Update to new numpy

folie/domains/__init__.py

@@ -53,8 +53,8 @@ def Rd(cls, dim):
         if dim < 1:
             dim = 1
         range = np.empty((2, dim))
-        range[0, :] = -np.infty
-        range[1, :] = np.infty
+        range[0, :] = -np.inf
+        range[1, :] = np.inf
         return cls(range)
 
     @classmethod

folie/estimation/mle.py

@@ -176,7 +176,7 @@ def fit(self, data, minimizer=None, coefficients0=None, use_jac=True, callback=N
         do_init = not (self.warm_start and hasattr(self, "converged_"))
         n_init = self.n_init if do_init else 1
 
-        max_lower_bound = -np.infty
+        max_lower_bound = -np.inf
         self.converged_ = False
 
         # That becomes duplicate of callback
@@ -200,8 +200,8 @@ def fit(self, data, minimizer=None, coefficients0=None, use_jac=True, callback=N
             mu0 = np.zeros(self.model.dim_h)
             sig0 = np.identity(self.model.dim_h)
             self._print_verbose_msg_init_beg(init)
-            lower_bound = -np.infty if do_init else self.lower_bound_
-            lower_bound_m_step = -np.infty
+            lower_bound = -np.inf if do_init else self.lower_bound_
+            lower_bound_m_step = -np.inf
             # Algorithm loop
             for n_iter in range(1, self.max_iter + 1):
                 prev_lower_bound = lower_bound

folie/simulations/__init__.py

@@ -88,7 +88,7 @@ def _bias(self, xt):
 
 
 class ABMD_Simulator(BiasedSimulator):
-    def __init__(self, stepper, dt, k=1, xstop=np.infty, **kwargs):
+    def __init__(self, stepper, dt, k=1, xstop=np.inf, **kwargs):
         super().__init__(stepper, dt, **kwargs)
         self.xmax = None
         self.k = k
@@ -106,7 +106,7 @@ def _bias(self, xt):
 
 
 class ABMD_2D_to_1DColvar_Simulator(BiasedSimulator):  # user must provide both colvar function and its gradient in colvar element
-    def __init__(self, stepper, dt, colvar, k=1, qstop=np.infty, **kwargs):
+    def __init__(self, stepper, dt, colvar, k=1, qstop=np.inf, **kwargs):
         super().__init__(stepper, dt, **kwargs)
         self.qmax = None
         self.k = k