\(\renewcommand\AA{\unicode{x212B}}\)

`mantid.plots`¶

The functions in this module are intended to be used with matplotlib’s object oriented abstract program interface (API). matplotlib’s (stateful) functional interface is discouraged by matplotlib. The object oriented API allow for customization as well.

The plotting of a mantid.api.MatrixWorkspace or a mantid.api.IMDHistoWorkspace can happen in two different ways. The use of a mantid projection allows most matplotlib-like experience:

import matplotlib.pyplot as plt
from mantid import plots

#some code here to get a workspace, and x, y, yerr arrays

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.errorbar(workspace,'rs',specNum=1) #for workspaces
ax.errorbar(x,y,yerr,'bo')            #for arrays
fig.show()

If the mantid projection is not used, the plotting functions take a matplotlib.axes.Axes and a mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace, with some keywords that are specific to Mantid an the type or workspace used. While there are defaults for the labels, you can easily override them after the initial plotting is called. A useful reference is matplotlib’s anatomy of a figure.

Note

To run these usage examples please first download the usage data, and add these to your path. In Mantid this is done using Manage User Directories.

All of the examples below can be run with the following imports, but not all are used in all places.

from mantid.simpleapi import mtd, Load, LoadEventNexus, Rebin, ConvertUnits, SofQW, Transpose
from mantid import plots
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm

First, load some diffraction data and see what the automatic axes will be using get_axes_labels().

Load(Filename="PG3_733", OutputWorkspace="PG3_733")
print(plots.helperfunctions.get_axes_labels(mtd['PG3_733']))

Which will print the y-label then the labels for all the other axes as properly escaped for use directly in matplotlib.axes.Axes.set_xlabel().

('Counts', 'd-Spacing ($\\AA$)', 'Spectrum')

To generate a 1D plots of some spectra with mantid projection:

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.plot(mtd['PG3_733'], 'go-', specNum=1, label='user label')
ax.errorbar(mtd['PG3_733'],  wkspIndex=2)
ax.legend()
fig.show()

or without:

fig, ax = plt.subplots()
plots.plotfunctions.plot(ax, mtd['PG3_733'], 'go-', specNum=1, label='user label')
plots.plotfunctions.errorbar(ax, mtd['PG3_733'],  wkspIndex=2)
ax.legend()
fig.show()

This example demonstrates adding multiple spectra onto a single 1D plot and overriding some of the default behavior. plot() is a normal line plot, while errorbar() adds the uncertainties. It should be warned that every call to one of the plot functions will automatically annotate the axes with the last one called being the one that takes effect.

The plot() function also allows plotting sample logs.

from mantid import plots
import matplotlib.pyplot as plt
w = LoadEventNexus(Filename='CNCS_7860_event.nxs')
fig = plt.figure()
ax1 = fig.add_subplot(211, projection = 'mantid')
ax2 = fig.add_subplot(212, projection = 'mantid')
ax1.plot(w, LogName = 'ChopperStatus5')
ax1.set_title('From run start')
ax2.plot(w, LogName = 'ChopperStatus5', FullTime = True)
ax2.set_title('Absolute time')
fig.tight_layout()
fig.show()

../../../../_images/mantid_plots_1Dlogs.png

Two common ways to look at 2D plots are contourf() and pcolormesh(). The difference between these is the contourf() calculates smooth lines of constant value, where the pcolormesh() is the actual data values. pcolormesh() is similar to pcolor(), but uses a different mechanism and returns a different object; pcolor returns a PolyCollection but pcolormesh returns a QuadMesh. It is much faster, so it is almost always preferred for large arrays.

LoadEventNexus(Filename='CNCS_7860_event.nxs', OutputWorkspace='CNCS_7860_event')
ConvertUnits(InputWorkspace='CNCS_7860_event', OutputWorkspace='CNCS_7860_event', Target='DeltaE', EMode='Direct', EFixed=3)
Rebin(InputWorkspace='CNCS_7860_event', OutputWorkspace='CNCS_7860_event', Params='-3,0.05,3')
SofQW(InputWorkspace='CNCS_7860_event', OutputWorkspace='CNCS_7860_sqw', QAxisBinning='0,0.05,3', EMode='Direct', EFixed=3)
Transpose(InputWorkspace='CNCS_7860_sqw',  OutputWorkspace='CNCS_7860_sqw')

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
c = ax.contourf(mtd['CNCS_7860_sqw'], norm=LogNorm())
ax.set_xlabel('awesome label')
fig.colorbar(c)
fig.show()

../../../../_images/mantid_plots_contourf.png

Similarly, showing the actual values with pcolormesh()

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
c = ax.pcolormesh(mtd['CNCS_7860_sqw'], norm=LogNorm())
fig.colorbar(c)
fig.show()

../../../../_images/mantid_plots_pcolormesh.png

A couple of notes about pcolor(), pcolormesh(), and pcolorfast():

If the mantid.api.MatrixWorkspace has unequal bins, the polygons/meshes will have sides not aligned with the axes. One can override this behavior by using the axisaligned keyword, and setting it to True
If the mantid.api.MatrixWorkspace has different numbers of bins the above functions will automatically use the axisaligned behavior (cannot be overridden). contour() and the like cannot plot these type of workspaces.

In addition to the mantid projection, there is also the mantid3d projection for 3d plots. Can be used much the same as the mantid projection, but by instead specifying mantid3d when giving the projection:

import matplotlib.pyplot as plt
from mantid import plots

#some code here to get a workspace, and x, y, yerr arrays

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.plot_wireframe(workspace)   #for workspaces
ax.plot_wireframe(x,y,z)       #for arrays
fig.show()

Types of functions¶

Informational

get_axes_labels()

1D Plotting

plot() - Plot lines and/or markers
errorbar() - Plot values with errorbars
scatter() - Make a scatter plot

2D Plotting

contour() - Draw contours at specified levels
contourf() - Draw contours at calculated levels
pcolor() - Draw a pseudocolor plot of a 2-D array
pcolorfast() - Draw a pseudocolor plot of a 2-D array
pcolormesh() - Draw a quadrilateral mesh
tripcolor() - Draw a pseudocolor plot of an unstructured triangular grid
tricontour() - Draw contours at specified levels on an unstructured triangular grid
tricontourf() - Draw contours at calculated levels on an unstructured triangular grid

3D Plotting

plot() - Draws a line plot in 3D space
scatter() - Draws a scatter plot in 3d space
plot_wireframe() - Draws a wire frame linking all adjacent data plots
plot_surface() - Draws a surface linking all adjacent data points
contour() - Draws contour lines at specified levels of the data
contourf() - Draws filled contour lines at specified levels of the data

matplotlib demonstrates the difference between uniform and nonuniform grids well in this example

Available Functions¶

When using `mantid` projection¶

class mantid.plots.MantidAxes(*args, **kwargs)¶

This class defines the mantid projection for 2d plotting. One chooses this projection using:

import matplotlib.pyplot as plt
from mantid import plots
fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})

or:

import matplotlib.pyplot as plt
from mantid import plots
fig = plt.figure()
ax = fig.add_subplot(111,projection='mantid')

The mantid projection allows replacing the array objects with mantid workspaces.

contour(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.contour() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.contour(workspace) #for workspaces
ax.contour(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.contour()

contourf(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.contourf() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.contourf(workspace) #for workspaces
ax.contourf(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.contourf()

errorbar(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.errorbar() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.errorbar(workspace,'rs',specNum=1) #for workspaces
ax.errorbar(x,y,yerr,'bo')            #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.errorbar()

pcolor(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.pcolor() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.pcolor(workspace) #for workspaces
ax.pcolor(x,y,C)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.pcolor()

pcolorfast(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.pcolorfast() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matpolotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.pcolorfast(workspace) #for workspaces
ax.pcolorfast(x,y,C)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.pcolorfast()

pcolormesh(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.pcolormesh() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.pcolormesh(workspace) #for workspaces
ax.pcolormesh(x,y,C)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.pcolormesh()

plot(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.plot() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.plot(workspace,'rs',specNum=1) #for workspaces
ax.plot(x,y,'bo')                 #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.plot().

scatter(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.scatter() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.scatter(workspace,'rs',specNum=1) #for workspaces
ax.scatter(x,y,'bo')                 #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.scatter()

tripcolor(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.tripcolor() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.tripcolor(workspace) #for workspaces
ax.tripcolor(x,y,C)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.tripcolor()

tricontour(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.tricontour() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.tricontour(workspace) #for workspaces
ax.tricontour(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.tricontour()

tricontourf(*args, **kwargs)¶

If the mantid projection is chosen, it can be used the same as matplotlib.axes.Axes.tricontourf() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid'})
ax.tricontourf(workspace) #for workspaces
ax.tricontourf(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions.tricontourf()

When using `mantid3d` projection¶

class mantid.plots.MantidAxes3D(*args, **kwargs)¶

This class defines the mantid3d projection for 3d plotting. One chooses this projection using:

import matplotlib.pyplot as plt
from mantid import plots
fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})

or:

import matplotlib.pyplot as plt
from mantid import plots
fig = plt.figure()
ax = fig.add_subplot(111,projection='mantid3d')

The mantid3d projection allows replacing the array objects with mantid workspaces.

contour(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.contour() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.contour(workspace) #for workspaces
ax.contour(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.contour()

contourf(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.contourf() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.contourf(workspace) #for workspaces
ax.contourf(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.contourf()

plot(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.plot() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.plot(workspace) #for workspaces
ax.plot(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.plot3D()

plot_surface(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.plot_surface() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.plot_surface(workspace) #for workspaces
ax.plot_surface(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.plot_surface()

plot_wireframe(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.plot_wireframe() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.plot_wireframe(workspace) #for workspaces
ax.plot_wireframe(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.wireframe()

scatter(*args, **kwargs)¶

If the mantid3d projection is chosen, it can be used the same as matplotlib.axes.Axes3D.scatter() for arrays, or it can be used to plot mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace. You can have something like:

import matplotlib.pyplot as plt
from mantid import plots

...

fig, ax = plt.subplots(subplot_kw={'projection':'mantid3d'})
ax.scatter(workspace) #for workspaces
ax.scatter(x,y,z)     #for arrays
fig.show()

For keywords related to workspaces, see plotfunctions3D.scatter()

Functions to use when mantid projection is not available¶

mantid.plots.axesfunctions.contour(axes, workspace, *args, **kwargs)¶

Essentially the same as matplotlib.axes.Axes.contour() but calculates the countour levels. Currently this only works with workspaces that have a constant number of bins between spectra.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

mantid.plots.axesfunctions.contourf(axes, workspace, *args, **kwargs)¶

Essentially the same as matplotlib.axes.Axes.contourf() but calculates the countour levels. Currently this only works with workspaces that have a constant number of bins between spectra.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

mantid.plots.axesfunctions.errorbar(axes, workspace, *args, **kwargs)¶

Unpack mantid workspace and render it with matplotlib. args and kwargs are passed to matplotlib.axes.Axes.errorbar() after special keyword arguments are removed. This will automatically label the line according to the spectrum number unless specified otherwise.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
specNum – spectrum number to plot if MatrixWorkspace
wkspIndex – workspace index to plot if MatrixWorkspace
distribution – None (default) asks the global setting. False means divide by bin width. True means do not divide by bin width. Applies only when the workspace is a MatrixWorkspace histogram.
normalize_by_bin_width – Plot the workspace as a distribution. If None default to global setting: config[‘graph1d.autodistribution’]
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
axis – Specify which axis will be plotted. Use axis=MantidAxType.BIN to plot a bin, and axis=MantidAxType.SPECTRUM to plot a spectrum. The default value is axis=1, plotting spectra by default.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use indices=(5, slice(None), 10) where the 5/10 are the bins selected for the other 2 axes.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use slicepoint=(1.0, None, 2.0) where the 1.0/2.0 are the dimension selected for the other 2 axes.

For matrix workspaces with more than one spectra, either specNum or wkspIndex needs to be specified. Giving both will generate a RuntimeError. There is no similar keyword for MDHistoWorkspaces. These type of workspaces have to have exactly one non integrated dimension

mantid.plots.axesfunctions.pcolor(axes, workspace, *args, **kwargs)¶

Essentially the same as matplotlib.axes.Axes.pcolor()

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

mantid.plots.axesfunctions.pcolorfast(axes, workspace, *args, **kwargs)¶

Essentially the same as matplotlib.axes.Axes.pcolorfast()

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

mantid.plots.axesfunctions.pcolormesh(axes, workspace, *args, **kwargs)¶

Essentially the same as matplotlib.axes.Axes.pcolormesh().

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

mantid.plots.axesfunctions.plot(axes, workspace, *args, **kwargs)¶

Unpack mantid workspace and render it with matplotlib. args and kwargs are passed to matplotlib.axes.Axes.plot() after special keyword arguments are removed. This will automatically label the line according to the spectrum number unless specified otherwise.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
specNum – spectrum number to plot if MatrixWorkspace
wkspIndex – workspace index to plot if MatrixWorkspace
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the workspace is a MatrixWorkspace histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
normalize_by_bin_width – None (default) ask the workspace. It can override the value from distribution. Is implemented so get_normalize_by_bin_width only need to be run once.
LogName – if specified, it will plot the corresponding sample log. The x-axis of the plot is the time difference between the log time and the first value of the proton_charge log (if available) or the sample log’s first time.
StartFromLog – False by default. If True the time difference will be from the sample log’s first time, even if proton_charge log is available.
FullTime – False by default. If true, the full date and time will be plotted on the axis instead of the time difference
ExperimentInfo – for MD Workspaces with multiple mantid.api.ExperimentInfo is the ExperimentInfo object from which to extract the log. It’s 0 by default
axis – Specify which axis will be plotted. Use axis=MantidAxType.BIN to plot a bin, and axis=MantidAxType.SPECTRUM to plot a spectrum. The default value is axis=1, plotting spectra by default.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use indices=(5, slice(None), 10) where the 5/10 are the bins selected for the other 2 axes.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use slicepoint=(1.0, None, 2.0) where the 1.0/2.0 are the dimension selected for the other 2 axes.

mantid.plots.axesfunctions.scatter(axes, workspace, *args, **kwargs)¶

Unpack mantid workspace and render it with matplotlib. args and kwargs are passed to matplotlib.axes.Axes.scatter() after special keyword arguments are removed. This will automatically label the line according to the spectrum number unless specified otherwise.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
specNum – spectrum number to plot if MatrixWorkspace
wkspIndex – workspace index to plot if MatrixWorkspace
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the workspace is a MatrixWorkspace histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use indices=(5, slice(None), 10) where the 5/10 are the bins selected for the other 2 axes.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use slicepoint=(1.0, None, 2.0) where the 1.0/2.0 are the dimension selected for the other 2 axes.

mantid.plots.axesfunctions.tricontour(axes, workspace, *args, **kwargs)¶

Essentially the same as mantid.plots.contour(), but works for non-uniform grids. Currently this only works with workspaces that have a constant number of bins between spectra or with MDHistoWorkspaces.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

See matplotlib.axes.Axes.tricontour() for more information.

mantid.plots.axesfunctions.tricontourf(axes, workspace, *args, **kwargs)¶

Essentially the same as mantid.plots.contourf(), but works for non-uniform grids. Currently this only works with workspaces that have a constant number of bins between spectra or with MDHistoWorkspaces.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

See matplotlib.axes.Axes.tricontourf() for more information.

mantid.plots.axesfunctions.tripcolor(axes, workspace, *args, **kwargs)¶

To be used with non-uniform grids. Currently this only works with workspaces that have a constant number of bins between spectra or with MDHistoWorkspaces.

Parameters:

axes – matplotlib.axes.Axes object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
distribution – None (default) asks the workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the matrix workspace is a histogram.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.
normalization – None (default) ask the workspace. Applies to MDHisto workspaces. It can override the value from displayNormalizationHisto. It checks only if the normalization is mantid.api.MDNormalization.NumEventsNormalization
transpose – bool to transpose the x and y axes of the plotted dimensions of an MDHistoWorkspace

See matplotlib.axes.Axes.tripcolor() for more information.

Functions to use when mantid3d projection is not available¶

mantid.plots.axesfunctions3D.contour(axes, workspace, *args, **kwargs)¶

Contour plots

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
extend3d – Whether to extend contour in 3D (default: False)
stride – Stride (step size) for extending contour
zdir – The direction to use: x, y or z (default)
offset – If specified plot a projection of the contour lines on this position in plane normal to zdir
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.

mantid.plots.axesfunctions3D.contourf(axes, workspace, *args, **kwargs)¶

Filled Contour plots

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
zdir – The direction to use: x, y or z (default)
offset – If specified plot a projection of the filled contour on this position in plane normal to zdir
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.

mantid.plots.axesfunctions3D.plot(axes, workspace, *args, **kwargs)¶

3D plots - line plots

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
zdir – Which direction to use as z (‘x’, ‘y’ or ‘z’) when plotting a 2D set.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use indices=(5, slice(None), 10) where the 5/10 are the bins selected for the other 2 axes.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the second axis to plot from a 3D volume use slicepoint=(1.0, None, 2.0) where the 1.0/2.0 are the dimension selected for the other 2 axes.

mantid.plots.axesfunctions3D.plot_surface(axes, workspace, *args, **kwargs)¶

Surface plots

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
rstride – Array row stride (step size)
cstride – Array column stride (step size)
rcount – Use at most this many rows, defaults to 100 (vs matplotlib default of 50)
ccount – Use at most this many columns, defaults to 100 (vs matplotlib default of 50)
color – Color of the surface patches
cmap – A colormap for the surface patches.
norm – An instance of Normalize to map values to colors
vmin – Minimum value to map
vmax – Maximum value to map
shade – Whether to shade the facecolors
facecolors – Face colors for the individual patches
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.

mantid.plots.axesfunctions3D.plot_wireframe(axes, workspace, *args, **kwargs)¶

Wire-frame plot

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
rstride – Array row stride (step size), defaults to 1
cstride – Array column stride (step size), defaults to 1
rcount – Use at most this many rows, defaults to 50
ccount – Use at most this many columns, defaults to 50
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.

mantid.plots.axesfunctions3D.scatter(axes, workspace, *args, **kwargs)¶

Scatter plots

Parameters:

axes – class:matplotlib.axes.Axes3D object that will do the plotting
workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace to extract the data from
zdir – Which direction to use as z (‘x’, ‘y’ or ‘z’) when plotting a 2D set.
s – Size in points^2. It is a scalar or an array of the same length as x and y.
c – A color. c can be a single color format string, or a sequence of color specifications of length N, or a sequence of N numbers to be mapped to colors using the cmap and norm specified via kwargs (see below). Note that c should not be a single numeric RGB or RGBA sequence because that is indistinguishable from an array of values to be colormapped. c can be a 2-D array in which the rows are RGB or RGBA, however, including the case of a single row to specify the same color for all points.
depthshade – Whether or not to shade the scatter markers to give the appearance of depth. Default is True.
indices – Specify which slice of an MDHistoWorkspace to use when plotting. Needs to be a tuple and will be interpreted as a list of indices. You need to use slice(None) to select which dimensions to plot. e.g. to select the last two axes to plot from a 3D volume use indices=(5, slice(None), slice(None)) where the 5 is the bin selected for the first axis.
slicepoint – Specify which slice of an MDHistoWorkspace to use when plotting in the dimension units. You need to use None to select which dimension to plot. e.g. to select the last two axes to plot from a 3D volume use slicepoint=(1.0, None, None) where the 1.0 is the value of the dimension selected for the first axis.

Helper functions¶

mantid.plots.datafunctions.boundaries_from_points(input_array)¶

The function tries to guess bin boundaries from bin centers

Parameters:: input_array – a numpy.ndarray of bin centers

mantid.plots.datafunctions.get_axes_labels(workspace, indices=None, normalize_by_bin_width=True, use_latex=True)¶

Get axis labels from a Workspace2D or an MDHistoWorkspace Returns a tuple. The first element is the quantity label, such as “Intensity” or “Counts”. All other elements in the tuple are labels for axes. Some of them are latex formatted already.

If MDWorkspace then the last element will be the values selected by the indices, to be set as title.

Parameters:

workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace
indices
normalize_by_bin_width – bool: Plotting workspace normalized by bin width
use_latex – bool: return y-unit label in Latex form

mantid.plots.datafunctions.get_distribution(workspace, **kwargs)¶: Determine whether or not the data is a distribution. If the workspace is a distribution return true, else the value in kwargs wins. Applies to Matrix workspaces only :param workspace: mantid.api.MatrixWorkspace to extract the data from

mantid.plots.datafunctions.get_matrix_2d_data(workspace, distribution, histogram2D=False, transpose=False)¶

Get all data from a Matrix workspace that has the same number of bins in every spectrum. It is used for 2D plots

Parameters:

workspace – Matrix workspace to extract the data from
distribution – if False, and the workspace contains histogram data, the intensity will be divided by the x bin width
histogram2D – flag that specifies if the coordinates in the output are -bin centers (such as for contour) for False, or -bin edges (such as for pcolor) for True.

Returns x,y,z 2D arrays

mantid.plots.datafunctions.get_md_data(workspace, normalization, indices=None, withError=False)¶

Generic function to extract data from an MDHisto workspace

Parameters:

workspace – mantid.api.IMDHistoWorkspace containing data
normalization – if mantid.api.MDNormalization.NumEventsNormalization it will divide intensity by the number of corresponding MDEvents
indices – slice indices to select data
withError – flag for if the error is calculated. If False, err is returned as None

returns a tuple containing bin boundaries for each dimension, the (maybe normalized) signal and error arrays

mantid.plots.datafunctions.get_md_data1d(workspace, normalization, indices=None)¶: Function to transform data in an MDHisto workspace with exactly one non-integrated dimension into arrays of bin centers, data, and error, to be used in 1D plots (plot, scatter, errorbar)

mantid.plots.datafunctions.get_md_data2d_bin_bounds(workspace, normalization, indices=None, transpose=False)¶

Function to transform data in an MDHisto workspace with exactly two non-integrated dimension into arrays of bin boundaries in each dimension, and data. To be used in 2D plots (pcolor, pcolorfast, pcolormesh)

Note: return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions

mantid.plots.datafunctions.get_md_data2d_bin_centers(workspace, normalization, indices=None, transpose=False)¶

Function to transform data in an MDHisto workspace with exactly two non-integrated dimension into arrays of bin centers in each dimension, and data. To be used in 2D plots (contour, contourf, tricontour, tricontourf, tripcolor)

Note: return coordinates are 1d vectors. Use numpy.meshgrid to generate 2d versions

mantid.plots.datafunctions.get_normalization(md_workspace, **kwargs)¶

Gets the normalization flag of an MDHistoWorkspace. For workspaces derived similar to MSlice/Horace, one needs to average data, the so-called “number of events” normalization.

Parameters:: md_workspace – mantid.api.IMDHistoWorkspace to extract the data from

mantid.plots.datafunctions.get_spectrum(workspace, wkspIndex, normalize_by_bin_width, withDy=False, withDx=False)¶

Extract a single spectrum and process the data into a frequency

Parameters:

workspace – a Workspace2D or an EventWorkspace
wkspIndex – workspace index
normalize_by_bin_width – flag to divide the data by bin width. The same effect can be obtained by running the ConvertToDistribution v1 algorithm
withDy – if True, it will return the error in the “counts”, otherwise None
Dx (with) – if True, and workspace has them, it will return errors in the x coordinate, otherwise None

Note that for workspaces containing bin boundaries, this function will return the bin centers for x. To be used in 1D plots (plot, scatter, errorbar)

mantid.plots.datafunctions.get_uneven_data(workspace, distribution)¶

Function to get data for uneven workspace2Ds, such as that pcolor, pcolorfast, and pcolormesh will plot axis aligned rectangles

Parameters:

workspace – a workspace2d
distribution – if False, and the workspace contains histogram data, the intensity will be divided by the x bin width

Returns three lists. Each element in the x list is an array of boundaries for a spectra. Each element in the y list is a 2 element array with the extents of a particular spectra. The z list contains arrays of intensities at bin centers

mantid.plots.datafunctions.get_wksp_index_dist_and_label(workspace, axis=MantidAxType.SPECTRUM, **kwargs)¶

Get workspace index, whether the workspace is a distribution, and label for the spectrum

Parameters:

workspace – a Workspace2D or an EventWorkspace
axis – The axis on which we’re operating
kwargs – Keyword arguments passed to the plot function, passed by reference as it is mutated

mantid.plots.datafunctions.points_from_boundaries(input_array)¶

The function returns bin centers from bin boundaries

Parameters:: input_array – a numpy.ndarray of bin boundaries

mantid.plots¶

Types of functions¶

Available Functions¶

When using mantid projection¶

When using mantid3d projection¶

Functions to use when mantid projection is not available¶

Functions to use when mantid3d projection is not available¶

Helper functions¶

`mantid.plots`¶

When using `mantid` projection¶

When using `mantid3d` projection¶