fitpy.dataset module
Datasets: units containing data and metadata.
The dataset is one key concept of the ASpecD framework and hence the FitPy package derived from it, consisting of the data as well as the corresponding metadata. Storing metadata in a structured way is a prerequisite for a semantic understanding within the routines. Furthermore, a history of every processing, analysis and annotation step is recorded as well, aiming at a maximum of reproducibility. This is part of how the ASpecD framework and therefore the FitPy package tries to support good scientific practice.
Therefore, each processing and analysis step of data should always be performed using the respective methods of a dataset, at least as long as it can be performed on a single dataset.
The reason for providing an own class for calculated datasets is to ensure
a consistent handling of fit results and provide means for specialised
plotters available in the fitpy.plotting
module to check for their
applicability.
- class fitpy.dataset.CalculatedDataset[source]
Bases:
aspecd.dataset.CalculatedDataset
Dataset containing results of fitting a model to data.
- data
numeric data, residual, and axes
In contrast to other datasets, it contains the residual (difference between fitted model and original data) as well.
- Type
- metadata
hierarchical key-value store of metadata
- class fitpy.dataset.CalculatedDatasetLHS[source]
Bases:
fitpy.dataset.CalculatedDataset
Dataset containing results of fitting a model to data.
- data
numeric data, residual, and axes
In contrast to other datasets, it contains the residual (difference between fitted model and original data) as well.
- Type
- metadata
hierarchical key-value store of metadata
- class fitpy.dataset.Data[source]
Bases:
aspecd.dataset.Data
Unit containing numeric data, residual, and corresponding axes.
In contrast to the base class of the ASpecD framework, it contains the residual (difference between fitted model and original data) as well.
- property residual
Residual (difference between fitted model and original data).
A residual need always to have the same shape as the corresponding data. If you try to set a residual not conforming to this condition, a
ValueError
will be raised.
- class fitpy.dataset.CalculatedDatasetMetadata[source]
Bases:
aspecd.metadata.CalculatedDatasetMetadata
Metadata for a dataset with calculated data.
This class contains the metadata for a dataset consisting of calculated data, i.e.,
CalculatedDataset
.Metadata can be converted to dict via
aspecd.utils.ToDictMixin.to_dict()
, e.g., for generating reports using templates and template engines.- calculation
Information on the calculation.
Contain, inter alia, the parameters of the calculation.
- data
Details of the data the model has been fitted to
- Type
class:DataMetadata
- class fitpy.dataset.CalculatedDatasetLHSMetadata[source]
Bases:
fitpy.dataset.CalculatedDatasetMetadata
Metadata for a dataset with calculated data.
This class contains the metadata for a dataset consisting of calculated data, i.e.,
CalculatedDataset
.Metadata can be converted to dict via
aspecd.utils.ToDictMixin.to_dict()
, e.g., for generating reports using templates and template engines.- calculation
Information on the calculation.
Contain, inter alia, the parameters of the calculation.
- data
Details of the data the model has been fitted to
- Type
class:DataMetadata
- class fitpy.dataset.Model[source]
Bases:
aspecd.metadata.Metadata
Metadata of a model fitted to data of a dataset.
Part of the metadata of a
CalculatedDataset
containing the data of the model fitted to the data of another (experimental) dataset.- from_model(model)[source]
Set attributes from
aspecd.model.Model
.- Parameters
model (
aspecd.model.Model
) – Model the attributes should be obtained from
- class fitpy.dataset.DataMetadata[source]
Bases:
aspecd.metadata.Metadata
Metadata of the data(set) a model has been fitted to.
Part of the metadata of a
CalculatedDataset
containing information of the (experimental) dataset the model has been fitted to.- label
Short description of the dataset
Can be set by the user, defaults to the value set as
aspecd.dataset.Dataset.id
by the importer.- Type
- from_dataset(dataset)[source]
Set attributes from
aspecd.dataset.Dataset
.- Parameters
dataset (
aspecd.dataset.Dataset
) – Dataset the attributes should be obtained from
- class fitpy.dataset.Result[source]
Bases:
aspecd.metadata.Metadata
Metadata of results of fitting a model to data of a dataset.
Part of the metadata of a
CalculatedDataset
containing the data of the model fitted to the data of another (experimental) dataset.While resembling the structure of the
lmfit.minimizer.MinimizerResult
class, this class tries to abstract away from the attributes in terms of their names and introduces more readable (and more lengthily) attribute names.- parameters
The best-fit parameters resulting from the fit.
- chi_square
Chi-square value
For this value to be meaningful, the residual function needs to be scaled properly to the uncertainties in the data.
- Type
- reduced_chi_square
Reduced chi-square value
For this value to be meaningful, the residual function needs to be scaled properly to the uncertainties in the data.
- Type
- covariance_matrix
Covariance matrix from minimisation.
Rows and columns correspond to
variable_names
- Type
- initial_values
List of initial values for variable parameters.
For the corresponding parameter names see
variable_names
.- Type
- to_dict(remove_empty=False)[source]
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
- from_lmfit_minimizer_result(result)[source]
Set attributes from
lmfit.minimizer.MinimizerResult
.- Parameters
result (
lmfit.minimizer.MinimizerResult
) – Result of a minimisation using lmfit
- class fitpy.dataset.LHS[source]
Bases:
aspecd.metadata.Metadata
Metadata of the LHS and its full results for each sampling.
Part of the metadata of a
CalculatedDatasetLHS
containing the data of the model fitted to the data of another (experimental) dataset.- samples
Grid of the Latin Hypercube
- Type
numpy.array
- discrepancy
Discrepancy of the sample.
The discrepancy is a uniformity criterion used to assess the space filling of a number of samples in a hypercube. A discrepancy quantifies the distance between the continuous uniform distribution on a hypercube and the discrete uniform distribution on distinct sample points. (from
scipy.stats.qmc.discrepancy()
)- Type
- from_lmfit_minimizer_results(results)[source]
Set attributes from
lmfit.minimizer.MinimizerResult
.- Parameters
results (
list
) –List of results of a minimisation using lmfit
Each result is an instance of
lmfit.minimizer.MinimizerResult
and gets transferred to an instance ofResult
.