text: """ Implements a NUTS Sampler for emcee
      http://dan.iel.fm/emcee/
  """
  import numpy as np
  from .pinnuts import pinnuts
  from .helpers import PinNutsSampler_fn_wrapper
  from emcee.sampler import Sampler
  
  
  __all__ = ['NUTSSampler', 'test_sampler']
  
  
  class PinNUTSSampler(Sampler):
      """ A sampler object mirroring emcee.sampler object definition"""
  
      def __init__(self, dim, lnprobfn, gradfn=None, *args, **kwargs):
              self.dim = dim
              self.f = PinNutsSampler_fn_wrapper(lnprobfn, gradfn, *args, **kwargs)
              self.lnprobfn = self.f.lnp_func
              self.gradfn = self.f.gradlnp_func
              self.reset()
  
      @property
      def random_state(self):
          """
          The state of the internal random number generator. In practice, it's
          the result of calling ``get_state()`` on a
          ``numpy.random.mtrand.RandomState`` object. You can try to set this
          property but be warned that if you do this and it fails, it will do
          so silently.
          """
          pass
  
      @random_state.setter  # NOQA
      def random_state(self, state):
          """
          Try to set the state of the random number generator but fail silently
          if it doesn't work. Don't say I didn't warn you...
  
          """
          pass
  
      @property
      def flatlnprobability(self):
          """
          A shortcut to return the equivalent of ``lnprobability`` but aligned
          to ``flatchain`` rather than ``chain``.
  
          """
          return self.lnprobability.flatten()
  
      def get_lnprob(self, p):
          """Return the log-probability at the given position."""
          return self.lnprobfn(p)
  
      def get_gradlnprob(self, p, dx=1e-3, order=1):
          """Return the log-probability at the given position."""
          return self.gradfn(p)
  
      def reset(self):
          """
          Clear ``chain``, ``lnprobability`` and the bookkeeping parameters.
  
          """
          self._lnprob = []
          self._chain = []
          self._epsilon = 0.
  
      @property
      def iterations(self):
          return len(self._lnprob)
  
      def clear_chain(self):
          """An alias for :func:`reset` kept for backwards compatibility."""
          return self.reset()
  
      def _sample_fn(self, p, dx=1e-3, order=1):
          """ proxy function for nuts6 """
          lnprob = self.lnprobfn(p)
          gradlnp = self.gradfn(p)
          return lnprob, gradlnp
  
      def sample(self, pos0, M, Madapt, delta=0.6, **kwargs):
          """ Runs NUTS6 """
          samples, lnprob, epsilon = pinnuts(self._sample_fn, M, Madapt, pos0, delta)
          self._chain = samples
          self._lnprob = lnprob
          self._epsilon = epsilon
  
          return samples
  
      def run_mcmc(self, pos0, M, Madapt, delta=0.6, **kwargs):
          """
          Iterate :func:`sample` for ``N`` iterations and return the result.
  
          :param pos0:
              The initial position vector.
  
          :param M:
              The number of steps to run.
  
          :param Madapt:
              The number of steps to run during the burning period.
  
          :param delta: (optional, default=0.6)
              Initial step size.
  
          :param kwargs: (optional)
              Other parameters that are directly passed to :func:`sample`.
  
          """
  
          print('Running HMC with dual averaging and trajectory length %0.2f...' % delta)
          return self.sample(pos0, M, Madapt, delta, **kwargs)
  
  
  class _function_wrapper(object):
      """
      This is a hack to make the likelihood function pickleable when ``args``
      are also included.
  
      """
      def __init__(self, f, args):
          self.f = f
          self.args = args
  
      def __call__(self, x):
          try:
              return self.f(x, *self.args)
          except:
              import traceback
              print("PinNUTS: Exception while calling your likelihood function:")
              print("  params:", x)
              print("  args:", self.args)
              print("  exception:")
              traceback.print_exc()
              raise
  
  
  def test_sampler():
      """ Example usage of PinNUTS_sampler: sampling a 2d highly correlated Gaussian distribution """
  
      def correlated_normal(theta):
          """
          Example of a target distribution that could be sampled from using NUTS.
          (Although of course you could sample from it more efficiently)
          Doesn't include the normalizing constant.
          """
  
          # Precision matrix with covariance [1, 1.98; 1.98, 4].
          # A = np.linalg.inv( cov )
          A = np.asarray([[50.251256, -24.874372],
                          [-24.874372, 12.562814]])
  
          grad = -np.dot(theta, A)
          logp = 0.5 * np.dot(grad, theta.T)
          return logp, grad
  
      def lnprobfn(theta):
          return correlated_normal(theta)[0]
  
      def gradfn(theta):
          return correlated_normal(theta)[1]
  
      D = 2
      M = 5000
      Madapt = 5000
      theta0 = np.random.normal(0, 1, D)
      delta = 0.6
  
      mean = np.zeros(2)
      cov = np.asarray([[1, 1.98],
                        [1.98, 4]])
  
      sampler = PinNUTSSampler(D, lnprobfn, gradfn)
      samples = sampler.run_mcmc(theta0, M, Madapt, delta)
  
      print('Percentiles')
      print (np.percentile(samples, [16, 50, 84], axis=0))
      print('Mean')
      print (np.mean(samples, axis=0))
      print('Stddev')
      print (np.std(samples, axis=0))
  
      samples = samples[1::10, :]
      import pylab as plt
      temp = np.random.multivariate_normal(mean, cov, size=500)
      plt.plot(temp[:, 0], temp[:, 1], '.')
      plt.plot(samples[:, 0], samples[:, 1], 'r+')
      plt.show()
  
      return sampler
  