def main4():
"""Animate the form of F_b with (crude) seasonal cycle."""
### Import data for ice-edge latitude:
tdef, xdef, Edef = filing.OpenData('DAT_constFb=4.0_constHML=75.0_HR')[0:3]
xideg_def = np.degrees(np.arcsin(WE.xi_seasonal(Edef, xdef)))
t, x_coords, E = filing.OpenData('DAT_t_DEPENDENCE')[0:3]
x = WE.xi_seasonal(E, x_coords)
xdeg = np.degrees(np.arcsin(x))
FB = np.zeros((len(t), len(x_coords)))
for i in xrange(len(t)):
FB[i] = JA.BasalFluxTimeDependent(x_coords, t[i], x[i])
fig, (ax1, ax2) = plt.subplots(1, 2)
### Fixed plot elements:
ax1.set_xlabel(r'$\phi$ ($^\circ$)')
ax1.set_ylabel(r'$F_\mathrm{b}(x,t)$ (W m$^{-2}$)')
ax1.set_xlim([0, 90])
ax1.set_ylim([0, 10])
fig, ax1 = pl.FormatAxis(fig, ax1)
ax2.plot(tdef, xideg_def, color='grey', linewidth=1.5)
ax2.plot(t, xdeg, color='k', linewidth=1.5)
ax2.set_xlabel(r'Time, $t$ (yr)')
ax2.set_ylabel(r'Ice-edge latitude, $\phi_\mathrm{i}$ ($^\circ$)')
ax2.set_title(r'Seasonal ice-edge latitude')
ax2.set_xlim([0, 1])
ax2.set_ylim([0, 90])
### Initial frame plot:
ax1.set_title(r'$t = %.2f$' % t[0])
line, = ax1.plot(np.degrees(np.arcsin(x_coords)),
FB[0],
color='k',
linewidth=1.5,
label=r'$t=%.2f$ yr' % t[0])
time, = ax2.plot(np.array([t[0], t[0]]),
np.array([0, 90]),
color='k',
linestyle='--')
fig, ax1 = pl.FormatAxis(fig, ax1)
fig, ax2 = pl.FormatAxis(fig, ax2)
def animate(i):
line.set_ydata(FB[i])
time.set_xdata(np.array([t[i], t[i]]))
ax1.set_title(r'$t=%.2f$ yr' % t[i])
return line
ani = animation.FuncAnimation(fig,
animate,
np.arange(1, len(t)),
interval=100,
blit=False)
fig.show()
return FB
def main(lowres=False, usesaved=False, savefigs=True, custom=False):
"""For the standard model implementation by Wagner and Eisenman (WE2015),
test whether it is conserving energy by calculating the integral over the
hemisphere of their equation (2). By integrating, the D*laplacian(T) term
drops out.
"""
if usesaved:
if custom:
t, x, E, T = filing.OpenData(params.custom_filename +
('LR' if lowres else 'HR'))
else:
t, x, E, T = filing.OpenData('WE2015_DEFAULT_DATA_' +
('LR' if lowres else 'HR'))
else:
t, x, E, T = WE.Integration(lowres, varyHML=False, varyFB=False)
filing.SaveData(
t, x, E, T, 'DAT_constFb=%.1f_constHML=%.1f_' %
(params.Fb, params.HML_OCEAN) + ('LR' if lowres else 'HR'))
# We now have an equilibrated seasonal cycle surface enthalpy E(x,t) and
# temperature profile T specified at certain times within the season.
dx = x[1] - x[0]
dt = t[1] - t[0]
dE = np.zeros(len(t) - 2)
for i in xrange(1, len(t) - 1):
dE[i - 1] = EnergyDiagnostic(np.array([enth[i - 1] for enth in E]),
np.array([enth[i] for enth in E]),
np.array([enth[i + 1] for enth in E]),
np.array([temp[i] for temp in T]),
x,
t[i],
dt=dt)
# Return also the integral of this curve (if zero, net energy loss within
# one seasonal cycle is zero so maybe okay...):
int_dE = np.sum(dE) * dt
print "The integral of dE over time = %.4f W yr m^-2" % int_dE
# Now use the second diagnostic:
E_stored = np.zeros(len(t))
F_leave = np.zeros(len(t))
for i in xrange(len(t)):
E_stored[i], F_leave[i] = EnergyDiagnostic2(
x, t[i], np.array([enth[i] for enth in E]),
np.array([temp[i] for temp in T]))
fig1, ax1 = pl.PlotEnergyDiagnostic1(t[1:len(t) - 1], dE)
fig1.canvas.set_window_title('cons_test_diag1_dx=%.2E_dt=%.2E' % (dx, dt))
fig2, ax2a, ax2b = pl.PlotEnergyDiagnostic2(t, E_stored, F_leave)
fig2.canvas.set_window_title('cons_test_diag2_dx=%.2E_dt=%.2E' % (dx, dt))
if savefigs:
filing.SaveFigures([fig1, fig2], 'conservation_tests')
filing.SaveFigures([fig1, fig2], 'conservation_tests', '.svg')
fig1.show()
fig2.show()
pass
def main(lowres=False,
usesaved=False,
times=[22, 73],
savefigs=False,
interactive=[False, False]):
"""Bla bla bla...
--Args--
lowres: boolean; if True, uses low resolution numerical parameters.
usesaved: boolean; if True, attempts to load relevant saved data rather
than calculating from scratch.
times: len-2 array; time *indexes* for winter and summer plots,
respectively.
savefigs: boolean; if True, saves figures (*.pdf) to the plots sub-
directory (will check before over-writing).
interactive: len-2 array of booleans; whether to use interactive form of
Fb(x, xi) [0] and Hml(x, xi) [1].
"""
if usesaved:
t, x, E, T = filing.OpenData(
'DAT_%.1f_Fb_%.1f_%.1f_Hml_%.1f' %
(params.FB_ICE, params.FB_ICE + params.DELTA_FB, params.HML_ICE,
params.HML_OCEAN) + ('_LR' if lowres else '_HR') +
('_interactiveFb' if interactive[0] else '') +
('_interactiveHml' if interactive[1] else ''))
else:
t, x, E, T = WE.Integration(lowres,
varyHML=True,
varyFB=True,
interactiveFB=interactive[0],
interactiveHML=interactive[1])
filing.SaveData(
t, x, E, T, 'DAT_%.1f_Fb_%.1f_%.1f_Hml_%.1f' %
(params.FB_ICE, params.FB_ICE + params.DELTA_FB, params.HML_ICE,
params.HML_OCEAN) + ('_LR' if lowres else '_HR') +
('_interactiveFb' if interactive[0] else '') +
('_interactiveHml' if interactive[1] else ''))
tdef, xdef, Edef, Tdef = filing.OpenData(
'DAT_constFb=4.0_constHML=75.0_HR')
xi_deg = np.degrees(np.arcsin(WE.xi_seasonal(E, x)))
xidef_deg = np.degrees(np.arcsin(WE.xi_seasonal(Edef, xdef)))
# Plot the ice-edge seasonal cycle:
details = r'(variable $H_\mathrm{ml}$, variable $F_\mathrm{b}$)'
f1, a1 = pl.PlotIceEdge(t, xi_deg, details=details)
a1.plot(tdef, xidef_deg, color='grey', linewidth=1.5)
a1.axvline(t[times[0]], color='k')
a1.axvline(t[times[1]], color='k', linestyle='--')
f1.tight_layout()
# Plot E(x, t) contour map:
f2, a2 = pl.PlotContour(t, np.degrees(np.arcsin(x)), E)
# Plot T(x, t) contour map:
f3, a3 = pl.PlotContour(t, np.degrees(np.arcsin(x)), T, type='T')
# Plot E(x) for winter and summer:
title = r'$%.1f< H_\mathrm{ml}(x,t)$ /m $<%.1f$, $%.1f<F_\mathrm{b}(x,t) /$Wm$^{-2} <%.1f$' % (
params.HML_ICE, params.HML_OCEAN, params.FB_ICE,
params.FB_ICE + params.DELTA_FB)
f4, a4 = pl.PlotContourWS(t, np.degrees(np.arcsin(x)), E, times, 'E',
title)
# Plot T(x) for winter and summer:
f5, a5 = pl.PlotContourWS(t, np.degrees(np.arcsin(x)), T, times, 'T',
title)
# Plot h(x) for winter and summer:
f6, a6 = pl.PlotIceThickness(t, np.degrees(np.arcsin(x)), E, times, title)
#### Add plot for constant Fb and constant Hml model manually:
h_def_win = Edef[:, times[0]] / -params.Lf
h_def_sum = Edef[:, times[1]] / -params.Lf
h_def_win = [h if h >= 0 else 0 for h in h_def_win]
h_def_sum = [h if h >= 0 else 0 for h in h_def_sum]
a6.plot(np.degrees(np.arcsin(xdef)),
h_def_win,
color='grey',
linewidth=1.5)
a6.plot(np.degrees(np.arcsin(xdef)),
h_def_sum,
color='grey',
linewidth=1.5,
linestyle='--')
# Plot the heat transport D(del^2)T(x,t) for winter and summer:
f7, a7 = pl.PlotHeatTransport(t, x, T, times, title=title)
figures = [f1, f2, f3, f4, f5, f6, f7]
if savefigs:
dirname = '%.1f_Fb_%.1f_%.1f_Hml_%.1f' % (
params.FB_ICE, params.FB_ICE + params.DELTA_FB, params.HML_ICE,
params.HML_OCEAN) + ('_interactiveFb' if interactive[0] else
'') + ('_interactiveHml'
if interactive[1] else '')
filing.SaveFigures(figures, dirname)
for fig in figures:
fig.show()
pass
def main(lowres=False,
usesaved=False,
custom=False,
times=[22, 73],
savefigs=False):
"""Run the model and generate all plots for the standard model as described
by Wagner and Eisenman (2015), i.e. with constant ocean mixed-layer depth
and constant ocean basal heat-flux (selects the parameter values in
params.py)
--Args--
lowres: boolean; if True, uses low resolution numerical parameters.
usesaved: boolean; if True, attempts to load relevant saved data rather
than calculating from scratch.
times: len-2 array; time *indexes* for winter and summer plots,
respectively.
savefigs: boolean; if True, saves figures (*.pdf) to the plots sub-
directory (will check before over-writing).
"""
if usesaved:
if custom:
t, x, E, T = filing.OpenData(params.custom_filename +
('LR' if lowres else 'HR'))
else:
t, x, E, T = filing.OpenData('DAT_constFb=%.1f_constHml=%.1f_' %
(params.Fb, params.HML_OCEAN) +
('LR' if lowres else 'HR'))
else:
t, x, E, T = WE.Integration(lowres, varyHML=False, varyFB=False)
if custom:
filing.SaveData(
t, x, E, T,
params.custom_filename + ('LR' if lowres else 'HR'))
else:
filing.SaveData(
t, x, E, T, 'DAT_constFb=%.1f_constHML=%.1f_' %
(params.Fb, params.HML_OCEAN) + ('LR' if lowres else 'HR'))
xi_deg = np.degrees(np.arcsin(WE.xi_seasonal(E, x)))
# Plot the ice-edge seasonal cycle:
details = r'(constant $F_\mathrm{b}=%.1f$ Wm$^{-2}$, constant' % params.Fb
details += r' $H_\mathrm{ml}=%.1f$ m)' % params.HML_OCEAN
f1, a1 = pl.PlotIceEdge(t, xi_deg, label='Default model', details=details)
a1.axvline(t[times[0]], color='k')
a1.axvline(t[times[1]], color='k', linestyle='--')
f1.tight_layout()
# Plot E(x, t) contour map:
if lowres:
f2, a2 = pl.PlotContour(t, np.degrees(np.arcsin(x)), E)
else:
f2, a2 = pl.PlotContour(t, np.degrees(np.arcsin(x)), E)
# Plot T(x, t) contour map:
f3, a3 = pl.PlotContour(t, np.degrees(np.arcsin(x)), T, type='T')
f3.tight_layout()
# Plot E(x) for winter and summer:
title = r'$F_\mathrm{b}(x,t)=%.1f$ Wm$^{-2}$, $H_\mathrm{ml}(x,t)=%.1f$ m' % (
params.Fb, params.HML_OCEAN)
f4, a4 = pl.PlotContourWS(t, np.degrees(np.arcsin(x)), E, times, 'E',
title)
# Plot T(x) for winter and summer:
f5, a5 = pl.PlotContourWS(t, np.degrees(np.arcsin(x)), T, times, 'T',
title)
# Plot h(x) for winter and summer:
f6, a6 = pl.PlotIceThickness(t, np.degrees(np.arcsin(x)), E, times, title)
# Plot the heat transport D(del^2)T(x,t) for winter and summer:
f7, a7 = pl.PlotHeatTransport(t, x, T, times, title=title)
figures = [f1, f2, f3, f4, f5, f6, f7]
if savefigs:
filing.SaveFigures(
figures,
'constantFb=%.1f_constantHml=%.1f' % (params.Fb, params.HML_OCEAN))
for fig in figures:
fig.show()
pass