:py:mod:`pygsti.modelmembers.povms.complementeffect`
====================================================

.. py:module:: pygsti.modelmembers.povms.complementeffect

.. autoapi-nested-parse::

   The ComplementPOVMEffect class and supporting functionality.






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

Classes
~~~~~~~

.. autoapisummary::

   pygsti.modelmembers.povms.complementeffect.ComplementPOVMEffect




.. py:class:: ComplementPOVMEffect(identity, other_effects, called_from_reduce=False)


   Bases: :py:obj:`pygsti.modelmembers.povms.conjugatedeffect.ConjugatedStatePOVMEffect`

   TODO: docstring
   A POVM effect vector that ensures that all the effects of a POVM sum to the identity.

   This POVM effect vector is paramterized as `I - sum(other_spam_vecs)` where `I` is
   a (static) identity element and `other_param_vecs` is a list of other spam
   vectors in the same parent :class:`POVM`.  This only *partially* implements
   the model-member interface (some methods such as `to_vector` and `from_vector`
   will thunk down to base class versions which raise `NotImplementedError`),
   as instances are meant to be contained within a :class:`POVM` which takes
   care of vectorization.

   Parameters
   ----------
   identity : array_like or POVMEffect
       a 1D numpy array representing the static identity operation from
       which the sum of the other vectors is subtracted.

   other_spamvecs : list of POVMEffects
       A list of the "other" parameterized POVM effect vectors which are
       subtracted from `identity` to compute the final value of this
       "complement" POVM effect vector.

   Initialize a new POVM effect Vector 

   .. py:property:: num_params

      Get the number of independent parameters which specify this POVM effect vector.

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


   .. py:attribute:: identity

      

   .. py:attribute:: other_effects

      

   .. 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:: submembers()

      Get the ModelMember-derived objects contained in this one.

      Returns
      -------
      list


   .. py:method:: to_vector()

      Get the POVM effect vector parameters as an array of values.

      Returns
      -------
      numpy array
          The parameters as a 1D array with length num_params().


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

      Initialize the POVM effect vector using a 1D array of parameters.

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

      close : bool, optional
          Whether `v` is close to this POVM effect vector'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 POVM effect vector.

      Construct a matrix whose columns are the derivatives of the POVM effect vector
      with respect to a single param.  Thus, each column is of length
      dimension and there is one column per POVM effect vector 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, num_params)


   .. py:method:: has_nonzero_hessian()

      Whether this POVM effect vector has a non-zero Hessian with respect to its parameters.

      Returns
      -------
      bool