4.0

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 MantidPlot 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()
../../../../_images/mantid_plots_1D.png

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

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.

When using mantid3d projection

class mantid.plots.MantidAxes3D(fig, rect=None, *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.plotfunctions.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 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
  • 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.

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.plotfunctions.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 workspace. False means divide by bin width. True means do not divide by bin width. Applies only when the 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
  • 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.

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.plotfunctions.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 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

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.plotfunctions.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 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
mantid.plots.plotfunctions.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 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
mantid.plots.plotfunctions.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 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
  • axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
mantid.plots.plotfunctions.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 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
  • axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
mantid.plots.plotfunctions.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 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
  • axisaligned – False (default). If True, or if the workspace has a variable number of bins, the polygons will be aligned with the axes
mantid.plots.plotfunctions.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 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

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

mantid.plots.plotfunctions.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 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

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

mantid.plots.plotfunctions.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 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

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

Functions to use when mantid3d projection is not available

mantid.plots.plotfunctions3D.plot(axes, workspace, *args, **kwargs)

3D plots - line plots

Parameters:
mantid.plots.plotfunctions3D.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.
mantid.plots.plotfunctions3D.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
mantid.plots.plotfunctions3D.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), 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
  • 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
mantid.plots.plotfunctions3D.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
mantid.plots.plotfunctions3D.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

Helper functions

mantid.plots.helperfunctions.get_distribution(workspace, **kwargs)

Determine whether or not the data is a distribution. The value in the kwargs wins. Applies to Matrix workspaces only

Parameters:workspace – mantid.api.MatrixWorkspace to extract the data from
mantid.plots.helperfunctions.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.helperfunctions.points_from_boundaries(input_array)

The function returns bin centers from bin boundaries

Parameters:input_array – a numpy.ndarray of bin boundaries
mantid.plots.helperfunctions.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.helperfunctions.get_wksp_index_dist_and_label(workspace, axis=<EnumValue: MantidAxType.SPECTRUM [value=1]>, **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.helperfunctions.get_md_data(workspace, normalization, withError=False)

Generic function to extract data from an MDHisto workspace

Parameters:

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

mantid.plots.helperfunctions.get_md_data1d(workspace, normalization)

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.helperfunctions.get_md_data2d_bin_bounds(workspace, normalization)

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.helperfunctions.get_md_data2d_bin_centers(workspace, normalization)

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.helperfunctions.get_spectrum(workspace, wkspIndex, distribution, 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
  • distribution – flag to divide the data by bin width. It happens only when this flag is False, the workspace contains histogram data, and the mantid configuration is set up to divide such workspaces 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.helperfunctions.get_matrix_2d_data(workspace, distribution, histogram2D=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.helperfunctions.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.helperfunctions.get_axes_labels(workspace)

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.

Parameters:workspace – mantid.api.MatrixWorkspace or mantid.api.IMDHistoWorkspace