Dataset structure

The dataset is an essential concept of the ASpecD framework, and hence of the FitPy package.

Developers frequently need to get an overview of the structure of the dataset as defined in the FitPy package. Whereas the API documentation of fitpy.dataset.CalculatedDataset and fitpy.dataset.CalculatedDatasetLHS provides a lot of information, a simple and accessible presentation of the dataset structure is often what is needed.

Therefore, the structures of the dataset classes defined in fitpy.dataset are provided below in YAML format, automatically generated from the actual source code.

Calculated dataset

class: fitpy.dataset.CalculatedDataset

data:
  calculated: true
  data:
    type: numpy.ndarray
    dtype: float64
    array: []
  axes:
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  residual:
    type: numpy.ndarray
    dtype: float64
    array: []
metadata:
  calculation:
    type: ''
    parameters: {}
  model:
    type: ''
    parameters: {}
  data:
    id: ''
    label: ''
  result:
    parameters: null
    success: false
    error_bars: false
    n_function_evaluations: 0
    n_variables: 0
    degrees_of_freedom: 0
    chi_square: 0.0
    reduced_chi_square: 0.0
    akaike_information_criterion: 0.0
    bayesian_information_criterion: 0.0
    variable_names: []
    covariance_matrix:
      type: numpy.ndarray
      dtype: float64
      array: []
    initial_values: []
    message: ''
history: []
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_origdata:
  calculated: true
  data:
    type: numpy.ndarray
    dtype: float64
    array: []
  axes:
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  residual:
    type: numpy.ndarray
    dtype: float64
    array: []
_package_name: fitpy
_history_pointer: -1

Calculated dataset LHS

class: fitpy.dataset.CalculatedDatasetLHS

data:
  calculated: true
  data:
    type: numpy.ndarray
    dtype: float64
    array: []
  axes:
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  residual:
    type: numpy.ndarray
    dtype: float64
    array: []
metadata:
  calculation:
    type: ''
    parameters: {}
  model:
    type: ''
    parameters: {}
  data:
    id: ''
    label: ''
  result:
    parameters: null
    success: false
    error_bars: false
    n_function_evaluations: 0
    n_variables: 0
    degrees_of_freedom: 0
    chi_square: 0.0
    reduced_chi_square: 0.0
    akaike_information_criterion: 0.0
    bayesian_information_criterion: 0.0
    variable_names: []
    covariance_matrix:
      type: numpy.ndarray
      dtype: float64
      array: []
    initial_values: []
    message: ''
  lhs:
    samples: null
    discrepancy: null
    results: []
history: []
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_origdata:
  calculated: true
  data:
    type: numpy.ndarray
    dtype: float64
    array: []
  axes:
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  - quantity: ''
    symbol: ''
    unit: ''
    label: ''
    values:
      type: numpy.ndarray
      dtype: float64
      array: []
    index: []
  residual:
    type: numpy.ndarray
    dtype: float64
    array: []
_package_name: fitpy
_history_pointer: -1