8.4.7. Data Management

DataDim(value)

Enum for dimensionality representation of data

DataSource(value)

Enum for source of data

DataDistribution(value)

Enum for distribution of data

Axis([label, units, data, index, scaling, ...])

Object holding info and data about physical axis of some data

DataBase(name[, source, dim, distribution, ...])

Base object to store homogeneous data and metadata generated by pymodaq's objects.

DataRaw(*args, **kwargs)

Specialized DataWithAxes set with source as 'raw'.

DataCalculated(*args[, axes])

Specialized DataWithAxes set with source as 'calculated'.

DataFromPlugins(*args, **kwargs)

Specialized DataWithAxes set with source as 'raw'.

DataFromRoi(*args[, axes])

Specialized DataWithAxes set with source as 'calculated'.To be used for processed data from region of interest

DataToExport(name[, data])

Object to store all raw and calculated DataWithAxes data for later exporting, saving, sending signal...

8.4.7.1. Axes

Created the 28/10/2022

@author: Sebastien Weber

class pymodaq.utils.data.Axis(label: str = '', units: str = '', data: Optional[ndarray] = None, index: int = 0, scaling=None, offset=None, size=None, spread_order: int = 0)[source]

Object holding info and data about physical axis of some data

In case the axis’s data is linear, store the info as a scale and offset else store the data

Parameters
  • label (str) – The label of the axis, for instance ‘time’ for a temporal axis

  • units (str) – The units of the data in the object, for instance ‘s’ for seconds

  • data (ndarray) – A 1D ndarray holding the data of the axis

  • index (int) – an integer representing the index of the Data object this axis is related to

  • scaling (float) – The scaling to apply to a linspace version in order to obtain the proper scaling

  • offset (float) – The offset to apply to a linspace/scaled version in order to obtain the proper axis

  • size (int) – The size of the axis array (to be specified if data is None)

  • spread_order (int) – An integer needed in the case where data has a spread DataDistribution. It refers to the index along the data’s spread_index dimension

Examples

>>> axis = Axis('myaxis', units='seconds', data=np.array([1,2,3,4,5]), index=0)
create_linear_data(nsteps: int)[source]

replace the axis data with a linear version using scaling and offset

find_index(threshold: float) int[source]

find the index of the threshold value within the axis

get_data() ndarray[source]

Convenience method to obtain the axis data (usually None because scaling and offset are used)

get_data_at(indexes: Union[int, Iterable, slice]) ndarray[source]

Get data at specified indexes

Parameters

indexes

get_scale_offset_from_data(data: Optional[ndarray] = None)[source]

Get the scaling and offset from the axis’s data

If data is not None, extract the scaling and offset

Parameters

data (ndarray) –

property data

get/set the data of Axis

Type

np.ndarray

property index: int

get/set the index this axis corresponds to in a DataWithAxis object

Type

int

property label: str

get/set the label of this axis

Type

str

property size: int

get/set the size/length of the 1D ndarray

Type

int

property units: str

get/set the units for this axis

Type

str

8.4.7.2. DataObjects

Created the 28/10/2022

@author: Sebastien Weber

class pymodaq.utils.data.DataBase(name: str, source: Optional[DataSource] = None, dim: Optional[DataDim] = None, distribution: DataDistribution = DataDistribution.uniform, data: Optional[List[ndarray]] = None, labels: Optional[List[str]] = None, origin: str = '', **kwargs)[source]

Base object to store homogeneous data and metadata generated by pymodaq’s objects.

To be inherited for real data

Parameters
  • name (str) – the identifier of these data

  • source (DataSource or str) – Enum specifying if data are raw or processed (for instance from roi)

  • dim (DataDim or str) – The identifier of the data type

  • distribution (DataDistribution or str) – The distribution type of the data: uniform if distributed on a regular grid or spread if on specific unordered points

  • data (list of ndarray) – The data the object is storing

  • labels (list of str) – The labels of the data nd-arrays

  • origin (str) – An identifier of the element where the data originated, for instance the DAQ_Viewer’s name. Used when appending DataToExport in DAQ_Scan to disintricate from which origin data comes from when scanning multiple detectors.

  • kwargs (named parameters) – All other parameters are stored dynamically using the name/value pair. The name of these extra parameters are added into the extra_attributes attribute

name

the identifier of these data

Type

str

source

Enum specifying if data are raw or processed (for instance from roi)

Type

DataSource or str

dim

The identifier of the data type

Type

DataDim or str

distribution

The distribution type of the data: uniform if distributed on a regular grid or spread if on specific unordered points

Type

DataDistribution or str

data

The data the object is storing

Type

list of ndarray

labels

The labels of the data nd-arrays

Type

list of str

origin

An identifier of the element where the data originated, for instance the DAQ_Viewer’s name. Used when appending DataToExport in DAQ_Scan to disintricate from which origin data comes from when scanning multiple detectors.

Type

str

shape

The shape of the underlying data

Type

Tuple[int]

size

The size of the ndarrays stored in the object

Type

int

length

The number of ndarrays stored in the object

Type

int

extra_attributes

list of string giving identifiers of the attributes added dynamically at the initialization (for instance to save extra metadata using the DataSaverLoader

Type

List[str]

See also

DataWithAxes, DataFromPlugins, DataRaw, DataSaverLoader

Examples

>>> import numpy as np
>>> from pymodaq.utils.data import DataBase, DataSource, DataDim, DataDistribution
>>> data = DataBase('mydata', source=DataSource['raw'], dim=DataDim['Data1D'],     distribution=DataDistribution['uniform'], data=[np.array([1.,2.,3.]), np.array([4.,5.,6.])],    labels=['channel1', 'channel2'], origin='docutils code')
>>> data.dim
<DataDim.Data1D: 1>
>>> data.source
<DataSource.raw: 0>
>>> data.shape
(3,)
>>> data.length
2
>>> data.size
3
abs()[source]

Take the absolute value of itself

as_dte(name: str = 'mydte') DataToExport[source]

Convenience method to wrap the DataWithAxes object into a DataToExport

average(other: DataBase, weight: int) DataBase[source]

Compute the weighted average between self and other DataBase

Parameters
  • other_data (DataBase) –

  • weight (int) – The weight the ‘other’ holds with respect to self

Returns

DataBase

Return type

the averaged DataBase object

fliplr()[source]

Reverse the order of elements along axis 1 (left/right)

flipud()[source]

Reverse the order of elements along axis 0 (up/down)

get_data_index(index: int = 0) ndarray[source]

Get the data by its index in the list, same as self[index]

get_dim_from_data(data: List[ndarray])[source]

Get the dimensionality DataDim from data

get_full_name() str[source]

Get the data ful name including the origin attribute into the returned value

Returns

str

Return type

the name of the ataWithAxes data constructed as : origin/name

Examples

d0 = DataBase(name=’datafromdet0’, origin=’det0’)

imag()[source]

Take the imaginary part of itself

pop(index: int) DataBase[source]

Returns a copy of self but with data taken at the specified index

real()[source]

Take the real part of itself

set_dim(dim: Union[DataDim, str])[source]

Addhoc modification of dim independantly of the real data shape, should be used with extra care

stack_as_array(axis=0, dtype=None) ndarray[source]

Stack all data arrays in a single numpy array

Parameters
  • axis (int) – The new stack axis index, default 0

  • dtype (str or np.dtype) – the dtype of the stacked array

Return type

np.ndarray

See also

np.stack()

property data: List[ndarray]

get/set (and check) the data the object is storing

Type

List[np.ndarray]

property dim

the enum representing the dimensionality of the stored data

Type

DataDim

property distribution

the enum representing the distribution of the stored data

Type

DataDistribution

property length

The length of data. This is the length of the list containing the nd-arrays

property shape

The shape of the nd-arrays

property size

The size of the nd-arrays

property source

the enum representing the source of the data

Type

DataSource

class pymodaq.utils.data.DataCalculated(*args, axes=[], **kwargs)[source]

Specialized DataWithAxes set with source as ‘calculated’. To be used for processed/calculated data

class pymodaq.utils.data.DataFromPlugins(*args, **kwargs)[source]

Specialized DataWithAxes set with source as ‘raw’. To be used for raw data generated by Detector plugins

It introduces by default to extra attributes, do_plot and do_save. Their presence can be checked in the extra_attributes list.

Parameters
  • do_plot (bool) – If True the underlying data will be plotted in the DAQViewer

  • do_save (bool) – If True the underlying data will be saved

do_plot

If True the underlying data will be plotted in the DAQViewer

Type

bool

do_save

If True the underlying data will be saved

Type

bool

class pymodaq.utils.data.DataFromRoi(*args, axes=[], **kwargs)[source]

Specialized DataWithAxes set with source as ‘calculated’.To be used for processed data from region of interest

class pymodaq.utils.data.DataRaw(*args, **kwargs)[source]

Specialized DataWithAxes set with source as ‘raw’. To be used for raw data

8.4.7.3. Data Characteristics

Created the 28/10/2022

@author: Sebastien Weber

class pymodaq.utils.data.DataDim(value)[source]

Enum for dimensionality representation of data

class pymodaq.utils.data.DataDistribution(value)[source]

Enum for distribution of data

class pymodaq.utils.data.DataSource(value)[source]

Enum for source of data

8.4.7.4. Union of Data

When exporting multiple set of Data objects, one should use a DataToExport

Created the 28/10/2022

@author: Sebastien Weber

class pymodaq.utils.data.DataToExport(name: str, data: List[DataWithAxes] = [], **kwargs)[source]

Object to store all raw and calculated DataWithAxes data for later exporting, saving, sending signal…

Includes methods to retrieve data from dim, source… Stored data have a unique identifier their name. If some data is appended with an existing name, it will replace the existing data. So if you want to append data that has the same name

Parameters
  • name (str) – The identifier of the exporting object

  • data (list of DataWithAxes) – All the raw and calculated data to be exported

name
timestamp
data
affect_name_to_origin_if_none()[source]

Affect self.name to all DataWithAxes children’s attribute origin if this origin is not defined

average(other: DataToExport, weight: int) DataToExport[source]

Compute the weighted average between self and other DataToExport and attributes it to self

Parameters
  • other (DataToExport) –

  • weight (int) – The weight the ‘other_data’ holds with respect to self

get_data_from_Naxes(Naxes: int, deepcopy: bool = False) DataToExport[source]

Get the data matching the given number of axes

Parameters

Naxes (int) – Number of axes in the DataWithAxes objects

Returns

DataToExport

Return type

filtered with data matching the number of axes

get_data_from_attribute(attribute: str, attribute_value: Any, deepcopy=False) DataToExport[source]

Get the data matching a given attribute value

Returns

DataToExport

Return type

filtered with data matching the attribute presence and value

get_data_from_dim(dim: DataDim, deepcopy=False) DataToExport[source]

Get the data matching the given DataDim

Returns

DataToExport

Return type

filtered with data matching the dimensionality

get_data_from_dims(dims: List[DataDim], deepcopy=False) DataToExport[source]

Get the data matching the given DataDim

Returns

DataToExport

Return type

filtered with data matching the dimensionality

get_data_from_full_name(full_name: str, deepcopy=False) DataWithAxes[source]

Get the DataWithAxes with matching full name

get_data_from_missing_attribute(attribute: str, deepcopy=False) DataToExport[source]

Get the data matching a given attribute value

Parameters
  • attribute (str) – a string of a possible attribute

  • deepcopy (bool) – if True the returned DataToExport will contain deepcopies of the DataWithAxes

Returns

DataToExport

Return type

filtered with data missing the given attribute

get_data_from_name(name: str) DataWithAxes[source]

Get the data matching the given name

get_data_from_name_origin(name: str, origin: str = '') DataWithAxes[source]

Get the data matching the given name and the given origin

get_data_from_sig_axes(Naxes: int, deepcopy: bool = False) DataToExport[source]

Get the data matching the given number of signal axes

Parameters

Naxes (int) – Number of signal axes in the DataWithAxes objects

Returns

DataToExport

Return type

filtered with data matching the number of signal axes

get_data_from_source(source: DataSource, deepcopy=False) DataToExport[source]

Get the data matching the given DataSource

Returns

DataToExport

Return type

filtered with data matching the dimensionality

get_data_with_naxes_lower_than(n_axes=2, deepcopy: bool = False) DataToExport[source]

Get the data with n axes lower than the given number

Parameters

Naxes (int) – Number of axes in the DataWithAxes objects

Returns

DataToExport

Return type

filtered with data matching the number of axes

get_full_names(dim: Optional[DataDim] = None)[source]

Get the ful names including the origin attribute into the returned value, eventually filtered by dim

Parameters

dim (DataDim or str) –

Returns

list of str

Return type

the names of the (filtered) DataWithAxes data constructed as : origin/name

Examples

d0 = DataWithAxes(name=’datafromdet0’, origin=’det0’)

get_names(dim: Optional[DataDim] = None) List[str][source]

Get the names of the stored DataWithAxes, eventually filtered by dim

Parameters

dim (DataDim or str) –

Returns

list of str

Return type

the names of the (filtered) DataWithAxes data

get_origins(dim: Optional[DataDim] = None)[source]

Get the origins of the underlying data into the returned value, eventually filtered by dim

Parameters

dim (DataDim or str) –

Returns

list of str

Return type

the origins of the (filtered) DataWithAxes data

Examples

d0 = DataWithAxes(name=’datafromdet0’, origin=’det0’)

index_from_name_origin(name: str, origin: str = '') List[DataWithAxes][source]

Get the index of a given DataWithAxes within the list of data

merge_as_dwa(dim: Union[str, DataDim], name: Optional[str] = None) DataRaw[source]

attempt to merge filtered dwa into one

Only possible if all filtered dwa and underlying data have same shape

Parameters
  • dim (DataDim or str) – will only try to merge dwa having this dimensionality

  • name (str) – The new name of the returned dwa

plot(plotter_backend: str = 'matplotlib', *args, **kwargs)[source]

Call a plotter factory and its plot method over the actual data

pop(index: int) DataWithAxes[source]

return and remove the DataWithAxes referred by its index

Parameters

index (int) – index as returned by self.index_from_name_origin

property data: List[DataWithAxes]

get the data contained in the object

Type

List[DataWithAxes]