6.4.7. Data Management¶

`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

Created the 28/10/2022

@author: Sebastien Weber

exception pymodaq.utils.data.DataDimWarning[source]¶

exception pymodaq.utils.data.DataIndexWarning[source]¶

exception pymodaq.utils.data.DataLengthError[source]¶

exception pymodaq.utils.data.DataShapeError[source]¶

exception pymodaq.utils.data.DataSizeWarning[source]¶

exception pymodaq.utils.data.DataTypeWarning[source]¶

pymodaq.utils.data.warning¶: alias of DataSizeWarning

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

For this particular data category, some explanation is needed, see example below:

Examples

One take images data (20x30) as a function of 2 parameters, say xaxis and yaxis non-linearly spaced on a regular grid.

data.shape = (150, 20, 30) data.nav_indexes = (0,)

The first dimension (150) corresponds to the navigation (there are 150 non uniform data points taken) The second and third could correspond to signal data, here an image of size (20x30) so: * nav_indexes is (0, ) * sig_indexes are (1, 2)

xaxis = Axis(name=xaxis, index=0, data…) length 150 yaxis = Axis(name=yaxis, index=0, data…) length 150

In fact from such a data shape the number of navigation axes in unknown . In our example, they are 2. To somehow keep track of some ordering in these navigation axes, one adds an attribute to the Axis object: the spread_order xaxis = Axis(name=xaxis, index=0, spread_order=0, data…) length 150 yaxis = Axis(name=yaxis, index=0, spread_order=1, data…) length 150

Methods

`compute_shape_from_axes`()	Get data shape from axes
`get_axis_from_index`(index[, create])	in spread mode, different nav axes have the same index (but not the same spread_order integer value) so may return multiple axis
`get_shape_from_index`(index)	Get the data shape at the given index

compute_shape_from_axes()[source]¶

Get data shape from axes

First get the nav length from one of the navigation axes Then check for signal axes

get_axis_from_index(index: int, create: bool = False) → List[Axis][source]¶

in spread mode, different nav axes have the same index (but not the same spread_order integer value) so may return multiple axis

No possible “linear” creation in this mode

get_shape_from_index(index: int) → int[source]¶: Get the data shape at the given index

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

Attributes

data: np.ndarray: get/set the data of Axis
index: int: get/set the index this axis corresponds to in a DataWithAxis object
label: str: get/set the label of this axis
offset
scaling
size: int: get/set the size/length of the 1D ndarray
units: str: get/set the units for this axis

Methods

`create_linear_data`(nsteps)	replace the axis data with a linear version using scaling and offset
`find_index`(threshold)	find the index of the threshold value within the axis
`get_data`()	Convenience method to obtain the axis data (usually None because scaling and offset are used)
`get_scale_offset_from_data`([data])	Get the scaling and offset from the axis's data

copy
create_simple_linear_data
find_indexes
is_axis_linear
max
mean
min

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_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

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: List[str] = [], origin: Optional[str] = None, **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

Attributes

data: List[np.ndarray]: get/set (and check) the data the object is storing
dim: DataDim: the enum representing the dimensionality of the stored data
distribution: DataDistribution: the enum representing the distribution of the stored data
labels
length: The length of data.
shape: The shape of the nd-arrays
size: The size of the nd-arrays
source: DataSource: the enum representing the source of the data

Methods

`average`(other, weight)	Compute the weighted average between self and other DataBase and attributes it to self
`get_data_index`([index])	Get the data by its index in the list
`get_dim_from_data`(data)	Get the dimensionality DataDim from data
`get_full_name`()	Get the data ful name including the origin attribute into the returned value
`set_dim`(dim)	Addhoc modification of dim independantly of the real data shape, should be used with extra care

append
check_shape_from_data

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

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

Parameters

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

get_data_index(index: int = 0)[source]¶: Get the data by its index in the list

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’)

set_dim(dim: Union[DataDim, str])[source]¶: Addhoc modification of dim independantly of the real data shape, should be used with extra care

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.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.DataFromPlugins(*args, **kwargs)[source]¶: Specialized DataWithAxes set with source as ‘raw’. To be used for raw data generated by plugins

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.DataLowLevel(name: str)[source]¶

Abstract object for all Data Object

Parameters: name (str) – the identifier of the data

name¶

Type: str

timestamp¶

Time in seconds since epoch. See method time.time()

Type: float

Attributes

name: str: the identifier of the data
timestamp: Get/Set the timestamp of when the object has been created

property name¶

the identifier of the data

Type: str

property timestamp¶: Get/Set the timestamp of when the object has been created

class pymodaq.utils.data.DataRaw(*args, **kwargs)[source]¶: Specialized DataWithAxes set with source as ‘raw’. To be used for raw data

class pymodaq.utils.data.DataScan(name: str, data: List[DataWithAxes] = [], **kwargs)[source]¶: Specialized DataToExport.To be used for data to be saved

class pymodaq.utils.data.DataSource(value)[source]¶: Enum for source of data

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¶

Attributes

data: List[DataWithAxes]: get the data contained in the object

Methods

`affect_name_to_origin_if_none`()	Affect self.name to all DataWithAxes children's attribute origin if this origin is not defined
`append`	Dispatch methods based on type signature
`average`(other, weight)	Compute the weighted average between self and other DataToExport and attributes it to self
`get_data_from_Naxes`(Naxes[, deepcopy])	Get the data matching the given number of axes
`get_data_from_dim`(dim[, deepcopy])	Get the data matching the given DataDim
`get_data_from_dims`(dims[, deepcopy])	Get the data matching the given DataDim
`get_data_from_full_name`(full_name[, deepcopy])	Get the DataWithAxes with matching full name
`get_data_from_name`(name)	Get the data matching the given name
`get_data_from_name_origin`(name[, origin])	Get the data matching the given name and the given origin
`get_data_from_source`(source[, deepcopy])	Get the data matching the given DataSource
`get_full_names`([dim])	Get the ful names including the origin attribute into the returned value, eventually filtered by dim
`get_names`([dim])	Get the names of the stored DataWithAxes, eventually filtered by dim
`index_from_name_origin`(name[, origin])	Get the index of a given DataWithAxes within the list of data
`pop`(index)	return and remove the DataWithAxes referred by its index

get_data_from_full_names
get_dim_presents
index
remove

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

Returns: DataToExport
Return type: filtered with data matching the number of axes

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_name(name: str) → List[DataWithAxes][source]¶: Get the data matching the given name

get_data_from_name_origin(name: str, origin: Optional[str] = None) → DataWithAxes[source]¶: Get the data matching the given name and the given origin

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_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)[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

index_from_name_origin(name: str, origin: Optional[str] = None) → List[DataWithAxes][source]¶: Get the index of a given DataWithAxes within the list of 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

`create_missing_axes`()	Check if given the data shape, some axes are missing to properly define the data (especially for plotting)
`deepcopy_with_new_data`([data, ...])	deepcopy without copying the initial data (saving memory)
`get_axis_indexes`()	Get all present different axis indexes
`get_dim_from_data_axes`()	Get the dimensionality DataDim from data taking into account nav indexes
`get_nav_axes_with_data`()	Get the data's navigation axes making sure there is data in the data field
`mean`([axis])	Process the mean of the data on the specified axis and returns the new data
`sort_data`([nav_axis])	Sort spread data along a given navigation axis, default is 0
`transpose`()	replace the data by their transposed version

deepcopy
get_axis_from_index
get_data_dimension
get_nav_axes
set_axes_manager