def plot(t_P, q, system, gens, mode_nums=None, verbose=False, tc="x", xmin=False, xmax=False, ymin=False, ymax=False):
""" a wrapper for plotting routines we'll use a lot """
n_l_m = system.network.nlm
ans = []
### amp-time
if verbose:
print "amp_time"
fig, ax = nmp.amp_plot(t_P, q, n_l_m=n_l_m, mode_nums=mode_nums)
ax.set_ylabel(r"$|" + tc + "_i|$")
ax.set_xlabel(r"$t/P_{\mathrm{orb}}$")
if ymin:
ax.set_ylim(ymin=ymin)
if ymax:
ax.set_ylim(ymax=ymax)
if xmin:
ax.set_xlim(xmin=xmin)
if xmax:
ax.set_xlim(xmax=xmax)
ans.append((fig, ax))
### multi-gen-amp-time
if verbose:
print "multi-gen_amp_time"
fig, axs = nmp.multi_gen_amp_plot(t_P, q, gens, n_l_m=n_l_m, mode_nums=mode_nums)
for ax in axs:
ax.set_ylabel(r"$|" + tc + "|$")
ax.set_xlabel(r"$t/P_{\mathrm{orb}}$")
if ymin:
ax.set_ylim(ymin=ymin)
if ymax:
ax.set_ylim(ymax=ymax)
if xmin:
ax.set_xlim(xmin=xmin)
if xmax:
ax.set_xlim(xmax=xmax)
ans.append((fig, axs))
### coupling diagrams
colormap = nmd.plt.get_cmap("jet")
### nl-placement disp
if verbose:
print "nl-placmenet disp"
myE = -np.array([np.mean(_) for _ in nms.viscous_disp(q, network, Eo=1.0)[-1]])
mode_colors = [colormap(_) for _ in myE]
mode_order = myE.argsort()
fig = nmd.plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.750, 0.8])
cbax = fig.add_axes([0.875, 0.1, 0.025, 0.8])
fig, ax = nmd.coupling_tree_nl(
system,
tree_type="placement",
genNos=[],
verbose=verbose,
mode_colors=mode_colors,
mode_order=mode_order,
fig_ax=(fig, ax),
mode_nums=mode_nums,
)
# colorbar = nmd.matplotlib.colorbar.ColorbarBase(cbax, cmap=colormap, orientation='vertical', norm=nmd.matplotlib.colors.LogNorm())
colorbar = nmd.matplotlib.colorbar.ColorbarBase(cbax, cmap=colormap, orientation="vertical")
colorbar.ax.set_ylabel(r"$\gamma_i A_i^2/\mathrm{max}\{\gamma_i A_i^2\}$")
ans.append((fig, ax, colorbar))
### nl-placement E
if verbose:
print "nl-placement E"
mE = np.array([np.mean(_) for _ in nms.compute_E(q, Eo=1.0)])
mode_colors = [colormap(_) for _ in mE / max(mE)]
mode_order = mE.argsort()
fig = nmd.plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.750, 0.8])
cbax = fig.add_axes([0.875, 0.1, 0.025, 0.8])
fig, ax = nmd.coupling_tree_nl(
system,
tree_type="placement",
genNos=[],
verbose=verbose,
mode_colors=mode_colors,
mode_order=mode_order,
fig_ax=(fig, ax),
mode_nums=mode_nums,
)
# colorbar = nmd.matplotlib.colorbar.ColorbarBase(cbax, cmap=colormap, orientation='vertical', norm=nmd.matplotlib.colors.LogNorm())
colorbar = nmd.matplotlib.colorbar.ColorbarBase(cbax, cmap=colormap, orientation="vertical")
colorbar.ax.set_ylabel(r"$A_i^2/\mathrm{max}\{A_i^2\}$")
ans.append((fig, ax, colorbar))
### nl-siblings gens
for genNo in xrange(1, len(gens)):
if verbose:
print "nl-siblings gen%d" % genNo
fig, ax = nmd.coupling_tree_nl(
system, tree_type="siblings", genNos=[genNo], verbose=verbose, mode_nums=mode_nums
)
ans.append((fig, ax))
return ans
mA /= max(mA)
mode_colors = [colormap(_) for _ in mA]
mode_order = mA.argsort() # smallest values plotted first
elif coloration == "E":
if opts.verbose: print "\t\t coloration: E"
mE = np.array([np.mean(_) for _ in nms.compute_E(q, Eo=1.0)])
mE /= max(mE)
mode_colors = [colormap(_) for _ in mE]
mode_order = mE.argsort()
elif coloration == "disp":
if opts.verbose: print "\t\t coloration: disp"
myE = np.array([-np.mean(_) for _ in nms.viscous_disp(q, network, Eo=1.0)[-1]])
myE /= max(myE)
mode_colors = [colormap(_) for _ in myE]
mode_order = myE.argsort()
else:
if opts.verbose: print "\t\tcoloration \"%s\" not understood. skipping..." % coloration
continue
fig = nmd.plt.figure()
ax = fig.add_axes([0.1, 0.1, 0.750, 0.8])
cbax = fig.add_axes([0.875, 0.1, 0.025, 0.8])
fig, ax = nmd.coupling_tree_nl(system, tree_type=diagram_type.split("-")[-1], genNos=[int(l) for l in opts.coupling_diagram_genNos.split()], verbose=opts.verbose, mode_colors=mode_colors, mode_order=mode_order, fig_ax=(fig,ax))
ax.grid(opts.grid, which="both")
print "separating modes by collective Ethr:"
collE_modeNos = {}
for collE in opts.collE:
if opts.verbose:
print "\tcollE:", collE
collE_modeNos[collE] = [
[network.modeNoD[nlms] for nlms in _]
for _ in pn.downselect_collE(collE, system, freqs=None, mode_nums=None)
]
# =================================================
### plot data!
# =================================================
### compute stuff for statistics
myE = -np.array([np.mean(_) for _ in nms.viscous_disp(q, network, Eo=1.0)[-1]])
myE /= sum(myE)
mE = np.array([np.mean(_) for _ in nms.compute_E(q, Eo=1.0)])
mE /= sum(mE)
pkl_obj = {"myE": myE, "mE": mE}
# === total data set
if opts.plot:
if opts.verbose:
print "total data"
tag = "" + opts.tag
savefig(
plot(t_P, q, system, gens, mode_nums=None, verbose=opts.vverbose, tc=opts.tc),
opts.outfilename,
opts.logfilename,
sAsin_lag.append( nm_s.sample_var(A, xo=mAsin_lag[-1])**0.5 )
lAsin_lag.append( A[-1] )
del x
msin_lag = []
ssin_lag = []
lsin_lag = []
for A in xx:
msin_lag.append( nm_s.sample_mean(A) )
ssin_lag.append( nm_s.sample_var(A, xo=msin_lag[-1])**0.5 )
lsin_lag.append( A[-1] )
del xx
# Asin_lag, sin_lag = nm_s.phase_lag(t_P, q, network, Eo=1.)
if opts.yE:
if opts.verbose: print "computing (viscous) energy disipation rates"
x, xx = nm_s.viscous_disp(q, network, Eo=1.)
mean_tot_yE = nm_s.sample_mean(x)
var_tot_yE = nm_s.sample_var(x, xo=mean_tot_yE)
del x
myE = []
syE = []
lyE = []
for A in xx:
myE.append( nm_s.sample_mean(A) )
syE.append( nm_s.sample_var(A, xo=myE[-1])**0.5 )
lyE.append( A[-1] )
del xx
if opts.conc_fit:
if opts.verbose: print "\tattempting to fit concentration diagram"
myE_fp, myE_cvr, myE_rchi2 = nm_s.broken_PowLaw_fitter(range(opts.conc_fit_start+1, opts.conc_fit_stop+1), np.array(sorted(myE)[opts.conc_fit_start:opts.conc_fit_stop])/mean_tot_yE, np.ones((opts.conc_fit_stop-opts.conc_fit_start)), max_iters=opts.conc_fit_iters, rtol=opts.conc_fit_rtol, verbose=opts.conc_verbose)
