nmraspecds.analysis module

analysis module of the nmraspecds package.

class nmraspecds.analysis.ChemicalShiftCalibration

Bases: SingleAnalysisStep

Calculate offset between transmitter and current spectrometer frequency.

As ssNMR is seldom referenced internally, external referencing is necessary to determine the correct frequency of the spectrometer. This is done on a standard sample whose chemical shift is known and can be set manually. From this, the offset from the spectrometer’s frequency is determined (this step) and has to be transferred to the sample of interest (see nmraspecds.processing.ExternalReferencing). Of course, the sample has to get measured shortly before or after the reference compound to avoid drift of the magnetic field that occurs over time.

Currently, the following standards are supported:

Substance	Name	Nucleus	chemical shift / ppm	Reference
Adamantane	adamantane	1H	1.8	[0]
Adamantane	adamantane	13C	37.77 (low field)	[0]
Ammoniumophosphate	NH4H2PO3	31P	1.33	[0]
Alanine	alanine	13C	176.8 (high field)	[0]
Q8M8	Q8M8	29Si	11.66	[1]
Al(H2O)3+	Aluminum	27Al	0	[0]

Q8M8 = Octakis(trimethylsiloxy)silsesquioxane

The column “name” here refers to the value the parameter standard can take (see below). These names are case-insensitive. If multiple peaks are present, the one indicated in the table above will be considered.

Eventually, the offset is returned which corresponds to the “SR” value in Bruker’s TopSpin software.

References

[1] Solid State Nucl. Magn. Res. 1992, 1, 41 - 44

parameters

All parameters necessary for this step.

chemical_shiftfloat

Chemical shift the largest peaks should be shifted to.

standardstr

Standard substance to take chemical shift from. Either the parameter “chemical_shift” or “standard” need to be provided.

return_typestr

Defines, type of output, can be “value” or “dict”. The latter contains additional information e.g. type of nucleus.

Default: value

Type:: dict

Returns:: Can be a single number or a dict. The dict additionally contains the nucleus that is given in the dataset. With this, in the next step, the offset is automatically converted if the dataset is acquired on another nucleus.
Return type:: offset
Raises:: ValueError – Either Standard sample or chemical shift to reference to needs to be provided.

Examples

- kind: singleanalysis
  type: ChemicalShiftCalibration
  properties:
    parameters:
      standard: adamantane
      nucleus: 1H
  result: offset

add_preprocessing_step(processingstep=None)

Add a preprocessing step to the internal list.

Some analyses need some preprocessing of the data. These preprocessing steps are contained in the preprocessing attribute.

Parameters:: processingstep (aspecd.processing.ProcessingStep) – processing step to be added to the list of preprocessing steps

analyse(dataset=None, from_dataset=False)

Perform the actual analysis step on the given dataset.

If no dataset is provided at method call, but is set as property in the SingleAnalysisStep object, the analyse method of the dataset will be called and thus the analysis added to the list of analyses of the dataset.

If no dataset is provided at method call nor as property in the object, the method will raise a respective exception.

The aspecd.dataset.Dataset object always call this method with the respective dataset as argument. Therefore, in this case setting the dataset property within the aspecd.analysis.SingleAnalysisStep object is not necessary.

The actual analysis step should be implemented within the non-public method _perform_task(). Besides that, the applicability of the analysis step to the given dataset will be checked automatically and the parameters will be sanitised by calling the non-public method _sanitise_parameters().

Additionally, each dataset will be automatically checked for applicability, using the aspecd.analysis.AnalysisStep.applicable() method. Make sure to override this method according to your needs.

Parameters:

dataset (aspecd.dataset.Dataset) – dataset to perform analysis for
from_dataset (boolean) –
whether we are called from within a dataset

Defaults to “False” and shall never be set manually.

Returns:

dataset – dataset analysis has been performed for

Return type:

aspecd.dataset.Dataset

Raises:

aspecd.exceptions.NotApplicableToDatasetError – Raised when analysis step is not applicable to dataset
aspecd.exceptions.MissingDatasetError – Raised when no dataset exists to act on

analyze(dataset=None, from_dataset=False)

Perform the actual analysis step on the given dataset.

Same method as self.analyse, but for those preferring AE over BE

static applicable(dataset)

Check whether analysis step is applicable to the given dataset.

Returns True by default and needs to be implemented in classes inheriting from SingleAnalysisStep according to their needs.

This is a static method that gets called automatically by each class inheriting from aspecd.analysis.AnalysisStep. Hence, if you need to override it in your own class, make the method static as well. An example of an implementation testing for two-dimensional data is given below:

@staticmethod
def applicable(dataset):
    return len(dataset.data.axes) == 3

Parameters:: dataset (aspecd.dataset.Dataset) – dataset to check
Returns:: applicable – True if successful, False otherwise.
Return type:: bool

create_dataset()

Create calculated dataset containing minimal metadata.

The following metadata are set:

Metadata	Value
calculation.type	`name`
calculation.parameters	`parameters`

Returns:: dataset – (Calculated) dataset containing minimal metadata.
Return type:: aspecd.dataset.CalculatedDataset

Added in version 0.2.

create_history_record()

Create history record to be added to the dataset.

Usually, this method gets called from within the aspecd.dataset.analyse() method of the aspecd.dataset.Dataset class and ensures the history of each analysis step to get written properly.

Returns:: history_record – history record for analysis step
Return type:: aspecd.history.AnalysisHistoryRecord

to_dict(remove_empty=False)

Create dictionary containing public attributes of an object.

Parameters:

remove_empty (bool) –

Whether to remove keys with empty values

Default: False

Returns:

public_attributes – Ordered dictionary containing the public attributes of the object

The order of attribute definition is preserved

Return type:

collections.OrderedDict

Changed in version 0.6: New parameter remove_empty

Changed in version 0.9: Settings for properties to exclude and include are not traversed

Changed in version 0.9.1: Dictionaries get copied before traversing, as otherwise, the special variables __dict__ and __0dict__ are modified, what may result in strange behaviour.

Changed in version 0.9.2: Dictionaries do not get copied by default, but there is a private method that can be overridden in derived classes to copy the dictionary.