def test_soil_water_potential_drops_faster_for_small_soil_reservoirs_than_bigger_ones():
for soil_class in hydraulic.def_param_soil().keys():
psi_soil_small = hydraulic.soil_water_potential(psi_soil_init=0., water_withdrawal=1., soil_class=soil_class,
soil_total_volume=1., psi_min=-3.)
psi_soil_big = hydraulic.soil_water_potential(psi_soil_init=0., water_withdrawal=1., soil_class=soil_class,
soil_total_volume=2., psi_min=-3.)
assert psi_soil_small < psi_soil_big
def test_soil_water_potential_decreases_as_water_withdrawal_increases():
for soil_class in hydraulic.def_param_soil().keys():
psi_soil = [
hydraulic.soil_water_potential(psi_soil_init=0.,
water_withdrawal=w,
soil_class=soil_class,
soil_total_volume=1,
psi_min=-3.)
for w in arange(0, 3, 0.1)
]
assert all(x >= y for x, y in zip(psi_soil, psi_soil[1:]))
def solve_interactions(g, meteo, psi_soil, t_soil, t_sky_eff, vid_collar,
vid_base, length_conv, time_conv, rhyzo_total_volume,
params, form_factors, simplified_form_factors):
"""Computes gas-exchange, energy and hydraulic structure of plant's shoot jointly.
Args:
g: MTG object
meteo (DataFrame): forcing meteorological variables
psi_soil (float): [MPa] soil (root zone) water potential
t_soil (float): [degreeC] soil surface temperature
t_sky_eff (float): [degreeC] effective sky temperature
vid_collar (int): id of the collar node of the mtg
vid_base (int): id of the basal node of the mtg
length_conv (float): [-] conversion factor from the `unit_scene_length` to 1 m
time_conv (float): [-] conversion factor from meteo data time step to seconds
rhyzo_total_volume (float): [m3] volume of the soil occupied with roots
params (params): [-] :class:`hydroshoot.params.Params()` object
"""
unit_scene_length = params.simulation.unit_scene_length
hydraulic_structure = params.simulation.hydraulic_structure
negligible_shoot_resistance = params.simulation.negligible_shoot_resistance
soil_water_deficit = params.simulation.soil_water_deficit
energy_budget = params.simulation.energy_budget
par_photo = params.exchange.par_photo
par_photo_n = params.exchange.par_photo_N
par_gs = params.exchange.par_gs
rbt = params.exchange.rbt
mass_conv = params.hydraulic.MassConv
xylem_k_max = params.hydraulic.Kx_dict
xylem_k_cavitation = params.hydraulic.par_K_vul
psi_min = params.hydraulic.psi_min
solo = params.energy.solo
irradiance_type2 = params.irradiance.E_type2
leaf_lbl_prefix = params.mtg_api.leaf_lbl_prefix
collar_label = params.mtg_api.collar_label
soil_class = params.soil.soil_class
dist_roots, rad_roots = params.soil.roots
temp_step = params.numerical_resolution.t_step
psi_step = params.numerical_resolution.psi_step
max_iter = params.numerical_resolution.max_iter
psi_error_threshold = params.numerical_resolution.psi_error_threshold
temp_error_threshold = params.numerical_resolution.t_error_crit
modelx, psi_critx, slopex = [
xylem_k_cavitation[ikey]
for ikey in ('model', 'fifty_cent', 'sig_slope')
]
if hydraulic_structure:
assert (
par_gs['model'] != 'vpd'
), "Stomatal conductance model should be linked to the hydraulic strucutre"
else:
par_gs['model'] = 'vpd'
negligible_shoot_resistance = True
print "par_gs: 'model' is forced to 'vpd'"
print "negligible_shoot_resistance is forced to True."
# Initialize all xylem potential values to soil water potential
for vtx_id in traversal.pre_order2(g, vid_base):
g.node(vtx_id).psi_head = psi_soil
# Temperature loop +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
t_error_trace = []
it_step = temp_step
# Initialize leaf temperature to air temperature
g.properties()['Tlc'] = energy.leaf_temperature_as_air_temperature(
g, meteo, leaf_lbl_prefix)
for it in range(max_iter):
t_prev = deepcopy(g.property('Tlc'))
# Hydraulic loop +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
if hydraulic_structure:
psi_error_trace = []
ipsi_step = psi_step
for ipsi in range(max_iter):
psi_prev = deepcopy(g.property('psi_head'))
# Compute gas-exchange fluxes. Leaf T and Psi are from prev calc loop
exchange.gas_exchange_rates(g, par_photo, par_photo_n, par_gs,
meteo, irradiance_type2,
leaf_lbl_prefix, rbt)
# Compute sap flow and hydraulic properties
hydraulic.hydraulic_prop(g,
mass_conv=mass_conv,
length_conv=length_conv,
a=xylem_k_max['a'],
b=xylem_k_max['b'],
min_kmax=xylem_k_max['min_kmax'])
# Update soil water status
psi_collar = hydraulic.soil_water_potential(
psi_soil,
g.node(vid_collar).Flux * time_conv, soil_class,
rhyzo_total_volume, psi_min)
if soil_water_deficit:
psi_collar = max(-1.3, psi_collar)
else:
psi_collar = max(-0.7, psi_collar)
for vid in g.Ancestors(vid_collar):
g.node(vid).psi_head = psi_collar
# Compute xylem water potential
n_iter_psi = hydraulic.xylem_water_potential(
g,
psi_soil=psi_collar,
model=modelx,
psi_min=psi_min,
psi_error_crit=psi_error_threshold,
max_iter=max_iter,
length_conv=length_conv,
fifty_cent=psi_critx,
sig_slope=slopex,
dist_roots=dist_roots,
rad_roots=rad_roots,
negligible_shoot_resistance=negligible_shoot_resistance,
start_vid=vid_collar,
stop_vid=None,
psi_step=psi_step)
psi_new = g.property('psi_head')
# Evaluate xylem conversion criterion
psi_error_dict = {}
for vtx_id in g.property('psi_head').keys():
psi_error_dict[vtx_id] = abs(psi_prev[vtx_id] -
psi_new[vtx_id])
psi_error = max(psi_error_dict.values())
psi_error_trace.append(psi_error)
print 'psi_error = ', round(
psi_error, 3
), ':: Nb_iter = %d' % n_iter_psi, 'ipsi_step = %f' % ipsi_step
# Manage temperature step to ensure convergence
if psi_error < psi_error_threshold:
break
else:
try:
if psi_error_trace[-1] >= psi_error_trace[
-2] - psi_error_threshold:
ipsi_step = max(0.05, ipsi_step / 2.)
except IndexError:
pass
psi_new_dict = {}
for vtx_id in psi_new.keys():
psix = psi_prev[vtx_id] + ipsi_step * (
psi_new[vtx_id] - psi_prev[vtx_id])
psi_new_dict[vtx_id] = psix
g.properties()['psi_head'] = psi_new_dict
else:
# Compute gas-exchange fluxes. Leaf T and Psi are from prev calc loop
exchange.gas_exchange_rates(g, par_photo, par_photo_n, par_gs,
meteo, irradiance_type2,
leaf_lbl_prefix, rbt)
# Compute sap flow and hydraulic properties
hydraulic.hydraulic_prop(g,
mass_conv=mass_conv,
length_conv=length_conv,
a=xylem_k_max['a'],
b=xylem_k_max['b'],
min_kmax=xylem_k_max['min_kmax'])
# End Hydraulic loop +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Compute leaf temperature
if energy_budget:
leaves_length = energy.get_leaves_length(
g,
leaf_lbl_prefix=leaf_lbl_prefix,
unit_scene_length=unit_scene_length)
leaf_wind_speed = energy.leaf_wind_as_air_wind(
g, meteo, leaf_lbl_prefix)
gbH = energy.heat_boundary_layer_conductance(
leaves_length, leaf_wind_speed)
t_init = g.property('Tlc')
ev = g.property('E')
ei = g.property('Ei')
g.properties()['Tlc'], t_iter = energy.leaf_temperature(
g,
meteo,
t_soil,
t_sky_eff,
t_init=t_init,
form_factors=form_factors,
gbh=gbH,
ev=ev,
ei=ei,
solo=solo,
ff_type=simplified_form_factors,
leaf_lbl_prefix=leaf_lbl_prefix,
max_iter=max_iter,
t_error_crit=temp_error_threshold,
t_step=temp_step)
# t_iter_list.append(t_iter)
t_new = deepcopy(g.property('Tlc'))
# Evaluation of leaf temperature conversion creterion
error_dict = {
vtx: abs(t_prev[vtx] - t_new[vtx])
for vtx in g.property('Tlc').keys()
}
t_error = round(max(error_dict.values()), 3)
print 't_error = ', t_error, 'counter =', it, 't_iter = ', t_iter, 'it_step = ', it_step
t_error_trace.append(t_error)
# Manage temperature step to ensure convergence
if t_error < temp_error_threshold:
break
else:
assert (it <= max_iter
), 'The energy budget solution did not converge.'
try:
if t_error_trace[
-1] >= t_error_trace[-2] - temp_error_threshold:
it_step = max(0.001, it_step / 2.)
except IndexError:
pass
t_new_dict = {}
for vtx_id in t_new.keys():
tx = t_prev[vtx_id] + it_step * (t_new[vtx_id] -
t_prev[vtx_id])
t_new_dict[vtx_id] = tx
g.properties()['Tlc'] = t_new_dict
def run(g, wd, scene=None, write_result=True, **kwargs):
"""
Calculates leaf gas and energy exchange in addition to the hydraulic structure of an individual plant.
:Parameters:
- **g**: a multiscale tree graph object
- **wd**: string, working directory
- **scene**: PlantGl scene
- **kwargs** can include:
- **psi_soil**: [MPa] predawn soil water potential
- **gdd_since_budbreak**: [°Cd] growing degree-day since bubreak
- **sun2scene**: PlantGl scene, when prodivided, a sun object (sphere) is added to it
- **soil_size**: [cm] length of squared mesh size
"""
print('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
print('+ Project: ', wd)
print('++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++')
time_on = datetime.now()
# Read user parameters
params_path = wd + 'params.json'
params = Params(params_path)
output_index = params.simulation.output_index
# ==============================================================================
# Initialisation
# ==============================================================================
# Climate data
meteo_path = wd + params.simulation.meteo
meteo_tab = read_csv(meteo_path, sep=';', decimal='.', header=0)
meteo_tab.time = DatetimeIndex(meteo_tab.time)
meteo_tab = meteo_tab.set_index(meteo_tab.time)
# Adding missing data
if 'Ca' not in meteo_tab.columns:
meteo_tab['Ca'] = [400.] * len(meteo_tab) # ppm [CO2]
if 'Pa' not in meteo_tab.columns:
meteo_tab['Pa'] = [101.3] * len(meteo_tab) # atmospheric pressure
# Determination of the simulation period
sdate = datetime.strptime(params.simulation.sdate, "%Y-%m-%d %H:%M:%S")
edate = datetime.strptime(params.simulation.edate, "%Y-%m-%d %H:%M:%S")
datet = date_range(sdate, edate, freq='H')
meteo = meteo_tab.loc[datet, :]
time_conv = {'D': 86.4e3, 'H': 3600., 'T': 60., 'S': 1.}[datet.freqstr]
# Reading available pre-dawn soil water potential data
if 'psi_soil' in kwargs:
psi_pd = DataFrame([kwargs['psi_soil']] * len(meteo.time),
index=meteo.time, columns=['psi'])
else:
assert (isfile(wd + 'psi_soil.input')), "The 'psi_soil.input' file is missing."
psi_pd = read_csv(wd + 'psi_soil.input', sep=';', decimal='.').set_index('time')
psi_pd.index = [datetime.strptime(s, "%Y-%m-%d") for s in psi_pd.index]
# Unit length conversion (from scene unit to the standard [m]) unit)
unit_scene_length = params.simulation.unit_scene_length
length_conv = {'mm': 1.e-3, 'cm': 1.e-2, 'm': 1.}[unit_scene_length]
# Determination of cumulative degree-days parameter
t_base = params.phenology.t_base
budbreak_date = datetime.strptime(params.phenology.emdate, "%Y-%m-%d %H:%M:%S")
if 'gdd_since_budbreak' in kwargs:
gdd_since_budbreak = kwargs['gdd_since_budbreak']
elif min(meteo_tab.index) <= budbreak_date:
tdays = date_range(budbreak_date, sdate, freq='D')
tmeteo = meteo_tab.loc[tdays, :].Tac.to_frame()
tmeteo = tmeteo.set_index(DatetimeIndex(tmeteo.index).normalize())
df_min = tmeteo.groupby(tmeteo.index).aggregate(np.min).Tac
df_max = tmeteo.groupby(tmeteo.index).aggregate(np.max).Tac
# df_tt = merge(df_max, df_min, how='inner', left_index=True, right_index=True)
# df_tt.columns = ('max', 'min')
# df_tt['gdd'] = df_tt.apply(lambda x: 0.5 * (x['max'] + x['min']) - t_base)
# gdd_since_budbreak = df_tt['gdd'].cumsum()[-1]
df_tt = 0.5 * (df_min + df_max) - t_base
gdd_since_budbreak = df_tt.cumsum()[-1]
else:
raise ValueError('Cumulative degree-days temperature is not provided.')
print('GDD since budbreak = %d °Cd' % gdd_since_budbreak)
# Determination of perennial structure arms (for grapevine)
# arm_vid = {g.node(vid).label: g.node(vid).components()[0]._vid for vid in g.VtxList(Scale=2) if
# g.node(vid).label.startswith('arm')}
# Soil reservoir dimensions (inter row, intra row, depth) [m]
soil_dimensions = params.soil.soil_dimensions
soil_total_volume = soil_dimensions[0] * soil_dimensions[1] * soil_dimensions[2]
rhyzo_coeff = params.soil.rhyzo_coeff
rhyzo_total_volume = rhyzo_coeff * np.pi * min(soil_dimensions[:2]) ** 2 / 4. * soil_dimensions[2]
# Counter clockwise angle between the default X-axis direction (South) and
# the real direction of X-axis.
scene_rotation = params.irradiance.scene_rotation
# Sky and cloud temperature [degreeC]
t_sky = params.energy.t_sky
t_cloud = params.energy.t_cloud
# Topological location
latitude = params.simulation.latitude
longitude = params.simulation.longitude
elevation = params.simulation.elevation
geo_location = (latitude, longitude, elevation)
# Pattern
ymax, xmax = map(lambda dim: dim / length_conv, soil_dimensions[:2])
pattern = ((-xmax / 2.0, -ymax / 2.0), (xmax / 2.0, ymax / 2.0))
# Label prefix of the collar internode
vtx_label = params.mtg_api.collar_label
# Label prefix of the leaves
leaf_lbl_prefix = params.mtg_api.leaf_lbl_prefix
# Label prefices of stem elements
stem_lbl_prefix = params.mtg_api.stem_lbl_prefix
E_type = params.irradiance.E_type
tzone = params.simulation.tzone
turtle_sectors = params.irradiance.turtle_sectors
icosphere_level = params.irradiance.icosphere_level
turtle_format = params.irradiance.turtle_format
energy_budget = params.simulation.energy_budget
print('Energy_budget: %s' % energy_budget)
# Optical properties
opt_prop = params.irradiance.opt_prop
print('Hydraulic structure: %s' % params.simulation.hydraulic_structure)
psi_min = params.hydraulic.psi_min
# Parameters of leaf Nitrogen content-related models
Na_dict = params.exchange.Na_dict
# Computation of the form factor matrix
form_factors = None
if energy_budget:
print('Computing form factors...')
form_factors = energy.form_factors_simplified(
g, pattern=pattern, infinite=True, leaf_lbl_prefix=leaf_lbl_prefix, turtle_sectors=turtle_sectors,
icosphere_level=icosphere_level, unit_scene_length=unit_scene_length)
# Soil class
soil_class = params.soil.soil_class
print('Soil class: %s' % soil_class)
# Rhyzosphere concentric radii determination
rhyzo_radii = params.soil.rhyzo_radii
rhyzo_number = len(rhyzo_radii)
# Add rhyzosphere elements to mtg
rhyzo_solution = params.soil.rhyzo_solution
print('rhyzo_solution: %s' % rhyzo_solution)
if rhyzo_solution:
if not any(item.startswith('rhyzo') for item in g.property('label').values()):
vid_collar = architecture.mtg_base(g, vtx_label=vtx_label)
vid_base = architecture.add_soil_components(g, rhyzo_number, rhyzo_radii,
soil_dimensions, soil_class, vtx_label)
else:
vid_collar = g.node(g.root).vid_collar
vid_base = g.node(g.root).vid_base
radius_prev = 0.
for ivid, vid in enumerate(g.Ancestors(vid_collar)[1:]):
radius = rhyzo_radii[ivid]
g.node(vid).Length = radius - radius_prev
g.node(vid).depth = soil_dimensions[2] / length_conv # [m]
g.node(vid).TopDiameter = radius * 2.
g.node(vid).BotDiameter = radius * 2.
g.node(vid).soil_class = soil_class
radius_prev = radius
else:
# Identifying and attaching the base node of a single MTG
vid_collar = architecture.mtg_base(g, vtx_label=vtx_label)
vid_base = vid_collar
g.node(g.root).vid_base = vid_base
g.node(g.root).vid_collar = vid_collar
# Initializing sapflow to 0
for vtx_id in traversal.pre_order2(g, vid_base):
g.node(vtx_id).Flux = 0.
# Addition of a soil element
if 'Soil' not in g.properties()['label'].values():
if 'soil_size' in kwargs:
if kwargs['soil_size'] > 0.:
architecture.add_soil(g, kwargs['soil_size'])
else:
architecture.add_soil(g, 500.)
# Suppression of undesired geometry for light and energy calculations
geom_prop = g.properties()['geometry']
vidkeys = []
for vid in g.properties()['geometry']:
n = g.node(vid)
if not n.label.startswith(('L', 'other', 'soil')):
vidkeys.append(vid)
[geom_prop.pop(x) for x in vidkeys]
g.properties()['geometry'] = geom_prop
# Attaching optical properties to MTG elements
g = irradiance.optical_prop(g, leaf_lbl_prefix=leaf_lbl_prefix,
stem_lbl_prefix=stem_lbl_prefix, wave_band='SW',
opt_prop=opt_prop)
# Estimation of Nitroen surface-based content according to Prieto et al. (2012)
# Estimation of intercepted irradiance over past 10 days:
if not 'Na' in g.property_names():
print('Computing Nitrogen profile...')
assert (sdate - min(
meteo_tab.index)).days >= 10, 'Meteorological data do not cover 10 days prior to simulation date.'
ppfd10_date = sdate + timedelta(days=-10)
ppfd10t = date_range(ppfd10_date, sdate, freq='H')
ppfd10_meteo = meteo_tab.loc[ppfd10t, :]
caribu_source, RdRsH_ratio = irradiance.irradiance_distribution(ppfd10_meteo, geo_location, E_type,
tzone, turtle_sectors, turtle_format,
None, scene_rotation, None)
# Compute irradiance interception and absorbtion
g, caribu_scene = irradiance.hsCaribu(mtg=g,
unit_scene_length=unit_scene_length,
source=caribu_source, direct=False,
infinite=True, nz=50, ds=0.5,
pattern=pattern)
g.properties()['Ei10'] = {vid: g.node(vid).Ei * time_conv / 10. / 1.e6 for vid in g.property('Ei').keys()}
# Estimation of leaf surface-based nitrogen content:
for vid in g.VtxList(Scale=3):
if g.node(vid).label.startswith(leaf_lbl_prefix):
g.node(vid).Na = exchange.leaf_Na(gdd_since_budbreak, g.node(vid).Ei10,
Na_dict['aN'],
Na_dict['bN'],
Na_dict['aM'],
Na_dict['bM'])
# Define path to folder
output_path = wd + 'output' + output_index + '/'
# Save geometry in an external file
# HSArc.mtg_save_geometry(scene, output_path)
# ==============================================================================
# Simulations
# ==============================================================================
sapflow = []
# sapEast = []
# sapWest = []
an_ls = []
rg_ls = []
psi_stem = {}
Tlc_dict = {}
Ei_dict = {}
an_dict = {}
gs_dict = {}
# The time loop +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
for date in meteo.time:
print("=" * 72)
print('Date', date, '\n')
# Select of meteo data
imeteo = meteo[meteo.time == date]
# Add a date index to g
g.date = datetime.strftime(date, "%Y%m%d%H%M%S")
# Read soil water potntial at midnight
if 'psi_soil' in kwargs:
psi_soil = kwargs['psi_soil']
else:
if date.hour == 0:
try:
psi_soil_init = psi_pd.loc[date, :][0]
psi_soil = psi_soil_init
except KeyError:
pass
# Estimate soil water potntial evolution due to transpiration
else:
psi_soil = hydraulic.soil_water_potential(psi_soil,
g.node(vid_collar).Flux * time_conv,
soil_class, soil_total_volume, psi_min)
if 'sun2scene' not in kwargs or not kwargs['sun2scene']:
sun2scene = None
elif kwargs['sun2scene']:
sun2scene = display.visu(g, def_elmnt_color_dict=True, scene=Scene())
# Compute irradiance distribution over the scene
caribu_source, RdRsH_ratio = irradiance.irradiance_distribution(imeteo, geo_location, E_type, tzone,
turtle_sectors, turtle_format, sun2scene,
scene_rotation, None)
# Compute irradiance interception and absorbtion
g, caribu_scene = irradiance.hsCaribu(mtg=g,
unit_scene_length=unit_scene_length,
source=caribu_source, direct=False,
infinite=True, nz=50, ds=0.5,
pattern=pattern)
# g.properties()['Ei'] = {vid: 1.2 * g.node(vid).Ei for vid in g.property('Ei').keys()}
# Trace intercepted irradiance on each time step
rg_ls.append(sum([g.node(vid).Ei / (0.48 * 4.6) * surface(g.node(vid).geometry) * (length_conv ** 2) \
for vid in g.property('geometry') if g.node(vid).label.startswith('L')]))
# Hack forcing of soil temperture (model of soil temperature under development)
t_soil = energy.forced_soil_temperature(imeteo)
# Climatic data for energy balance module
# TODO: Change the t_sky_eff formula (cf. Gliah et al., 2011, Heat and Mass Transfer, DOI: 10.1007/s00231-011-0780-1)
t_sky_eff = RdRsH_ratio * t_cloud + (1 - RdRsH_ratio) * t_sky
solver.solve_interactions(g, imeteo, psi_soil, t_soil, t_sky_eff,
vid_collar, vid_base, length_conv, time_conv,
rhyzo_total_volume, params, form_factors)
# Write mtg to an external file
if scene is not None:
architecture.mtg_save(g, scene, output_path)
# Plot stuff..
sapflow.append(g.node(vid_collar).Flux)
# sapEast.append(g.node(arm_vid['arm1']).Flux)
# sapWest.append(g.node(arm_vid['arm2']).Flux)
an_ls.append(g.node(vid_collar).FluxC)
psi_stem[date] = deepcopy(g.property('psi_head'))
Tlc_dict[date] = deepcopy(g.property('Tlc'))
Ei_dict[date] = deepcopy(g.property('Eabs'))
an_dict[date] = deepcopy(g.property('An'))
gs_dict[date] = deepcopy(g.property('gs'))
print('---------------------------')
print('psi_soil', round(psi_soil, 4))
print('psi_collar', round(g.node(3).psi_head, 4))
print('psi_leaf', round(np.median([g.node(vid).psi_head for vid in g.property('gs').keys()]), 4))
print('')
# print('Rdiff/Rglob ', RdRsH_ratio)
# print('t_sky_eff ', t_sky_eff)
print('gs', np.median(list(g.property('gs').values())))
print('flux H2O', round(g.node(vid_collar).Flux * 1000. * time_conv, 4))
print('flux C2O', round(g.node(vid_collar).FluxC, 4))
print('Tleaf ', round(np.median([g.node(vid).Tlc for vid in g.property('gs').keys()]), 2),
'Tair ', round(imeteo.Tac[0], 4))
print('')
print("=" * 72)
# End time loop +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Write output
# Plant total transpiration
sapflow = [flow * time_conv * 1000. for flow in sapflow]
# sapEast, sapWest = [np.array(flow) * time_conv * 1000. for i, flow in enumerate((sapEast, sapWest))]
# Median leaf temperature
t_ls = [np.median(list(Tlc_dict[date].values())) for date in meteo.time]
# Intercepted global radiation
rg_ls = np.array(rg_ls) / (soil_dimensions[0] * soil_dimensions[1])
results_dict = {
'Rg': rg_ls,
'An': an_ls,
'E': sapflow,
# 'sapEast': sapEast,
# 'sapWest': sapWest,
'Tleaf': t_ls
}
# Results DataFrame
results_df = DataFrame(results_dict, index=meteo.time)
# Write
if write_result:
results_df.to_csv(output_path + 'time_series.output',
sep=';', decimal='.')
time_off = datetime.now()
print("")
print("beg time", time_on)
print("end time", time_off)
print("--- Total runtime: %d minute(s) ---" % int((time_off - time_on).seconds / 60.))
return results_df