def plot_figure_8():
"""Generates figure 8 of the paper. This figure shows a snapshot at solar midday of leaf temperature distribution
across three virtual canopies.
"""
training_color = {'gdc': 'blue', 'vsp': 'red', 'lyre': 'green'}
obs_date = datetime(
2009,
7,
29,
14,
00,
0,
)
fig, axs = pyplot.subplots(nrows=3,
ncols=2,
sharex=True,
figsize=(6.69, 6),
gridspec_kw={'width_ratios': [0.8, 0.2]})
for i, training in enumerate(('vsp', 'gdc', 'lyre')):
pth = example_pth / 'virtual_canopies' / training
g, _ = mtg_load(
str(pth) + '/output/', 'mtg%s' % obs_date.strftime('%Y%m%d%H%M%S'))
axs[i, 0] = display.property_map(g,
'Tlc',
color=training_color[training],
ax=axs[i, 0])
axs[i, 0].set(ylim=(0, 250))
axs[i, 0].legend([training], prop={'size': 11})
axs[i, 0].xaxis.labelpad = 5
axs[i, 1].boxplot(g.property('Tlc').values(), vert=False, sym='')
# some layout
[ax.set_xlabel('') for ax in axs[:2, 0]]
for ax in axs[:, 1]:
ax.tick_params(axis='y', which='both', labelleft='off')
ax.set_xticklabels(range(32, 42), rotation=90)
axs[2, 1].set_xlabel('$\mathregular{T_{leaf}\/[^{\circ}C]}$')
fig.tight_layout()
fig.savefig('fig_8.png', dpi=600.)
pyplot.close(fig)
def plot_figure_12():
"""Generates figure 12 of the paper. This figure compares, leaf-to-leaf, simulated to observed leaf and leaf-to-air
temperature.
"""
fig, axs = pyplot.subplots(ncols=3, figsize=(6.69, 2.7))
[ax.grid() for ax in axs]
daily_temp_obs = np.array([])
daily_temp_sim = np.array([])
daily_temp_air = np.array([])
for iax, training in enumerate(('vsp_ww_grapevine', 'vsp_ws_grapevine')):
pth = example_pth / training
# set dates ************************
beg_date = datetime(
2009,
7,
29,
00,
00,
0,
)
end_date = datetime(
2009,
8,
1,
23,
00,
0,
)
datet = pd.date_range(beg_date, end_date, freq='H')
# read observations
obs = pd.read_csv(pth / 'temp.obs',
sep=';',
decimal='.',
index_col='date')
obs.index = [datetime.strptime(s, "%d/%m/%Y %H:%M") for s in obs.index]
# read simulations
sims = pd.read_csv(pth / 'output' / 'time_series.output',
sep=';',
decimal='.',
index_col='time')
sims.index = [
datetime.strptime(s, "%Y-%m-%d %H:%M:%S") for s in sims.index
]
sims.index = pd.DatetimeIndex(sims.index)
# plot simulated temperature magnitude
med_sim = np.array([])
q_1_sim = np.array([])
q_3_sim = np.array([])
for date in datet:
g, _ = mtg_load(
str(pth) + '/output/', 'mtg%s' % date.strftime('%Y%m%d%H%M%S'))
leaf_temp_sim = g.property('Tlc').values()
q_1_sim = np.append(q_1_sim, min(leaf_temp_sim))
q_3_sim = np.append(q_3_sim, max(leaf_temp_sim))
med_sim = np.append(med_sim, np.median(leaf_temp_sim))
print(date)
# get observed temperature magnitude
med_obs = np.array([])
q1_obs = np.array([])
q3_obs = np.array([])
for row, date in enumerate(datet):
pos = date.toordinal() + date.hour / 24.
leaf_temp_obs = obs.loc[
date, ['Tleaf%d' % d for d in (2, 3, 4, 5, 6, 8, 9, 10)]]
leaf_temp_obs = leaf_temp_obs[~np.isnan(leaf_temp_obs)]
q1_obs = np.append(q1_obs, min(leaf_temp_obs))
q3_obs = np.append(q3_obs, max(leaf_temp_obs))
med_obs = np.append(med_obs, np.median(leaf_temp_obs))
print(date)
# read meteo data
meteo_df = pd.read_csv(pth / 'meteo.input',
sep=';',
decimal='.',
index_col='time')
meteo_df.index = pd.DatetimeIndex(meteo_df.index)
air_temperature = meteo_df.loc[datet, 'Tac']
daily_temp_obs = np.append(daily_temp_obs, q1_obs)
daily_temp_obs = np.append(daily_temp_obs, q3_obs)
daily_temp_sim = np.append(daily_temp_sim, q_1_sim)
daily_temp_sim = np.append(daily_temp_sim, q_3_sim)
daily_temp_air = np.append(daily_temp_air, air_temperature)
daily_temp_air = np.append(daily_temp_air, air_temperature)
# minimum temperature
axs[0].plot(q1_obs - air_temperature,
q_1_sim - air_temperature, ['b^', 'r^'][iax],
label=['WW', 'WD'][iax])
# maximum temperature
axs[0].plot(q3_obs - air_temperature,
q_3_sim - air_temperature, ['bo', 'ro'][iax],
label=['WW', 'WD'][iax])
axs[1].plot(q1_obs,
q_1_sim, ['b^', 'r^'][iax],
label=['WW', 'WD'][iax])
axs[1].plot(q3_obs,
q_3_sim, ['bo', 'ro'][iax],
label=['WW', 'WD'][iax])
axs[2].plot(q3_obs - q1_obs,
q_3_sim - q_1_sim, ['bs', 'rs'][iax],
label=['WW', 'WD'][iax])
# some layout
axs[0].plot((-8, 12), (-8, 12), 'k--')
axs[0].set(xlabel='$\mathregular{T_{leaf, obs}-T_{air}\/[^\circ C]}$',
ylabel='$\mathregular{T_{leaf, sim}-T_{air}\/[^\circ C]}$',
xlim=(-8, 12),
ylim=(-8, 12))
axs[1].plot((10, 45), (10, 45), 'k--')
axs[1].set(xlabel='$\mathregular{T_{leaf, obs}\/[^\circ C]}$',
ylabel='$\mathregular{T_{leaf, sim}\/[^\circ C]}$')
axs[1].xaxis.set_major_locator(ticker.MultipleLocator(20))
axs[2].plot((-2, 14), (-2, 14), 'k--')
axs[2].set(xlabel='$\mathregular{\Delta T_{leaf, obs}\/[^\circ C]}$',
ylabel='$\mathregular{\Delta T_{leaf, sim}\/[^\circ C]}$')
axs[2].xaxis.set_major_locator(ticker.IndexLocator(base=2, offset=2))
axs[2].xaxis.set_major_locator(ticker.MultipleLocator(5))
for i in range(3):
if i == 0:
x, y = daily_temp_obs - daily_temp_air, daily_temp_sim - daily_temp_air
elif i == 1:
x, y = daily_temp_obs, daily_temp_sim
else:
x = np.array([])
x = np.append(x, daily_temp_obs[96:2 * 96] - daily_temp_obs[:96])
x = np.append(
x,
daily_temp_obs[3 * 96:4 * 96] - daily_temp_obs[2 * 96:3 * 96])
y = np.array([])
y = np.append(y, daily_temp_sim[96:2 * 96] - daily_temp_sim[:96])
y = np.append(
y,
daily_temp_sim[3 * 96:4 * 96] - daily_temp_sim[2 * 96:3 * 96])
diff_y_x = (y - x)[~np.isnan(y - x)]
bias = (diff_y_x).mean()
rmse = np.sqrt((diff_y_x**2).mean())
axs[i].text(0.05,
-0.6,
'$\mathregular{MBE\/=\/%.3f}$' % bias,
transform=axs[i].transAxes,
fontdict={'size': 11})
axs[i].text(0.05,
-0.7,
'$\mathregular{RMSE\/=\/%.1f}$' % rmse,
transform=axs[i].transAxes,
fontdict={'size': 11})
axs[i].text(0.05,
0.875, ['(a)', '(b)', '(c)'][i],
transform=axs[i].transAxes,
fontdict={'size': 11})
fig.tight_layout()
fig.subplots_adjust(bottom=0.4)
fig.savefig('fig_12.png', dpi=600.)
pyplot.close(fig)
def plot_figure_11():
"""Generates figure 11 of the paper. This figure compares simulated to observed leaf temperatures.
"""
fig, axs = pyplot.subplots(nrows=2,
ncols=2,
sharey='row',
figsize=(6.69, 6))
for iax, training in enumerate(('vsp_ww_grapevine', 'vsp_ws_grapevine')):
pth = example_pth / training
axs[0, iax].grid()
axs[1, iax].grid()
# set dates ************************
beg_date = datetime(
2009,
7,
29,
00,
00,
0,
)
end_date = datetime(
2009,
8,
1,
23,
00,
0,
)
datet = pd.date_range(beg_date, end_date, freq='H')
# read observations
obs = pd.read_csv(pth / 'temp.obs',
sep=';',
decimal='.',
index_col='date')
obs.index = [datetime.strptime(s, "%d/%m/%Y %H:%M") for s in obs.index]
# read simulations
sims = pd.read_csv(pth / 'output' / 'time_series.output',
sep=';',
decimal='.',
index_col='time')
sims.index = [
datetime.strptime(s, "%Y-%m-%d %H:%M:%S") for s in sims.index
]
sims.index = pd.DatetimeIndex(sims.index)
# plot median simulated leaf temperature
axs[0, iax].plot(sims['Tleaf'],
'-k',
label='$\mathregular{T_{leaf}}$',
linewidth=1)
# plot simulated temperature magnitude
med_sim = np.array([])
q1_sim = np.array([])
q3_sim = np.array([])
for date in datet:
g, _ = mtg_load(
str(pth) + '/output/', 'mtg%s' % date.strftime('%Y%m%d%H%M%S'))
leaf_temp_sim = g.property('Tlc').values()
q1_sim = np.append(q1_sim, min(leaf_temp_sim))
q3_sim = np.append(q3_sim, max(leaf_temp_sim))
med_sim = np.append(med_sim, np.median(leaf_temp_sim))
print(date)
axs[0, iax].fill_between(datet,
q1_sim,
q3_sim,
color='red',
alpha=0.5,
zorder=0)
# plot observed temperature magnitude
med_obs = np.array([])
q1_obs = np.array([])
q3_obs = np.array([])
for row, date in enumerate(datet):
pos = date.toordinal() + date.hour / 24.
leaf_temp_obs = obs.loc[
date, ['Tleaf%d' % d for d in (2, 3, 4, 5, 6, 8, 9, 10)]]
leaf_temp_obs = leaf_temp_obs[~np.isnan(leaf_temp_obs)]
axs[0, iax].boxplot(leaf_temp_obs, positions=[pos], widths=0.01)
q1_obs = np.append(q1_obs, min(leaf_temp_obs))
q3_obs = np.append(q3_obs, max(leaf_temp_obs))
med_obs = np.append(med_obs, np.median(leaf_temp_obs))
print(date)
# compare obs to sim temperature on 1:1 plot
axs[1, iax].plot((10, 50), (10, 50), 'k--')
axs[1, iax].errorbar(x=med_obs,
y=med_sim,
xerr=(np.nan_to_num(med_obs - q1_obs),
np.nan_to_num(q3_obs - med_obs)),
yerr=(np.nan_to_num(med_sim - q1_sim),
np.nan_to_num(q3_sim - med_sim)),
fmt='ro',
ecolor='0.5',
capthick=1)
# plot MBE and RMSE results on it
diff_obs_sim = (med_sim - med_obs)[~np.isnan(med_sim - med_obs)]
bias = diff_obs_sim.mean()
rmse = np.sqrt(diff_obs_sim**2).mean()
axs[1, iax].text(0.50,
0.20,
'$\mathregular{MBE\/=\/%.3f}$' % bias,
transform=axs[1, iax].transAxes,
fontdict={'size': 11})
axs[1, iax].text(0.50,
0.10,
'$\mathregular{RMSE\/=\/%.1f}$' % rmse,
transform=axs[1, iax].transAxes,
fontdict={'size': 11})
# some layout
axs[0, 0].set_ylabel('$\mathregular{Temperature\/[^{\circ}C]}$')
axs[1, 0].set_ylabel('$\mathregular{T_{leaf, sim}\/[^{\circ}C]}$')
for ax_upper, ax_lower in zip(axs[0, :], axs[1, :]):
ax_upper.set(xlim=(beg_date, end_date), ylim=(5, 50), xlabel='')
ax_upper.set_xticklabels(datet, rotation=45)
ax_upper.xaxis.set_major_locator(dates.DayLocator())
ax_upper.xaxis.set_major_formatter(dates.DateFormatter('%-d %b'))
ax_lower.set(xlim=(10, 50),
ylim=(10, 50),
xlabel='$\mathregular{T_{leaf, obs}\/[^{\circ}C]}$')
for iax, ax in enumerate(axs.flatten()):
ax.text(0.05,
0.875, ('(a)', '(c)', '(b)', '(d)')[iax],
transform=ax.transAxes,
fontdict={'size': 11})
fig.tight_layout()
fig.savefig('fig_11.png', dpi=600.)
pyplot.close(fig)
def plot_figure_10():
"""Generates figure 10 of the paper. This figure compares, simulated to observed stomatal conductance rates.
"""
beg_date = datetime(
2012,
8,
1,
00,
00,
0,
)
end_date = datetime(
2012,
8,
3,
00,
00,
0,
)
datet = pd.date_range(beg_date, end_date, freq='D')
fig, axs = pyplot.subplots(nrows=3,
ncols=3,
sharex='all',
sharey='all',
figsize=(6.69, 6))
for it, training in enumerate(
('gdc_can1_grapevine', 'gdc_can2_grapevine', 'gdc_can3_grapevine')):
pth = example_pth / training
for i_day, date in enumerate(datet):
ax = axs[it, i_day]
obs_date = date + pd.Timedelta(hours=13)
g, _ = mtg_load(
str(pth) + '/output/',
'mtg%s' % obs_date.strftime('%Y%m%d%H%M%S'))
ax = display.property_map(g,
'gs',
ax=ax,
prop2='Eabs',
color='grey',
colormap='autumn',
add_color_bar=False)
obs_df = pd.read_csv(pth / 'var.obs', sep=';', index_col='date')
obs_df.index = pd.DatetimeIndex(obs_df.index)
gs_leaf = obs_df.loc[date, 'gs'] / 1000.
ax.add_patch(
patches.Rectangle((max(gs_leaf), 50),
min(gs_leaf) - max(gs_leaf),
250,
color='0.8',
zorder=-1))
if i_day == 0:
ax.text(0.05,
0.075,
'Canopy%d' % (it + 1),
transform=ax.transAxes,
fontdict={'size': 11})
if it == 0:
ax.text(0.675,
0.825,
obs_date.strftime('%-d %b'),
transform=ax.transAxes,
fontdict={'size': 11})
[axi.set_xticklabels(axi.get_xticks(), rotation=90) for axi in axs[2]]
for ax, axi in enumerate(axs):
if ax < 2:
[axi[i_day].set_xlabel('') for i_day in range(3)]
[axi[i_day].set_ylabel('') for i_day in (1, 2)]
[axi[i_day].legend_.remove() for i_day in range(3)]
fig.tight_layout()
fig.subplots_adjust(bottom=0.3)
cbar_ax = fig.add_axes([0.395, 0.1, 0.30, 0.04])
norm = colors.Normalize(0, vmax=2000)
cbar = colorbar.ColorbarBase(cbar_ax,
cmap='autumn',
orientation='horizontal',
norm=norm)
cbar.ax.set_xticklabels(cbar.ax.get_xticklabels(), rotation=90)
cbar.set_label('$\mathregular{PPFD\/[\mu mol\/m^{-2}_{leaf}\/s^{-1}]}$',
labelpad=-20,
x=-0.4)
fig.savefig('fig_10.png', dpi=600.)
pyplot.close(fig)