def main():
lat = -23.575001
lon = 152.524994
doy = 180.0
#
## Met data ...
#
(par, tair, vpd) = get_met_data(lat, lon, doy)
wind = 2.5
pressure = 101325.0
Ca = 400.0
lat = 61.8474
lon = 24.2948
fpath = "/Users/mdekauwe/Downloads/"
fname = "Hyytiala_met_and_plant_data_drought_2003.csv"
fn = os.path.join(fpath, fname)
df = pd.read_csv(fn, skiprows=range(1, 2))
dox = int(doy) * 48
par = df.PPFD.iloc[dox:dox + 48].values
tair = df.Tair.iloc[dox:dox + 48].values
vpd = df.VPD.iloc[dox:dox + 48].values
wind = 2.5
pressure = 101325.0
Ca = 400.0
LAI = df.LAI[dox]
#
## Parameters
#
g0 = 1E-09
g1 = df.g1[0]
D0 = 1.5 # kpa
Vcmax25 = df.Vmax25[0]
#g0 = 0.001
#g1 = 2.0
#D0 = 1.5 # kpa
#Vcmax25 = 60.0
Jmax25 = Vcmax25 * 1.67
Rd25 = 2.0
Eaj = 30000.0
Eav = 60000.0
deltaSj = 650.0
deltaSv = 650.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 2.0
gamma = 0.0
leaf_width = 0.02
LAI = 1.5
# Cambell & Norman, 11.5, pg 178
# The solar absorptivities of leaves (-0.5) from Table 11.4 (Gates, 1980)
# with canopies (~0.8) from Table 11.2 reveals a surprising difference.
# 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
##
### Run Big-leaf
##
B = BigLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_bl = np.zeros(48)
gsw_bl = np.zeros(48)
et_bl = np.zeros(48)
tcan_bl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_bl[i], gsw_bl[i], et_bl[i],
tcan_bl[i]) = B.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf
##
T = TwoLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_tl = np.zeros(48)
gsw_tl = np.zeros(48)
et_tl = np.zeros(48)
tcan_tl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_tl[i], gsw_tl[i], et_tl[i],
tcan_tl[i]) = T.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
fig = plt.figure(figsize=(16, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.2)
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'] = 10
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.plot(np.arange(48) / 2., An_bl, label="Big leaf")
ax1.plot(np.arange(48) / 2., An_tl, label="Two leaf")
ax1.legend(numpoints=1, loc="best")
ax1.set_ylabel("$A_{\mathrm{n}}$ ($\mathrm{\mu}$mol m$^{-2}$ s$^{-1}$)")
ax2.plot(np.arange(48) / 2., et_bl * c.MOL_TO_MMOL, label="Big leaf")
ax2.plot(np.arange(48) / 2., et_tl * c.MOL_TO_MMOL, label="Two leaf")
ax2.set_ylabel("E (mmol m$^{-2}$ s$^{-1}$)")
ax2.set_xlabel("Hour of day")
ax3.plot(np.arange(48) / 2., tcan_bl, label="Tcanopy$_{1leaf}$")
ax3.plot(np.arange(48) / 2., tcan_tl, label="Tcanopy$_{2leaf}$")
ax3.plot(np.arange(48) / 2., tair, label="Tair")
ax3.set_ylabel("Temperature (deg C)")
ax3.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
plt.show()
fig.savefig("/Users/%s/Desktop/A_E_Tcan.pdf" % (os.getlogin()),
bbox_inches='tight',
pad_inches=0.1)
def main(met_fn, flx_fn, cab_fn, year_to_run, site):
#(site)
fpath = "/mnt/c/Users/Manon/Documents/CABLE_runs/"
fname = "%s_met_and_plant_data_drought_2003.csv" % (site)
fn = os.path.join(fpath, fname)
df = pd.read_csv(fn, skiprows=range(1, 2))
(df_cab) = read_cable_file(cab_fn)
df_cab = df_cab[df_cab.index.year == year_to_run]
(df_flx) = read_obs_file(flx_fn)
df_flx = df_flx[df_flx.index.year == year_to_run]
(df_met, lat, lon) = read_met_file(met_fn)
df_met = df_met[df_met.index.year == year_to_run]
par = df_met.PAR
tair = df_met.Tair
vpd = df_met.vpd
wind = df_met.Wind
pressure = df_met.PSurf
Ca = 400.0
#LAI = 3.0
LAI = df.LAI
#
## Parameters
#
g0 = 1E-09
g1 = df.g1[0] #4.12
D0 = 1.5 # kpa
Vcmax25 = df.Vmax25[0]
Jmax25 = Vcmax25 * 1.67
Rd25 = 2.0
Eaj = 30000.0
Eav = 60000.0
deltaSj = 650.0
deltaSv = 650.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 2.0
leaf_width = 0.02
gamma = 0 # doesn't do anything
# Cambell & Norman, 11.5, pg 178
# The solar absorptivities of leaves (-0.5) from Table 11.4 (Gates, 1980)
# with canopies (~0.8) from Table 11.2 reveals a surprising difference.
# 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
##
### Run 2-leaf
##
T = TwoLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
ndays = int(len(df_met) / 48)
Anc_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
LAI_store = np.zeros(ndays)
An_store = np.zeros(ndays)
An_sun_store = np.zeros(ndays)
An_sha_store = np.zeros(ndays)
par_sun_store = np.zeros(ndays)
par_sha_store = np.zeros(ndays)
lai_sun_store = np.zeros(ndays)
lai_sha_store = np.zeros(ndays)
E_store = np.zeros(ndays)
Anc_store = np.zeros(ndays)
Anc_sun_store = np.zeros(ndays)
Anc_sha_store = np.zeros(ndays)
parc_sun_store = np.zeros(ndays)
parc_sha_store = np.zeros(ndays)
laic_sun_store = np.zeros(ndays)
laic_sha_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
gpp_obs = np.zeros(ndays)
e_obs = np.zeros(ndays)
lai_obs = np.zeros(ndays)
et_conv = c.MOL_WATER_2_G_WATER * c.G_TO_KG * 1800.
an_conv = c.UMOL_TO_MOL * c.MOL_C_TO_GRAMS_C * 1800.
cnt = 0
for doy in range(ndays):
Anx = 0.0
Ex = 0.0
anxsun = 0.0
anxsha = 0.0
parxsun = 0.0
parxsha = 0.0
laixsun = 0.0
laixsha = 0.0
Anc = 0.0
Ec = 0.0
Acabx = 0.0
Ecabx = 0.0
Lcabx = 0.0
ancsun = 0.0
ancsha = 0.0
parcsun = 0.0
parcsha = 0.0
laicsun = 0.0
laicsha = 0.0
Aobsx = 0.0
Eobsx = 0.0
Lobsx = 0.0
for i in range(48):
if doy < 364:
laix = LAI[cnt]
else:
laix = LAI[cnt - 1]
#if doy == 164:
# print(laix)
#elif doy== 165:
# sys.exit()
hod = float(i) / 2. + 1800. / 3600. / 2.
(An, gsw, et, tcan, an_sun, an_sha, par_sun, par_sha, lai_sun,
lai_sha) = T.main(tair[cnt], par[cnt], vpd[cnt], wind[cnt],
pressure[cnt], Ca, doy + 1, hod, lat, lon, laix)
lambda_et = (c.H2OLV0 - 2.365E3 * tair[cnt]) * c.H2OMW
Anx += An * an_conv
anxsun += an_sun * an_conv
anxsha += an_sha * an_conv
parxsun += par_sun / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parxsha += par_sha / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laixsun += lai_sun
laixsha += lai_sha
Lobsx += laix
Ex += et * et_conv
#if not np.isclose(df_cab.LAI_shaded[cnt], lai_sha):
# print(cnt, df_cab.LAI_shaded[cnt], lai_sha)
# print(df_cab.LAI_shaded[cnt] + df_cab.LAI_sunlit[cnt], lai_sha + lai_sun)
# print(laix - (df_cab.LAI_shaded[cnt] + df_cab.LAI_sunlit[cnt]), laix - (lai_sha + lai_sun))
Anc += df_cab.GPP[cnt] * an_conv
ancsun += df_cab.GPP_sunlit[cnt] * an_conv
ancsha += df_cab.GPP_shaded[cnt] * an_conv
parcsun += df_cab.PAR_sunlit[cnt] / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parcsha += df_cab.PAR_shaded[
cnt] / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laicsun += df_cab.LAI_sunlit[cnt]
laicsha += df_cab.LAI_shaded[cnt]
Lcabx += df_cab.LAI[cnt]
Ec += et * et_conv
Aobsx += df_flx.GPP[cnt] * an_conv
Eobsx += df_flx.Qle[cnt] / lambda_et * et_conv
if cnt < LAI.index[-1]:
cnt += 1
else:
break
An_store[doy] = Anx
An_sun_store[doy] = anxsun
An_sha_store[doy] = anxsha
par_sun_store[doy] = parxsun
par_sha_store[doy] = parxsha
lai_sun_store[doy] = laixsun / 48.
lai_sha_store[doy] = laixsha / 48.
E_store[doy] = Ex
Anc_store[doy] = Anc
Anc_sun_store[doy] = ancsun
Anc_sha_store[doy] = ancsha
parc_sun_store[doy] = parcsun
parc_sha_store[doy] = parcsha
laic_sun_store[doy] = laicsun / 48.
laic_sha_store[doy] = laicsha / 48.
Ec_store[doy] = Ec
gpp_obs[doy] = Aobsx
e_obs[doy] = Eobsx
lai_obs[doy] = Lobsx / 48
#print(lai_sha_store - laic_sha_store)
window = 3
An_store = moving_average(An_store, n=window)
An_sun_store = moving_average(An_sun_store, n=window)
An_sha_store = moving_average(An_sha_store, n=window)
par_sun_store = moving_average(par_sun_store, n=window)
par_sha_store = moving_average(par_sha_store, n=window)
lai_sun_store = moving_average(lai_sun_store, n=window)
lai_sha_store = moving_average(lai_sha_store, n=window)
E_store = moving_average(E_store, n=window)
Anc_store = moving_average(Anc_store, n=window)
Anc_sun_store = moving_average(Anc_sun_store, n=window)
Anc_sha_store = moving_average(Anc_sha_store, n=window)
parc_sun_store = moving_average(parc_sun_store, n=window)
parc_sha_store = moving_average(parc_sha_store, n=window)
laic_sun_store = moving_average(laic_sun_store, n=window)
laic_sha_store = moving_average(laic_sha_store, n=window)
Ec_store = moving_average(Ec_store, n=window)
gpp_obs = moving_average(gpp_obs, n=window)
e_obs = moving_average(e_obs, n=window)
lai_obs = moving_average(lai_obs, n=window)
fig = plt.figure(figsize=(14, 8))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.1)
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(321)
ax2 = fig.add_subplot(322)
ax3 = fig.add_subplot(323)
ax4 = fig.add_subplot(324)
ax5 = fig.add_subplot(325)
ax6 = fig.add_subplot(326)
#ax1.plot(An_store, label="2-leaf - Sun")
#ax1.plot(Anc_store, label="2-leaf - CABLE")
ax1.plot(An_sun_store, label="Me")
ax1.plot(Anc_sun_store, label="CABLE")
ax1.set_title("Sun")
ax2.set_title("Shade")
ax1.set_ylabel("GPP (g C m$^{-2}$ d$^{-1}$)")
ax1.legend(numpoints=1, loc="best")
ax2.plot(An_sha_store)
ax2.plot(Anc_sha_store)
ax3.plot(par_sun_store)
ax3.plot(parc_sun_store)
ax3.set_ylabel("PAR (MJ m$^{-2}$ d$^{-1}$)")
ax4.plot(par_sha_store)
ax4.plot(parc_sha_store)
ax5.plot(lai_sun_store)
ax5.plot(laic_sun_store)
ax5.set_ylabel("LAI (m$^{2}$ m$^{-2}$)")
ax6.plot(lai_sha_store)
ax6.plot(laic_sha_store)
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
ax3.locator_params(nbins=6, axis="y")
ax4.locator_params(nbins=6, axis="y")
ax5.locator_params(nbins=6, axis="y")
ax6.locator_params(nbins=6, axis="y")
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax3.get_xticklabels(), visible=False)
plt.setp(ax4.get_xticklabels(), visible=False)
plt.show()
def main(met_fn, flx_fn, cab_fn, year_to_run, site):
fpath = "/Users/mdekauwe/Downloads/"
fname = "%s_met_and_plant_data_drought_2003.csv" % (site)
fn = os.path.join(fpath, fname)
df = pd.read_csv(fn, skiprows=range(1, 2))
(df_cab, p.lat, p.lon) = read_cable_file(cab_fn)
df_cab = df_cab[df_cab.index.year == year_to_run]
(df_flx) = read_obs_file(flx_fn)
df_flx = df_flx[df_flx.index.year == year_to_run]
(df_met, lat, lon) = read_met_file(met_fn)
df_met = df_met[df_met.index.year == year_to_run]
par = df_met.PAR
tair = df_met.Tair
vpd = df_met.vpd
wind = df_met.Wind
pressure = df_met.PSurf
Ca = 400.0
#LAI = 3.0
LAI = df.LAI
#
## Parameters
#
p.g0 = 1E-09
p.g1 = df.g1[0] #4.12
p.D0 = 1.5 # kpa
p.Vcmax25 = df.Vmax25[0]
p.Jmax25 = p.Vcmax25 * 1.67
p.Rd25 = None # Vcmax * 0.015
#p.Eaj = 30000.0
#p.Eav = 60000.0
#p.deltaSj = 650.0
#p.deltaSv = 650.0
#p.Hdv = 200000.0
#p.Hdj = 200000.0
p.Eaj = 50300.0
p.Eav = 73637.0
p.deltaSj = 495.0
p.deltaSv = 486.0
p.Hdv = 149252.0
p.Hdj = 152044.0
##
### Run 2-leaf
##
T = TwoLeaf(p, gs_model="medlyn")
ndays = int(len(df_met) / 48)
Anc_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
LAI_store = np.zeros(ndays)
An_store = np.zeros(ndays)
An_sun_store = np.zeros(ndays)
An_sha_store = np.zeros(ndays)
par_sun_store = np.zeros(ndays)
par_sha_store = np.zeros(ndays)
lai_sun_store = np.zeros(ndays)
lai_sha_store = np.zeros(ndays)
E_store = np.zeros(ndays)
Anc_store = np.zeros(ndays)
Anc_sun_store = np.zeros(ndays)
Anc_sha_store = np.zeros(ndays)
parc_sun_store = np.zeros(ndays)
parc_sha_store = np.zeros(ndays)
laic_sun_store = np.zeros(ndays)
laic_sha_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
gpp_obs = np.zeros(ndays)
e_obs = np.zeros(ndays)
lai_obs = np.zeros(ndays)
et_conv = c.MOL_WATER_2_G_WATER * c.G_TO_KG * 1800.
an_conv = c.UMOL_TO_MOL * c.MOL_C_TO_GRAMS_C * 1800.
cnt = 0
for doy in range(ndays - 1):
Anx = 0.0
Ex = 0.0
anxsun = 0.0
anxsha = 0.0
parxsun = 0.0
parxsha = 0.0
laixsun = 0.0
laixsha = 0.0
Anc = 0.0
Ec = 0.0
Acabx = 0.0
Ecabx = 0.0
Lcabx = 0.0
ancsun = 0.0
ancsha = 0.0
parcsun = 0.0
parcsha = 0.0
laicsun = 0.0
laicsha = 0.0
Aobsx = 0.0
Eobsx = 0.0
Lobsx = 0.0
for i in range(48):
if doy < 364:
laix = LAI[cnt]
else:
#print(cnt, len(LAI), LAI[17518])
laix = LAI[17518]
hod = float(i) / 2. + 1800. / 3600. / 2.
(An, et, Tcan, apar,
lai_leaf) = T.main(tair[cnt], par[cnt], vpd[cnt], wind[cnt],
pressure[cnt], Ca, doy + 1, hod, laix)
lambda_et = (c.H2OLV0 - 2.365E3 * tair[cnt]) * c.H2OMW
Anx += np.sum(An) * an_conv
anxsun += An[c.SUNLIT] * an_conv
anxsha += An[c.SHADED] * an_conv
parxsun += apar[c.SUNLIT] / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parxsha += apar[
c.SHADED] / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laixsun += lai_leaf[c.SUNLIT]
laixsha += lai_leaf[c.SHADED]
Lobsx += laix
Ex += np.sum(et) * et_conv
Anc += df_cab.GPP[cnt] * an_conv
ancsun += df_cab.GPP_sunlit[cnt] * an_conv
ancsha += df_cab.GPP_shaded[cnt] * an_conv
parcsun += df_cab.PAR_sunlit[cnt] / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parcsha += df_cab.PAR_shaded[
cnt] / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laicsun += df_cab.LAI_sunlit[cnt]
laicsha += df_cab.LAI_shaded[cnt]
Lcabx += df_cab.LAI[cnt]
#Ec += et * et_conv
Aobsx += df_flx.GPP[cnt] * an_conv
Eobsx += df_flx.Qle[cnt] / lambda_et * et_conv
cnt += 1
An_store[doy] = Anx
An_sun_store[doy] = anxsun
An_sha_store[doy] = anxsha
par_sun_store[doy] = parxsun
par_sha_store[doy] = parxsha
lai_sun_store[doy] = laixsun / 48.
lai_sha_store[doy] = laixsha / 48.
E_store[doy] = Ex
Anc_store[doy] = Anc
Anc_sun_store[doy] = ancsun
Anc_sha_store[doy] = ancsha
parc_sun_store[doy] = parcsun
parc_sha_store[doy] = parcsha
laic_sun_store[doy] = laicsun / 48.
laic_sha_store[doy] = laicsha / 48.
Ec_store[doy] = Ec
gpp_obs[doy] = Aobsx
e_obs[doy] = Eobsx
lai_obs[doy] = Lobsx / 48
window = 3
An_store = moving_average(An_store, n=window)
An_sun_store = moving_average(An_sun_store, n=window)
An_sha_store = moving_average(An_sha_store, n=window)
par_sun_store = moving_average(par_sun_store, n=window)
par_sha_store = moving_average(par_sha_store, n=window)
lai_sun_store = moving_average(lai_sun_store, n=window)
lai_sha_store = moving_average(lai_sha_store, n=window)
E_store = moving_average(E_store, n=window)
Anc_store = moving_average(Anc_store, n=window)
Anc_sun_store = moving_average(Anc_sun_store, n=window)
Anc_sha_store = moving_average(Anc_sha_store, n=window)
parc_sun_store = moving_average(parc_sun_store, n=window)
parc_sha_store = moving_average(parc_sha_store, n=window)
laic_sun_store = moving_average(laic_sun_store, n=window)
laic_sha_store = moving_average(laic_sha_store, n=window)
Ec_store = moving_average(Ec_store, n=window)
gpp_obs = moving_average(gpp_obs, n=window)
e_obs = moving_average(e_obs, n=window)
lai_obs = moving_average(lai_obs, n=window)
fig = plt.figure(figsize=(14, 8))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.1)
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(321)
ax2 = fig.add_subplot(322)
ax3 = fig.add_subplot(323)
ax4 = fig.add_subplot(324)
ax5 = fig.add_subplot(325)
ax6 = fig.add_subplot(326)
#ax1.plot(An_store, label="2-leaf - Sun")
#ax1.plot(Anc_store, label="2-leaf - CABLE")
ax1.plot(An_sun_store, label="Me")
ax1.plot(Anc_sun_store, label="CABLE")
#
ax1.set_title("Sun")
ax2.set_title("Shade")
ax1.set_ylabel("GPP (g C m$^{-2}$ d$^{-1}$)")
ax1.legend(numpoints=1, loc="best")
ax2.plot(An_sha_store)
ax2.plot(Anc_sha_store)
ax3.plot(par_sun_store)
ax3.plot(parc_sun_store)
ax3.set_ylabel("PAR (MJ m$^{-2}$ d$^{-1}$)")
ax4.plot(par_sha_store)
ax4.plot(parc_sha_store)
ax5.plot(lai_sun_store)
ax5.plot(laic_sun_store)
ax5.set_ylabel("LAI (m$^{2}$ m$^{-2}$)")
ax6.plot(lai_sha_store)
ax6.plot(laic_sha_store)
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
ax3.locator_params(nbins=6, axis="y")
ax4.locator_params(nbins=6, axis="y")
ax5.locator_params(nbins=6, axis="y")
ax6.locator_params(nbins=6, axis="y")
plt.setp(ax1.get_xticklabels(), visible=False)
plt.setp(ax2.get_xticklabels(), visible=False)
plt.setp(ax3.get_xticklabels(), visible=False)
plt.setp(ax4.get_xticklabels(), visible=False)
plt.show()
def main(met_fn, flx_fn, cab_fn, year_to_run, site):
fpath = "/Users/mdekauwe/Downloads/"
fname = "%s_met_and_plant_data_drought_2003.csv" % (site)
fn = os.path.join(fpath, fname)
df = pd.read_csv(fn, skiprows=range(1, 2))
(df_flx) = read_obs_file(flx_fn)
df_flx = df_flx[df_flx.index.year == year_to_run]
(df_met, lat, lon) = read_met_file(met_fn)
df_met = df_met[df_met.index.year == year_to_run]
par = df_met.PAR
tair = df_met.Tair
vpd = df_met.vpd
wind = df_met.Wind
pressure = df_met.PSurf
Ca = 400.0
#LAI = 3.0
LAI = df.LAI
#
## Parameters
#
p.g0 = 1E-09
p.g1 = df.g1[0] #4.12
p.D0 = 1.5 # kpa
p.Vcmax25 = df.Vmax25[0]
p.Jmax25 = p.Vcmax25 * 1.67
p.Rd25 = None # Vcmax * 0.015
p.Eaj = 30000.0
p.Eav = 60000.0
p.deltaSj = 650.0
p.deltaSv = 650.0
p.Hdv = 200000.0
p.Hdj = 200000.0
##
### Run 2-leaf
##
T = TwoLeaf(p, gs_model="medlyn")
C = TwoLeafCable(p, gs_model="medlyn")
ndays = int(len(df_met) / 48)
Anc_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
An_store = np.zeros(ndays)
An_sun_store = np.zeros(ndays)
An_sha_store = np.zeros(ndays)
par_sun_store = np.zeros(ndays)
par_sha_store = np.zeros(ndays)
lai_sun_store = np.zeros(ndays)
lai_sha_store = np.zeros(ndays)
E_store = np.zeros(ndays)
Tcan_store = np.zeros(ndays)
Anc_store = np.zeros(ndays)
Anc_sun_store = np.zeros(ndays)
Anc_sha_store = np.zeros(ndays)
parc_sun_store = np.zeros(ndays)
parc_sha_store = np.zeros(ndays)
laic_sun_store = np.zeros(ndays)
laic_sha_store = np.zeros(ndays)
Ec_store = np.zeros(ndays)
Tcanc_store = np.zeros(ndays)
Sunc_store = np.zeros(ndays)
Shac_store = np.zeros(ndays)
gpp_obs = np.zeros(ndays)
e_obs = np.zeros(ndays)
lai_obs = np.zeros(ndays)
et_conv = c.MOL_WATER_2_G_WATER * c.G_TO_KG * 1800.
an_conv = c.UMOL_TO_MOL * c.MOL_C_TO_GRAMS_C * 1800.
cnt = 0
for doy in range(ndays - 1):
Anx = 0.0
Ex = 0.0
anxsun = 0.0
anxsha = 0.0
parxsun = 0.0
parxsha = 0.0
laixsun = 0.0
laixsha = 0.0
Tcanx = 0.0
Anc = 0.0
Ec = 0.0
Acabx = 0.0
Ecabx = 0.0
Lcabx = 0.0
ancsun = 0.0
ancsha = 0.0
parcsun = 0.0
parcsha = 0.0
laicsun = 0.0
laicsha = 0.0
Tcancx = 0.0
Aobsx = 0.0
Eobsx = 0.0
Lobsx = 0.0
Sun_fracC = 0.0
Sha_fracC = 0.0
if doy == 0:
tsoil = np.mean(tair[cnt:cnt + 48])
else:
tsoil = np.mean(tair[cnt - 48:cnt])
hr_cnt = 0
for i in range(48):
if doy < 364:
laix = LAI[cnt]
else:
#print(cnt, len(LAI), LAI[17518])
laix = LAI[17518]
hod = float(i) / 2. + 1800. / 3600. / 2.
(An, et, Tcan, apar,
lai_leaf) = T.main(tair[cnt], par[cnt], vpd[cnt], wind[cnt],
pressure[cnt], Ca, doy + 1, hod, laix, tsoil)
lambda_et = (c.H2OLV0 - 2.365E3 * tair[cnt]) * c.H2OMW
Anx += np.sum(An) * an_conv
anxsun += An[c.SUNLIT] * an_conv
anxsha += An[c.SHADED] * an_conv
parxsun += apar[c.SUNLIT] / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parxsha += apar[
c.SHADED] / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laixsun += lai_leaf[c.SUNLIT]
laixsha += lai_leaf[c.SHADED]
sun_frac = lai_leaf[c.SUNLIT] / np.sum(lai_leaf)
sha_frac = lai_leaf[c.SHADED] / np.sum(lai_leaf)
if par[cnt] > 0.0:
Tcanx += (Tcan[c.SUNLIT] * sun_frac) + (Tcan[c.SHADED] *
sha_frac)
Lobsx += laix
Ex += np.sum(et) * et_conv
(An, et, Tcan, apar,
lai_leaf) = C.main(tair[cnt], par[cnt], vpd[cnt], wind[cnt],
pressure[cnt], Ca, doy + 1, hod, laix, tsoil)
Anc += np.sum(An) * an_conv
ancsun += An[c.SUNLIT] * an_conv
ancsha += An[c.SHADED] * an_conv
parcsun += apar[c.SUNLIT] / c.UMOLPERJ / c.MJ_TO_J * 1800.0
parcsha += apar[
c.SHADED] / c.UMOLPERJ / c.MJ_TO_J * 1800.0 # MJ m-2 d-1
laicsun += lai_leaf[c.SUNLIT]
laicsha += lai_leaf[c.SHADED]
if par[cnt] > 0.0:
Tcancx += Tcan
Ec += et * et_conv
Sun_fracC += sun_frac
Sha_fracC += sha_frac
Aobsx += df_flx.GPP[cnt] * an_conv
Eobsx += df_flx.Qle[cnt] / lambda_et * et_conv
cnt += 1
if par[cnt] > 0.0:
hr_cnt += 1
An_store[doy] = Anx
An_sun_store[doy] = anxsun
An_sha_store[doy] = anxsha
par_sun_store[doy] = parxsun
par_sha_store[doy] = parxsha
lai_sun_store[doy] = laixsun / 48.
lai_sha_store[doy] = laixsha / 48.
E_store[doy] = Ex
Tcan_store[doy] = (Tcanx / float(hr_cnt))
Anc_store[doy] = Anc
Anc_sun_store[doy] = ancsun
Anc_sha_store[doy] = ancsha
parc_sun_store[doy] = parcsun
parc_sha_store[doy] = parcsha
laic_sun_store[doy] = laicsun / 48.
laic_sha_store[doy] = laicsha / 48.
Ec_store[doy] = Ec
Tcanc_store[doy] = (Tcancx / float(hr_cnt))
Sunc_store[doy] = Sun_fracC / 48.
Shac_store[doy] = Sha_fracC / 48.
gpp_obs[doy] = Aobsx
e_obs[doy] = Eobsx
lai_obs[doy] = Lobsx / 48
window = 3
An_store = moving_average(An_store, n=window)
An_sun_store = moving_average(An_sun_store, n=window)
An_sha_store = moving_average(An_sha_store, n=window)
par_sun_store = moving_average(par_sun_store, n=window)
par_sha_store = moving_average(par_sha_store, n=window)
lai_sun_store = moving_average(lai_sun_store, n=window)
lai_sha_store = moving_average(lai_sha_store, n=window)
E_store = moving_average(E_store, n=window)
Tcan_store = moving_average(Tcan_store, n=window)
Anc_store = moving_average(Anc_store, n=window)
Anc_sun_store = moving_average(Anc_sun_store, n=window)
Anc_sha_store = moving_average(Anc_sha_store, n=window)
parc_sun_store = moving_average(parc_sun_store, n=window)
parc_sha_store = moving_average(parc_sha_store, n=window)
laic_sun_store = moving_average(laic_sun_store, n=window)
laic_sha_store = moving_average(laic_sha_store, n=window)
Ec_store = moving_average(Ec_store, n=window)
Sunc_store = moving_average(Sunc_store, n=window)
Shac_store = moving_average(Shac_store, n=window)
Tcanc_store = moving_average(Tcanc_store, n=window)
#gpp_obs = moving_average(gpp_obs, n=window)
#e_obs = moving_average(e_obs, n=window)
#lai_obs = moving_average(lai_obs, n=window)
fig = plt.figure(figsize=(9, 10))
fig.subplots_adjust(hspace=0.3)
fig.subplots_adjust(wspace=0.3)
plt.rcParams['text.usetex'] = False
plt.rcParams['font.family'] = "sans-serif"
plt.rcParams['font.sans-serif'] = "Helvetica"
plt.rcParams['axes.labelsize'] = 12
plt.rcParams['font.size'] = 12
plt.rcParams['legend.fontsize'] = 12
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
ax1 = fig.add_subplot(411)
ax2 = fig.add_subplot(412)
ax3 = fig.add_subplot(413)
ax4 = fig.add_subplot(414)
ax1.plot(An_store, label="2-leaf - Me")
ax1.plot(Anc_store, label="2-leaf - CABLE")
ax1.set_ylabel("GPP (g C m$^{-2}$ d$^{-1}$)")
ax1.legend(numpoints=1, loc="best")
ax2.plot(E_store, label="2-leaf - Me")
ax2.plot(Ec_store, label="2-leaf - CABLE")
ax2.set_ylabel("E (mm d$^{-1}$)")
ax3.plot(Tcan_store, label="2-leaf - Me")
ax3.plot(Tcanc_store, label="2-leaf - CABLE")
ax3.set_ylabel("T$_{canopy}$ (degrees C)")
print(np.mean(np.abs(Tcan_store - Tcanc_store)))
ax4.plot(Sunc_store, label="Sun")
ax4.plot(Shac_store, label="Shade")
ax4.set_ylabel("Frac (-)")
ax4.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
ax3.locator_params(nbins=6, axis="y")
ax4.locator_params(nbins=6, axis="y")
#plt.setp(ax1.get_xticklabels(), visible=False)
#plt.setp(ax2.get_xticklabels(), visible=False)
#plt.setp(ax3.get_xticklabels(), visible=False)
#plt.setp(ax4.get_xticklabels(), visible=False)
plt.show()
def main():
doy = 180.
#
## Met data ...
#
(par, tair, vpd) = get_met_data(p.lat, p.lon, doy)
# more realistic VPD
rh = 40.
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#
## Fixed met stuff
#
wind = 2.5
pressure = 101325.0
Ca = 400.0
LAI = p.LAI
##
### Run Big-leaf
##
B = BigLeaf(p, gs_model="medlyn")
An_bl = np.zeros(48)
gsw_bl = np.zeros(48)
et_bl = np.zeros(48)
tcan_bl = np.zeros(48)
for i in range(len(par)):
hod = float(i) / 2. + 1800. / 3600. / 2.
(An, gsw, et, Tcan) = B.main(p, tair[i], par[i], vpd[i], wind,
pressure, Ca, doy, hod, LAI)
An_bl[i] = An
et_bl[i] = et
tcan_bl[i] = Tcan
##
### Run 2-leaf
##
T = TwoLeaf(p, gs_model="medlyn")
An_tl = np.zeros(48)
et_tl = np.zeros(48)
tcan_tl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An, et, Tcan, apar, lai_leaf) = T.main(p, tair[i], par[i], vpd[i],
wind, pressure, Ca, doy,
hod / 2., LAI)
sun_frac = lai_leaf[c.SUNLIT] / np.sum(lai_leaf)
sha_frac = lai_leaf[c.SHADED] / np.sum(lai_leaf)
An_tl[i] = np.sum(An)
et_tl[i] = np.sum(et)
tcan_tl[i] = (Tcan[c.SUNLIT] * sun_frac) + (Tcan[c.SHADED] * sha_frac)
hod += 1
fig = plt.figure(figsize=(16, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.2)
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'] = 10
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
ax1.plot(np.arange(48) / 2., An_bl, label="Big leaf")
ax1.plot(np.arange(48) / 2., An_tl, label="Two leaf")
ax1.legend(numpoints=1, loc="best")
ax1.set_ylabel("$A_{\mathrm{n}}$ ($\mathrm{\mu}$mol m$^{-2}$ s$^{-1}$)")
ax2.plot(np.arange(48) / 2., et_bl * c.MOL_TO_MMOL, label="Big leaf")
ax2.plot(np.arange(48) / 2., et_tl * c.MOL_TO_MMOL, label="Two leaf")
ax2.set_ylabel("E (mmol m$^{-2}$ s$^{-1}$)")
ax2.set_xlabel("Hour of day")
ax3.plot(np.arange(48) / 2., tcan_bl, label="Tcanopy$_{1leaf}$")
ax3.plot(np.arange(48) / 2., tcan_tl, label="Tcanopy$_{2leaf}$")
ax3.plot(np.arange(48) / 2., tair, label="Tair")
ax3.set_ylabel("Temperature (deg C)")
ax3.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
plt.show()
fig.savefig("/Users/%s/Desktop/A_E_Tcan.pdf" % (os.getlogin()),
bbox_inches='tight',
pad_inches=0.1)
fname = "met_constant_forcing_eCO2.csv"
fn = os.path.join(fpath, fname)
df = pd.read_csv(fn)
#df = df.drop(df.columns[0], axis=1)
df.index = pd.to_datetime(df.DateTime)
# Add an LAI field, i.e. converting from per tree to m2 m-2
df = df.assign(lai=lambda x: x.leafArea / p.footprint)
## Fixed met stuff
#
#wind = 2.5
#pressure = 101325.0
#Ca = 400.0
T = TwoLeaf(p, gs_model="medlyn")
chambers = np.unique(df.chamber)
for chamber in chambers:
print(chamber)
dfx = df[(df.T_treatment == "ambient")
& (df.Water_treatment == "control") &
(df.chamber == chamber)].copy()
(out) = run_treatment(T, dfx, p, vary_vj=False)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
ofname = os.path.join(output_dir, "wtc_two_leaf_%s.csv" % (chamber))
if os.path.isfile(ofname):
def main():
lat = -23.575001
lon = 152.524994
doy = 180.0
#
## Met data ...
#
(par, tair, vpd) = get_met_data(lat, lon, doy)
wind = 2.5
pressure = 101325.0
Ca = 400.0
# more realistic VPD
rh = 40.
esat = calc_esat(tair)
ea = rh / 100. * esat
vpd = (esat - ea) * c.PA_2_KPA
vpd = np.where(vpd < 0.05, 0.05, vpd)
#plt.plot(vpd)
#plt.show()
#sys.exit()
## Parameters
#
g0 = 0.001
g1 = 1.5635 # Puechabon
D0 = 1.5 # kpa
Vcmax25 = 60.0
Jmax25 = Vcmax25 * 1.67
Rd25 = 2.0
Eaj = 30000.0
Eav = 60000.0
deltaSj = 650.0
deltaSv = 650.0
Hdv = 200000.0
Hdj = 200000.0
Q10 = 2.0
gamma = 0.0
leaf_width = 0.02
LAI = 3.
# Cambell & Norman, 11.5, pg 178
# The solar absorptivities of leaves (-0.5) from Table 11.4 (Gates, 1980)
# with canopies (~0.8) from Table 11.2 reveals a surprising difference.
# 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
##
### Run Big-leaf
##
B = BigLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_bl = np.zeros(48)
gsw_bl = np.zeros(48)
et_bl = np.zeros(48)
tcan_bl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_bl[i], gsw_bl[i], et_bl[i],
tcan_bl[i]) = B.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf
##
T = TwoLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_tl = np.zeros(48)
gsw_tl = np.zeros(48)
et_tl = np.zeros(48)
tcan_tl = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_tl[i], gsw_tl[i], et_tl[i],
tcan_tl[i]) = T.main(tair[i], par[i], vpd[i], wind, pressure, Ca, doy,
hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf opt
##
T = TwoLeafOpt(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
An_tlo = np.zeros(48)
gsw_tlo = np.zeros(48)
et_tlo = np.zeros(48)
tcan_tlo = np.zeros(48)
hod = 0
for i in range(len(par)):
(An_tlo[i], gsw_tlo[i], et_tlo[i],
tcan_tlo[i]) = T.main(tair[i], par[i], vpd[i], wind, pressure, Ca,
doy, hod, lat, lon, LAI)
hod += 1
##
### Run 2-leaf Manon
##
Ao = np.zeros(48)
gso = np.zeros(48)
Eo = np.zeros(48)
Aob = np.zeros(48)
gsob = np.zeros(48)
Eob = np.zeros(48)
hod = 0
for i in range(len(par)):
cos_zenith = calculate_solar_geometry(doy, hod, lat, lon)
zenith_angle = np.rad2deg(np.arccos(cos_zenith))
elevation = 90.0 - zenith_angle
if elevation > 0.0 and par[i] > 50.0:
p = declared_params()
p.PPFD = par[i]
p.sw_rad_day = par[i] * c.PAR_2_SW
p.LAI = LAI
p.coszen = cos_zenith
p.VPD = vpd[i]
p.precip = 0
p.Tair = tair[i]
p.Vmax25 = Vcmax25
p.g1 = g1
p.CO2 = Ca / 1000 * 101.325
p.JV = 1.67
p.Rlref = Rd25
p.Ej = Eaj
p.Ev = Eav
p.deltaSv = deltaSv
p.deltaSj = deltaSj
p.max_leaf_width = leaf_width
p.gamstar25 = 4.33 # 42.75 / 101.25 umol m-2 s-1
p.Kc25 = 41.0264925 # 404.9 umol m-2 s-1
p.Ko25 = 28208.88 # 278.4 mmol mol-1
p.O2 = 21.27825 # 210 *1000. / 101.25 210 mmol mol-1
p.alpha = 0.3
p.albedo = 0.2
p.Egamstar = 37830.0
p.Ec = 79430.0
p.Eo = 36380.0
p.P50 = 2.02 # Puechabon
p.P88 = 4.17
p.kmax = 0.862457122856143
_, _, fscale2can = absorbed_radiation_2_leaves(p)
p = p.append(pd.Series([np.nansum(fscale2can)], index=['fscale']))
Eo[i], gso[i], Ao[i], __, __ = solve_std(p, p.fc, photo='Farquhar')
#"""
try:
fstom_opt_psi, Eo[i], gso[i], Ao[i], _, _ = profit_psi(
p, photo='Farquhar', res='med', case=2)
p = p.append(
pd.Series([fstom_opt_psi], index=['fstom_opt_psi']))
except (ValueError, AttributeError):
(Eo[i], gso[i], Ao[i]) = (0., 0., 0.)
#"""
hod += 1
fig = plt.figure(figsize=(16, 4))
fig.subplots_adjust(hspace=0.1)
fig.subplots_adjust(wspace=0.2)
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'] = 10
plt.rcParams['xtick.labelsize'] = 14
plt.rcParams['ytick.labelsize'] = 14
almost_black = '#262626'
# change the tick colors also to the almost black
plt.rcParams['ytick.color'] = almost_black
plt.rcParams['xtick.color'] = almost_black
# change the text colors also to the almost black
plt.rcParams['text.color'] = almost_black
# Change the default axis colors from black to a slightly lighter black,
# and a little thinner (0.5 instead of 1)
plt.rcParams['axes.edgecolor'] = almost_black
plt.rcParams['axes.labelcolor'] = almost_black
ax1 = fig.add_subplot(131)
ax2 = fig.add_subplot(132)
ax3 = fig.add_subplot(133)
#ax1.plot(np.arange(48)/2., An_bl, label="Big leaf")
ax1.plot(np.arange(48) / 2., An_tl, label="Two leaf")
ax1.plot(np.arange(48) / 2., An_tlo, label="Two leaf Opt")
ax1.plot(np.arange(48) / 2., Ao, label="Two leaf Manon")
ax1.legend(numpoints=1, loc="best")
ax1.set_ylabel("$A_{\mathrm{n}}$ ($\mathrm{\mu}$mol m$^{-2}$ s$^{-1}$)")
#ax2.plot(np.arange(48)/2., et_bl * c.MOL_TO_MMOL, label="Big leaf")
ax2.plot(np.arange(48) / 2., et_tl * c.MOL_TO_MMOL, label="Two leaf")
ax2.plot(np.arange(48) / 2., et_tlo * c.MOL_TO_MMOL, label="Two leaf opt")
ax2.plot(np.arange(48) / 2., Eo, label="Two leaf Manon")
ax2.set_ylabel("E (mmol m$^{-2}$ s$^{-1}$)")
ax2.set_xlabel("Hour of day")
#ax3.plot(np.arange(48)/2., tcan_bl, label="Tcanopy$_{1leaf}$")
ax3.plot(np.arange(48) / 2., tcan_tl, label="Tcanopy$_{2leaf}$")
ax3.plot(np.arange(48) / 2., tair, label="Tair")
ax3.set_ylabel("Temperature (deg C)")
ax3.legend(numpoints=1, loc="best")
ax1.locator_params(nbins=6, axis="y")
ax2.locator_params(nbins=6, axis="y")
plt.show()
fig.savefig("/Users/%s/Desktop/A_E_Tcan.pdf" % (os.getlogin()),
bbox_inches='tight',
pad_inches=0.1)
# among leaves in a canopy and depends on the architecture of the canopy.
SW_abs = 0.8 # use canopy absorptance of solar radiation
##
### Run 2-leaf
##
T = TwoLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
Rd25,
Eaj,
Eav,
deltaSj,
deltaSv,
Hdv,
Hdj,
Q10,
leaf_width,
SW_abs,
gs_model="medlyn")
B = BigLeaf(g0,
g1,
D0,
gamma,
Vcmax25,
Jmax25,
def main(p, met, lai):
days = met.doy
hod = met.hod
ndays = int(len(days) / 24.)
nhours = len(met)
hours_in_day = int(nhours / float(ndays))
if hours_in_day == 24:
met_timestep = 60.
else:
met_timestep = 30.
timestep_sec = 60. * met_timestep
T = TwoLeaf(p, gs_model="medlyn")
#T = TwoLeaf(p, gs_model="leuning")
out, store = setup_output_dataframe(ndays, hours_in_day, p)
i = 0
hour_cnt = 1 # hour count
day_cnt = 0
while i < len(met):
year = met.index.year[i]
doy = met.doy[i]
hod = met.hod[i] + 1
if day_cnt - 1 == -1:
beta = calc_beta(p.theta_sat, p.theta_fc, p.theta_wp)
else:
beta = calc_beta(out.sw[day_cnt - 1], p.theta_fc, p.theta_wp)
#beta = calc_beta(store.sw[hour_cnt-1])
(An, et, Tcan, apar, lai_leaf) = T.main(met.tair[i],
met.par[i],
met.vpd[i],
met.wind[i],
met.press[i],
met.ca[i],
doy,
hod,
lai[i],
beta=beta)
if hour_cnt == hours_in_day: # End of the day
store_hourly(hour_cnt - 1, An, et, lai_leaf, met.precip[i], store,
hours_in_day)
store_daily(year, doy, day_cnt, store, beta, out, p)
hour_cnt = 1
day_cnt += 1
else:
store_hourly(hour_cnt - 1, An, et, lai_leaf, met.precip[i], store,
hours_in_day)
hour_cnt += 1
i += 1
return (out)