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Dataset structure
The dataset is an essential concept of the ASpecD framework and in turn the trepr package, 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 the trepr package 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, trepr.dataset.ExperimentalDataset and trepr.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
Base class for all kinds of datasets. Usually, either of the two subclasses will be used: ExperimentalDataset or CalculatedDataset. For both, the structure is documented below. For implementation details, see the API documentation of trepr.dataset.Dataset.
data:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
metadata: {}
history: []
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
_origdata:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
_package_name: trepr
_history_pointer: -1
Experimental dataset
Entity containing both, numerical data as well as the corresponding metadata that are specific for the trEPR method. For implementation details, see the API documentation of trepr.dataset.ExperimentalDataset and trepr.dataset.ExperimentalDatasetMetadata.
data:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
metadata:
measurement:
label: ''
start: null
end: null
purpose: ''
operator: ''
labbook_entry: ''
sample:
description: ''
solvent: ''
preparation: ''
tube: ''
name: ''
id: null
loi: ''
temperature_control:
cryostat: ''
cryogen: ''
temperature:
unit: ''
dimension: ''
name: ''
value: 0.0
controller: ''
transient:
points: null
length:
unit: ''
dimension: ''
name: ''
value: 0.0
trigger_position: null
experiment:
runs: null
shot_repetition_rate:
unit: ''
dimension: ''
name: ''
value: 0.0
spectrometer:
model: ''
software: ''
magnetic_field:
field_probe_type: ''
field_probe_model: ''
start:
unit: ''
dimension: ''
name: ''
value: 0.0
stop:
unit: ''
dimension: ''
name: ''
value: 0.0
step:
unit: ''
dimension: ''
name: ''
value: 0.0
sequence: ''
controller: ''
power_supply: ''
background:
field:
unit: ''
dimension: ''
name: ''
value: 0.0
occurrence: null
polarisation: ''
intensity:
unit: ''
dimension: ''
name: ''
value: 0.0
bridge:
model: ''
controller: ''
attenuation:
unit: ''
dimension: ''
name: ''
value: 0.0
power:
unit: ''
dimension: ''
name: ''
value: 0.0
detection: ''
frequency_counter: ''
mw_frequency:
unit: ''
dimension: ''
name: ''
value: 0.0
video_amplifier:
bandwidth:
unit: ''
dimension: ''
name: ''
value: 0.0
amplification:
unit: ''
dimension: ''
name: ''
value: 0.0
recorder:
model: ''
averages: null
time_base:
unit: ''
dimension: ''
name: ''
value: 0.0
bandwidth:
unit: ''
dimension: ''
name: ''
value: 0.0
pretrigger:
unit: ''
dimension: ''
name: ''
value: 0.0
coupling: ''
impedance:
unit: ''
dimension: ''
name: ''
value: 0.0
sensitivity:
unit: ''
dimension: ''
name: ''
value: 0.0
probehead:
type: ''
model: ''
coupling: ''
pump:
type: ''
model: ''
wavelength:
unit: ''
dimension: ''
name: ''
value: 0.0
power:
unit: ''
dimension: ''
name: ''
value: 0.0
repetition_rate:
unit: ''
dimension: ''
name: ''
value: 0.0
tunable_type: ''
tunable_model: ''
tunable_dye: ''
tunable_position: null
filter: ''
history: []
analyses: []
annotations: []
representations: []
id: ''
label: ''
references: []
tasks: []
time_stamp:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
microwave_frequency:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
_origdata:
calculated: false
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
_package_name: trepr
_history_pointer: -1
Calculated dataset
Entity consisting of calculated data and corresponding metadata. For implementation details, see the API documentation of trepr.dataset.CalculatedDataset and trepr.dataset.CalculatedDatasetMetadata.
data:
calculated: true
data:
type: numpy.ndarray
dtype: float64
array: []
axes:
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
metadata:
calculation:
type: ''
parameters: {}
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: []
- quantity: ''
symbol: ''
unit: ''
label: ''
values:
type: numpy.ndarray
dtype: float64
array: []
_package_name: trepr
_history_pointer: -1