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# MD Histogram Workspace¶

The MD Histogram Workspace[MDHistoWorkspace] is a simple multi-dimensional workspace. In contrast to the MDWorkspace, which contains points in space, the MDHistoWorkspace consists of a signal and error spread around space on a regular grid.

In a way, the MDHistoWorkspace is to a MDWorkspace is what the Workspace2D is to the EventWorkspace.

## Creating a MDHistoWorkspace¶

MDHistoWorkspaces typically have 3 or 4 dimensions, although they can be created in up to 9 dimensions.

• You can bin a MDWorkspace to a MDHistoWorkspace using the BinMD algorithm.
• You can use CreateMDWorkspace to create a blank MDWorkspace first, if you do not have data to bin.

## Viewing a MDHistoWorkspace¶

• You can right-click on the workspace and select:
• Plot MD: to perform a 1D plot of the signal in the workspace (only works on 1D MDHistoWorkspaces).
• Show Slice Viewer: to open the Sliceviewer, which shows 2D slices of the multiple-dimension workspace.

## Working with MD Histo Workspaces in Python¶

### Accessing Workspaces¶

The methods for getting a variable to an MDHistoWorkspace is the same as shown in the Workspace help page.

If you want to check if a variable points to something that is an MDHistoWorkspace you can use this:

from mantid.api import IMDHistoWorkspace

ws=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')

if isinstance(ws, IMDHistoWorkspace):
print(ws.name() + " is a " + ws.id())


Output:

ws is a MDHistoWorkspace


### MD Histo Workspace Properties¶

For a full list of the available properties and operation look at the IMDHistoWorkspace api page.

ws=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')

print("Number of events = {}".format(ws.getNEvents()))
print("Number of dimensions = {}".format(ws.getNumDims()))
print("Normalization = {}".format(ws.displayNormalization()))
for i in range(ws.getNumDims()):
dimension = ws.getDimension(i)
print("\tDimension {0} Name: {1}".format(i,
dimension.name))


#### Dimensions¶

As a generic multi dimensional container being able to access information about the dimensions is very important.

ws=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')

print("Number of dimensions = {}".format(ws.getNumDims()))
for i in range(ws.getNumDims()):
dimension = ws.getDimension(i)
print("\tDimension {0} Name: {1} id: {2} Range: {3}-{4} {5}".format(i,
dimension.getDimensionId(),
dimension.name,
dimension.getMinimum(),
dimension.getMaximum(),
dimension.getUnits()))

print("The dimension assigned to X = {}".format(ws.getXDimension().name))
print("The dimension assigned to Y = {}".format(ws.getYDimension().name))
try:
print("The dimension assigned to Z = " + ws.getZDimension().name)
except RuntimeError:
# if the dimension does not exist you will get a RuntimeError
print("Workspace does not have a Z dimension")

# you can also get a dimension by it's id
dim = ws.getDimensionIndexById("Dim1")
# or name
dim = ws.getDimensionIndexByName("Dim2")


### Accessing the Data¶

ws=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')

# To get the signal and error at a prticular position
index = ws.getLinearIndex(5,5)
print(ws.signalAt(index))
print(ws.errorSquaredAt(index))

# To extract the whole signal array
signalArray =  ws.getSignalArray()
# or the whole error squared array
errorSquaredArray =  ws.getErrorSquaredArray()


### Arithmetic Operations¶

The following algorithms allow you to perform simple arithmetic on the values:

These arithmetic operations propagate errors as described here. The formulas used are described in each algorithm’s wiki page.

The basic arithmetic operators are available from python. For example:

# Get two workspaces
A=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')
B = A.clone()

# Creating a new workspace
C = A + B
C = A - B
C = A * B
C = A / B
# Modifying a workspace in-place
C += A
C -= A
C *= A
C /= A
# Operators with doubles
C = A * 12.3
C *= 3.45

#Compound arithmetic expressions can be made, e.g:
E = (A - B) / (C * C)


### Boolean Operations¶

The MDHistoWorkspace can be treated as a boolean workspace. In this case, 0.0 is “false” and 1.0 is “true”.

The following operations can create a boolean MDHistoWorkspace:

These operations can combine/modify boolean MDHistoWorkspaces:

These boolean operators are available from python. Make sure you use the bitwise operators: & | ^ ~ , not the “word” operators (and, or, not). For example:

# Get two workspaces
A=CreateMDHistoWorkspace(Dimensionality=2,Extents='-3,3,-10,10', \
SignalInput=range(0,100),ErrorInput=range(0,100),\
NumberOfBins='10,10',Names='Dim1,Dim2',Units='MomentumTransfer,EnergyTransfer')
B = A.clone()

# Create boolean workspaces by comparisons
C = A > B
D = B < 12.34
# Combine boolean workspaces using not, or, and, xor:
not_C = ~C
C_or_D = C | D
C_and_D = C & D
C_xor_D = C ^ D
C |= D
C &= D
C ^= D
# Compound expressions can be used:
D = (A > 123) & (A > B) & (A < 456)