def plot_obs_east_west(ob, xa_east, xa_west, d_e, d_w):
"""Plots a specified observation using obs eqn in obs module. Takes an
observation string, a dataClass (dC) and a start and finish point.
"""
sns.set_context(rc={'lines.linewidth': .8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
me = mc.DalecModel(d_e)
mw = mc.DalecModel(d_w)
mod_lst_e = me.mod_list(xa_east)
obs_lst_e = me.oblist(ob, mod_lst_e)
mod_lst_w = mw.mod_list(xa_west)
obs_lst_w = mw.oblist(ob, mod_lst_w)
palette = sns.color_palette("colorblind", 11)
ax.plot(d_e.dates, obs_lst_e, color=palette[0], label='East')
ax.plot(d_w.dates, obs_lst_w, color=palette[2], label='West')
if ob in d_e.ob_dict.keys():
ax.plot(d_e.dates,
d_e.ob_dict[ob],
'o',
color=palette[0],
markeredgecolor='black',
markeredgewidth=0.5)
ax.plot(d_w.dates,
d_w.ob_dict[ob],
'o',
color=palette[2],
markeredgecolor='black',
markeredgewidth=0.5)
plt.legend()
ax.set_xlabel('Date')
ax.set_ylabel(ob)
plt.gcf().autofmt_xdate()
return ax, fig
def plot_scatter_twin(ob, pvals, dC, awindl, bfa='a'):
"""Plots scatter plot of obs vs model predicted values. Takes an initial
parameter set, a dataClass (must have only desired ob for comparison
specified in dC), assimilation window length and whether a comparison of
background 'b', forecast 'f' or analysis 'a' is desired.
"""
sns.set_context('poster',
font_scale=1.5,
rc={
'lines.linewidth': 1.,
'lines.markersize': 6.
})
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 10))
sns.set_style('ticks')
palette = sns.color_palette("colorblind", 11)
m = mc.DalecModel(dC)
mod_lst = m.mod_list(pvals)
mod_lst_truth = m.mod_list(dC.x_truth)
obs_lst = m.oblist(ob, mod_lst)
y_obs = m.oblist(ob, mod_lst_truth)
plt_ob_lst = (y_obs / y_obs) * obs_lst
if bfa == 'b' or bfa == 'a':
selection = xrange(0, awindl)
elif bfa == 'f':
selection = xrange(awindl, len(obs_lst))
else:
raise Exception('Please check function input for bfa variable')
ob_lst = plt_ob_lst[selection][np.isnan(y_obs[selection]) != True]
y_obs = y_obs[selection][np.isnan(y_obs[selection]) != True]
one_one = np.arange(int(min(min(y_obs), min(ob_lst))),
int(max(max(y_obs), max(ob_lst))))
plt.plot(one_one, one_one, color=palette[0])
print int(min(min(y_obs), min(ob_lst))), int(max(max(y_obs), max(ob_lst)))
ax.plot(y_obs, ob_lst, 'o', color=palette[1])
error = np.sqrt(np.sum((y_obs - ob_lst)**2) / len(y_obs))
yhx = np.mean(y_obs - ob_lst)
mod_obs_bar = np.mean(ob_lst)
std_mod_obs = np.nanstd(ob_lst)
obs_bar = np.mean(y_obs)
std_obs = np.std(y_obs)
rms = np.sqrt(
np.sum([((ob_lst[x] - mod_obs_bar) - (y_obs[x] - obs_bar))**2
for x in range(len(y_obs))]) / len(y_obs))
corr_coef = (np.sum([((ob_lst[x]-mod_obs_bar)*(y_obs[x]-obs_bar)) for x in range(len(y_obs))]) / len(y_obs)) / \
(std_mod_obs*std_obs)
plt.xlabel(ob.upper() + r' observations (g C m$^{-2}$ day$^{-1}$)')
plt.ylabel(ob.upper() + ' model (g C m$^{-2}$ day$^{-1}$)')
plt.title('mean(y-hx)=%.2f, rms=%.2f, corr_coef=%.2f' %
(yhx, rms, corr_coef))
print bfa + '_error=%f, mean(y-hx)=%f, rms=%f, corr_coef=%f' % (
error, yhx, rms, corr_coef)
#plt.xlim((-20, 15))
#plt.ylim((-20, 15))
return ax, fig
def plot_4dvar_twin(ob,
dC,
xb=None,
xa=None,
erbars=1,
awindl=None,
obdict_a=None):
"""Plots a model predicted observation value for two initial states (xb,xa)
and also the actual observations taken of the physical quantity. Takes a ob
string, two initial states (xb,xa), a dataClass and a start and finish
time step.
"""
sns.set_context(rc={'lines.linewidth': .8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
palette = sns.color_palette("colorblind", 11)
m = mc.DalecModel(dC)
mod_lst = m.mod_list(dC.x_truth)
obs_lst = m.oblist(ob, mod_lst)
ax.plot(dC.dates, obs_lst, color=palette[3])
if xb != None:
mod_lst = m.mod_list(xb)
obs_lst = m.oblist(ob, mod_lst)
ax.plot(dC.dates, obs_lst, ':', color=palette[0])
if xa != None:
mod_lst = m.mod_list(xa)
obs_lst = m.oblist(ob, mod_lst)
ax.plot(dC.dates, obs_lst, color=palette[1])
mod_lst = m.mod_list(dC.x_truth)
obs_lst = m.oblist(ob, mod_lst)
ax.plot(dC.dates, obs_lst, '--', color=palette[3])
# ob_dict = obdict_a
# ob_err_dict = dC.ob_err_dict
# if ob in ob_dict.keys():
# if erbars == True:
# ax.errorbar(dC.dates, ob_dict[ob], yerr=ob_err_dict[ob],
# fmt='o', label=ob+'_o', color=palette[2], alpha=0.7)
# else:
# ax.plot(dC.dates, ob_dict[ob], 'o', label=ob+'_o', color=palette[2])
if obdict_a != None:
ax.plot(dC.dates[0:len(obdict_a[ob])],
obdict_a[ob],
'o',
color=palette[2])
if awindl != None:
ax.axvline(x=dC.dates[awindl], color='k', ls='dashed')
ax.set_xlabel('Year')
ax.set_ylabel(ob)
plt.gcf().autofmt_xdate()
return ax, fig
def plot_gpp_sensitivity_ceff():
sns.set_context(rc={'lines.linewidth': .8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
palette = sns.color_palette("colorblind", 11)
d = dc.DalecDataTwin(1999, 2000, '')
m = mc.DalecModel(d)
m.x = 200
gpp = []
ceff_list = np.arange(10., 100.)
for ceff in ceff_list:
gpp.append(m.acm(180., 60., ceff, d.acm))
ax.plot(ceff_list, gpp, color=palette[0])
ax.set_xlabel('Canopy efficiency parameter')
ax.set_ylabel('GPP (g C m-2 day-1)')
return ax, fig
def plot_gpp_sensitivity_lai():
sns.set_context(rc={'lines.linewidth': .8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
palette = sns.color_palette("colorblind", 11)
d = dc.DalecDataTwin(1999, 2000, '')
m = mc.DalecModel(d)
m.x = 200
gpp = []
cf_list = np.arange(60., 600.)
for cf in cf_list:
gpp.append(m.acm(cf, 60., d.edinburgh_mean[10], d.acm))
ax.plot(cf_list / 60., gpp, color=palette[0])
ax.set_xlabel('LAI')
ax.set_ylabel('GPP (g C m-2 day-1)')
return ax, fig
def plot_obs(ob, pvals, dC):
"""Plots a specified observation using obs eqn in obs module. Takes an
observation string, a dataClass (dC) and a start and finish point.
"""
sns.set_context(rc={'lines.linewidth': 0.8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
m = mc.DalecModel(dC)
mod_lst = m.mod_list(pvals)
obs_lst = m.oblist(ob, mod_lst)
palette = sns.color_palette("colorblind", 11)
ax.plot(dC.dates, obs_lst, color=palette[0])
ax.set_xlabel('Year')
ax.set_ylabel(ob)
plt.gcf().autofmt_xdate()
return ax, fig
def plot_phi(pvals, dC):
"""Plots phi_onset and phi_fall fns controlling leaf on and leaf off. Takes
a parameter set (pvals) and a dataClass (dC).
"""
sns.set_context(rc={'lines.linewidth': .8, 'lines.markersize': 6})
fig, ax = plt.subplots(nrows=1, ncols=1)
m = mc.DalecModel(dC)
mod_lst = m.mod_list(pvals)
phi_on = m.oblist('phi_onset', mod_lst)
phi_off = m.oblist('phi_fall', mod_lst)
palette = sns.color_palette("colorblind", 11)
ax.plot(dC.dates, phi_on, color=palette[0], label='phi_onset')
ax.plot(dC.dates, phi_off, color=palette[2], label='phi_fall')
plt.legend()
ax.set_xlabel('Year')
ax.set_ylabel('Rate of leaf on/leaf off')
plt.gcf().autofmt_xdate()
return ax, fig