def testConv_script(NN):
NN=int(NN)
warmup=NN/2
params={'m':[1.,100.,0.],'T':[1e-1,1.],'L':1.,'D':1e-7,'d':0.,'a':[0.5,0.5],'b':[0.5,0.5],'kb':1.,'force':[0.,0.,0.],'accelFlag':'none'}
pt1=geometricPt(params=params)
pt2=geometricPt(params=params)
for i in xrange(NN):
pt1.step()
pt2.step()
rslt=np.zeros([2,NN-warmup])
rslt[0]=np.array(pt1.vt).T[1][warmup+1:]
rslt[1]=np.array(pt2.vt).T[1][warmup+1:]
return testConvergence(rslt)
def experimentFrameCoreQLR(fN, N=1e4, massRatio=[1]):
# heat transfer rate as a function of mass-ratio
qlrD = np.zeros(len(massRatio))
srD = np.zeros(len(massRatio))
paramsD = np.empty(len(massRatio), dtype=object)
for i in xrange(len(massRatio)):
params = {
"m": [0.01, 0.5, 0.5],
"T": [1e-1, 1.0],
"L": 1.0,
"D": 0.5,
"d": 0.25,
"a": [0.5, 0.5],
"b": [0.5, 0.5],
"kb": 1.0,
"force": [0.0, 0.0, 0.0],
"accelFlag": "none",
}
it = massRatio[i]
params["m"][1] = it / (1.0 + it)
params["m"][2] = 1 / (1.0 + it)
pt = geometricPt(params=params)
[pt.step() for n in xrange(int(N))]
qlr, qrr, sr, ql, qr, s = pt.heatTransferRate()
qlrD[i] = qlr[-1]
srD[i] = sr[-1]
resultsD = {"qlr": qlrD, "sr": srD, "params": paramsD, "massRatio": massRatio}
pickle.dump(resultsD, open(fN + ".p", "wr"))
def experimentFrameCore1(fN, N=1e6, massRatio=[1]):
# mass-ratio effect on velocity, q-rate, s-rate. time-path graphs.
resultsD = np.empty(len(massRatio), dtype=object)
for i in xrange(len(massRatio)):
params = {
"m": [0.01, 0.5, 0.5],
"T": [1e-1, 1.0],
"L": 1.0,
"D": 0.5,
"d": 0.25,
"a": [0.5, 0.5],
"b": [0.5, 0.5],
"kb": 1.0,
"force": [0.0, 0.0, 0.0],
"accelFlag": "none",
}
it = massRatio[i]
params["m"][1] = it / (1.0 + it)
params["m"][2] = 1 / (1.0 + it)
pt = geometricPt(params=params)
[pt.step() for n in xrange(int(N))]
ql, qr, s = pt.heatTransfer()
resultsD[i] = {
"t": np.array(pt.t),
"xt": np.array(pt.xt_noncyclic).T,
"wt": np.array(pt.wt),
"ql": ql,
"s": s,
"params": params,
}
pickle.dump(resultsD, open(fN + ".p", "wr"))
def experimentFrameCoreEquilibrium(fN, N=1e6):
# generate a few routine graphs demonstrating the convergence of quantities such as velocity, heat-transfer-rate, entropy-production-rate
params = {
"m": [0.01, 0.5, 0.5],
"T": [1e-1, 1.0],
"L": 1.0,
"D": 0.1,
"d": 0.01,
"a": [0.5, 0.5],
"b": [0.5, 0.5],
"kb": 1.0,
"force": [0.0, 0.0, 0.0],
"accelFlag": "none",
}
pt = geometricPt(params=params)
[pt.step() for n in xrange(int(N))]
_, _, _, ql, qr, s = pt.heatTransferRate()
ptDict = {
"t": np.array(pt.t),
"xt": np.array(pt.xt_noncyclic).T,
"wt": np.array(pt.wt),
"ql": ql,
"qr": qr,
"s": s,
"params": params,
}
pickle.dump(ptDict, open(fN + ".p", "wr"))
def experimentHeat(fN, N=1e6, a=[[1.0, 1.0], [0.5, 0.5]]):
a = np.array(a)
qlrD = np.zeros(np.shape(a)[1])
qrrD = np.zeros(np.shape(a)[1])
srD = np.zeros(np.shape(a)[1])
qlD = np.zeros(np.shape(a)[1])
qrD = np.zeros(np.shape(a)[1])
sD = np.zeros(np.shape(a)[1])
paramsD = np.empty(np.shape(a)[1], dtype=object)
for i in xrange(np.shape(a)[1]):
params = {
"m": [1.0, 0.0, 0.0],
"T": [1e-1, 1.0],
"L": 1.0,
"D": 0.0,
"d": 0.0,
"a": a.T[i],
"b": [0.0, 0.0],
"kb": 1.0,
"force": [0.0, 0.0, 0.0],
"accelFlag": "none",
}
pt = geometricPt(params=params)
[pt.step() for n in xrange(int(N))]
# aa=np.array(pt.heatTransferRate())
# pdb.set_trace()
qlrD[i], qrrD[i], srD[i], qlD[i], qrD[i], sD[i] = np.array(pt.heatTransferRate()).T[-1]
paramsD[i] = params
ptDict = {"a": a, "qlr": qlrD, "qrr": qrrD, "sr": srD, "ql": qlD, "qr": qrD, "s": sD, "params": paramsD}
pickle.dump(ptDict, open(fN + ".p", "wr"))
def exp_coreOne(mode="time", N=1e2, b=[1.0, 1.0], m_core=[1.0, 100.0], T=[0.1, 1.0]):
params = {
"m": [m_core[0], m_core[1], 0.0],
"T": T,
"L": 1.0,
"D": 1e-7,
"d": 0.0,
"a": [0.5, 0.5],
"b": b,
"kb": 1.0,
"force": [0.0, 0.0, 0.0],
"accelFlag": "none",
}
pt = geometricPt(params=params)
init_cutoff = int(N) / 2
if mode == "step":
[pt.step() for n in xrange(int(N))]
elif mode == "time":
while pt.t[-1] < N:
pt.step()
else:
raise ValueError("invalid mode for exp_core_One. Either time or step")
result_distr, result_running, result_end = summaryCalc_core(pt, init_cutoff)
return result_distr, result_running, result_end
