uvvispy.processing module¶
Processing steps of the UVVisPy package.
A processing step always operates on a dataset and usually modifies the numerical data contained therein. The result of a processing step is in any case again a dataset, in contrast to analysis steps where this is not necessarily the case. Typical routine processing steps are normalisation (to area, amplitude, maximum, minimum), and for UVVis spectroscopy such things as baseline correction.
Processing steps implemented¶
The processing steps implemented in this module can be separated into those specific for UVVis data and those that are generally applicable and were inherited from the ASpecD framework.
Specific processing steps for UVVis data¶
Currently, there are no specific processing steps implemented.
General processing steps inherited from the ASpecD framework¶
A number of further processing steps that are generally applicable to spectroscopic data have been inherited from the underlying ASpecD framework:

Correct baseline of dataset.

Normalise data to minimum, maximum, amplitude, area.

Perform scalar algebraic operation on one dataset.
Operations available: add, subtract, multiply, divide (by given scalar)

Perform scalar algebraic operation on axis values of a dataset.
Operations available: add, subtract, multiply, divide, power (by given scalar)

Perform scalar algebraic operation on two datasets.
Operations available: add, subtract

Extract range of data from a dataset.

Extract the common range of data for multiple datasets using interpolation.
Useful (and often necessary) for performing algebraic operations on datasets.

Interpolate data.

Filter data
Further processing steps implemented in the ASpecD framework can be used as
well, by importing the respective modules. In case of recipedriven data
analysis, simply prefix the kind with aspecd
:
 kind: aspecd.processing
type: <ClassNameOfProcessingStep>
Module documentation¶
What follows is the API documentation of each class implemented in this module.
 class uvvispy.processing.BaselineCorrection¶
Bases:
aspecd.processing.BaselineCorrection
Subtract baseline from dataset.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.BaselineCorrection
class for details.Note
There is only one difference to the ASpecD class: The area used for fitting is by default only the ten percent from the right, as optical spectra tend to have features towards the highenergy shortwavelength end.
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.In the simplest case, just invoke the baseline correction with default values:
 kind: processing type: BaselineCorrection
In this case, a zerothorder polynomial baseline will be subtracted from your dataset using ten percent to the right, and in case of a 2D dataset, the baseline correction will be performed along the first axis (index zero) for all indices of the second axis (index 1).
Of course, often you want to control a little bit more how the baseline will be corrected. This can be done by explicitly setting some parameters.
Suppose you want to perform a baseline correction with a polynomial of first order:
 kind: processing type: BaselineCorrection properties: parameters: order: 1
 class uvvispy.processing.Normalisation¶
Bases:
aspecd.processing.Normalisation
Normalise data.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.Normalisation
class for details.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.In the simplest case, just invoke the normalisation with default values:
 kind: processing type: Normalisation
This will normalise your data to their maximum.
Sometimes, normalising to maximum is not what you need, hence you can control in more detail the criterion using the appropriate parameter:
 kind: processing type: Normalisation properties: parameters: kind: amplitude
In this case, you would normalise to the amplitude, meaning setting the difference between minimum and maximum to one. For other kinds, see above.
If you want to normalise not over the entire range of the dataset, but only over a dedicated range, simply provide the necessary parameters:
 kind: processing type: Normalisation properties: parameters: range: [50, 150]
In this case, we assume a 1D dataset and use indices, requiring the data to span at least over 150 points. Of course, it is often more convenient to provide axis units. Here you go:
 kind: processing type: Normalisation properties: parameters: range: [340, 350] range_unit: axis
 class uvvispy.processing.ScalarAlgebra¶
Bases:
aspecd.processing.ScalarAlgebra
Perform scalar algebraic operation on one dataset.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.ScalarAlgebra
class for details.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.In case you would like to add a fixed value of 42 to your dataset:
 kind: processing type: ScalarAlgebra properties: parameters: kind: add value: 42
Similarly, you could use “minus”, “times”, “by”, “add”, “subtract”, “multiply”, or “divide” as kind  resulting in the given algebraic operation.
 class uvvispy.processing.ScalarAxisAlgebra¶
Bases:
aspecd.processing.ScalarAxisAlgebra
Perform scalar algebraic operation on the axis of a dataset.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.ScalarAxisAlgebra
class for details.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.In case you would like to add a fixed value of 42 to the first axis (index 0) your dataset:
 kind: processing type: ScalarAxisAlgebra properties: parameters: kind: plus axis: 0 value: 42
Similarly, you could use “minus”, “times”, “by”, “add”, “subtract”, “multiply”, “divide”, and “power” as kind  resulting in the given algebraic operation.
 class uvvispy.processing.DatasetAlgebra¶
Bases:
aspecd.processing.DatasetAlgebra
Perform scalar algebraic operation on two datasets.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.DatasetAlgebra
class for details.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.In case you would like to add the data of the dataset referred to by its label
label_to_other_dataset
to your dataset: kind: processing type: DatasetAlgebra properties: parameters: kind: plus dataset: label_to_other_dataset
Similarly, you could use “minus”, “add”, “subtract” as kind  resulting in the given algebraic operation.
As mentioned already, the data of both datasets need to have identical shape, and comparison is only meaningful if the axes are compatible as well. Hence, you will usually want to perform a CommonRangeExtraction processing step before doing algebra with two datasets:
 kind: multiprocessing type: CommonRangeExtraction results:  label_to_dataset  label_to_other_dataset  kind: processing type: DatasetAlgebra properties: parameters: kind: plus dataset: label_to_other_dataset apply_to:  label_to_dataset
 class uvvispy.processing.RangeExtraction¶
Bases:
aspecd.processing.RangeExtraction
Extract range of data from dataset.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.RangeExtraction
class for details.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.In the simplest case, just invoke the range extraction with one range only, assuming a 1D dataset:
 kind: processing type: RangeExtraction properties: parameters: range: [5, 10]
This will extract the range
data[5:10]
from your data (and adjust the axis accordingly). In case of 2D data, it would be fairly similar, except of now providing two ranges: kind: processing type: RangeExtraction properties: parameters: range:  [5, 10]  [3, 6]
Additionally, you can provide step sizes, just as you can do when slicing in Python:
 kind: processing type: RangeExtraction properties: parameters: range: [5, 10, 2]
This is equivalent to
data[5:10:2]
ordata[(slice(5, 10, 2))]
, accordingly.Sometimes, it is more convenient to give ranges in axis values rather than indices. This can be achieved by setting the parameter
unit
to “axis”: kind: processing type: RangeExtraction properties: parameters: range: [5, 10] unit: axis
Note that in this case, setting a step is meaningless and will be silently ignored. Furthermore, the nearest axis values will be used for the range.
In some cases you may want to extract a range by providing percentages instead of indices or axis values. Even this can be done:
 kind: processing type: RangeExtraction properties: parameters: range: [0, 10] unit: percentage
Here, the first ten percent of the data of the 1D dataset will be extracted, or more exactly the indices falling within the first ten percent. Note that in this case, setting a step is meaningless and will be silently ignored. Furthermore, the nearest axis values will be used for the range.
 class uvvispy.processing.CommonRangeExtraction¶
Bases:
aspecd.processing.CommonRangeExtraction
Extract the common range of data for multiple datasets using interpolation.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.CommonRangeExtraction
class for details.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.In case you would like to bring all datasets currently loaded into your recipe to a common range (use with caution, however), things can be as simple as:
 kind: multiprocessing type: CommonRangeExtraction
Note that this will operate on all datasets currently available in your recipe, including results from other processing steps. Therefore, it is usually better to be explicit, using
apply_to
. Otherwise, you can use this processing step early on in your recipe.Usually, however, you will want to restrict this to a subset using
apply_to
and provide labels for the results: kind: multiprocessing type: CommonRangeExtraction results:  dataset1_cut  dataset2_cut apply_tp:  dataset1  dataset2
If you want to perform algebraic operations on datasets, the data of both datasets need to have identical shape, and comparison is only meaningful if the axes are compatible as well. Hence, you will usually want to perform a CommonRangeExtraction processing step before doing algebra with two datasets:
 kind: multiprocessing type: CommonRangeExtraction results:  label_to_dataset  label_to_other_dataset  kind: processing type: DatasetAlgebra properties: parameters: kind: plus dataset: label_to_other_dataset apply_to:  label_to_dataset
For details of the algebraic operations on datasets, see
DatasetAlgebra
.
 class uvvispy.processing.Interpolation¶
Bases:
aspecd.processing.Interpolation
Interpolate data.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.Interpolation
class for details.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.Generally, interpolating requires to provide both, a range and a number of points:
 kind: processing type: Interpolation properties: parameters: range: [10, 100] npoints: 901
This would interpolate your data between their indices 10 and 100 using 901 points. As it is sometimes (often) more convenient to work with axis units, you can tell the processing step to use axis values instead of indices:
 kind: processing type: Interpolation properties: parameters: range: [400, 700] npoints: 1201 unit: axis
This would interpolate your (1D) data between the axis values 400 and 700 using 1201 points.
 class uvvispy.processing.Filtering¶
Bases:
aspecd.processing.Filtering
Filter data.
As the class is fully inherited from ASpecD for simple usage, see the ASpecD documentation for the
aspecd.processing.Filtering
class for details.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.Generally, filtering requires to provide both, a type of filter and a window length. Therefore, for uniform and Gaussian filters, this would be:
 kind: processing type: Filtering properties: parameters: type: uniform window_length: 10
Of course, at least uniform filtering (also known as boxcar or moving average) is strongly discouraged due to the artifacts introduced. Probably the best bet for applying a filter to smooth your data is the SavitzkyGolay filter:
 kind: processing type: Filtering properties: parameters: type: savitzkygolay window_length: 10 order: 3
Note that for this filter, you need to provide the polynomial order as well. To get best results, you will need to experiment with the parameters a bit.