apply_svd#

Autogenerated DPF operator classes.

class ansys.dpf.core.operators.compression.apply_svd.apply_svd(field_contaner_to_compress=None, scalar_int=None, scalar_double=None, boolean=None, config=None, server=None)#

Computes the coefficients (=U*Sigma) and VT components from SVD.

Parameters:

field_contaner_to_compress (FieldsContainer) – Fields container to be compressed
scalar_int (int) – Number of vectors (r) to keep for the future reconstraction of the matrix a, ex. a[m,n]=coef[m,r]*vt[r,n], where coef=u*sigma
scalar_double (float) – Threshold (precision) as a double, default value is 1e-7
boolean (bool) – Apply svd on the initial input data (true) or transposed (square matrix), default value is false

Returns:

us_svd (FieldsContainer) – The output entity is a field container (time dependant); it contains the multiplication of two matrices, u and s, where a=u.s.vt
vt_svd (FieldsContainer) – The output entity is a field container (space dependant), containing the vt, where a=u.s.vt
sigma (Field or FieldsContainer) – The output entity is a field (or a field container if input fc contains several labels, where field contains results per label), containing singular (s) values of the input data, where a=u.s.vt

Examples

>>> from ansys.dpf import core as dpf

>>> # Instantiate operator
>>> op = dpf.operators.compression.apply_svd()

>>> # Make input connections
>>> my_field_contaner_to_compress = dpf.FieldsContainer()
>>> op.inputs.field_contaner_to_compress.connect(my_field_contaner_to_compress)
>>> my_scalar_int = int()
>>> op.inputs.scalar_int.connect(my_scalar_int)
>>> my_scalar_double = float()
>>> op.inputs.scalar_double.connect(my_scalar_double)
>>> my_boolean = bool()
>>> op.inputs.boolean.connect(my_boolean)

>>> # Instantiate operator and connect inputs in one line
>>> op = dpf.operators.compression.apply_svd(
...     field_contaner_to_compress=my_field_contaner_to_compress,
...     scalar_int=my_scalar_int,
...     scalar_double=my_scalar_double,
...     boolean=my_boolean,
... )

>>> # Get output data
>>> result_us_svd = op.outputs.us_svd()
>>> result_vt_svd = op.outputs.vt_svd()
>>> result_sigma = op.outputs.sigma()

static default_config(server=None)#

Returns the default config of the operator.

This config can then be changed to the user needs and be used to instantiate the operator. The Configuration allows to customize how the operation will be processed by the operator.

Parameters:: server (server.DPFServer, optional) – Server with channel connected to the remote or local instance. When None, attempts to use the global server.

property inputs#

Enables to connect inputs to the operator

Returns:: inputs
Return type:: InputsApplySvd

property outputs#

Enables to get outputs of the operator by evaluating it

Returns:: outputs
Return type:: OutputsApplySvd

property config#

Copy of the operator’s current configuration.

You can modify the copy of the configuration and then use operator.config = new_config or instantiate an operator with the new configuration as a parameter.

For information on an operator’s options, see the documentation for that operator.

Returns:: Copy of the operator’s current configuration.
Return type:: ansys.dpf.core.config.Config

Examples

Modify the copy of an operator’s configuration and set it as current config of the operator.

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.math.add()
>>> config_add = op.config
>>> config_add.set_work_by_index_option(True)
>>> op.config = config_add

connect(pin, inpt, pin_out=0)#

Connect an input on the operator using a pin number.

Parameters:

pin (int) – Number of the input pin.
inpt (str, int, double, bool, list[int], list[float], Field, FieldsContainer, Scoping,) –
ScopingsContainer – Operator, os.PathLike Object to connect to.
MeshedRegion – Operator, os.PathLike Object to connect to.
MeshesContainer – Operator, os.PathLike Object to connect to.
DataSources – Operator, os.PathLike Object to connect to.
CyclicSupport – Operator, os.PathLike Object to connect to.
dict – Operator, os.PathLike Object to connect to.
Outputs – Operator, os.PathLike Object to connect to.
pin_out (int, optional) – If the input is an operator, the output pin of the input operator. The default is 0.

Examples

Compute the minimum of displacement by chaining the "U" and "min_max_fc" operators.

>>> from ansys.dpf import core as dpf
>>> from ansys.dpf.core import examples
>>> data_src = dpf.DataSources(examples.find_multishells_rst())
>>> disp_op = dpf.operators.result.displacement()
>>> disp_op.inputs.data_sources(data_src)
>>> max_fc_op = dpf.operators.min_max.min_max_fc()
>>> max_fc_op.inputs.connect(disp_op.outputs)
>>> max_field = max_fc_op.outputs.field_max()
>>> max_field.data
DPFArray([[0.59428386, 0.00201751, 0.0006032 ]]...

connect_operator_as_input(pin, op)#: Connects an operator as an input on a pin. :type pin: :param pin: Number of the output pin. The default is 0. :type pin: int :type op: :param op: Requested type of the output. The default is None. :type op: ansys.dpf.core.dpf_operator.Operator

eval(pin=None)#

Evaluate this operator.

Parameters:: pin (int) – Number of the output pin. The default is None.
Returns:: output – Returns the first output of the operator by default and the output of a given pin when specified. Or, it only evaluates the operator without output.
Return type:: FieldsContainer, Field, MeshedRegion, Scoping

Examples

Use the eval method.

>>> from ansys.dpf import core as dpf
>>> import ansys.dpf.core.operators.math as math
>>> from ansys.dpf.core import examples
>>> data_src = dpf.DataSources(examples.find_multishells_rst())
>>> disp_op = dpf.operators.result.displacement()
>>> disp_op.inputs.data_sources(data_src)
>>> normfc = math.norm_fc(disp_op).eval()

get_output(pin=0, output_type=None)#

Retrieve the output of the operator on the pin number.

To activate the progress bar for server version higher or equal to 3.0, use my_op.progress_bar=True

Parameters:

pin (int, optional) – Number of the output pin. The default is 0.
output_type (ansys.dpf.core.common.types, type, optional) – Requested type of the output. The default is None.

Returns:

Output of the operator.

Return type:

type

static operator_specification(op_name, server=None)#: Documents an Operator with its description (what the Operator does), its inputs and outputs and some properties

property progress_bar: bool#: With this property, the user can choose to print a progress bar when the operator’s output is requested, default is False

run()#: Evaluate this operator.

property specification#

Returns the Specification (or documentation) of this Operator

Return type:: Specification

class ansys.dpf.core.operators.compression.apply_svd.InputsApplySvd(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to connect user inputs to apply_svd operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> my_field_contaner_to_compress = dpf.FieldsContainer()
>>> op.inputs.field_contaner_to_compress.connect(my_field_contaner_to_compress)
>>> my_scalar_int = int()
>>> op.inputs.scalar_int.connect(my_scalar_int)
>>> my_scalar_double = float()
>>> op.inputs.scalar_double.connect(my_scalar_double)
>>> my_boolean = bool()
>>> op.inputs.boolean.connect(my_boolean)

property field_contaner_to_compress#

Allows to connect field_contaner_to_compress input to the operator.

Fields container to be compressed

Parameters:: my_field_contaner_to_compress (FieldsContainer) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> op.inputs.field_contaner_to_compress.connect(my_field_contaner_to_compress)
>>> # or
>>> op.inputs.field_contaner_to_compress(my_field_contaner_to_compress)

property scalar_int#

Allows to connect scalar_int input to the operator.

Number of vectors (r) to keep for the future reconstraction of the matrix a, ex. a[m,n]=coef[m,r]*vt[r,n], where coef=u*sigma

Parameters:: my_scalar_int (int) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> op.inputs.scalar_int.connect(my_scalar_int)
>>> # or
>>> op.inputs.scalar_int(my_scalar_int)

property scalar_double#

Allows to connect scalar_double input to the operator.

Threshold (precision) as a double, default value is 1e-7

Parameters:: my_scalar_double (float) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> op.inputs.scalar_double.connect(my_scalar_double)
>>> # or
>>> op.inputs.scalar_double(my_scalar_double)

property boolean#

Allows to connect boolean input to the operator.

Apply svd on the initial input data (true) or transposed (square matrix), default value is false

Parameters:: my_boolean (bool) –

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> op.inputs.boolean.connect(my_boolean)
>>> # or
>>> op.inputs.boolean(my_boolean)

connect(inpt)#

Connect any input (an entity or an operator output) to any input pin of this operator. Searches for the input type corresponding to the output.

Parameters:

inpt (str, int, double, bool, list[int], list[float], Field, FieldsContainer, Scoping,) –
ScopingsContainer (E501) – Input of the operator.
MeshedRegion (E501) – Input of the operator.
MeshesContainer (E501) – Input of the operator.
DataSources (E501) – Input of the operator.
CyclicSupport (E501) – Input of the operator.
Outputs (E501) – Input of the operator.
noqa (os.PathLike #) – Input of the operator.

class ansys.dpf.core.operators.compression.apply_svd.OutputsApplySvd(op: ansys.dpf.core.dpf_operator.Operator)#

Intermediate class used to get outputs from apply_svd operator.

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> # Connect inputs : op.inputs. ...
>>> result_us_svd = op.outputs.us_svd()
>>> result_vt_svd = op.outputs.vt_svd()
>>> result_sigma = op.outputs.sigma()

property us_svd#

Allows to get us_svd output of the operator

Returns:: my_us_svd
Return type:: FieldsContainer

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> # Connect inputs : op.inputs. ...
>>> result_us_svd = op.outputs.us_svd()

property vt_svd#

Allows to get vt_svd output of the operator

Returns:: my_vt_svd
Return type:: FieldsContainer

Examples

>>> from ansys.dpf import core as dpf
>>> op = dpf.operators.compression.apply_svd()
>>> # Connect inputs : op.inputs. ...
>>> result_vt_svd = op.outputs.vt_svd()