Replaced np.Inf with np.inf

src/seemps/analysis/evolution.py

@@ -67,7 +67,7 @@ def euler(
     """
     normalization_strategy = strategy.replace(normalize=True)
     state = CanonicalMPS(state, normalize=True)
-    results = EvolutionResults(state=state, energy=np.Inf, trajectory=[], Δβ=Δβ, β=[0])
+    results = EvolutionResults(state=state, energy=np.inf, trajectory=[], Δβ=Δβ, β=[0])
     for i in range(maxiter + 1):
         if i > 0:
             H_state = H.apply(state)
@@ -95,7 +95,7 @@ def improved_euler(
     """
     normalization_strategy = strategy.replace(normalize=True)
     state = CanonicalMPS(state, normalize=True)
-    results = EvolutionResults(state=state, energy=np.Inf, trajectory=[], Δβ=Δβ, β=[0])
+    results = EvolutionResults(state=state, energy=np.inf, trajectory=[], Δβ=Δβ, β=[0])
     for i in range(maxiter):
         if i > 0:
             H_state = H.apply(state)
@@ -125,7 +125,7 @@ def runge_kutta(
     """
     normalization_strategy = strategy.replace(normalize=True)
     state = CanonicalMPS(state, normalize=True)
-    results = EvolutionResults(state=state, energy=np.Inf, trajectory=[], Δβ=Δβ, β=[0])
+    results = EvolutionResults(state=state, energy=np.inf, trajectory=[], Δβ=Δβ, β=[0])
     for i in range(maxiter):
         if i > 0:
             H_state = H.apply(state)

src/seemps/optimization/descent.py

@@ -85,11 +85,11 @@ def gradient_descent(
     state = simplify(guess, strategy=strategy)
     results = OptimizeResults(
         state=state,
-        energy=np.Inf,
+        energy=np.inf,
         converged=False,
         message=f"Exceeded maximum number of steps {maxiter}",
     )
-    E = last_E = variance = avg_H2 = np.Inf
+    E = last_E = variance = avg_H2 = np.inf
     H_state: MPS
     with make_logger() as logger:
         logger(f"gradient_descent() invoked with {maxiter} iterations")

src/seemps/optimization/dmrg.py

@@ -180,7 +180,7 @@ def dmrg(
     logger(f"start, energy={energy}, variance={variance}")
     if callback is not None:
         callback(QF.state, results)
-    E: float = np.Inf
+    E: float = np.inf
     last_E: float = E
     strategy = strategy.replace(normalize=True)
     for step in range(maxiter):

src/seemps/optimization/power.py

@@ -73,7 +73,7 @@ def power_method(
     )
     results = PowerMethodOptimizeResults(
         state=state,
-        energy=np.Inf,
+        energy=np.inf,
         converged=False,
         trajectory=[],
         variances=[],
@@ -82,7 +82,7 @@ def power_method(
     # This extra field is needed because CGS consumes iterations
     # in itself.
     results.steps = []
-    last_energy = np.Inf
+    last_energy = np.inf
     logger = make_logger()
     logger(f"power_method() invoked with {maxiter} iterations")
     total_steps = 0