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Event Filtering

Given an EventWorkspace (additional docs) one will generally want to either remove events (commonly called filtering) or divide them into separate output workspaces (splitting). While the full list of algorithms can be found in the event filtering algorithm category, a high level summary of them is included here:

This document focuses on how to create workspaces for filtering and will largely ignore FilterByTime and FilterByLogValue.

How to generate event filters

Implicit filters

FilterByTime v1 and FilterByLogValue v1 internally generate event filters during execution that are not exposed to the user. These algorithms can only split the neutron events by pulse time and do not provide the equivalent of a FastLog=True option.

Explicit filters

FilterEvents v1 takes either a SplittersWorkspace, TableWorkspace, or MatrixWorkspace as the SplitterWorkspace. The events are split into output workspaces according to their pulse times or the times they arrive at detectors. Note, times in MatrixWorkspace and TableWorkspace are in seconds, while times in SplittersWorkspace are in nanoseconds.

GenerateEventsFilter will create a SplittersWorkspace based on its various options. This result can be supplied as the SplitterWorkspace input property of FilterEvents. It will also generate an InformationWorkspace which can be passed along to FilterEvents. Depending on the parameters in GenerateEventsFilter, the events will be filtered based on their pulse times or their absolute times. A neutron event’s absolute time is the sum of its pulse time and TOF.

Custom event filters

Sometimes one wants to filter events based on arbitrary conditions. In this case, one needs to go beyond what existing algorithms can do. For this, one must generate their own splitters workspace. The workspace is generally 3 columns, with the first two being start and stop times and the third being the workspace index to put the events into. For filtering with time relative to the start of the run, the first two columns can be either integer or floating-point values. To specify the times as absolute, the first two columns should be of long64 type. For both of the examples below, the filter workspaces are created using the following function:

def create_table_workspace(table_ws_name, column_def_list):
   CreateEmptyTableWorkspace(OutputWorkspace=table_ws_name)
   table_ws = mtd[table_ws_name]
   for col_tup in column_def_list:
       data_type = col_tup[0]
       col_name = col_tup[1]
       table_ws.addColumn(data_type, col_name)

   return table_ws

Relative time

The easiest way to generate a custom event filter is to make one relative to the start time of the run. Note, the times in the table are in seconds.

filter_rel = create_table_workspace('custom_relative', [('float', 'start'), ('float', 'stop'), ('str', 'target')])
filter_rel.addRow((0,9500, '0'))
filter_rel.addRow((9500,19000, '1'))
FilterEvents(InputWorkspace='ws', SplitterWorkspace=filter_rel,
             GroupWorkspaces=True, OutputWorkspaceBaseName='relative', RelativeTime=True)

This will generate an event filter relative to the start of the run. Specifying the FilterStartTime in FilterEvents, one can specify a different time that filtering will be relative to.

Absolute time

If instead a custom filter is to be created with absolute time, the time must be processed somewhat to go into the table workspace:

abs_times = [datetime64('2014-12-12T09:11:22.538096666'), datetime64('2014-12-12T11:45:00'), datetime64('2014-12-12T14:14:00')]
# convert to time relative to GPS epoch
abs_times = [time - datetime64('1990-01-01T00:00') for time in abs_times]
# convert to number of seconds
abs_times = [float(time / timedelta64(1, 's')) for time in abs_times]

filter_abs = create_table_workspace('custom_absolute', [('float', 'start'), ('float', 'stop'), ('str', 'target')])
filter_abs.addRow((abs_times[0], abs_times[1], '0'))
filter_abs.addRow((abs_times[1], abs_times[2], '1'))
FilterEvents(InputWorkspace='PG3_21638', SplitterWorkspace=filter_abs,
             GroupWorkspaces=True, OutputWorkspaceBaseName='absolute', RelativeTime=False)

Be warned that specifying RelativeTime=True with a table full of absolute times will almost certainly generate output workspaces without any events in them.

Time average mean and stddev of logs

In general, the simple mathematical mean of a log is not the value of interest. It is the mean weighted by time, referred to here as the time-average mean. The method for calculating the time-average mean and standard deviation is explained in detail in [1]. We define that a log is represented by the right-continuous multi-step function \(L(t)\) (the Kernel::TimeSeriesProperty class) and a region of interest in time (the Kernel::TimeROI class) is represented by the function \(M(t)\) which is zero when the data should not be included and one when it should be. The time-average mean, \(\mu_T\) is given by

\[\mu_T = \frac{\int_0^T M(t) L(t) dt}{\int_0^T M(t) dt}\]

The denominator is correctly observed to be the duration. The variance (standard deviation squared) is

\[\sigma_T^2 = \frac{\int_0^T M(t) (L(t) - \mu_T)^2 dt}{\int_0^T M(t) dt}\]

In the cases of properties (including time series) with only a single value, \(L\), these values become \(\mu_T = L\) and \(\sigma_T^2=0\) independent of the time region of interest, as expected. When all data is to be used (i.e. \(M(t) = 1\)), the equations simplify to the values weighted by their observed durations, or

\[\mu_T = \frac{\int_0^T L(t) dt}{\int_0^T dt} = \frac{1}{T} \int_0^T L(t) dt\]

References

Category: Concepts