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cwepr.plotting module

Plotting: Graphical representations of data extracted from datasets.

Graphical representations of cw-EPR data are an indispensable aspect of data analysis. To facilitate this, a series of different plotters are available.

Plotting relies on matplotlib, and mainly its object-oriented interface should be used for the actual plotting.

Generally, two types of plotters can be distinguished:

In the first case, the plot is usually handled using the plot() method of the respective cwepr.dataset.Dataset object. Additionally, those plotters always only operate on the data of a single dataset, and the plot can easily be attached as a representation to the respective dataset. Plotters handling single datasets should always inherit from the aspecd.plotting.SinglePlotter class.

In the second case, the plot is handled using the plot() method of the aspecd.plotting.Plotter object, and the datasets are stored as a list within the plotter. As these plots span several datasets, there is no easy connection between a single dataset and such a plot in sense of representations stored in datasets. Plotters handling multiple datasets should always inherit from the aspecd.plotting.MultiPlotter class.

In a certain sense, there is a third type of plotters:

However, practically mostly these composite plotters will behave like plotters handling either single or multiple datasets. Generally, these composite plotters will use other types of plotters to perform the actual plot tasks. This modular approach allows for great flexibility.

A note on array dimensions and axes

Something often quite confusing is the apparent inconsistency between the order of array dimensions and the order of axes. While we are used to assign axes in the order x, y, z, and assuming x to be horizontal, y vertical (and z sticking out of the paper plane), arrays are usually indexed row-first, column-second. That means, however, that if you simply plot a 2D array in axes, your first dimension is along the y axis, the second dimension along the x axis.

Therefore, as the axes of your datasets will always correspond to the array dimensions of your data, in case of 2D plots you will need to either use the information contained in the second axis object for your x axis label, and the information from the first axis object for your y axis label, or to transpose the data array.

Another aspect to have in mind is the position of the origin. Usually, in a Cartesian coordinate system, convention is to have the origin (0, 0) in the lower left of the axes (for the positive quadrant). However, for images, convention is to have the corresponding (0, 0) pixel located in the upper left edge of your image. Therefore, those plotting methods dealing with images will usually revert the direction of your y axis. Most probably, eventually you will have to check with real data and ensure the plotters to plot data and axes in a consistent fashion.

Types of concrete plotters

The cwepr package comes with a series of concrete plotters included ready to be used, thanks to inheriting from the underlying ASpecD framework. As stated above, plotters can generally be divided into two types: plotters operating on single datasets and plotters combining the data of multiple datasets into a single figure.

Additionally, plotters can be categorised with regard to creating figures consisting of a single or multiple axes. The latter are plotters inheriting from the aspecd.plotting.CompositePlotter class. The latter can be thought of as templates for the other plotters to operate on, i.e. they provide the axes for other plotters to display their results.

Concrete plotters for single datasets

  • cwepr.plotting.SinglePlotter1D

    Basic line plots for single datasets, allowing to plot a series of line-type plots, including (semi)log plots

  • cwepr.plotting.SinglePlotter2D

    Basic 2D plots for single datasets, allowing to plot a series of 2D plots, including contour plots and image-type display

  • aspecd.plotting.SinglePlotter2DStacked

    Stacked plots of 2D data, converting a 2D display into a series of 1D line plots stacked on top of each other.

  • cwepr.plotting.SingleCompositePlotter

    Composite plotter for single datasets, allowing to plot different views of one and the same datasets by using existing plotters for single datasets.

  • cwepr.plotting.GoniometerSweepPlotter

    Composite plotter for single datasets representing goniometer sweeps, i.e. angular-dependent cw-EPR measurements.

Concrete plotters for multiple datasets

A note for developers

As each kind of spectroscopy comes with own needs for extensions, there is a class PlotterExtensions that can be used as a mixin class for other plotters to provide additional functionality for all plotters.

Make sure when implementing functionality here that it really works with all types of plotters, i.e. both SinglePlotters and MultiPlotters. This is particularly relevant if you need to get information from dataset(s), as a SinglePlotter will have an attribute dataset, while a MultiPlotter will have an attribute datasets.

Module documentation

class cwepr.plotting.GoniometerSweepPlotter

Bases: SingleCompositePlotter

Overview of the results of a goniometer sweep.

A goniometer sweep, i.e. a series of cw-EPR spectra as a function of the angle of the sample with respect to the external magnetic field, is usually performed over at least 180°, regardless of the step size. The reason is simply that the spectra for 0° and 180° should be identical due to the underlying physics of magnetic resonance.

The plotter will create three subpanels:

  • A 2D plot (scaled image plot) as a general overview.

  • A 1D multiplot comparing the signals for 0° and 180° to check for consistency during the measurement.

  • A stacked plot showing all angular positions, providing an alternative view of the angular-dependent signal changes compared to the 2D plot.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class.

To get an overview of your goniometer sweep, just invoke the plotter with default values:

- kind: singleplot
  type: GoniometerSweepPlotter
  properties:
    filename: output.pdf
class cwepr.plotting.PowerSweepAnalysisPlotter

Bases: MultiPlotter1D

Plot results of a power saturation analysis with second axis for mw power.

To determine the microwave power level not saturating the cw-EPR signal, usually a “power sweep” (power saturation study) is carried out with systematically varying the incident microwave power. The signal amplitude of the resulting data is plotted vs. the square root of the microwave power, resulting in a power saturation curve. As long as the signal is not saturated, the graph shows a linear relationship.

As the class inherits from aspecd.plotting.MultiPlotter1D see there for additional details of the parameters that can be set.

parameters

All parameters necessary for the plot, implicit and explicit

Most parameters are documented in the base class. Here, only the additional parameters or parameters with specific settings are documented.

mw-axisclass:bool

Whether to show an additional microwave axis in units of power.

The main x axis gives the square root of the microwave power, but as the microwave power needs to be set in power units (typically mW), it is convenient to have this available as well.

Default: True

tight_layout: bool

Whether to adjust the plot to fit into the figure area

For details see matplotlib.figure.Figure.tight_layout().

Default: True

Type:

dict

Examples

The class basically works like a usual MultiPlotter1D. A full power saturation analysis may look like this:

datasets:
  - PowerSweep
tasks:
  - kind: singleanalysis
    type: AmplitudeVsSqrtPower
    apply_to:
      - PowerSweep
    result: power_sweep_analysis
  - kind: singleanalysis
    type: FitOnData
    properties:
      parameters:
        order: 1
        points: 5
        return_type: dataset
    apply_to:
      - power_sweep_analysis
    result: fit
  - kind: multiplot
    type: PowerSweepAnalysisPlotter
    properties:
      properties:
        drawings:
          - marker: '*'
          - color: red
        grid:
          show: true
          axis: both
        axes:
          ylabel: '$EPR\\ amplitude$'
      filename: powersweepanalysis.pdf
    apply_to:
      - power_sweep_analysis
      - fit

This would result in a power saturation curve (EPR signal amplitude as a function of the square root of the microwave power, the latter usually in mW), and a linear fit covering in this case the first five data points.

Added in version 0.2.

class cwepr.plotting.PlotterExtensions

Bases: object

Extensions for plots of cw-EPR data.

This class is meant as a mixin class for plotters of the cwepr package and provides functionality specific for cw-EPR-spectroscopic data.

Hence it can only be used as mixin in addition to a plotter class.

parameters

All parameters necessary for the plot, implicit and explicit

The following keys exist, in addition to those defined by the actual plotter:

g-axis: bool

Whether to show an additional g axis opposite of the magnetic field axis

This assumes the magnetic field axis to be the x axis and the magnetic field values to be in millitesla (mT), as it calls cwepr.utils.convert_mT2g().

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.

Type:

dict

Added in version 0.2.

class cwepr.plotting.SinglePlotter1D

Bases: SinglePlotter1D, PlotterExtensions

1D plots of single datasets.

Convenience class taking care of 1D plots of single datasets.

As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation of the aspecd.plotting.SinglePlotter1D class for details.

Furthermore, the class inhertis all functionality from PlotterExtensions. See there for additional details.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.

In the simplest case, just invoke the plotter with default values:

- kind: singleplot
  type: SinglePlotter1D
  properties:
    filename: output.pdf

In case you would like to have a g axis plotted as a second x axis on top:

- kind: singleplot
  type: SinglePlotter1D
  properties:
    parameters:
      g-axis: true
    filename: output.pdf

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.

class cwepr.plotting.SinglePlotter2D

Bases: SinglePlotter2D, PlotterExtensions

2D plots of single datasets.

Convenience class taking care of 2D plots of single datasets.

As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation of the aspecd.plotting.SinglePlotter2D class for details.

Furthermore, the class inherits all functionality from PlotterExtensions. See there for additional details.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.

In the simplest case, just invoke the plotter with default values:

- kind: singleplot
  type: SinglePlotter2D
  properties:
    filename: output.pdf

To change the axes (flip x and y axis):

- kind: singleplot
  type: SinglePlotter2D
  properties:
    filename: output.pdf
    parameters:
      switch_axes: True

To use another type (here: contour):

- kind: singleplot
  type: SinglePlotter2D
  properties:
    filename: output.pdf
    type: contour

To set the number of levels of a contour plot to 10:

- kind: singleplot
  type: SinglePlotter2D
  properties:
    filename: output.pdf
    type: contour
    parameters:
      levels: 10

To change the colormap (cmap) used:

- kind: singleplot
  type: SinglePlotter2D
  properties:
    filename: output.pdf
    properties:
      drawing:
        cmap: RdGy

Make sure to check the documentation of the ASpecD aspecd.plotting module for further parameters that can be set.

In case you would like to have a g axis plotted as a second x axis on top:

- kind: singleplot
  type: SinglePlotter2D
  properties:
    parameters:
      g-axis: true
    filename: output.pdf

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.

class cwepr.plotting.SinglePlotter2DStacked

Bases: SinglePlotter2DStacked, PlotterExtensions

Stacked plots of 2D data.

A stackplot creates a series of lines stacked on top of each other from a 2D dataset.

As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation of the aspecd.plotting.SinglePlotter2DStacked class for details.

Furthermore, the class inherits all functionality from PlotterExtensions. See there for additional details.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.

In the simplest case, just invoke the plotter with default values:

- kind: singleplot
  type: SinglePlotter2DStacked
  properties:
    filename: output.pdf

If you need to more precisely control the formatting of the y tick labels, particularly the number of decimals shown, you can set the formatting accordingly:

- kind: singleplot
  type: SinglePlotter2DStacked
  properties:
    filename: output.pdf
    parameters:
      yticklabelformat: '%.2f'

In this particular case, the y tick labels will appear with only two decimals. Note that currently, the “old style” formatting specifications are used due to their widespread use in other programming languages and hence the familiarity of many users with this particular notation.

Sometimes you want to have horizontal “zero lines” appear for each individual trace of the stacked plot. This can be achieved explicitly setting the “show_zero_lines” parameter to “True” that is set to “False” by default:

- kind: singleplot
  type: SinglePlotter2DStacked
  properties:
    filename: output.pdf
    parameters:
      show_zero_lines: True

In case you would like to have a g axis plotted as a second x axis on top:

- kind: singleplot
  type: SinglePlotter2DStacked
  properties:
    parameters:
      g-axis: true
    filename: output.pdf

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.

class cwepr.plotting.MultiPlotter1D

Bases: MultiPlotter1D, PlotterExtensions

1D plots of multiple datasets.

Convenience class taking care of 1D plots of multiple datasets.

As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation of the aspecd.plotting.MultiPlotter1D class for details.

Furthermore, the class inherits all functionality from PlotterExtensions. See there for additional details.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.

In the simplest case, just invoke the plotter with default values:

- kind: multiplot
  type: MultiPlotter1D
  properties:
    filename: output.pdf

To change the settings of each individual line (here the colour and label), supposing you have three lines, you need to specify the properties in a list for each of the drawings:

- kind: multiplot
  type: MultiPlotter1D
  properties:
    filename: output.pdf
    properties:
      drawings:
        - color: '#FF0000'
          label: foo
        - color: '#00FF00'
          label: bar
        - color: '#0000FF'
          label: foobar

Important

If you set colours using the hexadecimal RGB triple prefixed by #, you need to explicitly tell YAML that these are strings, surrounding the values by quotation marks.

In case you would like to have a g axis plotted as a second x axis on top:

- kind: multiplot
  type: MultiPlotter1D
  properties:
    parameters:
      g-axis: true
    filename: output.pdf

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.

class cwepr.plotting.MultiPlotter1DStacked

Bases: MultiPlotter1DStacked, PlotterExtensions

Stacked 1D plots of multiple datasets.

Convenience class taking care of 1D plots of multiple datasets.

As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation of the aspecd.plotting.MultiPlotter1DStacked class for details.

Furthermore, the class inherits all functionality from PlotterExtensions. See there for additional details.

Examples

For convenience, a series of examples in recipe style (for details of the recipe-driven data analysis, see aspecd.tasks) is given below for how to make use of this class. Of course, all parameters settable for the superclasses can be set as well. The examples focus each on a single aspect.

In the simplest case, just invoke the plotter with default values:

- kind: multiplot
  type: MultiPlotter1DStacked
  properties:
    filename: output.pdf

To change the settings of each individual line (here the colour and label), supposing you have three lines, you need to specify the properties in a list for each of the drawings:

- kind: multiplot
  type: MultiPlotter1DStacked
  properties:
    filename: output.pdf
    properties:
      drawings:
        - color: '#FF0000'
          label: foo
        - color: '#00FF00'
          label: bar
        - color: '#0000FF'
          label: foobar

Important

If you set colours using the hexadecimal RGB triple prefixed by #, you need to explicitly tell YAML that these are strings, surrounding the values by quotation marks.

Sometimes you want to have horizontal “zero lines” appear for each individual trace of the stacked plot. This can be achieved explicitly setting the “show_zero_lines” parameter to “True” that is set to “False” by default:

- kind: multiplot
  type: MultiPlotter1DStacked
  properties:
    filename: output.pdf
    parameters:
      show_zero_lines: True

In case you would like to have a g axis plotted as a second x axis on top:

- kind: multiplot
  type: MultiPlotter1DStacked
  properties:
    parameters:
      g-axis: true
    filename: output.pdf

Important

If you add a g axis to your plot, and at the same time specify a figure title, this will result in the figure title clashing with the g axis. The solution: set an axes title rather than a figure title.