Multiple events

MultipleEventExtractor

class apav.core.multipleevent.MultipleEventExtractor(roi, multiplicity, extents=((0, 200), (0, 200)))[source]

Handle extraction and access of multiple-event related data from an Roi

This class is responsible for extracting arbitrary multiple event data from Rois. The output is formatted in as pairs, i.e. a 6th order multiple event of the composition ABCDEF is formatted into the 15 ion pairs:

AB AC AD AE AF BC BD BE BF CD CE CF DE DF EF

The number of ion pairs generated from any nth order multiplicity can be calculated using pairs_per_multiplicity().

This class supports any multiplicity > 1, and also supports extracting the combined pairs all multiple events. For example, all of these are valid:

>>> roi = Roi.from_epos("path_to_epos_file.epos")
>>> mevents = MultipleEventExtractor(roi, multiplicity=2)  # Ion pairs from 2nd order multiple events
>>> mevents = MultipleEventExtractor(roi, multiplicity=11)  # Ion pairs from 11th order multiple events
>>> mevents = MultipleEventExtractor(roi, multiplicity="multiples")  # Ion pairs from all multiple events

The pairs can be access from

>>> mevents.pairs

More broadly, the indices of the pairs are accessed via

>>> mevents.pair_indices

Which allows for performing arbitrary analysis of multiple events on any data set attached to the Roi. For example, to form a plot of the difference in mass/charge ratio to distance between the ion pair in detector coordinates (to look at orientational properties of molecular dissociation):

>>> roi = Roi.from_epos("path_to_epos_file.epos")
>>> mevents = MultipleEventExtractor(roi, multiplicity="multiples")  # Use all multiple events
>>> mass = roi.mass[mevents.pair_indices]
>>> det_x = roi.misc["det_x"][mevents.pair_indices]
>>> det_y = roi.misc["det_y"][mevents.pair_indices]
>>> dx = np.diff(det_x)
>>> dy = np.diff(det_y)
>>> diff_det = np.linalg.norm([dx, dy], axis=0)
>>> diff_mass = np.diff(mass)
>>> plt.hist2d(diff_det, diff_mass, bins=100)
>>> plt.plot()
Parameters:

  • roi – region of interest

  • multiplicity (Union[int, str]) – multiplicity order (int > 1 or “multiples”)

  • extents (Tuple[Tuple, Tuple]) – two dimensional range to extract events from (think correlation histograms)

property extents: Tuple[Tuple[Number, Number], Tuple[Number, Number]]

Get the 2-dimensional boundary that was used to extract the ion pairs

property multiplicity: Union[int, str]

int>1 or “multiples

Type:

Get the multiplicity of multiple events in the MultipleEventExtractor. Either

property n_pairs: int

Get the number of pairs extracted

property pair_indices: ndarray

Get the array of indices for the pairs. This is the same shape as MultipleEventExtractor.pairs() but this is used to index map ion pairs to other positional data in the Roi.

property pairs: ndarray

Get the array of all ion pairs. These are ion pairs regardless of the multiplicity so the arrays is Mx2

property roi: Roi

Get the Roi used in the MultipleEventExtractor

pairs_per_multiplicity

apav.core.multipleevent.pairs_per_multiplicity(multiplicity)[source]

The number of unique ion pairs produced from a single multiple event of given multiplicity.

A 6th-order multiple event composed of ions ABCDEF produces the 15 ion pairs:

AB AC AD AE AF BC BD BE BF CD CE CF DE DF EF

>>> pairs_per_multiplicity(6)
15
Parameters:

multiplicity (int) – integer multiplicity order

get_mass_indices

apav.core.multipleevent.get_mass_indices(ipp, multiplicity)[source]

Get the array indices corresponding to multi-event events of a specific multiplicity

Parameters:

  • ipp (ndarray) – array of ions per pulse

  • multiplicity (Union[int, str]) – vent multiplicity

Return type:

ndarray

Returns:

array of indices into ipp