Scan

class escape.Scan(parameter={}, step_lengths=None, array=None, data=None, grid_specs=None)

Bases: object

Scan grouping of an Array across defined steps and optional grid metadata.

Scan partitions an Array into sequential steps defined by step_lengths and exposes step-level metadata through parameter. When grid_specs is provided, Scan also constructs a Grid that maps scan steps onto an N-D grid layout.

Args:

parameter: Metadata describing step parameters and values. step_lengths: List of integer lengths for each scan step. array: Underlying Array instance for this scan. data: Optional raw data used directly by the scan. grid_specs: Optional grid metadata forwarded to Grid.

__getitem__(sel, grid_specs=None)

array getter for scan

__init__(parameter={}, step_lengths=None, array=None, data=None, grid_specs=None)

__len__()

all(*args, **kwargs)

any(*args, **kwargs)

append_parameter(parameter: {'par_name': {'values': <class 'list'>}})

append_step(parameter, step_length)

average(*args, **kwargs)

corr_ana_plot(referece, scanpar_name=None, axis=None)

correlation_analysis_to(ref, *args, **kwargs)

count()

get_parameter_array(key=None)

get_parameter_selection(selection)

get_step_array(n, grid_specs=None)

array getter for scan

get_step_data(n)

data getter for scan

get_step_indexes(ix_step)

“array getter for multiple steps, more efficient than get_step_array

hist(cut_percentage=0, bins='auto', normalize_to=None, scanpar_name=None, plot_results=True, plot_axis=None, **kwargs)

max(*args, **kwargs)

mean(*args, **kwargs)

median(*args, **kwargs)

median_and_mad(axis=None, k_dist=1.4826, norm_samples=False)

Calculate median and median absolute deviation for steps of a scan.

Args:

axis (int, sequence of int, None, optional): axis argument for median calls. k_dist (float, optional): distribution scale factor, should be

1 for real MAD. Defaults to 1.4826 for gaussian distribution.

merge_scans(*others, roundto_interval=None, par_name=None)

min(*args, **kwargs)

nancount()

nanmax(*args, **kwargs)

nanmean(*args, **kwargs)

nanmedian(*args, **kwargs)

nanmin(*args, **kwargs)

nanpercentile(*args, **kwargs)

nanquantile(*args, **kwargs)

nanstd(*args, **kwargs)

nansum(*args, **kwargs)

property par_steps

pandas.DataFrame with one row per scan step.

Columns are the scan parameter names (one column per parameter) plus a step_length column containing the number of events in each step. The integer row index corresponds to the step number.

Return type:

pandas.DataFrame

plot(weights=None, scanpar_name=None, norm_samples=True, axis=None, use_quantiles=True, *args, **kwargs)

std(*args, **kwargs)

steps_where(data_condition=None)

Get step indices where condition is true.

Args:

data_condition (function, optional): function which gets data array as input and returns boolean array.

sum(*args, **kwargs)

property tools

weighted_avg_and_std(weights=None, norm_samples=False, axis=0)

weighted_stat(weights=None)

Grid

class escape.storage.storage.Grid(shape, positions, scan=None, grid_dimension_names=None, **kwargs)

Bases: object

__getitem__(sel)

__init__(shape, positions, scan=None, grid_dimension_names=None, **kwargs)

average(*args, **kwargs)

correlation_analysis_to(*args, **kwargs)

count(*args, **kwargs)

fill_count()

Return (filled_positions, total_positions, percent_filled).

filled_positions is the number of unique grid indices present in the associated Scan. total_positions is the product of self.shape.

get_grid_indices()

get_grid_specs()

max(*args, **kwargs)

mean(*args, **kwargs)

median(*args, **kwargs)

median_and_mad(*args, **kwargs)

min(*args, **kwargs)

nancount(*args, **kwargs)

nanmax(*args, **kwargs)

nanmean(*args, **kwargs)

nanmedian(*args, **kwargs)

nanmin(*args, **kwargs)

nanpercentile(*args, **kwargs)

nanquantile(*args, **kwargs)

nanstd(*args, **kwargs)

nansum(*args, **kwargs)

std(*args, **kwargs)

sum(*args, **kwargs)

to_grid(data)

weighted_avg_and_std(*args, **kwargs)

weighted_stats(*args, **kwargs)