48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
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import pynucastro as pyna
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from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
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from gridfire.solver import DirectNetworkSolver
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from gridfire.type import NetIn
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from fourdst.composition import Composition
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symbols : list[str] = ["H-1", "He-3", "He-4", "C-12", "N-14", "O-16", "Ne-20", "Mg-24"]
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X : list[float] = [0.708, 2.94e-5, 0.276, 0.003, 0.0011, 9.62e-3, 1.62e-3, 5.16e-4]
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comp = Composition()
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comp.registerSymbol(symbols)
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comp.setMassFraction(symbols, X)
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comp.finalize(True)
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print(f"Initial H-1 mass fraction {comp.getMassFraction("H-1")}")
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netIn = NetIn()
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netIn.composition = comp
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netIn.temperature = 1.5e7
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netIn.density = 1.6e2
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netIn.tMax = 1e-9
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netIn.dt0 = 1e-12
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baseEngine = GraphEngine(netIn.composition, 2)
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equiv_species = baseEngine.getNetworkSpecies()
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equiv_species = [x.name().replace("-","").lower() for x in equiv_species]
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equiv_species = [x if x != 'h1' else 'p' for x in equiv_species]
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equiv_species = [x if x != 'n1' else 'n' for x in equiv_species]
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rl = pyna.ReacLibLibrary()
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# equiv_species = ['p', 'd', 'he3', 'he4', 'c12', 'n14', 'o16', 'ne20', 'mg24']
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full_lib = rl.linking_nuclei(equiv_species)
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print(f"\nFound {len(full_lib.get_rates())} rates.")
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net = pyna.PythonNetwork(libraries=[full_lib])
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output_filename = "pp_chain_robust.py"
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net.write_network(output_filename)
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print(f"\nSuccessfully wrote robust PP-chain network to: {output_filename}")
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