Numpy 2.0 compatibility

pyddm/functions.py

@@ -873,7 +873,7 @@ def gddm(drift=0, noise=1, bound=1, nondecision=0, starting_position=0, mixture_
 
     for name,p in parameters.items():
         assert name not in ["x", "t", "T", "dx"], f"Parameters cannot be named 'x', 't', 'T', or 'dx'.  Invalid name '{name}'."
-        assert isinstance(p, (int,float,np.float_,np.int_)) or (isinstance(p, tuple) and len(p) == 2 and isinstance(p[0], (float,int,np.int_,np.float_)) and isinstance(p[1], (float,int,np.int_,np.float_))), f"Parameters must be a single number or a tuple of numbers (representing a range for fitting).  Invalid parameter '{name}'."
+        assert isinstance(p, (int,float,np.floating,np.integer)) or (isinstance(p, tuple) and len(p) == 2 and isinstance(p[0], (float,int,np.integer,np.floating)) and isinstance(p[1], (float,int,np.integer,np.floating))), f"Parameters must be a single number or a tuple of numbers (representing a range for fitting).  Invalid parameter '{name}'."
         assert name.isidentifier() and not keyword.iskeyword(name), f"Parameter names must be valid names for variables in Python.  Invalid parameter name '{name}'."
 
     # Either the fittable or the constant value
@@ -888,7 +888,7 @@ def _parse_dep(val, name, special="xt"):
             val = eval(f"lambda {val}: {val}")
         if val in parameters.keys():
             return "val",None,_fittables[val]
-        elif isinstance(val, (int,float,np.float_,np.int_)):
+        elif isinstance(val, (int,float,np.floating,np.integer)):
             return "val",None,val
         elif hasattr(val, "__call__"):
             sig = inspect.getfullargspec(val)

pyddm/model.py

@@ -641,7 +641,7 @@ def solve_analytical(self, conditions={}, force_python=False):
         if isinstance(self.get_dependence('IC'),(ICPoint, ICPointRatio)):
             ic = self.IC(conditions=conditions)
             assert np.count_nonzero(ic)==1, "Cannot solve analytically for models with non-point initial conditions"
-            shift = np.flatnonzero(ic) / (len(ic) - 1) #rescale to proprotion of total bound height
+            shift = np.flatnonzero(ic)[0] / (len(ic) - 1) #rescale to proprotion of total bound height
         else:
             shift = None
 
@@ -660,9 +660,7 @@ def solve_analytical(self, conditions={}, force_python=False):
                                                        force_python=force_python)
 
         ## Remove some abnormalities such as NaN due to trivial reasons.
-        anal_pdf_choice_upper[anal_pdf_choice_upper==np.NaN] = 0. # FIXME Is this a bug? You can't use == to compare nan to nan...
         anal_pdf_choice_upper[0] = 0.
-        anal_pdf_choice_lower[anal_pdf_choice_lower==np.NaN] = 0.
         anal_pdf_choice_lower[0] = 0.
 
         # Fix numerical errors

pyddm/sample.py

@@ -209,7 +209,7 @@ def pt(x): # Pythonic types
             arr = np.asarray(x, dtype=object)
             # The following is somewhat of a hack to get rid of object arrays
             # when a condition is not a number (e.g. string or tuple)
-            if len(arr) > 0 and not isinstance(arr[0], (float, int, np.float_, np.int_)):
+            if len(arr) > 0 and not isinstance(arr[0], (float, int, np.floating, np.integer)):
                 return arr
             arr = np.asarray(arr.tolist())
             try:
@@ -271,7 +271,7 @@ def pt(x): # Pythonic types
             arr = np.asarray(x, dtype=object)
             # The following is somewhat of a hack to get rid of object arrays
             # when a condition is not a number (e.g. string or tuple)
-            if len(arr) > 0 and not isinstance(arr[0], (float, int, np.float_, np.int_)):
+            if len(arr) > 0 and not isinstance(arr[0], (float, int, np.floating, np.integer)):
                 return arr
             arr = np.asarray(arr.tolist())
             try:

setup.py

@@ -32,7 +32,7 @@
                              sources=['pyddm/csolve.c'],
                              include_dirs=[np.get_include()],
                            )],
-    install_requires = ['numpy >= 1.9.2', 'scipy >= 0.16', 'matplotlib', 'paranoid-scientist >= 0.2.1'],
+    install_requires = ['numpy >= 1.9.2', 'scipy >= 0.16', 'matplotlib', 'paranoid-scientist >= 0.2.3', 'pandas'],
     classifiers = [
         'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',