4.1.1 pde.fields.base module

Defines base class of fields or collections, which are discretized on grids

class FieldBase(grid, data, *, label=None)[source]

Bases: object

abstract base class for describing (discretized) fields

Parameters:

grid (GridBase) – Grid defining the space on which this field is defined
data (ndarray, optional) – Field values at the support points of the grid and the ghost cells
label (str, optional) – Name of the field

apply(func, out=None, *, label=None, evaluate_args=None)[source]

applies a function/expression to the data and returns it as a field

Parameters:

func (callable or str) – The (vectorized) function being applied to the data or an expression that can be parsed using sympy (evaluate() is used in this case). The local field values can be accessed using the field labels for a field collection and via the variable c otherwise.
out (FieldBase, optional) – Optional field into which the data is written
label (str, optional) – Name of the returned field
evaluate_args (dict) – Additional arguments passed to evaluate(). Only used when func is a string.
self (TField)

Returns:

Field with new data. Identical to out if given.

Return type:

FieldBase

assert_field_compatible(other, accept_scalar=False)[source]

checks whether other is compatible with the current field

Parameters:

other (FieldBase) – The other field this one is compared to
accept_scalar (bool, optional) – Determines whether it is acceptable that other is an instance of ScalarField.

Return type:

None

property attributes: dict[str, Any]

describes the state of the instance (without the data)

Type:: dict

property attributes_serialized: dict[str, str]

serialized version of the attributes

Type:: dict

conjugate()[source]

returns complex conjugate of the field

Returns:: the complex conjugated field
Return type:: FieldBase
Parameters:: self (TField)

abstract copy(*, label=None, dtype=None)[source]

return a new field with the data (but not the grid) copied

Parameters:

label (str, optional) – Name of the returned field
dtype (numpy dtype) – The data type of the field. If omitted, it will be determined from data automatically or the dtype of the current field is used.
self (TField)

Returns:

A copy of the current field

Return type:

DataFieldBase

property data: ndarray

discretized data at the support points

Type:: ndarray

the numpy dtype of the underlying data

Type:: DTypeLike

classmethod from_file(filename)[source]

create field from data stored in a file

Field can be written to a file using FieldBase.to_file().

Example

Write a field to a file and then read it back:

field = pde.ScalarField(...)
field.write_to("test.hdf5")

field_copy = pde.FieldBase.from_file("test.hdf5")

Parameters:: filename (str) – Path to the file being read
Returns:: The field with the appropriate sub-class
Return type:: FieldBase

classmethod from_state(attributes, data=None)[source]

create a field from given state.

Parameters:

attributes (dict) – The attributes that describe the current instance
data (ndarray, optional) – Data values at the support points of the grid defining the field

Returns:

The field created from the state

Return type:

FieldBase

abstract get_image_data()[source]

return data for plotting an image of the field

Parameters:

scalar (str or int) – The method for extracting scalars as described in DataFieldBase.to_scalar().
transpose (bool) – Determines whether the transpose of the data should is plotted
**kwargs – Additional parameters are forwarded to grid.get_image_data

Returns:

Information useful for plotting an image of the field

Return type:

dict

abstract get_line_data(scalar='auto', extract='auto')[source]

return data for a line plot of the field

Parameters:

scalar (str or int) – The method for extracting scalars as described in DataFieldBase.to_scalar().
extract (str) – The method used for extracting the line data. See the docstring of the grid method get_line_data to find supported values.

Returns:

Information useful for performing a line plot of the field

Return type:

dict

property grid: GridBase

The grid on which the field is defined

Type:: base,GridBase

property imag: TField

Imaginary part of the field

Type:: FieldBase

property is_complex: bool

whether the field contains real or complex data

Type:: bool

property label: str | None

the name of the field

Type:: str

abstract plot(*args, **kwargs)[source]: visualize the field

plot_interactive(viewer_args=None, **kwargs)[source]

create an interactive plot of the field using napari

For a detailed description of the launched program, see the napari webpage.

Parameters:

viewer_args (dict) – Arguments passed to napari.viewer.Viewer to affect the viewer.
**kwargs – Extra arguments passed to the plotting function

Return type:

None

property real: TField

Real part of the field

Type:: FieldBase

split_mpi(decomposition=-1)[source]

splits the field onto subgrids in an MPI run

In a normal serial simulation, the method simply returns the field itself. In contrast, in an MPI simulation, the field provided on the main node is split onto all nodes using the given decomposition. The field data provided on all other nodes is not used.

Parameters:

decomposition (list of ints) – Number of subdivision in each direction. Should be a list of length field.grid.num_axes specifying the number of nodes for this axis. If one value is -1, its value will be determined from the number of available nodes. The default value decomposed the first axis using all available nodes
self (TField)

Returns:

The part of the field that corresponds to the subgrid associated with the current MPI node.

Return type:

FieldBase

to_file(filename, **kwargs)[source]

store field in a file

The extension of the filename determines what format is being used. If it ends in .h5 or .hdf, the Hierarchical Data Format is used. The other supported format are images, where only the most typical formats are supported.

To load the field back from the file, you may use FieldBase.from_file().

Example

Write a field to a file and then read it back:

field = pde.ScalarField(...)
field.write_to("test.hdf5")

field_copy = pde.FieldBase.from_file("test.hdf5")

Parameters:

filename (str) – Path where the data is stored
**kwargs – Additional parameters may be supported for some formats

Return type:

None

classmethod unserialize_attributes(attributes)[source]

unserializes the given attributes

Parameters:: attributes (dict) – The serialized attributes
Returns:: The unserialized attributes
Return type:: dict

property writeable: bool

whether the field data can be changed or not

Type:: bool

exception RankError[source]

Bases: TypeError

error indicating that the field has the wrong rank