:py:mod:`pygsti.modelmembers.operations.eigpdenseop`
====================================================

.. py:module:: pygsti.modelmembers.operations.eigpdenseop

.. autoapi-nested-parse::

   The EigenvalueParamDenseOp class and supporting functionality.






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

Classes
~~~~~~~

.. autoapisummary::

   pygsti.modelmembers.operations.eigpdenseop.EigenvalueParamDenseOp




Attributes
~~~~~~~~~~

.. autoapisummary::

   pygsti.modelmembers.operations.eigpdenseop.IMAG_TOL


.. py:data:: IMAG_TOL
   :value: '1e-07'

   

.. py:class:: EigenvalueParamDenseOp(matrix, include_off_diags_in_degen_blocks=False, tp_constrained_and_unital=False, basis=None, evotype='default', state_space=None)


   Bases: :py:obj:`pygsti.modelmembers.operations.denseop.DenseOperator`

   A real operation matrix parameterized only by its eigenvalues.

   These eigenvalues are assumed to be either real or to occur in
   conjugate pairs.  Thus, the number of parameters is equal to the
   number of eigenvalues.

   Parameters
   ----------
   matrix : numpy array
       a square 2D numpy array that gives the raw operation matrix to
       paramterize.  The shape of this array sets the dimension
       of the operation.

   include_off_diags_in_degen_blocks : bool or int
       If True, include as parameters the (initially zero)
       off-diagonal elements in degenerate blocks of the
       the diagonalized operation matrix.  If an integer, no
       off-diagonals are included in blocks larger than n x n, where
       `n == include_off_diags_in_degen_blocks`.  This is an option
       specifically used in the per-germ-power fiducial pair
       reduction (FPR) algorithm.

   tp_constrained_and_unital : bool
       If True, assume the top row of the operation matrix is fixed
       to `[1, 0, ... 0]` and should not be parameterized, and verify
       that the matrix is unital.  In this case, "1" is always a
       fixed (not-paramterized) eigenvalue with eigenvector
       `[1,...0]` and if include_off_diags_in_degen_blocks is True
       any off diagonal elements lying on the top row are *not*
       parameterized as implied by the TP constraint.

   basis : Basis or {'pp','gm','std'} or None
       The basis used to construct the Hilbert-Schmidt space representation
       of this state as a super-operator.  If None, certain functionality,
       such as access to Kraus operators, will be unavailable.

   evotype : Evotype or str, optional
       The evolution type.  The special value `"default"` is equivalent
       to specifying the value of `pygsti.evotypes.Evotype.default_evotype`.

   state_space : StateSpace, optional
       The state space for this operation.  If `None` a default state space
       with the appropriate number of qubits is used.

   Initialize a new LinearOperator 

   .. py:property:: num_params

      Get the number of independent parameters which specify this operation.

      Returns
      -------
      int
          the number of independent parameters.


   .. py:attribute:: evals

      

   .. py:attribute:: B

      

   .. py:attribute:: Bi

      

   .. py:attribute:: options

      

   .. py:attribute:: params
      :value: '[]'

      

   .. py:attribute:: paramvals

      

   .. py:method:: to_memoized_dict(mmg_memo)

      Create a serializable dict with references to other objects in the memo.

      Parameters
      ----------
      mmg_memo: dict
          Memo dict from a ModelMemberGraph, i.e. keys are object ids and values
          are ModelMemberGraphNodes (which contain the serialize_id). This is NOT
          the same as other memos in ModelMember (e.g. copy, allocate_gpindices, etc.).

      Returns
      -------
      mm_dict: dict
          A dict representation of this ModelMember ready for serialization
          This must have at least the following fields:
          module, class, submembers, params, state_space, evotype
          Additional fields may be added by derived classes.


   .. py:method:: to_vector()

      Extract a vector of the underlying operation parameters from this operation.

      Returns
      -------
      numpy array
          a 1D numpy array with length == num_params().


   .. py:method:: from_vector(v, close=False, dirty_value=True)

      Initialize the operation using a vector of parameters.

      Parameters
      ----------
      v : numpy array
          The 1D vector of operation parameters.  Length
          must == num_params()

      close : bool, optional
          Whether `v` is close to this operation's current
          set of parameters.  Under some circumstances, when this
          is true this call can be completed more quickly.

      dirty_value : bool, optional
          The value to set this object's "dirty flag" to before exiting this
          call.  This is passed as an argument so it can be updated *recursively*.
          Leave this set to `True` unless you know what you're doing.

      Returns
      -------
      None


   .. py:method:: deriv_wrt_params(wrt_filter=None)

      The element-wise derivative this operation.

      Construct a matrix whose columns are the vectorized
      derivatives of the flattened operation matrix with respect to a
      single operation parameter.  Thus, each column is of length
      op_dim^2 and there is one column per operation parameter.

      Parameters
      ----------
      wrt_filter : list or numpy.ndarray
          List of parameter indices to take derivative with respect to.
          (None means to use all the this operation's parameters.)

      Returns
      -------
      numpy array
          Array of derivatives, shape == (dimension^2, num_params)


   .. py:method:: has_nonzero_hessian()

      Whether this operation has a non-zero Hessian with respect to its parameters.

      (i.e. whether it only depends linearly on its parameters or not)

      Returns
      -------
      bool