base_slopes#

class arte.dataelab.base_slopes.BaseSlopes(data, time_vector=None, astropy_unit=Unit('signal'), data_label='slopes', axes=None)#

Bases: BaseTimeSeries

Wavefront sensor slopes time series.

This class handles slopes (wavefront gradients) recorded from wavefront sensors. Slopes are stored as vectors where typically half the elements are x-slopes and half are y-slopes.

By default, slopes are in xxxxyyyy order (all X slopes followed by all Y slopes). Individual slope components can be accessed using ‘x’ and ‘y’ selectors.

Slopes can be remapped into 2D arrays representing the telescope pupil, where each subaperture position shows its measured slope value.

Parameters:

data (array_like or DataLoader) – Slope data with shape (nframes, nslopes) or (nslopes,)
time_vector (array_like or DataLoader, optional) – Time vector for each frame
astropy_unit (astropy.units.Unit, optional) – Physical unit for slope values (default: signal_unit)
data_label (str, optional) – Label for plots (default: ‘slopes’)
axes (sequence, optional) – Names for data axes

Examples

>>> slopes = BaseSlopes('slopes.fits')
>>> sx = slopes.get_data('x')  # Get x-slopes only
>>> sy = slopes.get_data('y')  # Get y-slopes only
>>> slopes.imshow()  # Display as 2D pupil maps
>>> slopes.vecshow()  # Display as vector field

Notes

Derived classes can customize the slope order by providing a different indexer implementation.

Attributes:

axes

Data series shape

delta_time

Property with the interval between samples.

If no interval can be determined (time vector too short), returns 1 with the correct unit if applicable.

ensemble_mean

Mean over ensemble dimensions (chainable property).

ensemble_median

Median over ensemble dimensions (chainable property).

ensemble_ptp

Peak-to-peak over ensemble dimensions (chainable property).

ensemble_rms

RMS over ensemble dimensions (chainable property).

ensemble_std

Standard deviation over ensemble dimensions (chainable property).

shape

Data series shape

TODO depending on the data loader, it could be found without loading the entire data array

time_mean

Mean over time dimension (chainable property).

time_median

Median over time dimension (chainable property).

time_ptp

Peak-to-peak over time dimension (chainable property).

time_rms

RMS over time dimension (chainable property).

time_std

Standard deviation over time dimension (chainable property).

value

Extract final value(s) from TimeSeries (convenience accessor).

Methods

`astropy_unit`()	Data unit as an astropy unit
`data_label`()	Override to return a string with a readable unit name
`data_unit`()	Data unit string (for plots)
`ensemble_size`()	Number of distinct series in this time ensemble
`filename`()	Data filename (full path)
`filter`(args, *kwargs)	Create filtered TimeSeries by applying ensemble and/or time selection.
`get_data`(*args[, times, axes])	Retrieve time series data with optional filtering and indexing.
`get_display`(*args[, times])	Display cube for the specified time interval
`get_display_axes`()	Display cube axes names as a list of 3 strings
`get_ensemble_average`(*args[, times])	Average across series at each sampling time
`get_ensemble_median`(*args[, times])	Median across series at each sampling time
`get_ensemble_ptp`(*args[, times])	Peak-to-peak (max - min) across series at each sampling time
`get_ensemble_rms`(*args[, times])	Root-Mean-Square across series at each sampling time (legacy method).
`get_ensemble_std`(*args[, times])	Standard deviation across series at each sampling time
`get_index_of`(args, *kwargs)	Return a selection index
`get_time_average`(*args[, times])	Average value over time for each series
`get_time_median`(*args[, times])	Median over time for each series
`get_time_ptp`(*args[, times])	Peak-to-peak (max - min) over time for each series
`get_time_rms`(*args[, times])	Root-Mean-Square value over time for each series
`get_time_std`(*args[, times])	Standard deviation over time for each series
`get_time_vector`()	Return the series time vector
`help`([search, prefix])	Interactive help
`imshow`([cut_wings])	Display X and Y slope 2d images cut_wings=x means that colorbar is saturated for array values below x percentile and above 100-x percentile.
`movie`(*args[, interval])	Display data as a movie
`plot`(*args[, cut_wings, title, xlabel, ylabel])	Plot timeseries
`plot_cumulative_spectra`(*args[, from_freq, ...])	Plot cumulative PSD
`plot_hist`(*args[, from_t, to_t, overplot, ...])	Plot histogram.
`plot_spectra`(*args[, from_freq, to_freq, ...])	Plot PSD
`power`(*args[, from_freq, to_freq, ...])	Compute Power Spectral Density using Welch's method.
`savegif`(filename, *args[, interval, loop])	Save data as an animated GIF
`tile`(*args[, rowlength])	Display data as a tiled 2d frame
`time_size`()	Number of time samples in this time ensemble
`vecshow`()	Display slopes as vector field
`with_times`(times)	Filter to specific time interval (convenience alias for filter(times=...)).

frequency
last_cut_frequency

get_index_of(*args, **kwargs)#

Return a selection index

Parameters:

*args (tuple) – user-defined arguments for data selection
**kwargs (dict, optional) – extra user-defined arguments for data selection

Returns:

index – indexes to select a data subset. If None, all data is selected. An integer, a list of integers or a slice will select those rows. If the data is multidimensional, a tuple can be returned where each element will select data across a single dimension. For a detailed explanation, see the “Advanced indexing” topic at https://numpy.org/doc/stable/user/basics.indexing.html#advanced-indexing

Return type:

None, integer, list of integers, slice objects, or a tuple of the previous possibilities.

imshow(cut_wings=0)#: Display X and Y slope 2d images cut_wings=x means that colorbar is saturated for array values below x percentile and above 100-x percentile. Default is 0, i.e. all data are displayed; values below 0 are forced to 0, values above 50 are set to 50.

vecshow()#: Display slopes as vector field