refactor(reaction): refactored to an abstract reaction class in prep for weak reactions

validation/pynucastro/GridFireEquiv/evolve.py

@@ -0,0 +1,107 @@
+import numpy as np
+from scipy.integrate import solve_ivp
+
+import pp_chain_robust as network
+
+
+T = 1.5e7
+rho = 1.5e2  
+
+Y0 = np.zeros(network.nnuc)
+Y0[network.jp] = 0.702583  
+Y0[network.jhe3] = 9.74903e-6  
+Y0[network.jhe4] = 0.068963
+Y0[network.jc12] = 0.000250029
+Y0[network.jn14] = 7.85632e-5
+Y0[network.jo16] = 0.00060151
+Y0[network.jne20] = 8.10399e-5
+Y0[network.jmg24] = 2.15159e-5
+
+t_start = 0.0
+t_end = 3.14e17
+t_span = (t_start, t_end)
+
+sol = solve_ivp(
+    network.rhs,
+    t_span=t_span,
+    y0=Y0,
+    method='Radau',
+    jac=network.jacobian,
+    args=(rho, T),
+    dense_output=True,
+    rtol=1e-8,
+    atol=1e-8
+)
+
+
+with open("pynucastro_results.csv", 'w') as f:
+    f.write("t,h1,h2,he3,he4,c12,n14,o16,ne20,mg24\n")
+    for (t,h1,h2,he3,he4,c12,n14,o16,ne20,mg24) in zip(sol.t, sol.y[network.jp, :], sol.y[network.jd, :], sol.y[network.jhe3, :], sol.y[network.jhe4, :], sol.y[network.jc12, :], sol.y[network.jn14, :], sol.y[network.jo16, :], sol.y[network.jne20, :], sol.y[network.jmg24, :]):
+        f.write(f"{t},{h1},{h2},{he3},{he4},{c12},{n14},{o16},{ne20},{mg24}\n")
+
+from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
+from gridfire.solver import DirectNetworkSolver
+from gridfire.type import NetIn
+
+from fourdst.composition import Composition
+from fourdst.atomic import species
+
+symbols : list[str] = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
+X : list[float]     = [0.708, 2.94e-5, 0.276, 0.003, 0.0011, 9.62e-3, 1.62e-3, 5.16e-4]
+
+
+comp = Composition()
+comp.registerSymbol(symbols)
+comp.setMassFraction(symbols, X)
+comp.finalize(True)
+
+print(f"Initial H-1 mass fraction {comp.getMassFraction("H-1")}")
+
+netIn = NetIn()
+netIn.composition = comp
+netIn.temperature = 1.5e7
+netIn.density = 1.6e2
+netIn.tMax = 3.14e17
+netIn.dt0 = 1e-12
+
+baseEngine = GraphEngine(netIn.composition, 2)
+baseEngine.setUseReverseReactions(False)
+
+qseEngine = MultiscalePartitioningEngineView(baseEngine)
+
+adaptiveEngine = AdaptiveEngineView(qseEngine)
+
+solver = DirectNetworkSolver(adaptiveEngine)
+
+data = []
+def callback(context):
+    H1Index = context.engine.getSpeciesIndex(species["H-1"])
+    H2Index = context.engine.getSpeciesIndex(species["H-2"])
+    He3Index = context.engine.getSpeciesIndex(species["He-3"])
+    He4Index = context.engine.getSpeciesIndex(species["He-4"])
+    C12Index = context.engine.getSpeciesIndex(species["C-12"])
+    N14Index = context.engine.getSpeciesIndex(species["N-14"])
+    O16Index = context.engine.getSpeciesIndex(species["O-16"])
+    Ne20Index = context.engine.getSpeciesIndex(species["Mg-24"])
+    Mg24Index = context.engine.getSpeciesIndex(species["Mg-24"])
+    data.append([context.t,
+                 context.state[H1Index],
+                 context.state[H2Index],
+                 context.state[He3Index],
+                 context.state[He4Index],
+                 context.state[C12Index],
+                 context.state[N14Index],
+                 context.state[O16Index],
+                 context.state[Ne20Index],
+                 context.state[Mg24Index]
+            ])
+
+
+solver.set_callback(callback)
+results = solver.evaluate(netIn)
+
+with open("gridfire_results.csv", 'w') as f:
+    f.write("t,h1,h2,he3,he4,c12,n14,o16,ne20,mg24\n")
+    for row in data:
+        rowStr = ','.join([str(x) for x in row])
+        f.write(f"{rowStr}\n")