change: facilitate recovery

src/scgenerator/evaluator.py

@@ -234,7 +234,7 @@ class Evaluator:
             if dico is None, update the internal dict of parameters with params
         """
         for k, v in params.items():
-            self.main_map[k] = EvaluatedValue(v, np.inf)
+            self.main_map[k] = EvaluatedValue(v, INF)
 
     def reset(self):
         self.main_map = {}

src/scgenerator/parameter.py

@@ -494,9 +494,9 @@ class Parameters:
     def copy(self, deep: bool = True, freeze: bool = False) -> Parameters:
         """create a deep copy of self. if freeze is True, the returned copy is read-only"""
         if deep:
-            params = Parameters(**deepcopy(self.strip_params_dict()))
+            params = Parameters(**deepcopy(self._param_dico))
         else:
-            params = Parameters(**self.strip_params_dict())
+            params = Parameters(**self._param_dico)
         if freeze:
             params.freeze()
         return params

src/scgenerator/solver.py

@@ -161,19 +161,20 @@ def solve43(
     else:
         h = 0.000664237859  # from Luna
         const_step_size = False
-    k5 = nonlinear(spec, 0)
     z = 0
     stats = {}
     rejected = []
     if targets is not None:
+        if len(targets) <= 1:
+            return
         targets = list(sorted(set(targets)))
-        if targets[0] == 0:
+        z = targets[0]
-            targets.pop(0)
+        h = min(h, (targets[1] - targets[0]) / 2)
-        h = min(h, targets[0] / 2)
+        targets.pop(0)
 
+    k5 = nonlinear(spec, z)
     step_ind = 0
     msg = TimedMessage(2)
-    running = True
     last_error = 0
     error = 0
     store_next = False
@@ -183,7 +184,7 @@ def solve43(
 
     yield spec, stats() | dict(h=0)
 
-    while running:
+    while True:
         expD = np.exp(h * 0.5 * linear(z))
 
         A_I = expD * spec

tests/test_integrator.py

@@ -17,7 +17,7 @@ def test_rk43_absorbtion_only():
     )
     non_lin = op.no_op_freq(n)
 
-    res = sc.integrate(spec0, 1.0, lin, non_lin, targets=[1.0])
+    res = sc.integrate(spec0, 1.0, lin, non_lin, targets=[0, 1.0])
     assert np.max(sc.abs2(res.spectra[-1])) == pytest.approx(0.5)