123 lines
3.7 KiB
Python
123 lines
3.7 KiB
Python
# import numpy as np
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# from scipy.integrate import solve_ivp
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#
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# import pp_chain_robust as network
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#
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#
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# T = 1e7
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# rho = 1.5e2
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#
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# Y0 = np.zeros(network.nnuc)
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# Y0[network.jp] = 0.702583
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# Y0[network.jhe3] = 9.74903e-6
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# Y0[network.jhe4] = 0.068963
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# Y0[network.jc12] = 0.000250029
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# Y0[network.jn14] = 7.85632e-5
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# Y0[network.jo16] = 0.00060151
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# Y0[network.jne20] = 8.10399e-5
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# Y0[network.jmg24] = 2.15159e-5
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#
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# t_start = 0.0
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# t_end = 3.14e17
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# t_span = (t_start, t_end)
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#
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# sol = solve_ivp(
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# network.rhs,
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# t_span=t_span,
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# y0=Y0,
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# method='Radau',
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# jac=network.jacobian,
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# args=(rho, T),
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# dense_output=True,
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# rtol=1e-8,
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# atol=1e-8
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# )
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#
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#
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# with open("pynucastro_results.csv", 'w') as f:
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# f.write("t,h1,h2,he3,he4,c12,n14,o16,ne20,mg24\n")
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# 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, :]):
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# f.write(f"{t},{h1},{h2},{he3},{he4},{c12},{n14},{o16},{ne20},{mg24}\n")
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import sys
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from gridfire.engine import GraphEngine, MultiscalePartitioningEngineView, AdaptiveEngineView
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from gridfire.solver import CVODESolverStrategy
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from gridfire.type import NetIn
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from fourdst.composition import Composition
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from fourdst.atomic import species, Species
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from datetime import datetime
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from typing import List, Dict, Set, Tuple
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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 = 3e17
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netIn.dt0 = 1e-12
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baseEngine = GraphEngine(netIn.composition, 2)
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baseEngine.setUseReverseReactions(False)
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qseEngine = MultiscalePartitioningEngineView(baseEngine)
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adaptiveEngine = AdaptiveEngineView(qseEngine)
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solver = CVODESolverStrategy(adaptiveEngine)
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data: List[Tuple[float, Dict[str, Tuple[float, float]]]] = []
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def callback(context):
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engine = context.engine
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abundances: Dict[str, Tuple[float, float]] = {}
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for species in engine.getNetworkSpecies():
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sid = engine.getSpeciesIndex(species)
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abundances[species.name()] = (species.mass(), context.state[sid])
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data.append((context.t,abundances))
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solver.set_callback(callback)
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try:
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results = solver.evaluate(netIn, False)
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print(f"H-1 final molar abundance: {results.composition.getMolarAbundance("H-1"):0.3f}")
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except:
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print("Warning: solver did not converge", file=sys.stderr)
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uniqueSpecies: Set[Tuple[str, float]] = set()
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for t, timestep in data:
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for (name, (mass, abundance)) in timestep.items():
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uniqueSpecies.add((name, mass))
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sortedUniqueSpecies = list(sorted(uniqueSpecies, key=lambda e: e[1]))
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with open(f"gridfire_results_{datetime.now().strftime("%H-%M-%d_%m_%Y")}.csv", 'w') as f:
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f.write('t,')
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for i, (species,mass) in enumerate(sortedUniqueSpecies):
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f.write(f"{species}")
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if i < len(sortedUniqueSpecies)-1:
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f.write(",")
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f.write("\n")
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for t, timestep in data:
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f.write(f"{t},")
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for i, (species, mass) in enumerate(sortedUniqueSpecies):
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if timestep.get(species, None) is not None:
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f.write(f"{timestep.get(species)[1]}")
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else:
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f.write(f"")
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if i < len(sortedUniqueSpecies) - 1:
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f.write(",")
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f.write("\n")
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