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0cb2c69b3f
This cleans up the test to remove all unused code, making it smaller, a bit faster to deploy to a target to run, and also use less RAM on the target (which may help it run on targets that are just slightly out of memory running it). Signed-off-by: Damien George <damien@micropython.org>
99 lines
3.2 KiB
Python
99 lines
3.2 KiB
Python
# evolve the RGEs of the standard model from electroweak scale up
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# by dpgeorge
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import math
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class RungeKutta(object):
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def __init__(self, functions, initConditions, t0, dh, save=True):
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self.Trajectory, self.save = [[t0] + initConditions], save
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self.functions = [lambda *args: 1.0] + list(functions)
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self.N, self.dh = len(self.functions), dh
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self.coeff = [1.0 / 6.0, 2.0 / 6.0, 2.0 / 6.0, 1.0 / 6.0]
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self.InArgCoeff = [0.0, 0.5, 0.5, 1.0]
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def iterate(self):
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step = self.Trajectory[-1][:]
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istep, iac = step[:], self.InArgCoeff
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k, ktmp = self.N * [0.0], self.N * [0.0]
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for ic, c in enumerate(self.coeff):
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for if_, f in enumerate(self.functions):
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arguments = [(x + k[i] * iac[ic]) for i, x in enumerate(istep)]
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try:
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feval = f(*arguments)
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except OverflowError:
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return False
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if abs(feval) > 1e2: # stop integrating
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return False
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ktmp[if_] = self.dh * feval
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k = ktmp[:]
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step = [s + c * k[ik] for ik, s in enumerate(step)]
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if self.save:
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self.Trajectory += [step]
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else:
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self.Trajectory = [step]
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return True
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def solve(self, finishtime):
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while self.Trajectory[-1][0] < finishtime:
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if not self.iterate():
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break
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# 1-loop RGES for the main parameters of the SM
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# couplings are: g1, g2, g3 of U(1), SU(2), SU(3); yt (top Yukawa), lambda (Higgs quartic)
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# see arxiv.org/abs/0812.4950, eqs 10-15
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sysSM = (
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lambda *a: 41.0 / 96.0 / math.pi**2 * a[1] ** 3, # g1
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lambda *a: -19.0 / 96.0 / math.pi**2 * a[2] ** 3, # g2
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lambda *a: -42.0 / 96.0 / math.pi**2 * a[3] ** 3, # g3
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lambda *a: 1.0
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/ 16.0
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/ math.pi**2
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* (
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9.0 / 2.0 * a[4] ** 3
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- 8.0 * a[3] ** 2 * a[4]
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- 9.0 / 4.0 * a[2] ** 2 * a[4]
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- 17.0 / 12.0 * a[1] ** 2 * a[4]
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), # yt
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lambda *a: 1.0
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/ 16.0
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/ math.pi**2
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* (
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24.0 * a[5] ** 2
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+ 12.0 * a[4] ** 2 * a[5]
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- 9.0 * a[5] * (a[2] ** 2 + 1.0 / 3.0 * a[1] ** 2)
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- 6.0 * a[4] ** 4
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+ 9.0 / 8.0 * a[2] ** 4
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+ 3.0 / 8.0 * a[1] ** 4
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+ 3.0 / 4.0 * a[2] ** 2 * a[1] ** 2
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), # lambda
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)
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def singleTraj(system, trajStart, h=0.02, tend=1.0):
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is_REPR_C = float("1.0000001") == float("1.0")
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tstart = 0.0
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# compute the trajectory
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rk = RungeKutta(system, trajStart, tstart, h)
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rk.solve(tend)
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# print out trajectory
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for i in range(len(rk.Trajectory)):
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tr = rk.Trajectory[i]
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tr_str = " ".join(["{:.4f}".format(t) for t in tr])
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if is_REPR_C:
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# allow two small deviations for REPR_C
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if tr_str == "1.0000 0.3559 0.6485 1.1944 0.9271 0.1083":
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tr_str = "1.0000 0.3559 0.6485 1.1944 0.9272 0.1083"
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if tr_str == "16.0000 0.3894 0.5793 0.7017 0.5686 -0.0168":
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tr_str = "16.0000 0.3894 0.5793 0.7017 0.5686 -0.0167"
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print(tr_str)
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# initial conditions at M_Z
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singleTraj(sysSM, [0.354, 0.654, 1.278, 0.983, 0.131], h=0.5, tend=math.log(10**17)) # true values
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