QueryMDWorkspace dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| InputWorkspace | Input | IMDWorkspace | Mandatory | An input MDWorkspace. |
| OutputWorkspace | Output | TableWorkspace | Mandatory | The output Tableworkspace with columns containing key summary information about the MDWorkspace. |
| LimitRows | Input | boolean | True | Limit the report output to a maximum number of rows |
| MaximumRows | Input | number | 100000 | The number of neighbours to utilise. Defaults to 100000. |
| Normalisation | Input | string | none | What normalisation do you wish to apply none: No normalisation. volume: Normalise by the volume. number of events: Normalise by the number of events. Allowed values: [‘none’, ‘volume’, ‘number of events’] |
| TransformCoordsToOriginal | Input | boolean | True | Output box coordinates in terms of original workspace coordinates |
| BoxDataTable | Output | TableWorkspace | Optional output data table with MDEventWorkspace-specific box data. |
This algorithm outputs a table workspace containing summary data about each box within an IMDWorkspace. The table workspace can be used as a basis for plotting within MantidPlot.
Example - query MD workspace and look at the result:
# create sample inelastic workspace for MARI instrument containing 1 at all spectra
ws1 = CreateSimulationWorkspace(Instrument='MAR', BinParams='-10,1,10', UnitX='DeltaE')
AddSampleLog(ws1, 'Ei', '12.', 'Number')
ws2 = ws1 * 2;
# Convert to MD
mdWs1 = ConvertToMD(InputWorkspace=ws1, QDimensions='|Q|', QConversionScales='Q in A^-1', SplitInto='10,10', MaxRecursionDepth='1')
# get the query
table = QueryMDWorkspace(InputWorkspace=mdWs1)
# look at the output:
col_names = table.keys();
name0 = col_names[0];
nRows = len(table.column(name0));
print("Table contains {0} rows".format(nRows))
print("first 11 of them are:")
print("--------------------------------------------------------------------------------------------------------------")
print(' '.join('| {0:19}'.format(name) for name in col_names) + ' |')
print("--------------------------------------------------------------------------------------------------------------")
for i in range(0,11):
print(' '.join('| {0:>19.4f}'.format(table.column(name)[i]) for name in col_names) + ' |')
Output:
Table contains 100 rows
first 11 of them are:
--------------------------------------------------------------------------------------------------------------
| Signal/none | Error/none | Number of Events | |Q|/MomentumTransfer | DeltaE/DeltaE |
--------------------------------------------------------------------------------------------------------------
| 0.0000 | 0.0000 | 0.0000 | 0.3982 | -9.0000 |
| 338.0000 | 0.0000 | 338.0000 | 0.9065 | -9.0000 |
| 164.0000 | 0.0000 | 164.0000 | 1.4149 | -9.0000 |
| 164.0000 | 0.0000 | 164.0000 | 1.9233 | -9.0000 |
| 153.0000 | 0.0000 | 153.0000 | 2.4317 | -9.0000 |
| 162.0000 | 0.0000 | 162.0000 | 2.9401 | -9.0000 |
| 171.0000 | 0.0000 | 171.0000 | 3.4485 | -9.0000 |
| 195.0000 | 0.0000 | 195.0000 | 3.9568 | -9.0000 |
| 231.0000 | 0.0000 | 231.0000 | 4.4652 | -9.0000 |
| 258.0000 | 0.0000 | 258.0000 | 4.9736 | -9.0000 |
| 28.0000 | 0.0000 | 28.0000 | 0.3982 | -7.0000 |
Categories: Algorithms | MDAlgorithms\Utility\Workspace
C++ source: QueryMDWorkspace.cpp (last modified: 2017-05-26)
C++ header: QueryMDWorkspace.h (last modified: 2016-06-14)