takes a scan segment (e.g., 2M or 1M or 0M) and divides it into x and y blocks. Each segment is a list of [x_avg_scan, y_avg_scan]. Those pairs form 1 record by combining all their measurement sites and counts (considering the block_size!). This procedure is repeated, and a dictionary of measurement records is returned. If a MeasurementRecord object is provided, the counts are substituted by seg, and the record is truncated. If agnostic mode is chosen, c0 is estimated from record; otherwise, the default from the provided experiment is chosen.

Parameters:

  • seg (list) – List of x/y segments.

  • exp (Experiment) – Experiment object with information on how to construct the record.

  • brightness (list) – Estimated brightness.

  • restore_line_order (bool) – Option to assemble blocks as in the original scan or keep plain x-y scans.

  • agnostic (bool) – If True, be agnostic about c0 and estimate from counts.

Returns:

Record object.

Return type:

MeasurementRecord

_assemble_blocks(x_scans, y_scans, seg_idcs)#

Re-assemble the x- and y-segments to an array with alternating x- and y-blocks according to the segment indices.

Parameters:

  • x_scans (list) – List of x-scans.

  • y_scans (list) – List of y-scans.

  • seg_idcs (numpy.ndarray) – Segment indices.

Returns:

Assembled array with alternating x- and y-blocks.

Return type:

numpy.ndarray