text: """
  Basic PDFs are provided here. Gauss, exponential... that can be used together with Functors to
  build larger models.
  """
  
  #  Copyright (c) 2020 zfit
  import contextlib
  import math as mt
  
  import numpy as np
  import tensorflow as tf
  
  import zfit.z.math
  from zfit import z
  from ..core.basepdf import BasePDF
  from ..core.space import Space, ANY_LOWER, ANY_UPPER
  from ..util import ztyping
  from ..util.exception import AnalyticIntegralNotImplementedError, BreakingAPIChangeError
  from ..util.warnings import warn_advanced_feature
  
  infinity = mt.inf
  
  
  class CustomGaussOLD(BasePDF):
  
      def __init__(self, mu, sigma, obs, name="Gauss"):
          super().__init__(name=name, obs=obs, params=dict(mu=mu, sigma=sigma))
  
      def _unnormalized_pdf(self, x):
          x = x.unstack_x()
          mu = self.params['mu']
          sigma = self.params['sigma']
          gauss = tf.exp(- 0.5 * tf.square((x - mu) / sigma))
  
          return gauss
  
  
  def _gauss_integral_from_inf_to_inf(limits, params, model):
      return tf.sqrt(2 * z.pi) * params['sigma']
  
  
  CustomGaussOLD.register_analytic_integral(func=_gauss_integral_from_inf_to_inf,
                                            limits=Space(limits=(-infinity, infinity), axes=(0,)))
  
  
  class Exponential(BasePDF):
      _N_OBS = 1
  
      def __init__(self, lam=None, obs: ztyping.ObsTypeInput = None, name: str = "Exponential", lambda_=None):
          """Exponential function exp(lambda * x).
  
          The function is normalized over a finite range and therefore a pdf. So the PDF is precisely
          defined as :math:`\\frac{ e^{\\lambda \\cdot x}}{ \\int_{lower}^{upper} e^{\\lambda \\cdot x} dx}`
  
          Args:
              lam: Accessed as parameter "lambda".
              obs: The :py:class:`~zfit.Space` the pdf is defined in.
              name: Name of the pdf.
              dtype:
          """
          if lambda_ is not None:
              if lam is None:
                  lam = lambda_
              else:
                  raise BreakingAPIChangeError("The 'lambda' parameter has been renamed from 'lambda_' to 'lam'.")
          params = {'lambda': lam}
          super().__init__(obs, name=name, params=params)
  
          self._calc_numerics_data_shift = lambda: z.constant(0.)
  
          if not self.space.has_limits:
              warn_advanced_feature("Exponential pdf relies on a shift of the input towards 0 to keep the numerical "
                                    f"stability high. The space {self.space} does not have limits set and no shift"
                                    f" will occure. To set it manually, set _numerics_data_shift to the expected"
                                    f" average values given to this function _in case you want things to be set_."
                                    f"If this sounds unfamiliar, regard this as an error and use a normalization range.",
                                    identifier='exp_shift')
          self._set_numerics_data_shift(self.space)
  
      def _unnormalized_pdf(self, x):
          lambda_ = self.params['lambda']
          x = x.unstack_x()
          probs = z.exp(lambda_ * (self._shift_x(x)))
          tf.debugging.assert_all_finite(probs, f"Exponential PDF {self} has non valid values. This is likely caused"
                                                f" by numerical problems: if the exponential is too steep, this will"
                                                f" yield NaNs or infs. Make sure that your lambda is small enough and/or"
                                                f" the initial space is in the same"
                                                f" region as your data (and norm_range, if explicitly set differently)."
                                                f" If this issue still persists, please oben an issue on Github:"
                                                f" https://github.com/zfit/zfit")
          return probs  # Don't use exp! will overflow.
  
      def _shift_x(self, x):
          return x - self._calc_numerics_data_shift()
  
      @contextlib.contextmanager
      def _set_numerics_data_shift(self, limits):
          if limits:
              def calc_numerics_data_shift():
                  lower, upper = [], []
                  for limit in limits:
                      low, up = limit.rect_limits
                      lower.append(z.convert_to_tensor(low[:, 0]))
                      upper.append(z.convert_to_tensor(up[:, 0]))
                  lower = z.convert_to_tensor(lower)
                  upper = z.convert_to_tensor(upper)
                  lower_val = tf.math.reduce_min(lower, axis=0)
                  upper_val = tf.math.reduce_max(upper, axis=0)
  
                  return (upper_val + lower_val) / 2
  
              old_value = self._calc_numerics_data_shift
  
              self._calc_numerics_data_shift = calc_numerics_data_shift
              yield
              self._calc_numerics_data_shift = old_value
          else:
              yield
  
      # All hooks are needed to set the right shift when "entering" the pdf. The norm range is taken where both are
      # available. No special need needs to be taken for sampling (it samples from the correct region, the limits, and
      # uses the predictions by the `unnormalized_prob` -> that is shifted correctly
      def _single_hook_integrate(self, limits, norm_range):
          with self._set_numerics_data_shift(norm_range):
              return super()._single_hook_integrate(limits, norm_range)
  
      def _single_hook_analytic_integrate(self, limits, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_analytic_integrate(limits, norm_range)
  
      #
      def _single_hook_numeric_integrate(self, limits, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_numeric_integrate(limits, norm_range)
  
      def _single_hook_partial_integrate(self, x, limits, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_partial_integrate(x, limits, norm_range)
  
      def _single_hook_partial_analytic_integrate(self, x, limits, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_partial_analytic_integrate(x, limits, norm_range)
  
      def _single_hook_partial_numeric_integrate(self, x, limits, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_partial_numeric_integrate(x, limits, norm_range)
  
      # def _single_hook_normalization(self, limits):
      #     with self._set_numerics_data_shift(limits=limits):
      #         return super()._single_hook_normalization(limits)
  
      #
      # # TODO: remove component_norm_range? But needed for integral?
      # def _single_hook_unnormalized_pdf(self, x, name):
      #     if component_norm_range.limits_are_false:
      #         component_norm_range = self.space
      #     if component_norm_range.limits_are_set:
      #         with self._set_numerics_data_shift(limits=component_norm_range):
      #             return super()._single_hook_unnormalized_pdf(x, name)
      #     else:
      #         return super()._single_hook_unnormalized_pdf(x, name)
      #
      def _single_hook_pdf(self, x, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_pdf(x, norm_range)
  
      #
      def _single_hook_log_pdf(self, x, norm_range):
          with self._set_numerics_data_shift(limits=norm_range):
              return super()._single_hook_log_pdf(x, norm_range)
  
      def _single_hook_sample(self, n, limits):
          with self._set_numerics_data_shift(limits=limits):
              return super()._single_hook_sample(n, limits)
  
  
  def _exp_integral_from_any_to_any(limits, params, model):
      lambda_ = params['lambda']
      lower, upper = limits.rect_limits_np
      if any(np.isinf([lower, upper])):
          raise AnalyticIntegralNotImplementedError
  
      integral = _exp_integral_func_shifting(lambd=lambda_, lower=lower, upper=upper, model=model)
      return integral[0]
  
  
  def _exp_integral_func_shifting(lambd, lower, upper, model):
      def raw_integral(x):
          return z.exp(lambd * (model._shift_x(x))) / lambd  # needed due to overflow in exp otherwise
  
      lower_int = raw_integral(x=z.constant(lower))
      upper_int = raw_integral(x=z.constant(upper))
      integral = (upper_int - lower_int)
      return integral
  
  
  def exp_icdf(x, params, model):
      lambd = params['lambda']
      x = z.unstack_x(x)
      x = model._shift_x(x)
      return zfit.z.math.log(lambd * x) / lambd
  
  
  # Exponential.register_inverse_analytic_integral(exp_icdf)  # TODO: register icdf for exponential
  # TODO: cleanup, make cdf registrable _and_ inverse integral, but real
  
  limits = Space(axes=0, limits=(ANY_LOWER, ANY_UPPER))
  Exponential.register_analytic_integral(func=_exp_integral_from_any_to_any, limits=limits)
  