content: __all__ = [
      'wigner', 'qfunc', 'QFunc', 'spin_q_function', 'spin_wigner',
      'wigner_transform',
  ]
  
  import numpy as np
  import warnings
  from numpy import (
      zeros, array, arange, exp, real, conj, pi, copy, sqrt, meshgrid, cos, sin,
  )
  import scipy.sparse as sp
  import scipy.fftpack as ft
  import scipy.linalg as la
  import scipy.special
  from scipy.special import genlaguerre, binom, factorial
  
  try:
      from scipy.special import sph_harm_y
  except ImportError:
      from scipy.special import sph_harm
  
      def sph_harm_y(n, m, polar, azimuthal):
          # sph_harm is set for removal.
          # Same function, but changed parameter order
          return sph_harm(m, n, azimuthal, polar)
  
  import qutip
  from qutip import Qobj, ket2dm, jmat
  from .solver.parallel import parallel_map
  from .utilities import clebsch
  from .core import data as _data
  from .core.data.eigen import eigh
  
  
  def wigner_transform(psi, j, fullparity, steps, slicearray):
      """takes the density matrix or state vector of any finite state and
      generates the Wigner function for that state on a sphere, generating a spin
      Wigner function useful for displaying the quasi-probability for a qubit or
      any qudit. For the standard, continuous-variable Wigner function for
      position and momentum variables, wigner() should be used.
  
      Parameters
      ----------
          psi : qobj
                a state vector or density matrix.
          j : int
              the total angular momentum of the quantum state.
          fullparity : bool
                       should the parity of the full SU space be used?
          steps : int
                  number of points at which the Wigner transform is calculated.
          slicearray : list of str
                       the angle slice to be used for each particle in case of a
                       multi-particle quantum state. 'l' yields an equal angle
                       slice. 'x', 'y' and 'z' angle slices can also be chosen.
  
      Returns
      ----------
          wigner : list of float
                   the wigner transformation at `steps` different theta and phi.
  
      Raises
      ------
      ComplexWarning
          This can be ignored as it is caused due to rounding errors.
  
      Notes
      ------
      See example notebook wigner_visualisation.
  
      References
      ------
      [1] T. Tilma, M. J. Everitt, J. H. Samson, W. J. Munro,
          and K. Nemoto, Phys. Rev. Lett. 117, 180401 (2016).
      [2] R. P. Rundle, P. W. Mills, T. Tilma, J. H. Samson, and
          M. J. Everitt, Phys. Rev. A 96, 022117 (2017).
      """
      if not (psi.type == 'ket' or psi.type == 'operator' or psi.type == 'bra'):
          raise TypeError('Input state is not a valid operator.')
  
      if psi.isket or psi.isbra:
          rho = psi.proj()
      else:
          rho = psi
  
      sun = 2   # The order of the SU group
  
      # calculate total number of particles in quantum state:
      N = np.int32(np.log(np.shape(rho)[0]) / np.log(2 * j + 1))
  
      theta = np.zeros((N, steps))
      phi = np.zeros((N, steps))
  
      for i in range(N):
          theta[i, :] = np.linspace(0, np.pi, steps)
          phi[i, :] = np.linspace(0, 2 * np.pi, steps)
  
      theta, phi = _angle_slice(np.array(slicearray, dtype=str), theta, phi)
  
      wigner = np.zeros((steps, steps))
      if fullparity:
          pari = _parity(sun**N, j)
      else:
          pari = _parity(sun, j)
      for t in range(steps):
          for p in range(steps):
              kernel = _kernelsu2(theta[:, t], phi[:, p], N, j, pari, fullparity)
              kernel = _data.dense.fast_from_numpy(kernel)
              wigner[t, p] = _data.expect(rho.data, kernel).real
      return wigner
  
  
  def _parity(N, j):
      """Private function to calculate the parity of the quantum system.
      """
      if j == 0.5:
          pi = np.identity(N) - np.sqrt((N - 1) * N * (N + 1) / 2) * _lambda_f(N)
          return pi / N
      elif j > 0.5:
          mult = np.int32(2 * j + 1)
          matrix = np.zeros((mult, mult))
          foo = np.ones(mult)
          for n in np.arange(-j, j + 1, 1):
              for l in np.arange(0, mult, 1):
                  foo[l] = (2 * l + 1) * clebsch(j, l, j, n, 0, n)
              matrix[np.int32(n + j), np.int32(n + j)] = np.sum(foo)
          return matrix / mult
  
  
  def _lambda_f(N):
      """Private function needed for the calculation of the parity.
      """
      matrix = np.sqrt(2 / (N * (N - 1))) * np.identity(N)
      matrix[-1, -1] = - np.sqrt(2 * (N - 1) / N)
      return matrix
  
  
  def _kernelsu2(theta, phi, N, j, parity, fullparity):
      """Private function that calculates the kernel for the SU2 unitary group.
      """
      U = np.ones(1)
      # calculate the total rotation matrix (tensor product for each particle):
      for i in range(0, N):
          U = np.kron(U, _rotation_matrix(theta[i], phi[i], j))
      if not fullparity:
          op_parity = parity   # The parity for a one particle system
          for i in range(1, N):
              parity = np.kron(parity, op_parity)
      matrix = U @ parity @ U.conj().T
      return matrix
  
  
  def _rotation_matrix(theta, phi, j):
      """Private function to calculate the rotation operator for the SU2 kernel.
      """
      return la.expm(1j * phi * jmat(j, 'z').full()) @ \
             la.expm(1j * theta * jmat(j, 'y').full())
  
  
  def _angle_slice(slicearray, theta, phi):
      """Private function to modify theta and phi for angle slicing.
      """
      xind = np.where(slicearray == 'x')
      theta[xind, :] = np.pi - theta[xind, :]
      phi[xind, :] = -phi[xind, :]
      yind = np.where(slicearray == 'y')
      theta[yind, :] = np.pi - theta[yind, :]
      phi[yind, :] = np.pi - phi[yind, :]
      zind = np.where(slicearray == 'z')
      phi[zind, :] = phi[zind, :] + np.pi
      return theta, phi
  
  
  def wigner(psi, xvec, yvec=None, method='clenshaw', g=sqrt(2),
             sparse=False, parfor=False):
      """Wigner function for a state vector or density matrix at points
      `xvec + i * yvec`.
  
      Parameters
      ----------
  
      state : qobj
          A state vector or density matrix.
  
      xvec : array_like
          x-coordinates at which to calculate the Wigner function.
  
      yvec : array_like
          y-coordinates at which to calculate the Wigner function.  Does not
          apply to the 'fft' method.
  
      g : float, default: sqrt(2)
          Scaling factor for `a = 0.5 * g * (x + iy)`, default `g = sqrt(2)`.
          The value of `g` is related to the value of `hbar` in the commutation
          relation `[x, y] = i * hbar` via `hbar=2/g^2` giving the default
          value `hbar=1`.
  
      method : string {'clenshaw', 'iterative', 'laguerre', 'fft'}, default: 'clenshaw'
          Select method 'clenshaw' 'iterative', 'laguerre', or 'fft', where 'clenshaw'
          and 'iterative' use an iterative method to evaluate the Wigner functions for density
          matrices :math:`|m><n|`, while 'laguerre' uses the Laguerre polynomials
          in scipy for the same task. The 'fft' method evaluates the Fourier
          transform of the density matrix. The 'iterative' method is default, and
          in general recommended, but the 'laguerre' method is more efficient for
          very sparse density matrices (e.g., superpositions of Fock states in a
          large Hilbert space). The 'clenshaw' method is the preferred method for
          dealing with density matrices that have a large number of excitations
          (>~50). 'clenshaw' is a fast and numerically stable method.
  
      sparse : bool, optional
          Tells the default solver whether or not to keep the input density
          matrix in sparse format.  As the dimensions of the density matrix
          grow, setthing this flag can result in increased performance.
  
      parfor : bool, optional
          Flag for calculating the Laguerre polynomial based Wigner function
          method='laguerre' in parallel using the parfor function.
  
  
      Returns
      -------
  
      W : array
          Values representing the Wigner function calculated over the specified
          range [xvec,yvec].
  
      yvex : array
          FFT ONLY. Returns the y-coordinate values calculated via the Fourier
          transform.
  
      Notes
      -----
      The 'fft' method accepts only an xvec input for the x-coordinate.
      The y-coordinates are calculated internally.
  
      References
      ----------
  
      Ulf Leonhardt,
      Measuring the Quantum State of Light, (Cambridge University Press, 1997)
  
      """
  
      if not (psi.type == 'ket' or psi.type == 'oper' or psi.type == 'bra'):
          raise TypeError('Input state is not a valid operator.')
  
      if method == 'fft':
          return _wigner_fourier(psi, xvec, g)
  
      if psi.isket or psi.isbra:
          rho = psi.proj()
      else:
          rho = psi
  
      if method == 'iterative':
          return _wigner_iterative(rho, xvec, yvec, g)
  
      elif method == 'laguerre':
          return _wigner_laguerre(rho, xvec, yvec, g, parfor)
  
      elif method == 'clenshaw':
          return _wigner_clenshaw(rho, xvec, yvec, g, sparse=sparse)
  
      else:
          raise TypeError(
              "method must be either 'iterative', 'laguerre', or 'fft'.")
  
  
  def _wigner_iterative(rho, xvec, yvec, g=sqrt(2)):
      r"""
      Using an iterative method to evaluate the wigner functions for the Fock
      state :math:`|m><n|`.
  
      The Wigner function is calculated as
      :math:`W = \sum_{mn} \rho_{mn} W_{mn}` where :math:`W_{mn}` is the Wigner
      function for the density matrix :math:`|m><n|`.
  
      In this implementation, for each row m, Wlist contains the Wigner functions
      Wlist = [0, ..., W_mm, ..., W_mN]. As soon as one W_mn Wigner function is
      calculated, the corresponding contribution is added to the total Wigner
      function, weighted by the corresponding element in the density matrix
      :math:`rho_{mn}`.
      """
  
      M = np.prod(rho.shape[0])
      X, Y = meshgrid(xvec, yvec)
      A = 0.5 * g * (X + 1.0j * Y)
  
      Wlist = array([zeros(np.shape(A), dtype=complex) for k in range(M)])
      Wlist[0] = exp(-2.0 * abs(A) ** 2) / pi
  
      W = real(rho[0, 0]) * real(Wlist[0])
      for n in range(1, M):
          Wlist[n] = (2.0 * A * Wlist[n - 1]) / sqrt(n)
          W += 2 * real(rho[0, n] * Wlist[n])
  
      for m in range(1, M):
          temp = copy(Wlist[m])
          Wlist[m] = (2 * conj(A) * temp - sqrt(m) * Wlist[m - 1]) / sqrt(m)
  
          # Wlist[m] = Wigner function for |m><m|
          W += real(rho[m, m] * Wlist[m])
  
          for n in range(m + 1, M):
              temp2 = (2 * A * Wlist[n - 1] - sqrt(m) * temp) / sqrt(n)
              temp = copy(Wlist[n])
              Wlist[n] = temp2
  
              # Wlist[n] = Wigner function for |m><n|
              W += 2 * real(rho[m, n] * Wlist[n])
  
      return 0.5 * W * g ** 2
  
  
  def _wigner_laguerre(rho, xvec, yvec, g, parallel):
      r"""
      Using Laguerre polynomials from scipy to evaluate the Wigner function for
      the density matrices :math:`|m><n|`, :math:`W_{mn}`. The total Wigner
      function is calculated as :math:`W = \sum_{mn} \rho_{mn} W_{mn}`.
      """
  
      M = np.prod(rho.shape[0])
      X, Y = meshgrid(xvec, yvec)
      A = 0.5 * g * (X + 1.0j * Y)
      W = zeros(np.shape(A))
  
      # compute wigner functions for density matrices |m><n| and
      # weight by all the elements in the density matrix
      B = 4 * abs(A) ** 2
      if sp.isspmatrix_csr(rho.data):
          # for compress sparse row matrices
          if parallel:
              iterator = (
                  (m, rho, A, B) for m in range(len(rho.data.indptr) - 1))
              W1_out = parallel_map(_par_wig_eval, iterator)
              W += sum(W1_out)
          else:
              for m in range(len(rho.data.indptr) - 1):
                  for jj in range(rho.data.indptr[m], rho.data.indptr[m + 1]):
                      n = rho.data.indices[jj]
  
                      if m == n:
                          W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
  
                      elif n > m:
                          W += 2.0 * real(rho[m, n] * (-1) ** m *
                                          (2 * A) ** (n - m) *
                                          sqrt(factorial(m) / factorial(n)) *
                                          genlaguerre(m, n - m)(B))
      else:
          # for dense density matrices
          B = 4 * abs(A) ** 2
          for m in range(M):
              if abs(rho[m, m]) > 0.0:
                  W += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
              for n in range(m + 1, M):
                  if abs(rho[m, n]) > 0.0:
                      W += 2.0 * real(rho[m, n] * (-1) ** m *
                                      (2 * A) ** (n - m) *
                                      sqrt(factorial(m) / factorial(n)) *
                                      genlaguerre(m, n - m)(B))
  
      return 0.5 * W * g ** 2 * np.exp(-B / 2) / pi
  
  
  def _par_wig_eval(args):
      """
      Private function for calculating terms of Laguerre Wigner function
      using parfor.
      """
      m, rho, A, B = args
      W1 = zeros(np.shape(A))
      for jj in range(rho.data.indptr[m], rho.data.indptr[m + 1]):
          n = rho.data.indices[jj]
  
          if m == n:
              W1 += real(rho[m, m] * (-1) ** m * genlaguerre(m, 0)(B))
  
          elif n > m:
              W1 += 2.0 * real(rho[m, n] * (-1) ** m *
                               (2 * A) ** (n - m) *
                               sqrt(factorial(m) / factorial(n)) *
                               genlaguerre(m, n - m)(B))
      return W1
  
  
  def _wigner_fourier(psi, xvec, g=np.sqrt(2)):
      """
      Evaluate the Wigner function via the Fourier transform.
      """
      if psi.type == 'bra':
          psi = psi.dag()
      if psi.type == 'ket':
          return _psi_wigner_fft(psi.full(), xvec, g)
      elif psi.type == 'oper':
          eig_vals, eig_vecs = eigh(psi.full())
          W = 0
          for ii in range(psi.shape[0]):
              W1, yvec = _psi_wigner_fft(
                  np.reshape(eig_vecs[:, ii], (psi.shape[0], 1)), xvec, g)
              W += eig_vals[ii] * W1
          return W, yvec
  
  
  def _psi_wigner_fft(psi, xvec, g=sqrt(2)):
      """
      FFT method for a single state vector.  Called multiple times when the
      input is a density matrix.
      """
      n = len(psi)
      A = _osc_eigen(n, xvec * g / np.sqrt(2))
      xpsi = np.dot(psi.T, A)
      W, yvec = _wigner_fft(xpsi, xvec * g / np.sqrt(2))
      return (0.5 * g ** 2) * np.real(W.T), yvec * np.sqrt(2) / g
  
  
  def _wigner_fft(psi, xvec):
      """
      Evaluates the Fourier transformation of a given state vector.
      Returns the corresponding density matrix and range
      """
      n = 2*len(psi.T)
      r1 = np.concatenate(
          (np.array([0]), np.fliplr(psi.conj()).ravel(), np.zeros(n//2 - 1))
      )
      r2 = np.concatenate(
          (np.array([0]), psi.ravel(), np.zeros(n//2 - 1))
      )
      w = la.toeplitz(np.zeros(n//2), r1) * \
          np.flipud(la.toeplitz(np.zeros(n//2), r2))
      w = np.concatenate((w[:, n//2:n], w[:, 0:n//2]), axis=1)
      w = ft.fft(w)
      w = np.real(np.concatenate((w[:, 3*n//4:n+1], w[:, 0:n//4]), axis=1))
      p = np.arange(-n/4, n/4)*np.pi / (n*(xvec[1] - xvec[0]))
      w = w / (p[1] - p[0]) / n
      return w, p
  
  
  def _osc_eigen(N, pnts):
      """
      Vector of and N-dim oscillator eigenfunctions evaluated
      at the points in pnts.
      """
      pnts = np.asarray(pnts)
      lpnts = len(pnts)
      A = np.zeros((N, lpnts))
      A[0, :] = np.exp(-pnts ** 2 / 2.0) / pi ** 0.25
      if N == 1:
          return A
      else:
          A[1, :] = np.sqrt(2) * pnts * A[0, :]
          for k in range(2, N):
              A[k, :] = np.sqrt(2.0 / k) * pnts * A[k - 1, :] - \
                  np.sqrt((k - 1.0) / k) * A[k - 2, :]
          return A
  
  
  def _wigner_clenshaw(rho, xvec, yvec, g=sqrt(2), sparse=False):
      r"""
      Using Clenshaw summation - numerically stable and efficient
      iterative algorithm to evaluate polynomial series.
  
      The Wigner function is calculated as
      :math:`W = e^(-0.5*x^2)/pi * \sum_{L} c_L (2x)^L / \sqrt(L!)` where
      :math:`c_L = \sum_n \rho_{n,L+n} LL_n^L` where
      :math:`LL_n^L = (-1)^n \sqrt(L!n!/(L+n)!) LaguerreL[n,L,x]`
      """
  
      M = np.prod(rho.shape[0])
      X,Y = np.meshgrid(xvec, yvec)
      #A = 0.5 * g * (X + 1.0j * Y)
      A2 = g * (X + 1.0j * Y) #this is A2 = 2*A
  
      B = np.abs(A2)
      B *= B
      w0 = (2*rho[0, -1])*np.ones_like(A2)
      L = M-1
      #calculation of \sum_{L} c_L (2x)^L / \sqrt(L!)
      #using Horner's method
      if not sparse:
          rho = rho.full() * (2*np.ones((M,M)) - np.diag(np.ones(M)))
          while L > 0:
              L -= 1
              #here c_L = _wig_laguerre_val(L, B, np.diag(rho, L))
              w0 = _wig_laguerre_val(L, B, np.diag(rho, L)) + w0 * A2 * (L+1)**-0.5
      else:
          # TODO: fix dispatch.
          _rho = _data.to(_data.CSR, rho.data).as_scipy()
          while L > 0:
              L -= 1
              diag = _rho.diagonal(L)
              if L != 0:
                  diag *= 2
              #here c_L = _wig_laguerre_val(L, B, np.diag(rho, L))
              w0 = _wig_laguerre_val(L, B, diag) + w0 * A2 * (L+1)**-0.5
  
      return w0.real * np.exp(-B*0.5) * (g*g*0.5 / pi)
  
  
  def _wig_laguerre_val(L, x, c):
      r"""
      this is evaluation of polynomial series inspired by hermval from numpy.
      Returns polynomial series
  
      .. math:
          \sum_n b_n LL_n^L,
  
      where
  
      .. math:
          LL_n^L = (-1)^n \sqrt(L!n!/(L+n)!) LaguerreL[n,L,x]
  
      The evaluation uses Clenshaw recursion.
      """
  
      if len(c) == 1:
          y0 = c[0]
          y1 = 0
      elif len(c) == 2:
          y0 = c[0]
          y1 = c[1]
      else:
          k = len(c)
          y0 = c[-2]
          y1 = c[-1]
          for i in range(3, len(c) + 1):
              k -= 1
              y0,    y1 = c[-i] - y1 * (float((k - 1)*(L + k - 1))/((L+k)*k))**0.5, \
              y0 - y1 * ((L + 2*k -1) - x) * ((L+k)*k)**-0.5
  
      return y0 - y1 * ((L + 1) - x) * (L + 1)**-0.5
  
  
  # -----------------------------------------------------------------------------
  # Q FUNCTION
  #
  def _qfunc_check_state(state: Qobj):
      if not isinstance(state, Qobj):
          raise TypeError(f"state must be Qobj, but is {state}")
      # This is only approximate, but it's enough for our purposes; doing more
      # than this would take computational effort we don't _need_ to do.
      isdm = (
          state.isoper
          and state._dims[0] == state._dims[1]
          and state.isherm
          and abs(state.tr() - 1) < qutip.settings.core['atol']
      )
      if not (state.isket or isdm):
          raise ValueError(
              f"state must be a ket or density matrix, but is {state}"
          )
      if len(state.dims[0]) != 1:
          raise ValueError(
              "state must not have tensor structure, but has dimensions:"
              f" {state.dims}"
          )
      return state
  
  
  def _qfunc_check_coordinates(xvec, yvec):
      if np.isscalar(xvec) or xvec is None:
          raise TypeError("xvec must be array-like, but is " + repr(xvec))
      if np.isscalar(yvec) or yvec is None:
          raise TypeError("yvec must be array-like, but is " + repr(yvec))
      xvec = np.asarray(xvec, dtype=np.float64)
      yvec = np.asarray(yvec, dtype=np.float64)
      if xvec.ndim != 1 or yvec.ndim != 1:
          raise ValueError(
              f"xvec and yvec must be 1D, but have shapes {xvec.shape} and {yvec.shape}."
          )
      return xvec, yvec
  
  
  class _QFuncCoherentGrid:
      """
      Internal function to compute coherent state operators corresponding to a
      grid of complex values in phase space.  For efficiency reasons, this class
      produces the adjoint of the coherent states, to save allocations when
      calculating inner products later.
  
      Examples
      --------
      Initialise the grid calculator.
  
      >>> xvec = yvec = np.linspace(-1, 1, 21)
      >>> g = np.sqrt(0.5)
      >>> max_ns = 10
      >>> grid = _QFuncCoherentGrid(xvec, yvec, g)
  
      The naive construction of the grid is
  
      >>> xs, ys = np.meshgrid(xvec, yvec)
      >>> all_alphas = 0.5 * g * (xs + 1j*ys)
      >>> naive = np.array([
      ...     [
      ...         qutip.coherent(max_ns, alpha, method='analytic')
      ...             .dag().full().ravel()
      ...         for alpha in x_alphas
      ...     ]
      ...     for y_alphas in all_alphas
      ... ])
  
      The naive approach is typically several of orders of magnitude slower than
      this class, which uses much simpler vectorised operations.  The outputs are
      within close tolerance, however:
  
      >>> np.allclose(naive, grid(max_ns))
      True
      >>> np.allclose(naive[:, :, 4:7], grid(4, 7))
      True
      """
      def __init__(self, xvec, yvec, g: float):
          self.xvec, self.yvec = _qfunc_check_coordinates(xvec, yvec)
          x, y = np.meshgrid(0.5 * g * self.xvec, 0.5 * g * self.yvec)
          self.grid = np.empty(x.shape, dtype=np.complex128)
          self.grid.real = x
          # We produce the adjoint of the coherent states to save an operation
          # later when computing dot products, hence the negative imaginary part.
          self.grid.imag = -y
          self.prefactor = np.exp(-0.5 * (x * x + y * y)).astype(np.complex128)
  
      def _start(self, first: int):
          """
          Get the coherent state matrix corresponding to the first needed Fock
          state.
          """
          if first == 0:
              return self.prefactor.copy()
          out = np.power(self.grid, first)
          out *= self.prefactor
          return out
  
      def __call__(self, first: int, last: int = None):
          """
          Get a 3D array of shape ``(yvec.size, xvec.size, last - first)`` of the
          coherent-state vectors for all the Fock states in the range ``first``
          to ``last``, excluding the end point.  The first two axes are the y-
          and x-coordinates of phase space (i.e. Cartesian indexing, like
          ``numpy.meshgrid``), and the last runs over the selected range of
          Fock-space dimensions.
          """
          ns = np.arange(first, last).reshape(1, 1, -1)
          # Technically we could avoid hitting the limits of floating-point
          # exponents for longer by doing all this in logarithmic space (using
          # scipy.special.gammaln), but that ends up involving more
          # floating-point operations overall, and needs special care around the
          # point alpha = 0 to avoid nan appearing, due to how Python handles
          # mixed-width arithmetic operations.
          out = np.empty(self.grid.shape + (ns.size,), dtype=np.complex128)
          out[:, :, 0] = self._start(ns.flat[0])
          for i in range(ns.size - 1):
              out[:, :, i+1] = out[:, :, i] * self.grid
          out /= np.sqrt(scipy.special.factorial(ns))
          return out
  
  
  class QFunc:
      r"""
      Class-based method of calculating the Husimi-Q function of many different
      quantum states at fixed phase-space points ``0.5*g* (xvec + i*yvec)``.
      This class has slightly higher first-usage costs than :obj:`.qfunc`, but
      subsequent operations will be several times faster. However, it can require
      quite a lot of memory. Call the created object as a function to retrieve
      the Husimi-Q function.
  
      Parameters
      ----------
      xvec, yvec : array_like
          x- and y-coordinates at which to calculate the Husimi-Q function.
  
      g : float, default: sqrt(2)
          Scaling factor for ``a = 0.5 * g * (x + iy)``.  The value of `g` is
          related to the value of `hbar` in the commutation relation
          :math:`[x,\,y] = i\hbar` via :math:`\hbar=2/g^2`, so the default
          corresponds to :math:`\hbar=1`.
  
      memory : real, default: 1024
          Size in MB that may be used internally as workspace.  This class will
          raise ``MemoryError`` if subsequently passed a state of sufficiently
          large dimension that this bound would be exceeded.  In those cases, use
          :obj:`.qfunc` with ``precompute_memory=None`` instead to force using
          the slower, more memory-efficient algorithm.
  
      Examples
      --------
      Initialise the class for a square set of coordinates, with some states we
      want to investigate.
  
      >>> xvec = np.linspace(-2, 2, 101)
      >>> states = [qutip.rand_dm(10) for _ in [None]*10]
      >>> qfunc = qutip.QFunc(xvec, xvec)
  
      Now we can calculate the Husimi-Q function over each of the states more
      efficiently with:
  
      >>> husimiq = np.array([qfunc(state) for state in states])
  
      See Also
      --------
      :obj:`.qfunc` :
          A single function version, which will involve computing several
          quantities multiple times in order to use less memory.
      """
  
      def __init__(
          self, xvec, yvec, g: float = np.sqrt(2), memory: float = 1024
      ):
          self._g = g
          self._coherent_grid = _QFuncCoherentGrid(xvec, yvec, g)
          # 16 bytes per complex, 1024**2 bytes per MB.
          self._size_mb = self._coherent_grid.grid.size * 16 / (1024 ** 2)
          self._memory_mb = memory
          self._max_size = int(self._memory_mb // self._size_mb)
          self._current_size = 0
          self._cache = None
  
      def _alphas(self, size: int):
          r"""
          Retrive the full tensor of (the conjugate of) coherent states over all
          values of :math:`\alpha`, for states of dimension ``size``.
          """
          if self._current_size >= size:
              return self._cache[:, :, :size]
          if size > self._max_size:
              requirement = self._size_mb * size
              raise MemoryError(
                  f"Refusing to precompute up to {size} basis states."
                  f" This would require {requirement:.2f} MB,"
                  f" but only {self._memory_mb} MB is allowed."
              )
          if self._cache is None:
              self._cache = self._coherent_grid(self._current_size, size)
          else:
              self._cache = np.dstack(
                  [self._cache, self._coherent_grid(self._current_size, size)]
              )
          self._current_size = size
          return self._cache
  
      def _single(self, vector: np.ndarray, alphas: np.ndarray):
          r"""
          Get the Q function (without the :math:`\pi` scaling factor) of a single
          state vector.
          """
          return np.abs(np.dot(alphas, (self._g * 0.5) * vector)) ** 2
  
      def __call__(self, state: Qobj):
          """
          Get the Husimi-Q function for the given state vector or density matrix,
          over the coordinates used to initialise the class.  If called multiple
          times, the states do not need to have the same dimensions, but none of
          them can have tensor-product structure.
          """
          state = _qfunc_check_state(state)
          alphas = self._alphas(state.shape[0])
          if state.isket:
              return self._single(state.full().ravel(), alphas) / np.pi
          # We don't use Qobj.eigenstates() to avoid building many unnecessary
          # CSR versions of dense matrices.
          values, vectors = eigh(state.full())
          vectors = vectors.T
          out = values[0] * self._single(vectors[0], alphas)
          for value, vector in zip(values[1:], vectors[1:]):
              out += value * self._single(vector, alphas)
          return out / np.pi
  
  
  def _qfunc_iterative_single(
      vector: np.ndarray, alpha_grid: _QFuncCoherentGrid, g: float,
  ):
      r"""
      Get the Q function (without the :math:`\pi` scaling factor) of a single
      state vector, using the iterative algorithm which recomputes the powers of
      the coherent-state matrix.
      """
      ns = np.arange(vector.shape[0])
      out = np.polyval(
          (0.5*g * vector / np.sqrt(scipy.special.factorial(ns)))[::-1],
          alpha_grid.grid,
      )
      out *= alpha_grid.prefactor
      return np.abs(out)**2
  
  
  def qfunc(
      state: Qobj,
      xvec,
      yvec,
      g: float = sqrt(2),
      precompute_memory: float = 1024,
  ):
      r"""
      Husimi-Q function of a given state vector or density matrix at phase-space
      points ``0.5 * g * (xvec + i*yvec)``.
  
      Parameters
      ----------
      state : :obj:`.Qobj`
          A state vector or density matrix.  This cannot have tensor-product
          structure.
  
      xvec, yvec : array_like
          x- and y-coordinates at which to calculate the Husimi-Q function.
  
      g : float, default: sqrt(2)
          Scaling factor for ``a = 0.5 * g * (x + iy)``.  The value of `g` is
          related to the value of :math:`\hbar` in the commutation relation
          :math:`[x,\,y] = i\hbar` via :math:`\hbar=2/g^2`, so the default
          corresponds to :math:`\hbar=1`.
  
      precompute_memory : real, default: 1024
          Size in MB that may be used during calculations as working space when
          dealing with density-matrix inputs.  This is ignored for state-vector
          inputs.  The bound is not quite exact due to other, order-of-magnitude
          smaller, intermediaries being necessary, but is a good approximation.
          If you want to use the same iterative algorithm for density matrices
          that is used for single kets, set ``precompute_memory=None``.
  
      Returns
      -------
      ndarray
          Values representing the Husimi-Q function calculated over the specified
          range ``[xvec, yvec]``.
  
      See Also
      --------
      :obj:`.QFunc` :
          a class-based version, more efficient if you want to calculate the
          Husimi-Q function for several states over the same coordinates.
      """
      state = _qfunc_check_state(state)
      xvec, yvec = _qfunc_check_coordinates(xvec, yvec)
      required_memory = state.shape[0] * xvec.size * yvec.size * 16 / (1024 ** 2)
      enough_memory = (
          precompute_memory is not None
          and precompute_memory > required_memory
      )
      if state.isoper and enough_memory:
          return QFunc(xvec, yvec, g)(state)
      if precompute_memory is not None and state.isoper:
          warnings.warn(
              "Falling back to iterative algorithm due to lack of memory."
              f" Needed {required_memory:.2f} MB, but only allowed to use"
              f" {precompute_memory:.2f} MB.  Increase `precompute_memory` to"
              " raise limit, or set to `None` to suppress warning."
          )
      alpha_grid = _QFuncCoherentGrid(xvec, yvec, g)
      if state.isket:
          out = _qfunc_iterative_single(state.full().ravel(), alpha_grid, g)
          out /= np.pi
          return out
      # We don't use Qobj.eigenstates() to avoid building many unnecessary CSR
      # versions of dense matrices.
      values, vectors = eigh(state.full())
      vectors = vectors.T
      out = values[0] * _qfunc_iterative_single(vectors[0], alpha_grid, g)
      for value, vector in zip(values[1:], vectors[1:]):
          out += value * _qfunc_iterative_single(vector, alpha_grid, g)
      out /= np.pi
      return out
  
  
  # -----------------------------------------------------------------------------
  # PSEUDO DISTRIBUTION FUNCTIONS FOR SPINS
  #
  def spin_q_function(rho, theta, phi):
      r"""The Husimi Q function for spins is defined as ``Q(theta, phi) =
      SCS.dag() * rho * SCS`` for the spin coherent state ``SCS = spin_coherent(
      j, theta, phi)`` where j is the spin length.
      The implementation here is more efficient as it doesn't
      generate all of the SCS at theta and phi (see references).
  
      The spin Q function is normal when integrated over the surface of the
      sphere
  
      .. math:: \frac{4 \pi}{2j + 1}\int_\phi \int_\theta
                Q(\theta, \phi) \sin(\theta) d\theta d\phi = 1
  
      Parameters
      ----------
      state : qobj
          A state vector or density matrix for a spin-j quantum system.
      theta : array_like
          Polar (colatitude) angle at which to calculate the Husimi-Q function.
      phi : array_like
          Azimuthal angle at which to calculate the Husimi-Q function.
  
      Returns
      -------
      Q, THETA, PHI : 2d-array
          Values representing the spin Husimi Q function at the values specified
          by THETA and PHI.
  
      References
      ----------
      [1] Lee Loh, Y., & Kim, M. (2015). American J. of Phys., 83(1), 30–35.
      https://doi.org/10.1119/1.4898595
  
      """
  
      if rho.isket or rho.isbra:
          rho = rho.proj()
  
      J = rho.shape[0]
      j = (J - 1) / 2
  
      THETA, PHI = meshgrid(theta, phi)
  
      Q = np.zeros_like(THETA, dtype=complex)
      data = rho.full()
  
      for m1 in arange(-j, j + 1):
          Q += binom(2 * j, j + m1) * cos(THETA / 2) ** (2 * (j + m1)) * \
               sin(THETA / 2) ** (2 * (j - m1)) * \
               data[int(j - m1), int(j - m1)]
  
          for m2 in arange(m1 + 1, j + 1):
              Q += (sqrt(binom(2 * j, j + m1)) * sqrt(binom(2 * j, j + m2)) *
                    cos(THETA / 2) ** (2 * j + m1 + m2) *
                    sin(THETA / 2) ** (2 * j - m1 - m2)) * \
               (exp(1j * (m1 - m2) * PHI) * data[int(j - m1), int(j - m2)] +
                exp(1j * (m2 - m1) * PHI) * data[int(j - m2), int(j - m1)])
  
      return Q.real, THETA, PHI
  
  
  def _rho_kq(rho, j, k, q):
      """
      This calculates the trace of the multipole operator T_kq and the density
      matrix rho for use in the spin Wigner quasiprobability distribution.
  
      Parameters
      ----------
      rho : qobj
          A density matrix for a spin-j quantum system.
      j : float
          The spin length of the system.
      k : int
          Spherical harmonic degree
      q : int
  