You're reading the documentation for a development version. For the latest released version, please have a look at v0.11.
Dataset structure
The dataset is an essential concept of the ASpecD framework, as it abstracts the different vendor formats and combines both, numerical data and metadata, in an easily accessible way. Even more, the general structure of a dataset allows to compare data of entirely different origin (read: spectroscopic method), as long as their axes are compatible.
Developers of both, the ASpecD framework and even more of packages built upon the ASpecD framework, frequently need to get an overview of the structure of the dataset and its different subclasses, namely the ExperimentalDataset
and CalculatedDataset
. Whereas the API documentation of each class, aspecd.dataset.ExperimentalDataset
and aspecd.dataset.CalculatedDataset
, provides a lot of information, a simple and accessible presentation of the dataset structure is often what is needed.
Therefore, the structure of each of the dataset classes is provided below in YAML format, automatically generated from the actual source code.
Basic dataset
data:
calculated: false
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: []
_origdata:
calculated: false
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: []
device_data: {}
metadata: {}
history: []
_history_pointer: -1
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_package_name: aspecd
Experimental dataset
While generally, the propery device_data
is empty when creating a dataset object, here, the structure of the device_data
is shown explicitly for one device named example
. For more details regarding device data, see the documentation in the dataset module.
data:
calculated: false
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: []
_origdata:
calculated: false
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: []
device_data:
example:
calculated: false
metadata:
label: ''
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: []
metadata:
measurement:
start: null
end: null
purpose: ''
operator: ''
labbook_entry: ''
sample:
name: ''
id: null
loi: ''
temperature_control:
temperature:
unit: ''
dimension: ''
name: ''
value: 0.0
controller: ''
history: []
_history_pointer: -1
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_package_name: aspecd
Calculated dataset
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: []
_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: []
device_data: {}
metadata:
calculation:
type: ''
parameters: {}
history: []
_history_pointer: -1
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_package_name: aspecd