def to_str(self): """ converts to standard string form for mode list ASCii files """ x, expx = nm_u.float_to_scientific(self.metric_value) k, expk = nm_u.float_to_scientific(self.k) return "%.14fe%d\t%s\t%s\t%.14fe%d" % (x, expx, self.dmode1.to_str_nlmwy(tuple=True), self.dmode2.to_str_nlmwy(tuple=True), k, expk)
def to_str_nlmwy(self, tuple=False):
w, wexp = float_to_scientific(self.w)
y, yexp = float_to_scientific(self.y)
if tuple:
return "(%d, %d, %d, %.14fe%d, %.14fe%d)" % (self.n, self.l, self.m, w, wexp, y, yexp)
else:
return "%d %d %d %.14fe%d %.14fe%d" % (self.n, self.l, self.m, w, wexp, y, yexp)
)
# === Ethr
if opts.verbose:
print "Ethr"
pkl_obj["Ethr"] = {}
for ethr in sorted(ethr_modeNos.keys()):
g, l = ethr_modeNos[ethr]
pkl_obj["Ethr"][ethr] = [(g, sum(mE[g]), sum(myE[g])), (l, sum(mE[l]), sum(myE[l]))]
if opts.verbose:
print "\tEthr=", ethr, "\n\tN>=", len(g), "\n\t\tmE=", sum(mE[g]), "\n\t\tmyE=", sum(
myE[g]
), "\n\tN<=", len(l), "\n\t\tmE=", sum(mE[l]), "\n\t\tmyE=", sum(myE[l])
if opts.plot:
tag = "Ethr-g-%.3fe%d" % nmu.float_to_scientific(ethr) + opts.tag
if opts.vverbose:
print tag
savefig(
plot(
t_P,
q,
system,
gens,
mode_nums=g,
verbose=opts.vverbose,
tc=opts.tc,
xmin=opts.time_xmin,
xmax=opts.time_xmax,
ymin=opts.time_ymin,
ymax=opts.time_ymax,
print "\n%f < t < %f" % rt[0], rt[-1]
print "dt = %f" % rt[1]-rt[0]
print "num samples = %d" % len(rt)
##############################################################################################
#
# compute and report individual statistics
#
##############################################################################################
print "Network Statistics:"
if opts.liapunov: # compute the numerical value of the liapunov exponent: essentially fit residuals to an exponential
(_, Ty_fit, Tchi2_red), (_, y_fit, chi2_red) = liapunov.nmode_liapunov_exp(rt, r)
# network totals for each mode
print "\ty_fit = %fe%d" % nmu.float_to_scientific(Ty_fit)
print "\tchi2_red = %f" % Tchi2_red
if opts.sum_sqr_err: # compute the sum-square-errors statistic
tot_SSE, SSE = liapunov.sum_sqr_err(r)
# these are normalized by the number of sample points
print "\tsum{r**2}/len(r) = %fe%d" % nmu.float_to_scientific(tot_SSE)
if opts.chi2: # compute the chi2 goodness of fit statistic
Tchi2s, chi2s = liapunov.nmode_chi2(rq1, rq2)
# returns the reduced chi2 goodness of fit test between the two time series
print "\tchi2{rq1,rq2}/len(rq1) = %fe%d" % nmu.float_to_scientific(Tchi2s)
if opts.inner_product: # compute the inner product of the data, normalized by the inner products of the separate time streams
Tinner_prod, inner_prods = liapunov.nmode_inner_product(rq1, rq2)
# standard definition of inner product in time domain
df_F : %f
f_F[-1] : %f""" % (opts.freqfilename, opts.fcurrent, freq[0], freq[1]-freq[0], freq[-1])
### add to pickle object
if opts.pstefilename:
sdata["freq_domain"] = {"frqfilename":opts.freqfilename, "var":opts.fcurrent, "f_F[0]":freq[0], "df_F":freq[1]-freq[0], "f_F[-1]":freq[-1]}
### print parameters of the system
print >> stefile, """SYSTEM
logfilename : %s
Nmodes : %d
Ngens : %d
Ntriples : %d
Mprim/Msun : %f
Rprim/Rsun : %f
Mcomp/Mjup : %f""" % (opts.logfilename, N_m, len(network.gens()[0]), len(network.to_triples()), Mprim, Rprim, Mcomp)
_val, _valexp = nm_u.float_to_scientific(dimEo)
print >> stefile, "\tEo : %fe%d %s" % (_val, _valexp, nm_u.units['energy'])
_val, _valexp = nm_u.float_to_scientific(Porb)
print >> stefile, "\tPorb : %fe%d %s" % (_val, _valexp, nm_u.units['time'])
_val, _valexp = nm_u.float_to_scientific(Oorb)
print >> stefile, "\tOorb : %fe%d rad/%s" % (_val, _valexp, nm_u.units['time'])
print >> stefile, "\tecc : %f%d" % nm_u.float_to_scientific(system.eccentricity)
_val, _valexp = nm_u.float_to_scientific(aorb)
print >> stefile, "\taorb : %fe%d %s" % (_val, _valexp, nm_u.units['length'])
_val, _valexp = nm_u.float_to_scientific(Eorb)
print >> stefile, "\tEorb : %fe%d %s" % (_val, _valexp, nm_u.units['energy'])
### add to pickle object
if opts.pstefilename:
sdata["system"] = {"logfilename":opts.logfilename, "Nmodes":N_m, "Ngens":len(network.gens()[0]), "Ntriples":len(network.to_triples()), "Mprim/Msun":Mprim, "Rprim/Rsun":Rprim, "Mcomp/Mjup":Mcomp, "Eo":dimEo, "Porb":Porb, "ecc":system.eccentricity, "Eorb":Eorb, "Ngi":[len(gen) for gen in network.gens()[0]]}
### print network totals
####################################################################################################
if opts.verbose: print "downselecting a subset of modes based on integration data"
if opts.verbose: print "reading in network parameters from %s" % opts.logfilename
system = nm_u.load_log(opts.logfilename)
network = system.network
Oorb = system.Oorb
Porb = 2*np.pi/Oorb
if opts.verbose: print "reading time-domain data from %s" % opts.filename
t_P, q, N_m = nm_u.load_out(opts.filename, tmin=opts.min_t_Porb, tmax=opts.max_t_Porb, downsample=opts.file_downsample)
remove = []
for Athr, method in zip(Athrs, downselection_methods):
if opts.verbose: _athr, _athrexp = nm_u.float_to_scientific(Athr)
if method == "Alast":
if opts.verbose: print "downselecting modes using %s and Athr=%fe%d" % (method, _athr, _athrexp)
remove += pn.downselect_Alast(q, Athr, network)[1]
elif method == "Amean":
if opts.verbose: print "downselecting modes using %s and Athr=%fe%d" % (method, _athr, _athrexp)
remove += pn.downselect_Amean(q, Athr, network)[1]
elif method == "Afit":
if opts.verbose: print "downselecting modes using %s and Athr=%fe%d" % (method, _athr, _athrexp)
remove += pn.downselect_Afit(q, Athr, network, t_P=t_P)[1]
else:
raise ValueError, "unknown downselection method: %s\nplease supply at last one downselection method from the following:\n\tAlst\n\tAmean\n\tAfit"
remove = list(set(remove)) # generate a unique set of modes to remove
if opts.verbose: print "attempting to remove %d/%d modes from the network" % (len(remove), N_m)
