dedalus.core.field
==================

.. py:module:: dedalus.core.field

.. autoapi-nested-parse::

   Class for data fields.









Module Contents
---------------

.. py:class:: Field(dist, bases=None, name=None, tensorsig=None, dtype=None)



   Scalar field over a domain.

   :Parameters: * **domain** (*domain object*) -- Problem domain
                * **name** (*str, optional*) -- Field name (default: Python object id)

   :ivar layout: Current layout of field
   :vartype layout: layout object
   :ivar data: View of internal buffer in current layout
   :vartype data: ndarray


   .. py:method:: allgather_data(layout=None)

      Build global data on all processes.



   .. py:method:: allreduce_L2_norm(normalize_volume=True)


   .. py:method:: allreduce_data_max(layout=None)


   .. py:method:: allreduce_data_norm(layout=None, order=2)


   .. py:method:: broadcast_ghosts(output_nonconst_dims)

      Copy data over constant distributed dimensions for arithmetic broadcasting.



   .. py:method:: change_layout(layout)

      Change data to specified layout.



   .. py:method:: change_scales(scales)

      Change data to specified scales.



   .. py:method:: copy()


   .. py:method:: fill_random(layout=None, scales=None, seed=None, chunk_size=2**20, distribution='standard_normal', **kw)

      Fill field with random data. If a seed is specified, the global data is
      reproducibly generated for any process mesh.

      :Parameters: * **layout** (*Layout object, 'c', or 'g', optional*) -- Layout for setting field data. Default: current layout.
                   * **scales** (*number or tuple of numbers, optional*) -- Scales for setting field data. Default: current scales.
                   * **seed** (*int, optional*) -- RNG seed. Default: None.
                   * **chunk_size** (*int, optional*) -- Chunk size for drawing from distribution. Should be less than locally
                     available memory. Default: 2**20, corresponding to 8 MB of float64.
                   * **distribution** (*str, optional*) -- Distribution name, corresponding to numpy random Generator method.
                     Default: 'standard_normal'.
                   * **\*\*kw** (*dict*) -- Other keywords passed to the distribution method.



   .. py:method:: gather_data(root=0, layout=None)


   .. py:method:: get_basis(coord)


   .. py:method:: high_pass_filter(shape=None, scales=None)

      Apply a spectral high-pass filter by zeroing modes below specified relative scales.
      The scales can be specified directly or deduced from a specified global grid shape.

      :Parameters: * **shape** (*tuple of ints, optional*) -- Global grid shape for inferring truncation scales.
                   * **scales** (*float or tuple of floats, optional*) -- Scale factors for truncation.



   .. py:method:: load_from_global_coeff_data(global_data, pre_slices=tuple(), func=None)

      Load local coeff data from array-like global coeff data.



   .. py:method:: load_from_global_grid_data(global_data, pre_slices=tuple(), func=None)

      Load local grid data from array-like global grid data.



   .. py:method:: load_from_hdf5(file, index, task=None, func=None)

      Load grid data from an hdf5 file. Task corresponds to field name by default.



   .. py:method:: local_elements()


   .. py:method:: low_pass_filter(shape=None, scales=None)

      Apply a spectral low-pass filter by zeroing modes above specified relative scales.
      The scales can be specified directly or deduced from a specified global grid shape.

      :Parameters: * **shape** (*tuple of ints, optional*) -- Global grid shape for inferring truncation scales.
                   * **scales** (*float or tuple of floats, optional*) -- Scale factors for truncation.



   .. py:method:: normalize(normalize_volume=True)

      Normalize field inplace using L2 norm.

      :Parameters: **normalize_volume** (*bool, optional*) -- Normalize inner product by domain volume. Default: True.



   .. py:method:: require_coeff_space(axis=None)

      Require one axis (default: all axes) to be in coefficient space.



   .. py:method:: require_grid_space(axis=None)

      Require one axis (default: all axes) to be in grid space.



   .. py:method:: require_local(axis)

      Require an axis to be local.



   .. py:method:: set_global_data(global_data)


   .. py:method:: set_local_data(local_data)


   .. py:method:: towards_coeff_space()

      Change to next layout towards coefficient space.



   .. py:method:: towards_grid_space()

      Change to next layout towards grid space.



   .. py:attribute:: buffer_size
      :value: -1



   .. py:attribute:: dist


   .. py:attribute:: domain


   .. py:attribute:: dtype
      :value: None



   .. py:property:: global_shape


   .. py:property:: is_scalar


   .. py:attribute:: layout


   .. py:attribute:: name
      :value: None



   .. py:attribute:: scales
      :value: None



   .. py:attribute:: tensorsig
      :value: None



.. py:class:: LockedField(dist, bases=None, name=None, tensorsig=None, dtype=None)



   Field locked to particular layouts, disallowing changes to other layouts.


   .. py:method:: change_scales(scales)

      Change data to specified scales.



   .. py:method:: lock_axis_to_grid(axis)


   .. py:method:: lock_to_layouts(*layouts)


   .. py:method:: towards_coeff_space()

      Change to next layout towards coefficient space.



   .. py:method:: towards_grid_space()

      Change to next layout towards grid space.



   .. py:method:: unlock()

      Return regular Field object with same data and no layout locking.



.. py:function:: TensorField(dist, coordsys, *args, order=2, **kw)

.. py:function:: VectorField(dist, coordsys, *args, **kw)

.. py:data:: ScalarField