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nmraspecds.dataset module

dataset module of the nmraspecds package.

class nmraspecds.dataset.DatasetFactory

Bases: DatasetFactory

Factory for creating dataset objects based on the source provided.

Particularly in case of recipe-driven data analysis (c.f. aspecd.tasks), there is a need to automatically retrieve datasets using nothing more than a source string that can be, e.g., a path or LOI.

The DatasetFactory operates in conjunction with a cwepr.io.factory.DatasetImporterFactory to import the actual dataset. See the respective class documentation for more details.

importer_factory

ImporterFactory instance used for importing datasets

Type:

cwepr.io.factory.DatasetImporterFactory

get_dataset(source='', importer='', parameters=None)

Return dataset object for dataset specified by its source.

The import of data into the dataset is handled using an instance of aspecd.io.DatasetImporterFactory.

The actual code for deciding which type of dataset to return in what case should be implemented in the non-public method _create_dataset() in any package based on the ASpecD framework.

Parameters:

  • source (str) –

    string describing the source of the dataset

    May be a filename or path, a URL/URI, a LOI, or similar

  • importer (str) –

    Name of the importer to use for importing the dataset

    Default: ‘’

    Added in version 0.2.

  • parameters (dict) –

    Additional parameters for controlling the import

    Default: None

    Added in version 0.2.

Returns:

dataset – Dataset object of appropriate class

Return type:

aspecd.dataset.Dataset

Raises:
class nmraspecds.dataset.ExperimentalDataset

Bases: ExperimentalDataset

Set of data uniting all relevant information.

Core element of the package as all io, processing, analysis ans plotting steps are wrapped around a dataset which contains numerical data and metadata.

add_reference(dataset=None)

Add a reference to another dataset to the list of references.

A reference is always an object of type aspecd.dataset.DatasetReference that will be automatically created from the dataset provided.

Parameters:

dataset (aspecd.dataset.Dataset) – dataset a reference for should be added to the list of references

Raises:

aspecd.exceptions.MissingDatasetError – Raised if no dataset was provided

analyse(analysis_step=None)

Apply analysis to dataset.

Every analysis step is an object of type aspecd.analysis.SingleAnalysisStep and is passed as an argument to analyse().

The information necessary to reproduce an analysis is stored in the analyses attribute as object of class aspecd.dataset.AnalysisHistoryRecord. This record contains as well a (deep) copy of the complete history of the dataset stored in history.

Parameters:

analysis_step (aspecd.analysis.SingleAnalysisStep) – analysis step to apply to the dataset

Returns:

analysis_step – analysis step applied to the dataset

Return type:

aspecd.analysis.SingleAnalysisStep

analyze(analysis_step=None)

Apply analysis to dataset.

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

annotate(annotation_=None)

Add annotation to dataset.

Parameters:

annotation (aspecd.annotation.DatasetAnnotation) – annotation to add to the dataset

append_history_record(history_record)

Append history record to dataset history.

This method should never be called manually, but only from within classes of the ASpecD framework, at least as long as you are not interested in Orwellian History.

Parameters:

history_record (aspecd.history.HistoryRecord) – History record (of a processing step) to be appended.

Changed in version 0.2: Converted into a public method, due to needs of aspecd.processing.MultiProcessingStep

delete_analysis(index=None)

Remove analysis step record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

delete_annotation(index=None)

Remove annotation record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

delete_representation(index=None)

Remove representation record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

export_to(exporter=None)

Export data and metadata.

This requires initialising an aspecd.io.DatasetImporter object first that is provided as an argument for this method.

Note

The same operation can be performed by calling the export_from() method of an aspecd.io.Exporter object taking an aspecd.dataset.Dataset object as argument.

However, as usually the dataset is already at hand, first creating an instance of a respective exporter and then calling export_to() of the dataset is the preferred way.

Parameters:

exporter (aspecd.io.DatasetExporter) – Exporter writing data and metadata to specific output format

from_dict(dict_=None)

Set properties from dictionary.

Only parameters in the dictionary that are valid properties of the class are set accordingly.

Note

In conjunction with the aspecd.dataset.to_dict() method, this method allows to serialise and deserialise dataset objects, i.e. all kinds of storage to the persistence layer.

Parameters:

dict (dict) – Dictionary containing properties to set

import_from(importer=None)

Import data and metadata contained in importer object.

This requires initialising an aspecd.io.Importer object first that is provided as an argument for this method.

Note

The same operation can be performed by calling the import_into() method of an aspecd.io.Importer object taking an aspecd.dataset.Dataset object as argument.

However, as usually one wants to continue working with a dataset, first creating an instance of a dataset and a respective importer and then calling import_from() of the dataset is the preferred way.

Parameters:

importer (aspecd.io.DatasetImporter) – Importer containing data and metadata read from some source

load(filename=None)

Load dataset object from persistence layer.

The dataset will be loaded from a file conforming to the ASpecD dataset format (adf). For details, see the aspecd.io.AdfExporter class.

property package_name

Return package name.

The name of the package the dataset is implemented in is a crucial detail for writing the history. The value is set automatically and is read-only.

plot(plotter=None)

Perform plot with data of current dataset.

Every plotter is an object of type aspecd.plotting.Plotter and is passed as an argument to plot().

The information necessary to reproduce a plot is stored in the representations attribute as object of class aspecd.dataset.PlotHistoryRecord. This record contains as well a (deep) copy of the complete history of the dataset stored in history. Besides being a necessary prerequisite to reproduce a plot, this allows to automatically recreate plots requiring different incompatible preprocessing steps in arbitrary order.

Parameters:

plotter (aspecd.plotting.Plotter) – plot to perform with data of current dataset

Returns:

plotter – plot performed on the current dataset

Return type:

aspecd.plotting.Plotter

Raises:

aspecd.exceptions.MissingPlotterError – Raised when trying to plot without plotter

process(processing_step=None)

Apply processing step to dataset.

Every processing step is an object of type aspecd.processing.SingleProcessingStep and is passed as argument to process().

Calling this function ensures that the history record is added to the dataset as well as a few basic checks are performed such as for leading history, meaning that the _history_pointer is not set to the current tip of the history of the dataset. In this case, an error is raised.

Note

If processing_step is undoable, all previous plots stored in the list of representations will be removed, as these plots cannot be reproduced due to a change in _origdata.

Parameters:

processing_step (aspecd.processing.SingleProcessingStep) – processing step to apply to the dataset

Returns:

processing_step – processing step applied to the dataset

Return type:

aspecd.processing.SingleProcessingStep

Raises:

aspecd.exceptions.ProcessingWithLeadingHistoryError – Raised when trying to process with leading history

redo()

Reapply previously undone processing step.

Raises:

aspecd.exceptions.RedoAlreadyAtLatestChangeError – Raised when trying to redo with empty history

remove_reference(dataset_id=None)

Remove a reference to another dataset from the list of references.

A reference is always an object of type aspecd.dataset.DatasetReference that was automatically created from the respective dataset when adding the reference.

Parameters:

dataset_id (string) – ID of the dataset the reference should be removed for

Raises:

aspecd.exceptions.MissingDatasetError – Raised if no dataset ID was provided

save(filename=None)

Save dataset to persistence layer.

The dataset will be saved in ASpecD dataset format (adf). For details, see the aspecd.io.AdfExporter class.

strip_history()

Remove leading history, if any.

If a dataset has a leading history, i.e., its history pointer does not point to the last entry of the history, and you want to perform a processing step on this very dataset, you need first to strip its history, as otherwise, a ProcessingWithLeadingHistoryError will be raised.

tabulate(table=None)

Create table from data of current dataset.

Every table is an object of type aspecd.table.Table and is passed as an argument to tabulate().

The information necessary to reproduce a table is stored in the representations attribute as object of class aspecd.dataset.TableHistoryRecord.

Parameters:

table (aspecd.table.Table) – table created from the data of the current dataset

Returns:

table – table created from the data of the current dataset

Return type:

aspecd.table.Table

Raises:

TypeError – Raised when trying to tabulate without table

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.

undo()

Revert last processing step.

Actually, the history pointer is decremented and starting from the _origdata, all processing steps are reapplied to the data up to this point in history.

Raises:
class nmraspecds.dataset.CalculatedDataset

Bases: CalculatedDataset

Base class for datasets containing calculated data.

add_reference(dataset=None)

Add a reference to another dataset to the list of references.

A reference is always an object of type aspecd.dataset.DatasetReference that will be automatically created from the dataset provided.

Parameters:

dataset (aspecd.dataset.Dataset) – dataset a reference for should be added to the list of references

Raises:

aspecd.exceptions.MissingDatasetError – Raised if no dataset was provided

analyse(analysis_step=None)

Apply analysis to dataset.

Every analysis step is an object of type aspecd.analysis.SingleAnalysisStep and is passed as an argument to analyse().

The information necessary to reproduce an analysis is stored in the analyses attribute as object of class aspecd.dataset.AnalysisHistoryRecord. This record contains as well a (deep) copy of the complete history of the dataset stored in history.

Parameters:

analysis_step (aspecd.analysis.SingleAnalysisStep) – analysis step to apply to the dataset

Returns:

analysis_step – analysis step applied to the dataset

Return type:

aspecd.analysis.SingleAnalysisStep

analyze(analysis_step=None)

Apply analysis to dataset.

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

annotate(annotation_=None)

Add annotation to dataset.

Parameters:

annotation (aspecd.annotation.DatasetAnnotation) – annotation to add to the dataset

append_history_record(history_record)

Append history record to dataset history.

This method should never be called manually, but only from within classes of the ASpecD framework, at least as long as you are not interested in Orwellian History.

Parameters:

history_record (aspecd.history.HistoryRecord) – History record (of a processing step) to be appended.

Changed in version 0.2: Converted into a public method, due to needs of aspecd.processing.MultiProcessingStep

delete_analysis(index=None)

Remove analysis step record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

delete_annotation(index=None)

Remove annotation record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

delete_representation(index=None)

Remove representation record from dataset.

Parameters:

index (int) – Number of analysis in analyses to delete

export_to(exporter=None)

Export data and metadata.

This requires initialising an aspecd.io.DatasetImporter object first that is provided as an argument for this method.

Note

The same operation can be performed by calling the export_from() method of an aspecd.io.Exporter object taking an aspecd.dataset.Dataset object as argument.

However, as usually the dataset is already at hand, first creating an instance of a respective exporter and then calling export_to() of the dataset is the preferred way.

Parameters:

exporter (aspecd.io.DatasetExporter) – Exporter writing data and metadata to specific output format

from_dict(dict_=None)

Set properties from dictionary.

Only parameters in the dictionary that are valid properties of the class are set accordingly.

Note

In conjunction with the aspecd.dataset.to_dict() method, this method allows to serialise and deserialise dataset objects, i.e. all kinds of storage to the persistence layer.

Parameters:

dict (dict) – Dictionary containing properties to set

import_from(importer=None)

Import data and metadata contained in importer object.

This requires initialising an aspecd.io.Importer object first that is provided as an argument for this method.

Note

The same operation can be performed by calling the import_into() method of an aspecd.io.Importer object taking an aspecd.dataset.Dataset object as argument.

However, as usually one wants to continue working with a dataset, first creating an instance of a dataset and a respective importer and then calling import_from() of the dataset is the preferred way.

Parameters:

importer (aspecd.io.DatasetImporter) – Importer containing data and metadata read from some source

load(filename=None)

Load dataset object from persistence layer.

The dataset will be loaded from a file conforming to the ASpecD dataset format (adf). For details, see the aspecd.io.AdfExporter class.

property package_name

Return package name.

The name of the package the dataset is implemented in is a crucial detail for writing the history. The value is set automatically and is read-only.

plot(plotter=None)

Perform plot with data of current dataset.

Every plotter is an object of type aspecd.plotting.Plotter and is passed as an argument to plot().

The information necessary to reproduce a plot is stored in the representations attribute as object of class aspecd.dataset.PlotHistoryRecord. This record contains as well a (deep) copy of the complete history of the dataset stored in history. Besides being a necessary prerequisite to reproduce a plot, this allows to automatically recreate plots requiring different incompatible preprocessing steps in arbitrary order.

Parameters:

plotter (aspecd.plotting.Plotter) – plot to perform with data of current dataset

Returns:

plotter – plot performed on the current dataset

Return type:

aspecd.plotting.Plotter

Raises:

aspecd.exceptions.MissingPlotterError – Raised when trying to plot without plotter

process(processing_step=None)

Apply processing step to dataset.

Every processing step is an object of type aspecd.processing.SingleProcessingStep and is passed as argument to process().

Calling this function ensures that the history record is added to the dataset as well as a few basic checks are performed such as for leading history, meaning that the _history_pointer is not set to the current tip of the history of the dataset. In this case, an error is raised.

Note

If processing_step is undoable, all previous plots stored in the list of representations will be removed, as these plots cannot be reproduced due to a change in _origdata.

Parameters:

processing_step (aspecd.processing.SingleProcessingStep) – processing step to apply to the dataset

Returns:

processing_step – processing step applied to the dataset

Return type:

aspecd.processing.SingleProcessingStep

Raises:

aspecd.exceptions.ProcessingWithLeadingHistoryError – Raised when trying to process with leading history

redo()

Reapply previously undone processing step.

Raises:

aspecd.exceptions.RedoAlreadyAtLatestChangeError – Raised when trying to redo with empty history

remove_reference(dataset_id=None)

Remove a reference to another dataset from the list of references.

A reference is always an object of type aspecd.dataset.DatasetReference that was automatically created from the respective dataset when adding the reference.

Parameters:

dataset_id (string) – ID of the dataset the reference should be removed for

Raises:

aspecd.exceptions.MissingDatasetError – Raised if no dataset ID was provided

save(filename=None)

Save dataset to persistence layer.

The dataset will be saved in ASpecD dataset format (adf). For details, see the aspecd.io.AdfExporter class.

strip_history()

Remove leading history, if any.

If a dataset has a leading history, i.e., its history pointer does not point to the last entry of the history, and you want to perform a processing step on this very dataset, you need first to strip its history, as otherwise, a ProcessingWithLeadingHistoryError will be raised.

tabulate(table=None)

Create table from data of current dataset.

Every table is an object of type aspecd.table.Table and is passed as an argument to tabulate().

The information necessary to reproduce a table is stored in the representations attribute as object of class aspecd.dataset.TableHistoryRecord.

Parameters:

table (aspecd.table.Table) – table created from the data of the current dataset

Returns:

table – table created from the data of the current dataset

Return type:

aspecd.table.Table

Raises:

TypeError – Raised when trying to tabulate without table

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.

undo()

Revert last processing step.

Actually, the history pointer is decremented and starting from the _origdata, all processing steps are reapplied to the data up to this point in history.

Raises: