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cwepr.analysis module¶
Module containing the analysis steps of the cwEPR package.
An analysis step, in contrast to a processing step, does not simply modify a
given dataset, but extracts some characteristic of this dataset that is
contained in its results
property. This can be as simple as a number,
e.g., in case of the signaltonoise figure of a data trace, but as
complicated as a (calculated) dataset on its own containing some measure as
function of some parameter, such as the microwave frequency for each of a
series of recordings, allowing to visualise drifts that may or may not
impact data analysis.
Note to developers¶
Processing steps can be based on analysis steps, but not inverse! Otherwise, we get cyclic dependencies what should obviously be avoided in order to keep code working.

class
cwepr.analysis.
FieldCalibration
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Determine offset value for a magnetic field calibration.
While the microwave frequency of an EPR spectrometer can be measured with high accuracy independent of the resonator (and cryostat) used, the magnetic field values recorded are usually measured by a Hall probe or NMR teslameter away from the actual sample position. Hence, calibrating the recorded magnetic field value is necessary in case you are interested in quantitative gvalue measurements or accurate comparisons between measurements.
While spectrometers without removable probeheads (typically benchtop devices) come often calibrated by the manufacturer, in the typical research setup with probeheads and cryostats changing more frequent, field calibration is typically performed by the individual researcher recording the EPR spectrum of a standard sample with known g value.
Correcting the magnetic field axis of a recorded EPR spectrum is in such cases actually a twostep process:
Obtain the magnetic field offset value from the EPR spectrum of a field standard with known g value.
Add the obtained value to the values of the magnetic field axis of the EPR spectrum that should be fieldcorrected.
This class provides the first step, obtaining the magnetic field offset value for a number of wellknown standards. And in case you use another field standard, you can simply provide the g value for this standard as well. The second step, correcting the magnetic field axis of your spectrum, is taken care of by the class
cwepr.processing.FieldCorrection
. See the examples section below for further details.Note
The method currently relies on the recorded spectrum of the field standard to consist of a single symmetric line accurately recorded with sufficient resolution. The g value
Currently, the following field standards are supported:
Substance
Name
g Value
Reference
Li:LiF
LiLiF
2.002293 ± 0.000002
[1]
DPPH
DPPH
2.0036 ± 0.0002
[2]
References
[1] Stesmans and Van Gorp, Rev. Sci. Instrum. 60(1989):2949–2952.
[2] Yordanov, Appl. Magn. Reson. 10(1996):339–350.
The column “name” here refers to the value the parameter
standard
can take (see below). These names are caseinsensitive.
parameters
¶ All parameters necessary for this step.
 standard
str
Field standard that should be applied.
For valid values, see the table above.
 g_value
float
g value of the standard sample.
If you provide a standard by name using the
standard
parameter above, this is not necessary. Providing a value here overrides the value for the standard. Hence use with care not to confuse you afterwards, when standard name and g value are inconsistent. mw_frequency
float
Microwave frequency to be corrected for.
In general, this value is taken from the dataset and should therefore usually not be provided explicitly. Providing a value overrides reading from the dataset.
 Type
 standard
 Raises
ValueError – Raised if no microwave frequency or g value is available.
See also
cwepr.processing.FieldCorrection
Correct magnetic field axis by a linear offset
Examples
For convenience, a series of examples in recipe style (for details of the recipedriven data analysis, see
aspecd.tasks
) is given below for how to make use of this class. The examples focus each on a single aspect.Suppose you have recorded the spectrum of a Li:LiF field standard and now you would like to obtain the field offset to correct your other spectra:
 kind: singleanalysis type: FieldCalibration properties: parameters: standard: LiLiF result: deltaB0
This result can now be used within the same recipe to perform a field correction of your other data. A more detailed example may look as follows:
datasets:  /path/to/LiLiF/dataset label: lilif  /path/to/my/first/dataset label: first  /path/to/my/second/dataset label: second tasks:  kind: singleanalysis type: FieldCalibration properties: parameters: standard: LiLiF result: deltaB0 apply_to: lilif  kind: processing type: FieldCorrection properties: parameters: offset: deltaB0 apply_to:  first  second
Here, we start by loading three datasets, a standard (LiLiF in this case) and two actual spectra. The first task is the analysis step to obtain the field offset value, performed only on the LiLiF spectrum, and the second is a processing step performed only on the two actual spectra to correct their field axis.
Suppose you have used a standard that’s currently not supported by name. Therefore, you will want to provide both, name and g value of the standard:
 kind: singleanalysis type: FieldCalibration properties: parameters: standard: Strong pitch g_value: 2.0028 result: deltaB
Of course, providing the name of the standard does not change how the field offset is calculated, but it serves as documentation for you. Note that providing a g value overrides the value stored internally. Therefore, if you provide both, standard and g value, it is your sole responsibility to ensure consistency between those two parameters.
Changed in version 0.2: Renamed to FieldCalibration, added parameter
g_value

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Field calibration can only be applied to 1D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

class
cwepr.analysis.
LinewidthPeakToPeak
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Peak to peak linewidth in derivative spectrum.
The linewidth is given in a first derivative spectrum as difference between the two extreme points. However, this is valid only for simple spectra with just one line or signal. This analysis step simply takes the difference on the magnetic field axis which is then stored in the result. The task can be used as follows:
 kind: singleanalysis type: LinewidthPeakToPeak result: linewidth

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Line width detection can only be applied to 1D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

get_peak_to_peak_linewidth
()¶ Calculates the peaktopeak linewidth.
This is done by determining the distance between the maximum and the minimum in the derivative spectrum which should yield acceptable results in a symmetrical signal. Limitation: Spectrum contains only one line.
 Returns
linewidth – peak to peak linewidth
 Return type

static

class
cwepr.analysis.
LinewidthFWHM
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Full linewidth at half maximum (FWHM).
In EPR, this linewidth can be applied to integrated cwEPR spectra or spectra recorded in absorptive mode. The calculation is done by subtracting maximum/2, getting the absolute value and then determining the minima. The distance between these points corresponds to the FWHM linewidth.
Examples
Usage is quite simple as it requires no additional parameters:
 kind: singleanalysis type: LinewidthFWHM result: linewidth

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Line width detection can only be applied to 1D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

static

class
cwepr.analysis.
SignalToNoiseRatio
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Get a spectrum’s signal to noise ratio.
This is done by comparing the absolute maximum of the spectrum to the maximum of the edge part of the spectrum (i.e. a part which is considered to not contain any signal.

parameters['percentage']
percentage of the spectrum to be considered edge part on any side (i.e. 10 % means 10 % on each side).
Default: 10 %
 Type
Examples
The analysis can be applied either with or without the additional parameter “percentage”:
 kind: singleanalysis type: SignalToNoiseRatio properties: parameters: percentage: 7 result: SNR


class
cwepr.analysis.
Amplitude
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Determine amplitude of dataset.
Depending of the dimension of the dataset, the returned value is either a scalar (1D dataset) or a vector (2D dataset) containing the amplitude of each row respectively.
 Returns
amplitude(s) rowwise, thus scalar or vector.
 Return type
result
Examples
The analysis is again called without additional parameters:
 kind: singleanalysis type: Amplitude result: amplitude

class
cwepr.analysis.
AmplitudeVsPower
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Return a calculated dataset to further analyse a powersweep experiment.
The analysis of a power sweep results in a plot of the peak amplitude vs. the square root of the microwave power of the bridge. Both values are determined in this step and put together in a calculated dataset that can be used subsequently to perform the linear fit.
 Returns
result – Calculated Dataset where the data is the amplitude and the axis values is the root of the mwpower (in ascending order).
 Return type
Examples
For analysing a power sweep, extracting the amplitude and taking the root of the microwave power is the first step to success (see Power sweep analysis for further details) and can be done as follows without additional parameters:
 kind: singleanalysis type: AmplitudeVsPower result: power_sweep_analysis
A more complete example of a power sweep analysis including linear fit of the first n points and a graphical representation of the results may look as follows:
datasets:  PowerSweep tasks:  kind: singleanalysis type: AmplitudeVsPower 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.

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Extracting the EPR signal amplitude as function of the microwave power can only be applied to 2D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

class
cwepr.analysis.
FitOnData
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Perform polynomial fit on data and return its parameters or a dataset.
Developed tests first.

parameters
¶ All parameters necessary for this step.
 points
first n points that should taken into account
Default: 3
 order
order of the fit.
Default: 1
 return_type:class: str
Choose to returning the coefficients of the fit in order of increasing degree or a calculated dataset containing the curve to plot.
Default: coefficients.
Valid values: ‘coefficients’, ‘dataset’
 add_origin:class: bool
Adds the point (0,0) to the data and axes, but does not guarantee the fit really passes the origin.
Default: False.
 Type
Examples
Some Parameters can be chosen here, depending on the purpose and the following analysis and processing steps.
 kind: singleanalysis type: FitOnData properties: parameters: points: 5 order: 2 return_type: dataset add_origin: True result: fit
Changed in version 0.3: Rewrite to perform fits with fixed intercept, remove parameter “add_origin”, introduced “fixed_intercerpt”. Return coefficients in order of increasing degree

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Polynomial fitting can only be applied to 1D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type


class
cwepr.analysis.
PtpVsModAmp
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Create calculated dataset for modulation sweep analysis.
For a modulation sweep analysis, the first step is to get the peak to peak amplitude and correlate it to the modulation amplitude, see Modulation sweep analysis for further details.
Examples
The usage is also quite simple without additional parameters necessary:
 kind: singleanalysis type: PtpVsModAmp result: calc_dataset

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Extracting the peaktopeak linewidth as function of the modulation amplitude can only be applied to 2D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

static

class
cwepr.analysis.
AreaUnderCurve
¶ Bases:
aspecd.analysis.SingleAnalysisStep
Make definite integration, i.e. calculate the area under the curve.
Takes no other parameters and returns a single number. However, this step does not (yet) account for baselines or noise thus that the value obtained here is to be taken with care.
Examples
 kind: singleanalysis type: AreaUnderCurve result: area

static
applicable
(dataset)¶ Check whether analysis step is applicable to the given dataset.
Calculating the area can only be applied to 1D datasets.
 Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset to check Returns
applicable – Whether dataset is applicable
 Return type

static