Fix scalar conversion error with NumPy 2.x

pydream/Dream.py

@@ -465,7 +465,7 @@ def estimate_crossover_probabilities(self, ndim, q0, q_new, CR):
         cross_probs = Dream_shared_vars.cross_probs[0:self.nCR]
 
         #Compute squared normalized jumping distance
-        m_loc = int(np.where(self.CR_values == CR)[0])
+        m_loc = int(np.flatnonzero(self.CR_values == CR).item())
 
         Dream_shared_vars.ncr_updates[m_loc] += 1
 
@@ -520,7 +520,7 @@ def estimate_gamma_level_probs(self, ndim, q0, q_new, gamma_level):
 
         gamma_level_probs = Dream_shared_vars.gamma_level_probs[0:self.ngamma]
 
-        gamma_loc = int(np.where(self.gamma_level_values == gamma_level)[0])
+        gamma_loc = int(np.flatnonzero(self.gamma_level_values == gamma_level).item())
 
         Dream_shared_vars.ngamma_updates[gamma_loc] += 1
 
@@ -542,9 +542,10 @@ def estimate_gamma_level_probs(self, ndim, q0, q_new, gamma_level):
     def set_snooker(self):
         """Choose to run a snooker update on a given iteration or not."""
         if self.snooker != 0:
-            snooker_choice = np.where(np.random.multinomial(1, [self.snooker, 1-self.snooker])==1)
-
-            if snooker_choice[0] == 0:
+            snooker_choice = int(
+                np.random.multinomial(1, [self.snooker, 1-self.snooker]).argmax())
+
+            if snooker_choice == 0:
                 run_snooker = True
             else:
                 run_snooker = False
@@ -612,12 +613,13 @@ def set_gamma(self, DEpairs, snooker_choice, gamma_level_choice, d_prime):
         d_prime : int
             number of parameter dimensions to be updated on this step."""
 
-        gamma_unity_choice = np.where(np.random.multinomial(1, [self.p_gamma_unity, 1-self.p_gamma_unity])==1)
+        gamma_unity_choice = int(
+            np.random.multinomial(1, [self.p_gamma_unity, 1-self.p_gamma_unity]).argmax())
 
         if snooker_choice:
             gamma = np.random.uniform(1.2, 2.2)
 
-        elif gamma_unity_choice[0] == 0:
+        elif gamma_unity_choice == 0:
             gamma = 1.0
 
         else:
@@ -905,7 +907,7 @@ def mt_choose_proposal_pt(self, log_priors, log_likes, proposed_pts, T):
         #Calculate probabilities
         sum_proposal_logps = np.sum(log_ps_sub)
         logp_prob = log_ps_sub/sum_proposal_logps
-        best_logp_loc = int(np.squeeze(np.where(np.random.multinomial(1, logp_prob)==1)[0]))
+        best_logp_loc = int(np.random.multinomial(1, logp_prob).argmax())
 
         #Randomly select one of the tested points with probability proportional to the probability density at the point
         q_proposal = np.squeeze(proposed_pts[best_logp_loc])

pydream/core.py

@@ -44,12 +44,12 @@ def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None,
         """
 
     if restart:
-        if start == None:
+        if start is None:
             raise Exception('Restart run specified but no start positions given.')
         if 'model_name' not in kwargs:
             raise Exception('Restart run specified but no model name to load history and crossover value files from given.')
 
-    if type(parameters) is not list:
+    if not isinstance(parameters, list):
         parameters = [parameters]
 
     model = Model(likelihood=likelihood, sampled_parameters=parameters)
@@ -71,7 +71,7 @@ def run_dream(parameters, likelihood, nchains=5, niterations=50000, start=None,
 
         else:
 
-            if type(start) is list:
+            if isinstance(start, list):
                 args = zip([step_instance]*nchains, [niterations]*nchains, start, [verbose]*nchains, [nverbose]*nchains)
 
             else:
@@ -137,7 +137,7 @@ def _sample_dream_pt(nchains, niterations, step_instance, start, pool, verbose):
 
     step_instances = [step_instance]*nchains   
 
-    if type(start) is list:
+    if isinstance(start, list):
         args = list(zip(step_instances, start, T, [None]*nchains, [None]*nchains))
     else:
         args = list(zip(step_instances, [start]*nchains, T, [None]*nchains, [None]*nchains))
@@ -296,10 +296,10 @@ def _setup_mp_dream_pool(nchains, niterations, step_instance, start_pt=None, mp_
     n = ctx.Value('i', 0)
     tf = ctx.Value('c', b'F')
 
-    if step_instance.crossover_burnin == None:
+    if step_instance.crossover_burnin is None:
         step_instance.crossover_burnin = int(np.floor(niterations/10))
 
-    if start_pt != None:
+    if start_pt is not None:
         if step_instance.start_random:
             print('Warning: start position provided but random_start set to True.  Overrode random_start value and starting walk at provided start position.')
             step_instance.start_random = False

pydream/parameters.py

@@ -1,9 +1,8 @@
 # -*- coding: utf-8 -*-
 
 import numpy as np
-import time
 
-class SampledParam():
+class SampledParam:
     """A SciPy-based parameter prior class.
 
     Parameters