def adelR(nplants,dd):
devT = devCsv('./data/axeTCa0N.csv','./data/dimTCa0N.csv','./data/phenTCa0N.csv','./data/earTCa0N.csv','./data/ssi2sen.csv')
geoLeaf = genGeoLeaf()
geoAxe = genGeoAxe()
pars = setAdel(devT,geoLeaf,geoAxe,nplants)
cantable = RunAdel(dd,pars)
return pars,cantable
def adelR(nplants, dd):
devT = devCsv('./data/axeTCa0N.csv', './data/dimTCa0N.csv',
'./data/phenTCa0N.csv', './data/earTCa0N.csv',
'./data/ssi2sen.csv')
geoLeaf = genGeoLeaf()
geoAxe = genGeoAxe()
pars = setAdel(devT, geoLeaf, geoAxe, nplants)
cantable = RunAdel(dd, pars)
return pars, cantable
def devT():
from alinea.adel.AdelR import devCsv
axeT = datadir + '/data/axeTCa0N.csv'
dimT = datadir + '/data/dimTCa0N.csv'
phenT = datadir + '/data/phenTCa0N.csv'
earT = datadir + '/data/earTCa0N.csv'
ssisenT = datadir + '/data/ssi2sen.csv'
return devCsv(axeT, dimT, phenT, earT, ssisenT)
def devT():
from alinea.adel.AdelR import devCsv
axeT = datadir + '/data/axeTCa0N.csv'
dimT = datadir + '/data/dimTCa0N.csv'
phenT = datadir + '/data/phenTCa0N.csv'
earT = datadir + '/data/earTCa0N.csv'
ssisenT = datadir + '/data/ssi2sen.csv'
return devCsv(axeT,dimT,phenT,earT,ssisenT)
def test_organ_length():
dir = './data/test_Adel_Maxwell_plante11/Maxwell_'
sufix = '_plante11.csv'
axeTpath = dir + 'axeT' + sufix
dimTpath = dir + 'dimT' + sufix
phenTpath = dir + 'phenT' + sufix
earTpath = dir + 'earT.csv'
ssi2senpath = dir + 'ssi2sen.csv'
devT = devCsv(axeTpath, dimTpath, phenTpath, earTpath, ssi2senpath)
geoLeaf = genGeoLeaf()
geoAxe = genGeoAxe()
pars = setAdel(devT,geoLeaf,geoAxe,1,seed = 1)
cantables = map(lambda(x): RunAdel(x,pars), range(0,2300,100))
expected = csvAsDict(dir + 'reference_simulation.csv')
for k in ('TT','plant','numphy','Ll','Gl','El','Lv','Gv','Ev','Esen','Lsen','Gsen'):
sim = reduce(lambda x,y: x + y, (t[k].tolist() for t in cantables))
exp = expected[k]
np.testing.assert_allclose(sim,exp)
def test_organ_length():
dir = './data/test_Adel_Maxwell_plante11/Maxwell_'
sufix = '_plante11.csv'
axeTpath = dir + 'axeT' + sufix
dimTpath = dir + 'dimT' + sufix
phenTpath = dir + 'phenT' + sufix
earTpath = dir + 'earT.csv'
ssi2senpath = dir + 'ssi2sen.csv'
devT = devCsv(axeTpath, dimTpath, phenTpath, earTpath, ssi2senpath)
geoLeaf = genGeoLeaf()
geoAxe = genGeoAxe()
pars = setAdel(devT,geoLeaf,geoAxe,1,seed = 1)
cantables = map(lambda(x): RunAdel(x,pars), range(0,2300,100))
expected = csvAsDict(dir + 'reference_simulation.csv')
for k in ('TT','plant','numphy','Ll','Gl','El','Lv','Gv','Ev','Esen','Lsen','Gsen'):
sim = reduce(lambda x,y: x + y, (t[k].tolist() for t in cantables))
exp = expected[k]
#np.testing.assert_allclose(sim,exp)
def create_init_MTG_with_tillers(nplants=1,
sowing_density=250.,
plant_density=250.,
inter_row=0.15,
nff=12,
nsect=1,
seed=1,
leaves=None):
stand = AgronomicStand(sowing_density=sowing_density,
plant_density=plant_density,
inter_row=inter_row,
noise=0.04,
density_curve_data=None)
em = TillerEmission(primary_tiller_probabilities={
'T1': 1.,
'T2': 0.5,
'T3': 0.5,
'T4': 0.3
})
reg = TillerRegression(ears_per_plant=3)
axp = AxePop(MS_leaves_number_probabilities={str(nff): 1},
Emission=em,
Regression=reg)
plants = axp.plant_list(nplants=nplants)
hs = HaunStage(mean_nff=nff)
pgen = PlantGen(HSfit=hs)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort_values(['id_plt', 'id_cohort', 'N_phytomer'])
devT = devCsv(axeT, dimT, phenT)
adel = AdelWheatDyn(nplants=nplants,
nsect=nsect,
devT=devT,
stand=stand,
seed=seed,
sample='sequence',
leaves=leaves,
scene_unit='m')
age = hs.TT(
reg.hs_debreg(nff=nff)
) # date a laquelle debut des regression. Problemes : 1) toutes les feuilles pas encore visibles, 2) il y a des feuilles senescentes
g = adel.setup_canopy(
age
) # MG : je ne crois pas que id_cohort soit renvoyee par la fonction R runAdel
return adel, g
def create_init_MTG_with_tillers(nplants=1, sowing_density=250., plant_density=250.,
inter_row=0.15, nff=12, nsect=1, seed=1):
stand = AgronomicStand(sowing_density=sowing_density,
plant_density=plant_density, inter_row=inter_row,
noise=0.04, density_curve_data=None)
em = TillerEmission()
reg = TillerRegression(ears_per_plant=3)
axp = AxePop(MS_leaves_number_probabilities={str(nff): 1}, Emission=em, Regression=reg)
plants = axp.plant_list(nplants=nplants)
hs = HaunStage(mean_nff=nff)
pgen = PlantGen(HSfit=hs)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort_values(['id_plt', 'id_cohort', 'N_phytomer'])
devT = devCsv(axeT, dimT, phenT)
adel = AdelWheatDyn(nplants=nplants, nsect=nsect, devT=devT, stand=stand,
seed=seed, sample='sequence', scene_unit='m')
age = hs.TT(12)
g = adel.setup_canopy(age)
return adel, g
def alep_custom_reconstructions(variety='Tremie13', nplants=30,
sowing_density=250.,
plant_density=250., inter_row=0.15,
nsect=7, seed=1,
scale_HS = 1,
scale_leafDim_length = 1,
scale_leafDim_width = 1,
scale_leafRate=1,
scale_stemDim = 1,
scale_stemRate=1,
scale_fallingRate=1,
scale_leafSenescence=1,
scale_tillering=1,
**kwds):
parameters = reconstruction_parameters()
stand = AgronomicStand(sowing_density=sowing_density,
plant_density=plant_density,
inter_row=inter_row, noise=0.04,
density_curve_data = None)
n_emerged = nplants
MS_leaves_number_probabilities={'11': 0.3, '12': 0.7}
tiller_proba = {'T1':1., 'T2':0.5, 'T3':0.5, 'T4':0.3}
ears_per_plant = 2.5
if 'tiller_probability' in kwds:
tiller_proba = {k:min(1, p*kwds['tiller_probability']) for k,p in tiller_proba.iteritems()}
if 'proba_main_nff' in kwds:
MS_leaves_number_probabilities = {'11':1-kwds['proba_main_nff'], '12':kwds['proba_main_nff']}
# scale _tillering
tiller_proba = {k:min(1, p * scale_tillering) for k,p in tiller_proba.iteritems()}
em = pgen_ext.TillerEmission(primary_tiller_probabilities=tiller_proba)
reg = pgen_ext.TillerRegression(ears_per_plant = ears_per_plant * scale_tillering)
#generate plants
HSfit = HS_fit()[variety]
axp = pgen_ext.AxePop(MS_leaves_number_probabilities = MS_leaves_number_probabilities,
Emission=em, Regression=reg, std_em=HSfit.std_TT_hs_0)
plants = axp.plant_list(n_emerged)
# Measure and save Green leaf durations on the reference
adel_pars = parameters['adel_pars']
HSfit.mean_nff = axp.mean_nff()
Dimfit = dimension_fits(HS_fit(), **parameters)[variety]
GLfit = GL_fits(HS_fit(), **parameters)[variety]
pgen = pgen_ext.PlantGen(HSfit=HSfit, GLfit=GLfit, Dimfit=Dimfit, adel_pars = adel_pars)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort(['id_plt', 'id_cohort', 'N_phytomer'])
GreenDuration1_ref = axeT['TT_sen_phytomer1'] - axeT['TT_em_phytomer1']
phenT = phenT.sort(['id_phen', 'index_phytomer'])
dTTsen_ref = phenT['dTT_sen_phytomer']
dTTem_ref = phenT['dTT_em_phytomer']
# Modify fits
# HS_fit
# petit commentaire : scale_HS determine ligulation rate + emergence rate
# la duree emergence-ligulation est impactee du fait de ce changement (inevitable si on impose leaf_rate)
# mais aussi en raison de scale_leafRate(non souhaite => compensation ci dessous)
HS_ref = HSfit.a_cohort
leafDuration_ref = adel_pars['leafDuration']
HSfit.a_cohort /= scale_HS
# on compense en decalant l'emergence des plante
HSfit.TT_hs_0 += (1 - adel_pars['fracLeaf']) / HS_ref * leafDuration_ref * (float(scale_HS) / scale_leafRate - 1)
# Modify leaf dimension and growth rate
adel_pars['leafDuration'] = ((scale_leafDim_length*scale_leafDim_width) / scale_HS)* \
(leafDuration_ref / scale_leafRate)
Dimfit.scale['L_blade'] *= scale_leafDim_length
Dimfit.scale['W_blade'] *= scale_leafDim_width
# Modify stem dimension and growth rate
stemDuration_ref = adel_pars['stemDuration']
adel_pars['stemDuration'] = (scale_stemDim / scale_HS)* \
(stemDuration_ref / scale_stemRate)
Dimfit.scale['L_internode'] *= scale_stemDim
# Scale GL : GL flag, bolting and start_senescence : apply factor
# TEST EXTREME WITH LEAF RATE
#GLfit.GL_bolting *= scale_leafSenescence
#GLfit.GL_flag *= scale_leafSenescence
#GLfit.n0 *= scale_leafSenescence
# Modify green leaf durations and falling rate
pgen = pgen_ext.PlantGen(HSfit=HSfit, GLfit=GLfit, Dimfit=Dimfit, adel_pars = adel_pars)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort(['id_plt', 'id_cohort', 'N_phytomer'])
phenT = phenT.sort(['id_phen', 'index_phytomer'])
# Modify senescence
axeT['TT_sen_phytomer1'] = axeT['TT_em_phytomer1'] + scale_leafSenescence * GreenDuration1_ref
phenT['dTT_sen_phytomer'] = dTTsen_ref + (phenT['dTT_em_phytomer'] - dTTem_ref)
devT = devCsv(axeT, dimT, phenT)
leaves = leafshape_fits(**parameters)[variety]
bins_ref = leaves.bins
bins_ref[-1] = 21.
bins = [x * scale_HS / scale_fallingRate if i_x!=0 else x*scale_HS for i_x,x in enumerate(bins_ref)]
leaves.bins = bins
return AdelWheat(nplants = nplants, nsect=nsect, devT=devT, stand = stand ,
seed=seed, sample='sequence', leaves = leaves, run_adel_pars = adel_pars)
def alep_custom_reconstructions(variety='Tremie13',
nplants=30,
sowing_density=250.,
plant_density=250.,
inter_row=0.15,
nsect=7,
seed=1,
scale_HS=1,
scale_leafDim_length=1,
scale_leafDim_width=1,
scale_leafRate=1,
scale_stemDim=1,
scale_stemRate=1,
scale_fallingRate=1,
scale_leafSenescence=1,
scale_tillering=1,
**kwds):
parameters = reconstruction_parameters()
stand = AgronomicStand(sowing_density=sowing_density,
plant_density=plant_density,
inter_row=inter_row,
noise=0.04,
density_curve_data=None)
n_emerged = nplants
MS_leaves_number_probabilities = {'11': 0.3, '12': 0.7}
tiller_proba = {'T1': 1., 'T2': 0.5, 'T3': 0.5, 'T4': 0.3}
ears_per_plant = 2.5
if 'tiller_probability' in kwds:
tiller_proba = {
k: min(1, p * kwds['tiller_probability'])
for k, p in tiller_proba.iteritems()
}
if 'proba_main_nff' in kwds:
MS_leaves_number_probabilities = {
'11': 1 - kwds['proba_main_nff'],
'12': kwds['proba_main_nff']
}
# scale _tillering
tiller_proba = {
k: min(1, p * scale_tillering)
for k, p in tiller_proba.iteritems()
}
em = pgen_ext.TillerEmission(primary_tiller_probabilities=tiller_proba)
reg = pgen_ext.TillerRegression(ears_per_plant=ears_per_plant *
scale_tillering)
#generate plants
HSfit = HS_fit()[variety]
axp = pgen_ext.AxePop(
MS_leaves_number_probabilities=MS_leaves_number_probabilities,
Emission=em,
Regression=reg,
std_em=HSfit.std_TT_hs_0)
plants = axp.plant_list(n_emerged)
# Measure and save Green leaf durations on the reference
adel_pars = parameters['adel_pars']
HSfit.mean_nff = axp.mean_nff()
Dimfit = dimension_fits(HS_fit(), **parameters)[variety]
GLfit = GL_fits(HS_fit(), **parameters)[variety]
pgen = pgen_ext.PlantGen(HSfit=HSfit,
GLfit=GLfit,
Dimfit=Dimfit,
adel_pars=adel_pars)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort(['id_plt', 'id_cohort', 'N_phytomer'])
GreenDuration1_ref = axeT['TT_sen_phytomer1'] - axeT['TT_em_phytomer1']
phenT = phenT.sort(['id_phen', 'index_phytomer'])
dTTsen_ref = phenT['dTT_sen_phytomer']
dTTem_ref = phenT['dTT_em_phytomer']
# Modify fits
# HS_fit
# petit commentaire : scale_HS determine ligulation rate + emergence rate
# la duree emergence-ligulation est impactee du fait de ce changement (inevitable si on impose leaf_rate)
# mais aussi en raison de scale_leafRate(non souhaite => compensation ci dessous)
HS_ref = HSfit.a_cohort
leafDuration_ref = adel_pars['leafDuration']
HSfit.a_cohort /= scale_HS
# on compense en decalant l'emergence des plante
HSfit.TT_hs_0 += (1 -
adel_pars['fracLeaf']) / HS_ref * leafDuration_ref * (
float(scale_HS) / scale_leafRate - 1)
# Modify leaf dimension and growth rate
adel_pars['leafDuration'] = ((scale_leafDim_length*scale_leafDim_width) / scale_HS)* \
(leafDuration_ref / scale_leafRate)
Dimfit.scale['L_blade'] *= scale_leafDim_length
Dimfit.scale['W_blade'] *= scale_leafDim_width
# Modify stem dimension and growth rate
stemDuration_ref = adel_pars['stemDuration']
adel_pars['stemDuration'] = (scale_stemDim / scale_HS)* \
(stemDuration_ref / scale_stemRate)
Dimfit.scale['L_internode'] *= scale_stemDim
# Scale GL : GL flag, bolting and start_senescence : apply factor
# TEST EXTREME WITH LEAF RATE
#GLfit.GL_bolting *= scale_leafSenescence
#GLfit.GL_flag *= scale_leafSenescence
#GLfit.n0 *= scale_leafSenescence
# Modify green leaf durations and falling rate
pgen = pgen_ext.PlantGen(HSfit=HSfit,
GLfit=GLfit,
Dimfit=Dimfit,
adel_pars=adel_pars)
axeT, dimT, phenT = pgen.adelT(plants)
axeT = axeT.sort(['id_plt', 'id_cohort', 'N_phytomer'])
phenT = phenT.sort(['id_phen', 'index_phytomer'])
# Modify senescence
axeT['TT_sen_phytomer1'] = axeT[
'TT_em_phytomer1'] + scale_leafSenescence * GreenDuration1_ref
phenT['dTT_sen_phytomer'] = dTTsen_ref + (phenT['dTT_em_phytomer'] -
dTTem_ref)
devT = devCsv(axeT, dimT, phenT)
leaves = leafshape_fits(**parameters)[variety]
bins_ref = leaves.bins
bins_ref[-1] = 21.
bins = [
x * scale_HS / scale_fallingRate if i_x != 0 else x * scale_HS
for i_x, x in enumerate(bins_ref)
]
leaves.bins = bins
return AdelWheat(nplants=nplants,
nsect=nsect,
devT=devT,
stand=stand,
seed=seed,
sample='sequence',
leaves=leaves,
run_adel_pars=adel_pars)