code: """Decoding module."""
  import numpy as np
  import warnings
  from . import utils
  
  from numba import njit, int64, types, float64
  
  
  def decode(H, y, snr, maxiter=1000):
      """Decode a Gaussian noise corrupted n bits message using BP algorithm.
  
      Decoding is performed in parallel if multiple codewords are passed in y.
  
      Parameters
      ----------
      H: array (n_equations, n_code). Decoding matrix H.
      y: array (n_code, n_messages) or (n_code,). Received message(s) in the
          codeword space.
      maxiter: int. Maximum number of iterations of the BP algorithm.
  
      Returns
      -------
      x: array (n_code,) or (n_code, n_messages) the solutions in the
          codeword space.
  
      """
      m, n = H.shape
  
      bits_hist, bits_values, nodes_hist, nodes_values = utils._bitsandnodes(H)
  
      _n_bits = np.unique(H.sum(0))
      _n_nodes = np.unique(H.sum(1))
  
      if _n_bits * _n_nodes == 1:
          solver = _logbp_numba_regular
          bits_values = bits_values.reshape(n, -1)
          nodes_values = nodes_values.reshape(m, -1)
  
      else:
          solver = _logbp_numba
  
      var = 10 ** (-snr / 10)
  
      if y.ndim == 1:
          y = y[:, None]
      # step 0: initialization
  
      Lc = 2 * y / var
      _, n_messages = y.shape
  
      Lq = np.zeros(shape=(m, n, n_messages))
  
      Lr = np.zeros(shape=(m, n, n_messages))
      for n_iter in range(maxiter):
          Lq, Lr, L_posteriori = solver(bits_hist, bits_values, nodes_hist,
                                        nodes_values, Lc, Lq, Lr, n_iter)
          x = np.array(L_posteriori <= 0).astype(int)
          product = utils.incode(H, x)
          if product:
              break
      if n_iter == maxiter - 1:
          warnings.warn("""Decoding stopped before convergence. You may want
                         to increase maxiter""")
      return x.squeeze()
  
  
  output_type_log2 = types.Tuple((float64[:, :, :], float64[:, :, :],
                                 float64[:, :]))
  
  
  @njit(output_type_log2(int64[:], int64[:], int64[:], int64[:], float64[:, :],
                         float64[:, :, :],  float64[:, :, :], int64), cache=True)
  def _logbp_numba(bits_hist, bits_values, nodes_hist, nodes_values, Lc, Lq, Lr,
                   n_iter):
      """Perform inner ext LogBP solver."""
      m, n, n_messages = Lr.shape
      # step 1 : Horizontal
  
      bits_counter = 0
      nodes_counter = 0
      for i in range(m):
          # ni = bits[i]
          ff = bits_hist[i]
          ni = bits_values[bits_counter: bits_counter + ff]
          bits_counter += ff
          for j in ni:
              nij = ni[:]
  
              X = np.ones(n_messages)
              if n_iter == 0:
                  for kk in range(len(nij)):
                      if nij[kk] != j:
                          X *= np.tanh(0.5 * Lc[nij[kk]])
              else:
                  for kk in range(len(nij)):
                      if nij[kk] != j:
                          X *= np.tanh(0.5 * Lq[i, nij[kk]])
              num = 1 + X
              denom = 1 - X
              for ll in range(n_messages):
                  if num[ll] == 0:
                      Lr[i, j, ll] = -1
                  elif denom[ll] == 0:
                      Lr[i, j, ll] = 1
                  else:
                      Lr[i, j, ll] = np.log(num[ll] / denom[ll])
  
      # step 2 : Vertical
      for j in range(n):
          # mj = nodes[j]
          ff = nodes_hist[j]
          mj = nodes_values[nodes_counter: nodes_counter + ff]
          nodes_counter += ff
          for i in mj:
              mji = mj[:]
              Lq[i, j] = Lc[j]
  
              for kk in range(len(mji)):
                  if mji[kk] != i:
                      Lq[i, j] += Lr[mji[kk], j]
  
      # LLR a posteriori:
      L_posteriori = np.zeros((n, n_messages))
      nodes_counter = 0
      for j in range(n):
          ff = nodes_hist[j]
          mj = nodes_values[nodes_counter: nodes_counter + ff]
          nodes_counter += ff
          L_posteriori[j] = Lc[j] + Lr[mj, j].sum(axis=0)
  
      return Lq, Lr, L_posteriori
  
  
  @njit(output_type_log2(int64[:], int64[:, :], int64[:], int64[:, :],
                         float64[:, :], float64[:, :, :],  float64[:, :, :],
                         int64), cache=True)
  def _logbp_numba_regular(bits_hist, bits_values, nodes_hist, nodes_values, Lc,
                           Lq, Lr, n_iter):
      """Perform inner ext LogBP solver."""
      m, n, n_messages = Lr.shape
      # step 1 : Horizontal
      for i in range(m):
          ni = bits_values[i]
          for j in ni:
              nij = ni[:]
  
              X = np.ones(n_messages)
              if n_iter == 0:
                  for kk in range(len(nij)):
                      if nij[kk] != j:
                          X *= np.tanh(0.5 * Lc[nij[kk]])
              else:
                  for kk in range(len(nij)):
                      if nij[kk] != j:
                          X *= np.tanh(0.5 * Lq[i, nij[kk]])
              num = 1 + X
              denom = 1 - X
              for ll in range(n_messages):
                  if num[ll] == 0:
                      Lr[i, j, ll] = -1
                  elif denom[ll] == 0:
                      Lr[i, j, ll] = 1
                  else:
                      Lr[i, j, ll] = np.log(num[ll] / denom[ll])
  
      # step 2 : Vertical
      for j in range(n):
          mj = nodes_values[j]
          for i in mj:
              mji = mj[:]
              Lq[i, j] = Lc[j]
  
              for kk in range(len(mji)):
                  if mji[kk] != i:
                      Lq[i, j] += Lr[mji[kk], j]
  
      # LLR a posteriori:
      L_posteriori = np.zeros((n, n_messages))
      for j in range(n):
          mj = nodes_values[j]
          L_posteriori[j] = Lc[j] + Lr[mj, j].sum(axis=0)
  
      return Lq, Lr, L_posteriori
  
  
  def get_message(tG, x):
      """Compute the original `n_bits` message from a `n_code` codeword `x`.
  
      Parameters
      ----------
      tG: array (n_code, n_bits) coding matrix tG.
      x: array (n_code,) decoded codeword of length `n_code`.
  
      Returns
      -------
      message: array (n_bits,). Original binary message.
  
      """
      n, k = tG.shape
  
      rtG, rx = utils.gausselimination(tG, x)
  
      message = np.zeros(k).astype(int)
  
      message[k - 1] = rx[k - 1]
      for i in reversed(range(k - 1)):
          message[i] = rx[i]
          message[i] -= utils.binaryproduct(rtG[i, list(range(i+1, k))],
                                            message[list(range(i+1, k))])
  
      return abs(message)
  
apis: numpy.ones numpy.zeros numpy.log numpy.array numpy.tanh