par = 1500.0
wind = 2.5
pressure = 101325.0
vpd = 1.5
tair = 25
Ca = np.linspace(350, 700)
CM = CoupledModel(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
(gs, et, an, Cs, Ci) = get_values2(vpd, Ca, tair, par, pressure, CM)
from apsim_co2_response import apsim_co2_response
co2_mod = apsim_co2_response(tair, Ca)
apsim_an = an * co2_mod
def main(fname):
wtc = read_file(fname)
# create hourly values for subsequent averaging of fluxes
wtc["DateTime_hr"] = wtc["DateTime_hr"].apply(\
lambda x: pd.to_datetime(x).round('30min'))
wtc_m = wtc.groupby(
['DateTime_hr', 'chamber', 'T_treatment', 'HWtrt', 'combotrt'],
as_index=False).mean()
wtc_m = wtc_m.set_index('DateTime_hr')
st = pd.datetime.strptime('2016-10-20 00:00:00', '%Y-%m-%d %H:%M:%S')
en = pd.datetime.strptime('2016-11-11 20:00:00', '%Y-%m-%d %H:%M:%S')
wtc_m = wtc_m[(wtc_m.index >= st) & (wtc_m.index <= en)]
df_ct = wtc_m[(wtc_m["HWtrt"] == "C") & (wtc_m["PAR"] > 10.0)]
df_hw = wtc_m[(wtc_m["HWtrt"] == "HW") & (wtc_m["PAR"] > 10.0)]
PARlimit = 600
df_ct = df_ct[df_ct["PAR"] > PARlimit]
df_hw = df_hw[df_hw["PAR"] > PARlimit]
# Parameters
# A stuff
Vcmax25 = 34.0
Jmax25 = 60.0
Rd25 = 0.92
Eaj = 21640.
Eav = 51780.
deltaSj = 633.0
deltaSv = 640.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 1.92
# Misc stuff
leaf_width = 0.01
SW_abs = 0.86 # absorptance to short_wave rad [0,1], typically 0.4-0.6
lat = -33.611111
lon = 150.740694
# variables though obviously fixed here.
wind = 8.0
pressure = 100.0 * c.KPA_2_PA
g0 = 0.003
g1 = 2.9
Ca = 400.
D0 = 1.5 # Not used
gamma = 0.0 # Not used
C = CoupledModel(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
Et_hw = []
An_hw = []
for i in range(len(df_hw)):
(An, gsw, et, LE) = C.main(df_hw.Tair_al[i],
df_hw.PAR[i],
df_hw.VPD[i],
wind,
pressure,
Ca,
rnet=None)
Et_hw.append(et * c.MOL_2_MMOL) # mmol m-2 s-1
An_hw.append(An) # umol m-2 s-1
Et_hw2 = []
An_hw2 = []
for i in range(len(df_hw)):
ea = max(
0.0,
calc_esat(df_hw.Tair_al[i], pressure) -
(df_hw.VPD[i] * c.KPA_2_PA))
rnet = calc_net_radiation(df_hw.index[i].dayofyear,
df_hw.index[i].hour, lat, lon,
df_hw.PAR[i] * c.PAR_2_SW, df_hw.Tair_al[i],
ea)
(An, gsw, et, LE) = C.main(df_hw.Tair_al[i],
df_hw.PAR[i],
df_hw.VPD[i],
wind,
pressure,
Ca,
rnet=rnet)
Et_hw2.append(et * c.MOL_2_MMOL) # mmol m-2 s-1
An_hw2.append(An) # umol m-2 s-1
width = 9
height = width / 1.618
fig = plt.figure(figsize=(width, height))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.05)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 20
plt.rcParams['font.size'] = 18
plt.rcParams['legend.fontsize'] = 18
plt.rcParams['xtick.labelsize'] = 18
plt.rcParams['ytick.labelsize'] = 18
ax1 = fig.add_subplot(111)
x = df_hw.TargTempC_Avg
y = df_hw.Trans
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax1.scatter(x,
y,
c=z,
s=25,
edgecolor='',
cmap='Reds',
alpha=0.7,
label="HW")
ax1.scatter(x, Et_hw, color='black', s=5, alpha=0.7, label="Model")
ax1.scatter(x,
Et_hw2,
color='green',
s=5,
alpha=0.7,
label="Varying R$_{net}$")
ax1.legend(scatterpoints=1,
loc="upper right",
frameon=False,
handletextpad=0.1)
legend = ax1.get_legend()
legend.legendHandles[0].set_color("red")
legend.legendHandles[0]._sizes = [60]
legend.legendHandles[1]._sizes = [60]
legend.legendHandles[2]._sizes = [60]
ax1.locator_params(nbins=4, axis='x')
ax1.locator_params(nbins=4, axis='y')
ax1.set_ylim(0, 4)
ax1.set_xlim(15, 50)
ax1.set_ylabel('E$_{canopy}$ (mmol m$^{-2}$ s$^{-1}$)')
ax1.set_xlabel('Canopy temperature ($^\circ$C)')
#fig.savefig("plots/increasing_g0.pdf", bbox_inches='tight',
# pad_inches=0.1)
fig.savefig("plots/varying_rnet.png",
dpi=300,
bbox_inches='tight',
pad_inches=0.1)
# variables though obviously fixed here.
par = 1500.0
wind = 2.5
pressure = 101325.0
tair = 25.0
Ca = 380.
vpd = np.linspace(0.14, 9, 100)
g0 = 0.0
g1 = 9.0
D0 = 1.5
gamma = 0.0
CL = CoupledModel(g0, g1, D0, gamma, Vcmax25, Jmax25, Rd25,
Eaj, Eav,deltaSj, deltaSv, Hdv, Hdj, Q10, leaf_width,
SW_abs, alpha=alpha, gs_model="leuning")
g0 = 0.0
g1 = 3.0
CM = CoupledModel(g0, g1, D0, gamma, Vcmax25, Jmax25, Rd25,
Eaj, Eav,deltaSj, deltaSv, Hdv, Hdj, Q10, leaf_width,
SW_abs, alpha=alpha, gs_model="medlyn")
(gs, et, an) = get_values(vpd, Ca, tair, par, pressure, CM)
ax1.plot(vpd, et, "b-", label="MED")
ax3.plot(vpd, gs, "b-", label="MED")
(gs, et, an) = get_values(vpd, Ca, tair, par, pressure, CL)
ax1.plot(vpd, et, "r-", label="LEU")
# The higher absorptivityof canopies arises because of multiple reflections
# among leaves in a canopy and depends on the architecture of the canopy.
SW_abs = 0.8 # use canopy absorptance of solar radiation
# variables though obviously fixed here.
pressure = 101325.0
D0 = None
gamma = None
g0 = 0.0
g1 = 1.72
df = pd.read_csv("/Users/mdekauwe/Downloads/Rocca1_met_and_plant_data_drought_2003.csv")
CM = CoupledModel(g0, g1, D0, gamma, Vcmax25, Jmax25, Rd25,
Eaj, Eav,deltaSj, deltaSv, Hdv, Hdj, Q10, leaf_width,
SW_abs, gs_model="medlyn")
"""
gs_store = []
et_store = []
An_store = []
et_conv = c.MOL_WATER_2_G_WATER * c.G_TO_KG * c.SEC_TO_DAY
an_conv = c.UMOL_TO_MOL * c.MOL_C_TO_GRAMS_C * c.SEC_TO_DAY
Anx = 0.0
etx = 0.0
count = 0
for i in range(len(df)):
def main(fname):
wtc = read_file(fname)
# create hourly values for subsequent averaging of fluxes
wtc["DateTime_hr"] = wtc["DateTime_hr"].apply(\
lambda x: pd.to_datetime(x).round('30min'))
wtc_m = wtc.groupby(['DateTime_hr', 'chamber','T_treatment',
'HWtrt','combotrt'], as_index=False).mean()
wtc_m = wtc_m.set_index('DateTime_hr')
st = pd.datetime.strptime('2016-10-20 00:00:00', '%Y-%m-%d %H:%M:%S')
en = pd.datetime.strptime('2016-11-11 20:00:00', '%Y-%m-%d %H:%M:%S')
wtc_m = wtc_m[(wtc_m.index >= st) & (wtc_m.index <= en)]
df_ct = wtc_m[(wtc_m["HWtrt"] == "C") & (wtc_m["PAR"] > 10.0)]
df_hw = wtc_m[(wtc_m["HWtrt"] == "HW") & (wtc_m["PAR"] > 10.0)]
PARlimit = 600
df_ct = df_ct[df_ct["PAR"] > PARlimit]
df_hw = df_hw[df_hw["PAR"] > PARlimit]
# Parameters
# A stuff
Vcmax25 = 34.0
Jmax25 = 60.0
Rd25 = 0.92
Eaj = 21640.
Eav = 51780.
deltaSj = 633.0
deltaSv = 640.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 1.92
# Misc stuff
leaf_width = 0.01
SW_abs = 0.86 # absorptance to short_wave rad [0,1], typically 0.4-0.6
# variables though obviously fixed here.
wind = 8.0
pressure = 101.0 * c.KPA_2_PA
g0 = 0.003
g1 = 2.9
Ca = 400.
D0 = 1.5 # Not used
gamma = 0.0 # Not used
C = CoupledModel(g0, g1, D0, gamma, Vcmax25, Jmax25, Rd25,
Eaj, Eav,deltaSj, deltaSv, Hdv, Hdj, Q10, leaf_width,
SW_abs, gs_model="medlyn")
#(An, gsw, et, LE) = C.main(45., 2000.0, 4.0,
# wind, pressure, Ca)
#print(An, gsw, et, LE)
#sys.exit()
#"""
Et_ct = []
An_ct = []
Et_hw = []
An_hw = []
for i in range(len(df_ct)):
(An, gsw, et, LE) = C.main(df_ct.Tair_al[i], df_ct.PAR[i], df_ct.VPD[i],
wind, pressure, Ca)
Et_ct.append(et* c.MOL_2_MMOL) # mmol m-2 s-1
An_ct.append(An) # umol m-2 s-1
#"""
Et_hw = []
An_hw = []
for i in range(len(df_hw)):
(An, gsw, et, LE) = C.main(df_hw.Tair_al[i], df_hw.PAR[i], df_hw.VPD[i],
wind, pressure, Ca)
Et_hw.append(et * c.MOL_2_MMOL) # mmol m-2 s-1
An_hw.append(An) # umol m-2 s-1
# roughly paper size
#width = 8.0
#height = 8 #width / 1.618
width = 10.0
height = width / 1.618
fig = plt.figure(figsize=(width, height))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.05)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['font.size'] = 14
plt.rcParams['legend.fontsize'] = 14
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
x = df_ct.TargTempC_Avg
y = df_ct.Photo
dummy = np.ones(len(np.asarray(y.values))) * np.nan
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax1.scatter(x, y, c=z, s=25, edgecolor='', cmap='Blues', alpha=0.7,
label="CT")
ax1.scatter(x, dummy, s=25, edgecolor='', alpha=0.7, label="HW")
ax1.scatter(x, An_ct, color='black', s=5, alpha=0.7, label="Model")
ax1.legend(scatterpoints=1, loc="best", frameon=False, handletextpad=0.1)
legend = ax1.get_legend()
legend.legendHandles[0].set_color("blue")
legend.legendHandles[1].set_color("red")
legend.legendHandles[2].set_color("black")
legend.legendHandles[0]._sizes = [60]
legend.legendHandles[1]._sizes = [60]
legend.legendHandles[2]._sizes = [60]
x = df_hw.TargTempC_Avg
y = df_hw.Photo
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax2.scatter(x, y, c=z, s=25, edgecolor='', cmap='Reds', alpha=0.7)
ax2.scatter(x, An_hw, color='black', s=5, alpha=0.7)
x = df_ct.TargTempC_Avg
y = df_ct.Trans
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax3.scatter(x, y, c=z, s=25, edgecolor='', cmap='Blues', alpha=0.7)
ax3.scatter(x, Et_ct, color='black', s=5, alpha=0.7)
x = df_hw.TargTempC_Avg
y = df_hw.Trans
x = np.nan_to_num(x)
y = np.nan_to_num(y)
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax4.scatter(x, y, c=z, s=25, edgecolor='', cmap='Reds', alpha=0.7)
ax4.scatter(x, Et_hw, color='black', s=5, alpha=0.7)
ax1.set_ylim(-1, 15)
ax2.set_ylim(-1, 15)
ax3.set_ylim(0, 4)
ax4.set_ylim(0, 4)
ax1.set_xlim(15, 50)
ax2.set_xlim(15, 50)
ax3.set_xlim(15, 50)
ax4.set_xlim(15, 50)
ax1.locator_params(nbins=4, axis='y')
ax2.locator_params(nbins=4, axis='y')
ax2.locator_params(nbins=4, axis='y')
ax3.locator_params(nbins=4, axis='y')
ax1.locator_params(nbins=4, axis='x')
ax2.locator_params(nbins=4, axis='x')
ax2.locator_params(nbins=4, axis='x')
ax4.locator_params(nbins=4, axis='x')
ax1.set_ylabel(u'A$_{canopy}$ (μmol m$^{-2}$ s$^{-1}$)')
ax3.set_ylabel('E$_{canopy}$ (mmol m$^{-2}$ s$^{-1}$)')
ax3.set_xlabel('Canopy temperature ($^\circ$C)', position=(1.0, 0.5))
ax1.get_yaxis().set_label_coords(-0.1,0.5)
ax3.get_yaxis().set_label_coords(-0.1,0.5)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax2.get_yticklabels(), visible=False)
plt.setp(ax4.get_yticklabels(), visible=False)
#fig.savefig("plots/exp_plus_modellng.pdf", bbox_inches='tight',
# pad_inches=0.1)
fig.savefig("plots/exp_plus_modellng.png", dpi=300, bbox_inches='tight',
pad_inches=0.1)