CompressedPixelData#

class stixpy.product.sources.CompressedPixelData(*, meta, control, data, energies, idb_versions=None)[source]#

Bases: ScienceData, PixelPlotMixin, TimesSeriesPlotMixin, SpectrogramPlotMixin

Compressed count data from selected pixels, detectors and energies.

Examples

>>> from stixpy.data import test
>>> from stixpy.product import Product
>>> compressed_pd = Product("http://dataarchive.stix.i4ds.net/fits/L1/2020/05/05/SCI/"
...                         "solo_L1_stix-sci-xray-cpd_20200505T235959-20200506T000019_V02_0087031809-50883.fits")
>>> compressed_pd
CompressedPixelData   <sunpy.time.timerange.TimeRange object at ...
    Start: 2020-05-05 23:59:59
    End:   2020-05-06 00:00:19
    Center:2020-05-06 00:00:09
    Duration:0.00023148148148144365 days or
           0.005555555555554648 hours or
           0.33333333333327886 minutes or
           19.99999999999673 seconds
    DetectorMasks
    [0...4]: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]

    PixelMasks
    [0...4]: [['1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1' '1']]

    EnergyEdgeMasks
    [0]: [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]

Attributes Summary

detectors

A stixpy.science.DetectorMasks object representing the detectors contained in the data.

duration

An astropy.units.Quantiy array giving the duration or integration time

energies

A astropy.table.Table object representing the energies contained in the data.

level

pixels

A stixpy.science.PixelMasks object representing the pixels contained in the data

service_subtype

Service subtype

service_type

Service type

ssid

Science Structure ID for product

time_range

A sunpy.time.TimeRange for the data.

times

An astropy.time.Time array representing the center of the observed time bins.

Methods Summary

concatenate(others)

Concatenate two or more science products.

get_data([time_indices, energy_indices, ...])

Return the counts, errors, times, durations and energies for selected data.

is_datasource_for(*, meta, **kwargs)

Determines if meta data meach Raw Pixel Data

plot_pixels(*[, kind, time_indices, ...])

plot_spectrogram([axes, time_indices, ...])

Plot a spectrogram for the selected time and energies.

plot_timeseries([time_indices, ...])

Plot a times series of the selected times and energies.

Attributes Documentation

detectors#

A stixpy.science.DetectorMasks object representing the detectors contained in the data.

duration#

An astropy.units.Quantiy array giving the duration or integration time

energies#

A astropy.table.Table object representing the energies contained in the data.

level#
pixels#

A stixpy.science.PixelMasks object representing the pixels contained in the data

service_subtype#

Service subtype

service_type#

Service type

ssid#

Science Structure ID for product

time_range#

A sunpy.time.TimeRange for the data.

times#

An astropy.time.Time array representing the center of the observed time bins.

Methods Documentation

concatenate(others)#

Concatenate two or more science products.

Parameters:

others (list [stixpy.science.ScienceData]) – The other/s science products to concatenate

Returns:

stixpy.science.ScienceData – The concatenated science products

get_data(time_indices=None, energy_indices=None, detector_indices=None, pixel_indices=None, sum_all_times=False)#

Return the counts, errors, times, durations and energies for selected data.

Optionally summing in time and or energy.

Parameters:

  • time_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example time_indices=[0, 2, 5] would return only the first, third and sixth times while time_indices=[[0, 2],[3, 5]] would sum the data between.

  • energy_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example energy_indices=[0, 2, 5] would return only the first, third and sixth times while energy_indices=[[0, 2],[3, 5]] would sum the data between.

  • detector_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example detector_indices=[0, 2, 5] would return only the first, third and sixth detectors while detector_indices=[[0, 2],[3, 5]] would sum the data between.

  • pixel_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example pixel_indices=[0, 2, 5] would return only the first, third and sixth pixels while pixel_indices=[[0, 2],[3, 5]] would sum the data between.

  • sum_all_times (bool) – Flag to sum all give time intervals into one

Returns:

tuple – Counts, errors, times, deltatimes, energies

classmethod is_datasource_for(*, meta, **kwargs)[source]#

Determines if meta data meach Raw Pixel Data

plot_pixels(*, kind='pixel', time_indices=None, energy_indices=None, fig=None, cmap=None, **kwargs)#
plot_spectrogram(axes=None, time_indices=None, energy_indices=None, detector_indices='all', pixel_indices='all', **plot_kwargs)#

Plot a spectrogram for the selected time and energies.

Parameters:

  • axes (optional matplotlib.axes) – The axes the plot the spectrogram.

  • time_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example time_indices=[0, 2, 5] would return only the first, third and sixth times while time_indices=[[0, 2],[3, 5]] would sum the data between.

  • energy_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example energy_indices=[0, 2, 5] would return only the first, third and sixth times while energy_indices=[[0, 2],[3, 5]] would sum the data between.

  • **plot_kwargs (dict) – Any additional arguments are passed to pcolormesh().

Returns:

matplotlib.axes

plot_timeseries(time_indices=None, energy_indices=None, detector_indices='all', pixel_indices='all', axes=None, error_bar=False, **plot_kwarg)#

Plot a times series of the selected times and energies.

Parameters:

  • axes (optional matplotlib.axes) – The axes the plot the spectrogram.

  • time_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example time_indices=[0, 2, 5] would return only the first, third and sixth times while time_indices=[[0, 2],[3, 5]] would sum the data between.

  • energy_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example energy_indices=[0, 2, 5] would return only the first, third and sixth times while energy_indices=[[0, 2],[3, 5]] would sum the data between.

  • detector_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example detector_indices=[0, 2, 5] would return only the first, third and sixth detectors while detector_indices=[[0, 2],[3, 5]] would sum the data between.

  • pixel_indices (list or numpy.ndarray) – If an 1xN array will be treated as mask if 2XN array will sum data between given indices. For example pixel_indices=[0, 2, 5] would return only the first, third and sixth pixels while pixel_indices=[[0, 2],[3, 5]] would sum the data between.

  • error_bar (optional bool) – Add error bars to plot.

  • **plot_kwargs (dict) – Any additional arguments are passed to plot().

Returns:

matplotlib.axes