ImportMDHistoWorkspace dialog.
Table of Contents
| Name | Direction | Type | Default | Description |
|---|---|---|---|---|
| Filename | Input | string | Mandatory | File of type txt. Allowed extensions: [‘.txt’] |
| Dimensionality | Input | number | Mandatory | Dimensionality of the data in the file. |
| Extents | Input | dbl list | A comma separated list of min, max for each dimension, specifying the extents of each dimension. | |
| NumberOfBins | Input | int list | Number of bin in each dimension. | |
| Names | Input | str list | A comma separated list of the name of each dimension. | |
| Units | Input | str list | A comma separated list of the units of each dimension. | |
| OutputWorkspace | Output | IMDHistoWorkspace | Mandatory | MDHistoWorkspace reflecting the input text file. |
This algorithm takes a text file (.txt extension) containing two columns and converts it into an MDHistoWorkspace.
The columns are in the order signal then error. The file must only contain two columns, these may be separated by any whitespace character. The algorithm expects there to be 2*product(nbins in each dimension) entries in this file. So if you have set the dimensionality to be 4,4,4 then you will need to provide 64 rows of data, in 2 columns or 128 floating point entries.
The Names, Units, Extents and NumberOfBins inputs are all linked by the order they are provided in. For example, if you provide Names A, B, C and Units U1, U2, U3 then the dimension A will have units U1.
Signal and Error inputs are read in such that, the first entries in the file will be entered across the first dimension specified, and the zeroth index in the other dimensions. The second set of entries will be entered across the first dimension and the 1st index in the second dimension, and the zeroth index in the others.
A very similar algorithm to this is CreateMDHistoWorkspace, which takes it’s input signal and error values from arrays rather than a text file. Another alternative is to use ConvertToMD which works on MatrixWorkspaces, and allows log values to be included in the dimensionality.
Note
To run these usage examples please first download the usage data, and add these to your path. In MantidPlot this is done using Manage User Directories.
A 3D Example
ws = ImportMDHistoWorkspace('demo_mdhw.txt',Dimensionality='3',Extents='-1,1,-1,1,-1,1',
NumberOfBins='2,2,2',Names='A,B,C',Units='A,A,A')
print "Number of Dimensions =", ws.getNumDims()
index = (1,0,1)
print "Signal at "+str(index)+" =", ws.getSignalArray()[index]
print "Error Squared at "+str(index)+" =", ws.getErrorSquaredArray()[index]
Output:
Number of Dimensions = 3
Signal at (1, 0, 1) = 6.0
Error Squared at (1, 0, 1) = 37.21
A 2D Example
ws = ImportMDHistoWorkspace('demo_mdhw.txt',Dimensionality='2',Extents='-1,1,-1,1',
NumberOfBins='4,2',Names='A,B',Units='A,A')
print "Number of Dimensions =", ws.getNumDims()
index = (2,1)
print "Signal at "+str(index)+" =", ws.getSignalArray()[index]
print "Error Squared at "+str(index)+" =", ws.getErrorSquaredArray()[index]
Output:
Number of Dimensions = 2
Signal at (2, 1) = 7.0
Error Squared at (2, 1) = 50.41
Categories: Algorithms | MDAlgorithms