DependentVariable

class csdmpy.DependentVariable(*args, **kwargs)[source]

Bases: object

Create an instance of the DependentVariable class.

The instance of this class represents a dependent variable, \(\mathbf{U}\). A dependent variable holds \(p\)-component data values, where \(p>0\) is an integer. For example, a scalar is single-component (\(p=1\)), a vector may have up to n-components (\(p=n\)), while a second rank symmetric tensor have six unique component (\(p=6\)).

Creating a new dependent variable.

There are two ways of creating a new instance of a DependentVariable class.

From a python dictionary containing valid keywords.

>>> from csdmpy import DependentVariable
>>> import numpy as np
>>> numpy_array = np.arange(30).reshape(3, 10).astype(np.float32)

>>> dependent_variable_dictionary = {
...     "type": "internal",
...     "components": numpy_array,
...     "name": "star",
...     "unit": "W s",
...     "quantity_name": "energy",
...     "quantity_type": "pixel_3",
... }
>>> y = DependentVariable(dependent_variable_dictionary)

Here, dependent_variable_dictionary is the python dictionary.

From valid keyword arguments.

>>> y = DependentVariable(
...     type="internal",
...     name="star",
...     unit="W s",
...     quantity_type="pixel_3",
...     components=numpy_array,
... )

Attributes Summary

type

The dependent variable subtype.

description

Brief description of the dependent variables.

application

Application metadata of the DependentVariable object.

name

Name of the dependent variable.

unit

Unit associated with the dependent variable.

quantity_name

Quantity name of physical quantities associated with the dependent variable.

encoding

The encoding method used in representing the dependent variable.

numeric_type

The numeric type of the component values from the dependent variable.

quantity_type

Quantity type of the dependent variable.

component_labels

List of labels corresponding to the components of the dependent variable.

components

Component array of the dependent variable.

components_url

URL where the data components of the dependent variable are stored.

axis_label

List of formatted string labels for each component of the dependent variable.

data_structure

Json serialized string describing the DependentVariable class instance.

Methods Summary

to

Convert the unit of the dependent variable to the unit.

dict

Return DependentVariable object as a python dictionary.

to_dict

Alias to the dict() method of the class.

copy

Return a copy of the DependentVariable object.

Attributes Documentation

type

The dependent variable subtype.

There are two valid subtypes of DependentVariable class with the following enumeration literals,

internal
external

corresponding to Internal and External sub class. By default, all instances of the DependentVariable class are assigned as internal upon import. The user may update the value of this attribute, at any time, with a string containing a valid type literal, for example,

>>> print(y.type)
internal

>>> y.type = "external"

When type is external, the data values from the corresponding dependent variable are serialized to an external file within the same directory as the .csdfe file.

Returns:

A string with a valid dependent variable subtype.

Raises:

ValueError – When an invalid value is assigned.

description

Brief description of the dependent variables.

The default value is an empty string, ‘’.

>>> print(y.description)
A test image
>>> y.description = "A test pixel_3 image"
>>> print(y.description)
A test pixel_3 image
Returns:

A string of UTF-8 allowed characters describing the dependent variable.

Raises:

TypeError – When the assigned value is not a string.

application

Application metadata of the DependentVariable object.

>>> print(y.application)
None

The application attribute is where an application can place its own metadata as a python dictionary object containing the application specific metadata, using a reverse domain name notation string as the attribute key, for example,

>>> y.application = {"com.example.myApp": {"myApp_key": "myApp_metadata"}}
>>> print(y.application)
{'com.example.myApp': {'myApp_key': 'myApp_metadata'}}

Please refer to the Core Scientific Dataset Model article for details.

Returns:

A python dictionary containing dependent variable application metadata.

name

Name of the dependent variable.

>>> y.name
'star'
>>> y.name = "rock star"
Returns:

A string containing the name of the dependent variable.

Raises:

TypeError – When the assigned value is not a string.

unit

Unit associated with the dependent variable.

Note

The attribute cannot be modified. To convert the unit, use the to() method of the class instance.

Returns:

A Unit object from astropy.unit package.

Raises:

AttributeError – When assigned a value.

quantity_name

Quantity name of physical quantities associated with the dependent variable.

>>> y.quantity_name
'energy'
Returns:

A string with the quantity name associated with the dependent variable physical quantities .

Raises:

NotImplementedError – When assigning a value.

encoding

The encoding method used in representing the dependent variable.

The value of this attribute determines the method used when serializing or deserializing the data values to and from the file. Currently, there are three valid encoding methods:

raw
base64
none

A value, raw, means that the data values are serialized as binary data. The value, base64, implies that the data values are serialized as base64 strings, while, the value none refers to text-based serialization.

By default, the encoding attribute of all dependent variable object are set to base64 after import. The user may update this attribute, at any time, with a string containing a valid encoding literal, for example,

>>> y.encoding = "base64"

The value of this attribute will be used in serializing the data to the file, when using the save() method.

Returns:

A string with a valid encoding type.

Raises:

ValueError – If an invalid encoding value is assigned.

numeric_type

The numeric type of the component values from the dependent variable.

There are currently twelve valid numeric types in core scientific dataset model.

uint8

int8

float32

complex64

uint16

int16

float64

complex128

uint32

int32

uint64

int64

Besides, csdmpy also accepts any valid type object, such as int, float, np.complex64, as long as the type is consistent with the above twelve entries.

When assigning a valid value, this attribute updates the dtype of the Numpy array from the corresponding components attribute.

>>> y.numeric_type
'float32'

>>> print(y.components)
[[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9.]
 [10. 11. 12. 13. 14. 15. 16. 17. 18. 19.]
 [20. 21. 22. 23. 24. 25. 26. 27. 28. 29.]]

>>> y.numeric_type = "complex64"
>>> print(y.components[:, :5])
[[ 0.+0.j  1.+0.j  2.+0.j  3.+0.j  4.+0.j]
 [10.+0.j 11.+0.j 12.+0.j 13.+0.j 14.+0.j]
 [20.+0.j 21.+0.j 22.+0.j 23.+0.j 24.+0.j]]

>>> y.numeric_type = float  # python type object
>>> print(y.components[:, :5])
[[ 0.  1.  2.  3.  4.]
 [10. 11. 12. 13. 14.]
 [20. 21. 22. 23. 24.]]
Returns:

A string with a valid numeric type.

Raises:

ValueError – If an invalid numeric type value is assigned.

quantity_type

Quantity type of the dependent variable.

There are currently six valid quantity types,

scalar
vector_n
pixel_n
matrix_n_m
symmetric_matrix_n

where n and m are integers. The value of the attribute is modified with a string containing a valid quantity type.

>>> y.quantity_type
'pixel_3'
>>> y.quantity_type = "vector_3"
Returns:

A string with a valid quantity type.

Raises:

ValueError – If an invalid value is assigned.

component_labels

List of labels corresponding to the components of the dependent variable.

>>> y.component_labels
['', '', '']

To update the component_labels, assign an array of strings with same number of elements as the number of components.

>>> y.component_labels = ["channel 0", "channel 1", "channel 2"]

The individual labels are accessed with proper indexing, for example,

>>> y.component_labels[2]
'channel 2'
Returns:

A list of component label strings.

Raises:

TypeError – When the assigned value is not an array of strings.

components

Component array of the dependent variable.

The value of this attribute, \(\mathbb{U}\), is a Numpy array of shape \((p \times N_{d-1} \times ... N_1 \times N_0)\) where \(p\) is the number of components, and \(N_k\) is the number of points from the \(k^\mathrm{th}\) Dimension object.

Note

The shape of the components Numpy array, \((p \times N_{d-1} \times ... N_1 \times N_0)\), is reverse the shape of the components array, \((N_0 \times N_1 \times ... N_{d-1} \times p)\), from the CSD model. This is because CSD model utilizes a column-major order to shape the components array relative to the order of the dimension while Numpy utilizes a row-major order.

The dimensionality of this Numpy array is \(d+1\) where \(d\) is the number of dimension objects. The zeroth axis with \(p\) points is the number of components.

This attribute can only be updated when the shape of the new array is the same as the shape of the components array.

For example,

>>> print(y.components.shape)
(3, 10)
>>> y.numeric_type
'float32'

is a three-component dependent variable with ten data values per component. The numeric type of the data values, in this example, is float32. To update the components array, assign an array of shape (3, 10) to the components attribute. In the following example, we assign a Numpy array,

>>> y.components = np.linspace(0, 256, 30, dtype="u1").reshape(3, 10)
>>> y.numeric_type
'uint8'

Notice, the value of the numeric_type attribute is automatically updated based on the dtype of the Numpy array. In this case, from a float32 to uint8. In this other example,

>>> try: 
...     y.components = np.random.rand(1,10).astype('u1')
... except ValueError as e:
...     print(e)
The shape of the `ndarray`, `(1, 10)`, is inconsistent with the
shape of the components array, `(3, 10)`.

a ValueError is raised because the shape of the input array (1, 10) is not consistent with the shape of the components array, (3, 10).

Returns:

A Numpy array of components.

Raises:

ValueError – When assigning an array whose shape is not consistent with the shape of the components array.

components_url

URL where the data components of the dependent variable are stored.

This attribute is only informative and cannot be modified. Its value is a string containing the local or remote address of the file where the data values are stored. The attribute is only valid for dependent variable with type, external.

Returns:

A string containing the URL.

Raises:

AttributeError – When assigned a value.

axis_label

List of formatted string labels for each component of the dependent variable.

This attribute is not a part of the original core scientific dataset model, however, it is a convenient supplementary attribute that provides formatted string ready for labeling the components of the dependent variable. The string at index i is formatted as component_labels[i] / unit if component_labels[i] is a non-empty string, otherwise, quantity_name / unit. Here, quantity_name, component_labels, and unit`are the attributes of the :ref:`dv_api instance. For example,

Returns:

A list of formatted component label strings.

Raises:

AttributeError – When assigned a value.

data_structure

Json serialized string describing the DependentVariable class instance.

This supplementary attribute is useful for a quick preview of the dependent variable object. For convenience, the values from the components attribute are truncated to the first and the last two numbers per component. The encoding keyword is also hidden from this view.

Returns:

A json serialized string of the dependent variable object.

Raises:

AttributeError – When modified.

Method Documentation

to(unit)[source]

Convert the unit of the dependent variable to the unit.

Parameters:

unit – A string containing a unit with the same dimensionality as the components of the dependent variable.

>>> print(y.components[0, 5])
5.0
>>> y.to("mJ")
>>> y.unit
Unit("mJ")
>>> print(y.components[0, 5])
5000.0

Note

This method is a wrapper of the to method from the Quantity class.

dict()[source]

Return DependentVariable object as a python dictionary.

Example

>>> y.dict() 
{'type': 'internal', 'description': 'A test image', 'name': 'star',
'unit': 's * W', 'quantity_name': 'energy', 'encoding': 'none',
'numeric_type': 'float32', 'quantity_type': 'pixel_3',
'components': [[0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
[10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0],
[20.0, 21.0, 22.0, 23.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0]]}
to_dict()[source]

Alias to the dict() method of the class.

copy()[source]

Return a copy of the DependentVariable object.