# -*- coding: utf-8 -*-
# @Author: Alan Lau
# @Date: 2017-09-06 17:47:51
# @Last Modified by: Alan Lau
# @Last Modified time: 2017-09-06 17:47:51
from canopy import Canopy
import numpy as np
from PIL import Image
# dataset = np.random.rand(10, 3)
dataset = Image.open('1.jpg')
image = np.array(dataset, dtype=np.float64) / 255
# print(image)
print(image.shape)
# print(dataset)
gc = Canopy(image)
gc.setThreshold(0.6, 0.4)
canopies = gc.clustering()
print(len(canopies))
def worker(potentials, pft_height, pft_la_sa, pft_sapwood_density, pft_name, p,
total_exp, cpu_count, node):
g0 = 0.0
theta_J = 0.85
Rd25 = 0.92
Q10 = 1.92
Vcmax25 = p.Vcmax
Jmax25 = p.Jmax
Eav = 58550.0
deltaSv = 629.26
Eaj = 29680.
deltaSj = 631.88
FAO = False
psi_stem0 = -0.5
psi_f = p.psiv
kp_sat = p.kpsat
g1 = p.g1
s50 = p.s50
sf = p.sf
F = Canopy(g1=g1,
g0=g0,
theta_J=theta_J,
Rd25=Rd25,
Q10=Q10,
Vcmax25=Vcmax25,
Jmax25=Jmax25,
Eav=Eav,
deltaSv=deltaSv,
Eaj=Eaj,
deltaSj=deltaSj)
D = Desica(psi_stem0=psi_stem0,
psi_f=psi_f,
F=F,
g1=g1,
stop_dead=True,
FAO=FAO,
kp_sat=kp_sat,
s50=s50,
sf=sf,
height=pft_height,
la_sa=pft_la_sa,
sapwood_density=pft_sapwood_density)
names = ['Tmax', 'Dmax', 'Dmean', 'gmin', 'lai', 'p50', 'Cl', 'Cs', \
'b', 'psi_e', 'depth', 'psi_stem', 'plc', 'day_of_death']
df = pd.DataFrame(columns=names)
#count = 0
#last_progress = 9.0
for gmin, lai, p50, Cl, Cs, soil_depth, b, psi_e in potentials:
#"""
(D.gmin, D.lai, D.p50, D.Cl, D.Cs, D.soil_depth, D.b,
D.psi_e) = gmin, lai, p50, Cl, Cs, soil_depth, b, psi_e
out, day_of_death = D.run_simulation(met)
psi_stem = out.psi_stem.iloc[-1]
plc = out.plc.iloc[-1]
result = [Tmax, Dmax, Dmean, gmin, lai, p50, Cl, Cs, \
b, psi_e, soil_depth, psi_stem, plc, day_of_death]
print(day_of_death)
print(p)
print(" ")
s = pd.Series(result, index=df.columns)
df = df.append(s, ignore_index=True)
#"""
#progress = (count / total_exp) * 100.0
#if progress > last_progress:
# print(pft_name, "--", round(progress,3), count, ":", total_exp)
# last_progress += 9.
#count += 1
odir = "outputs"
if not os.path.exists(odir):
os.makedirs(odir)
ofn = "%s_trait_sensitivity_%d_%d.csv" % (pft_name, node, cpu_count)
ofname = os.path.join(odir, ofn)
df.to_csv(ofname, index=False)
# -*- coding: utf-8 -*- # @Author: Alan Lau # @Date: 2017-09-06 17:47:51 # @Last Modified by: Alan Lau # @Last Modified time: 2017-09-06 17:47:51 from canopy import Canopy import numpy as np dataset = np.random.rand(500, 2) gc = Canopy(dataset) gc.setThreshold(0.6, 0.4) canopies = gc.clustering() print(len(canopies))
deltaSj = 631.88
FAO = False
psi_stem0 = -0.5
psi_f = p.psiv
kp_sat = p.kpsat
g1 = p.g1
s50 = p.s50
sf = p.sf
AL = lai_mu
psi_e = -1.32 * c.KPA_2_MPA # Sandy clay loam, MPa
b = 6.77
#psi_e = -3.17 * c.KPA_2_MPA # Silty clay clay loam, MPa
#b = 10.39 # Silty clay, SW retention curve param
F = Canopy(g1=g1, g0=g0, theta_J=theta_J, Rd25=Rd25, Q10=Q10,
Vcmax25=Vcmax25, Jmax25=Jmax25, Eav=Eav,
deltaSv=deltaSv, Eaj=Eaj, deltaSj=deltaSj)
D = Desica(psi_stem0=psi_stem0, psi_f=psi_f, F=F, g1=g1, stop_dead=True,
FAO=FAO, kp_sat=kp_sat, s50=s50, sf=sf, AL=AL,
force_refilling=False)
out, day_of_death = D.run_simulation(met)
#odir = "/Users/mdekauwe/Desktop/refilling_plots"
#odir = "/Users/mdekauwe/Desktop/new_plots"
odir = "/Users/mdekauwe/Desktop/old_plots"
if not os.path.exists(odir):
os.makedirs(odir)
plot_time_to_mortality(odir, out, time_step, to_screen=False, pft=pft)
def main(pft_name, p, ranges):
time_step = 30
lat = -35.76
lon = 148.0
g0 = 0.0
theta_J = 0.85
Rd25 = 0.92
Q10 = 1.92
Vcmax25 = p.Vcmax
Jmax25 = p.Jmax
Eav = 58550.0
deltaSv = 629.26
Eaj = 29680.
deltaSj = 631.88
FAO = False
psi_stem0 = -0.5
psi_f = p.psiv
kp_sat = p.kpsat
g1 = p.g1
s50 = p.s50
sf = p.sf
AL = 2.0
F = Canopy(g1=g1,
g0=g0,
theta_J=theta_J,
Rd25=Rd25,
Q10=Q10,
Vcmax25=Vcmax25,
Jmax25=Jmax25,
Eav=Eav,
deltaSv=deltaSv,
Eaj=Eaj,
deltaSj=deltaSj)
D = Desica(psi_stem0=psi_stem0,
psi_f=psi_f,
F=F,
g1=g1,
stop_dead=True,
FAO=FAO,
kp_sat=kp_sat,
s50=s50,
sf=sf,
AL=AL)
names = ['Tmax', 'Dmax', 'Dmean', 'gmin', 'lai', 'p50', 'Cl', 'Cs', \
'depth', 'psi_stem', 'cwd', 'plc', 'day_of_death']
df = pd.DataFrame(columns=names)
Tmax = 35.
RH = 10.
met = generate_met_data(Tmin=15,
Tmax=Tmax,
RH=RH,
ndays=720,
lat=lat,
lon=lon,
time_step=time_step)
Dmax = np.max(met.vpd)
Dmean = np.mean(met.vpd)
for gmin, AL, p50, Cl, Cs, soil_depth, in \
itertools.product(*ranges):
#"""
(D.gmin, D.AL, D.p50, D.Cl, D.Cs,
D.soil_depth) = gmin, AL, p50, Cl, Cs, soil_depth
out, day_of_death = D.run_simulation(met)
psi_stem = out.psi_stem.iloc[-1]
plc = out.plc.iloc[-1]
cwd = out.cwd.iloc[-1]
result = [Tmax, Dmax, Dmean, gmin, AL, p50, Cl, Cs, \
soil_depth, psi_stem, cwd, plc, day_of_death]
s = pd.Series(result, index=df.columns)
df = df.append(s, ignore_index=True)
#"""
return df
time_step = 30
met = generate_met_data(Tmin=10, RH=30, ndays=200, time_step=time_step)
psi_stem0 = 0. # initial stem water potential, MPa
AL = 6. # plant leaf area, m2
p50 = -4. # xylem pressure inducing 50% loss of hydraulic conductivity
# due to embolism, MPa
psi_f = -3. # reference potential for Tuzet model, MPa
gmin = 10. # minimum stomatal conductance, mmol m-2 s-1
Cl = 10000. # leaf capacitance, mmol MPa-1 (total plant)
Cs = 120000. # stem capacitance, mmol MPa-1
g1 = 4.0 # sensitivity of stomatal conductance to the assimilation
# rate, kPa
F = Canopy(g1=g1)
D = Desica(psi_stem0=psi_stem0,
AL=AL,
p50=p50,
psi_f=psi_f,
gmin=gmin,
Cl=Cl,
Cs=Cs,
F=F,
g1=g1,
nruns=3,
stop_dead=True)
out = D.run_simulation(met)
make_plot(out, time_step)
plot_swp_sw(out)