bilby/core/prior/conditional.py

-import numpy as np
-
 from .base import Prior, PriorException
-from bilby.core.prior.interpolated import Interped
-from bilby.core.prior.analytical import DeltaFunction, PowerLaw, Uniform, LogUniform, \
+from .interpolated import Interped
+from .analytical import DeltaFunction, PowerLaw, Uniform, LogUniform, \
     SymmetricLogUniform, Cosine, Sine, Gaussian, TruncatedGaussian, HalfGaussian, \
     LogNormal, Exponential, StudentT, Beta, Logistic, Cauchy, Gamma, ChiSquared, FermiDirac
-from bilby.core.utils import infer_args_from_method, infer_parameters_from_function
+from ..utils import infer_args_from_method, infer_parameters_from_function
 
 
 def conditional_prior_factory(prior_class):
@@ -76,7 +74,9 @@ def conditional_prior_factory(prior_class):
             float: See superclass
 
             """
-            self.least_recently_sampled = self.rescale(np.random.uniform(0, 1, size), **required_variables)
+            from ..utils.random import rng
+
+            self.least_recently_sampled = self.rescale(rng.uniform(0, 1, size), **required_variables)
             return self.least_recently_sampled
 
         def rescale(self, val, **required_variables):
@@ -371,7 +371,8 @@ class DirichletElement(ConditionalBeta):
         self._required_variables = [
             label + str(ii) for ii in range(order)
         ]
-        self.__class__.__name__ = 'Dirichlet'
+        self.__class__.__name__ = 'DirichletElement'
+        self.__class__.__qualname__ = 'DirichletElement'
 
     def dirichlet_condition(self, reference_parms, **kwargs):
         remaining = 1 - sum(

bilby/core/prior/interpolated.py

 import numpy as np
-from scipy.integrate import cumtrapz
 from scipy.interpolate import interp1d
 
 from .base import Prior
-from bilby.core.utils import logger
+from ..utils import logger
 
 
 class Interped(Prior):
@@ -87,7 +86,6 @@ class Interped(Prior):
 
         This maps to the inverse CDF. This is done using interpolation.
         """
-        self.test_valid_for_rescaling(val)
         rescaled = self.inverse_cumulative_distribution(val)
         if rescaled.shape == ():
             rescaled = float(rescaled)
@@ -164,10 +162,11 @@ class Interped(Prior):
         self._initialize_attributes()
 
     def _initialize_attributes(self):
+        from scipy.integrate import cumulative_trapezoid
         if np.trapz(self._yy, self.xx) != 1:
             logger.debug('Supplied PDF for {} is not normalised, normalising.'.format(self.name))
         self._yy /= np.trapz(self._yy, self.xx)
-        self.YY = cumtrapz(self._yy, self.xx, initial=0)
+        self.YY = cumulative_trapezoid(self._yy, self.xx, initial=0)
         # Need last element of cumulative distribution to be exactly one.
         self.YY[-1] = 1
         self.probability_density = interp1d(x=self.xx, y=self._yy, bounds_error=False, fill_value=0)