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Characteristics of a dataset

Classes used:

Description

As with all kinds of data, one of the first and most important steps is to get an overview of the resulting data and their characteristics. Furthermore, with a growing number of different measurements, keeping an overview is crucial. This is the realm of well-crafted, expressive graphical representations that are automatically generated, thus uniform and highly appropriate to compare different datasets and focus on their respective differences.

For tr-EPR data in particular, things get somewhat more complicated due to the two-dimensional nature of the data (magnetic field, time). One strategy to get a decent overview of the characteristics of a dataset would be:

  • Graphical overview of the entire dataset (2D plot),

  • Transients (slices along the time axis) at both, global maximum and global minimum, and

  • Spectra (slices along the magnetic field axis) at both, global maximum and global minimum.

Of course, prior to plotting, the usual corrections (pretrigger offset compensation, background correction) need to be applied.

Recipe

Complete recipe for creating a graphical representation consisting of three subpanels showing an overview of the entire dataset as 2D plot as well as transients and spectra at the global maximum and minimum, respectively. While automatically taking the global extrema might not always be the best idea, it is pretty useful for a first overview, at least as long as your data are not too noisy.
  1format:
  2  type: ASpecD recipe
  3  version: '0.2'
  4
  5settings:
  6  default_package: trepr
  7  autosave_plots: false
  8
  9directories:
 10  datasets_source: ../../tests/testdata/fsc2/
 11  
 12datasets:
 13  #- radical-pair
 14  - triplet
 15
 16tasks:
 17  - kind: processing
 18    type: PretriggerOffsetCompensation
 19    comment: >
 20        Compensate for DC offsets of the signal prior to the laser flash
 21  - kind: processing
 22    type: BackgroundCorrection
 23    comment: >
 24        Correct for laser-induced background signal.
 25  - kind: singleanalysis
 26    type: BasicCharacteristics
 27    properties:
 28      parameters:
 29        kind: max
 30        output: indices
 31        axis: 0
 32    result: max_field
 33    comment: Extract index of field axis for global maximum of the dataset
 34  - kind: singleanalysis
 35    type: BasicCharacteristics
 36    properties:
 37      parameters:
 38        kind: min
 39        output: indices
 40        axis: 0
 41    result: min_field
 42    comment: Extract index of field axis for global minimum of the dataset
 43  - kind: singleanalysis
 44    type: BasicCharacteristics
 45    properties:
 46      parameters:
 47        kind: max
 48        output: indices
 49        axis: 1
 50    result: max_time
 51    comment: Extract index of time axis for global maximum of the dataset
 52  - kind: singleanalysis
 53    type: BasicCharacteristics
 54    properties:
 55      parameters:
 56        kind: min
 57        output: indices
 58        axis: 1
 59    result: min_time
 60    comment: Extract index of time axis for global minimum of the dataset
 61  - kind: processing
 62    type: SliceExtraction
 63    properties:
 64      parameters:
 65        position: max_field
 66        axis: 0
 67    result: time_trace_max
 68    comment: Extract transient for global maximum of the dataset
 69  - kind: processing
 70    type: SliceExtraction
 71    properties:
 72      parameters:
 73        position: min_field
 74        axis: 0
 75    result: time_trace_min
 76    comment: Extract transient for global minimum of the dataset
 77  - kind: processing
 78    type: SliceExtraction
 79    properties:
 80      parameters:
 81        position: max_time
 82        axis: 1
 83    result: spectrum_max
 84    comment: Extract spectrum for global maximum of the dataset
 85  - kind: processing
 86    type: SliceExtraction
 87    properties:
 88      parameters:
 89        position: min_time
 90        axis: 1
 91    result: spectrum_min
 92    comment: Extract spectrum for global minimum of the dataset
 93  - kind: multiplot
 94    type: MultiPlotter1D
 95    properties:
 96      parameters:
 97        switch_axes: true
 98        tight_layout: true
 99        tight: y
100      properties:
101        grid:
102          show: true
103          axis: y
104    apply_to:
105    - time_trace_min
106    - time_trace_max
107    result: transients
108  - kind: multiplot
109    type: MultiPlotter1D
110    properties:
111      parameters:
112        tight_layout: true
113        tight: x
114      properties:
115        grid:
116          show: true
117          axis: x
118    apply_to:
119    - spectrum_max
120    - spectrum_min
121    result: spectra
122  - kind: singleplot
123    type: SinglePlotter2D
124    properties:
125      parameters:
126        tight_layout: true
127      properties:
128        figure:
129          dpi: 300
130        drawing:
131          cmap: twilight_shifted
132      type: contourf
133    result: 2D
134  - kind: compositeplot
135    type: CompositePlotter
136    properties:
137      properties:
138        figure:
139          dpi: 150
140          size: [8, 8]
141      grid_dimensions: [2, 2]
142      subplot_locations:
143        - [0, 0, 1, 1]
144        - [0, 1, 1, 1]
145        - [1, 0, 1, 1]
146      plotter:
147        - 2D
148        - transients
149        - spectra
150      filename: characteristics.png

Result

../_images/show-characteristics.png

Characteristics of a tr-EPR dataset at a glance. The 2D plot in the top left panel provides an overview of the entire dataset, while the transients and spectra obtained for the global maximum and minimum, respectively, are presented parallel to the corresponding axes of the 2D plot. In this particular case, the spectra for maximum and minimum entirely overlap, meaning that maximum and minimum of the signal occur at the same time after the laser flash.