def _compair_pet():
"""just to compaire the pet and peyman results, the are slightly differnt,
but I think that is due to different methods of calculating PET,"""
from supporting_functions.plotting import plot_multiple_results
from basgra_python import run_basgra_nz
verbose = False
params, matrix_weather, days_harvest, doy_irr = establish_peyman_input(
False)
peyman_out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=verbose,
dll_path='default',
supply_pet=False)
params, matrix_weather, days_harvest, doy_irr = establish_peyman_input(
True)
pet_out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=verbose,
dll_path='default',
supply_pet=True)
from supporting_functions.plotting import plot_multiple_results
plot_multiple_results({'pet': pet_out, 'peyman': peyman_out})
return params, matrix_weather, days_harvest, doy_irr
def run_frequent_harvest(freq, trig, targ):
params, matrix_weather, days_harvest, doy_irr = establish_org_input(
'lincoln')
strs = [
'{}-{:03d}'.format(e, f)
for e, f in matrix_weather[['year', 'doy']].itertuples(False, None)
]
days_harvest = pd.DataFrame({
'year': matrix_weather.loc[:, 'year'],
'doy': matrix_weather.loc[:, 'doy'],
'frac_harv': np.ones(len(matrix_weather)), # set filler values
'harv_trig':
np.zeros(len(matrix_weather)) - 1, # set flag to not harvest
'harv_targ': np.zeros(len(matrix_weather)), # set filler values
'weed_dm_frac': np.zeros(len(matrix_weather)), # set filler values
})
# start harvesting at the same point
harv_days = pd.date_range(start='2011-09-03',
end='2017-04-30',
freq='{}D'.format(freq))
idx = np.in1d(pd.to_datetime(strs, format='%Y-%j'), harv_days)
days_harvest.loc[idx, 'harv_trig'] = trig
days_harvest.loc[idx, 'harv_targ'] = targ
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
return out
def run_nonirr_lincoln_low_basil(IBASAL):
params, matrix_weather, days_harvest, doy_irr = establish_org_input(
'lincoln')
matrix_weather = get_lincoln_broadfield()
matrix_weather.loc[:, 'max_irr'] = 10
matrix_weather.loc[:, 'irr_trig'] = 0
matrix_weather.loc[:, 'irr_targ'] = 1
matrix_weather = matrix_weather.loc[:, matrix_weather_keys_pet]
params['IRRIGF'] = 0 # no irrigation
params['BASALI'] = IBASAL # start at 20% basal
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
out.loc[:, 'per_fc'] = out.loc[:, 'WAL'] / out.loc[:, 'WAFC']
out.loc[:, 'per_paw'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
return out
def run_inital_basgra(basali, weed_dm_frac, harv_targ, harv_trig, freq):
"""
run an intial test
:param basali:
:param weed_dm_frac:
:return:
"""
params, matrix_weather, days_harvest, doy_irr = get_input_data(
basali,
weed_dm_frac,
harv_targ=harv_targ,
harv_trig=harv_trig,
freq=freq)
temp = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
out = {'temp': temp}
temp.to_csv(r"C:\Users\Matt Hanson\Downloads\test_get_time.csv")
plot_multiple_results(out,
out_vars=[
'DM', 'YIELD', 'DMH_RYE', 'DM_RYE_RM', 'IRRIG',
'PAW', 'DMH', 'BASAL'
])
def run_mod_past_basgra_dryland(return_inputs=False, site='oxford', reseed=True, pg_mode='from_dmh', fun='mean',
reseed_trig=0.06, reseed_basal=0.1, basali=0.2, weed_dm_frac=0.05,
use_defined_params_except_weed_dm_frac=True):
if not isinstance(weed_dm_frac, dict) and weed_dm_frac is not None:
weed_dm_frac = {e: weed_dm_frac for e in range(1, 13)}
mode = 'dryland'
print('running: {}, {}, reseed: {}'.format(mode, site, reseed))
weather = get_vcsn_record(site=site)
rest = None
params, doy_irr = get_params_doy_irr(mode)
matrix_weather = create_matrix_weather(mode, weather, rest, fix_leap=False)
days_harvest = create_days_harvest(mode, matrix_weather, site, fix_leap=False)
if not reseed:
days_harvest.loc[:, 'reseed_trig'] = -1
else:
if not use_defined_params_except_weed_dm_frac:
days_harvest.loc[days_harvest.reseed_trig > 0, 'reseed_trig'] = reseed_trig
days_harvest.loc[days_harvest.reseed_trig > 0, 'reseed_basal'] = reseed_basal
params['BASALI'] = basali
if weed_dm_frac is not None:
for m in range(1, 13):
days_harvest.loc[days_harvest.index.month == m, 'weed_dm_frac'] = weed_dm_frac[m]
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
out.loc[:, 'per_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(calc_pasture_growth(out, days_harvest, mode=pg_mode, resamp_fun=fun, freq='1d'))
out.loc[:, 'pg'] = pg.loc[:, 'pg']
out = calc_pasture_growth_anomaly(out, fun=fun)
if return_inputs:
return out, (params, doy_irr, matrix_weather, days_harvest)
return out
def run_example_basgra():
params, matrix_weather, days_harvest, doy_irr = establish_org_input()
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
def run_old_basgra():
params, matrix_weather, days_harvest, doy_irr = establish_org_input(
'lincoln')
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
return out
def _temp_basgra_run():
start_mod, dates, params, matrix_weather, days_harvest, doy_irr = _base_restriction_data(
)
print(start_mod)
temp = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
out = {'temp': temp}
temp.to_csv(r"C:\Users\Matt Hanson\Downloads\test_get_time.csv")
plot_multiple_results(out,
out_vars=[
'DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM',
'IRRIG', 'PAW'
])
def run_past_basgra_dryland(return_inputs=False, site='eyrewell', reseed=True, version='trended'):
mode = 'dryland'
print('running: {}, {}, reseed: {}'.format(mode, site, reseed))
weather = get_vcsn_record(site=site, version=version)
rest = None
params, doy_irr = get_params_doy_irr(mode)
matrix_weather = create_matrix_weather(mode, weather, rest, fix_leap=False)
days_harvest = create_days_harvest(mode, matrix_weather, site, fix_leap=False)
if not reseed:
days_harvest.loc[:, 'reseed_trig'] = -1
out = run_basgra_nz(params, matrix_weather, days_harvest, doy_irr, verbose=False)
out.loc[:, 'per_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(calc_pasture_growth(out, days_harvest, mode='from_yield', resamp_fun='mean', freq='1d'))
out.loc[:, 'pg'] = pg.loc[:, 'pg']
out = calc_pasture_growth_anomaly(out, fun='mean')
if return_inputs:
return out, (params, doy_irr, matrix_weather, days_harvest)
return out
def support_for_memory_usage():
params, matrix_weather, days_harvest, doy_irr = establish_org_input()
start_year = matrix_weather['year'].min()
start_day = matrix_weather.loc[matrix_weather.year == start_year,
'doy'].min()
# set up a maximum run time
idxs = np.random.random_integers(0, len(matrix_weather) - 1, 36600)
matrix_weather = matrix_weather.iloc[idxs]
expected_days = pd.Series(
pd.date_range(start=pd.to_datetime('{}-{}'.format(
start_year, start_day),
format='%Y-%j'),
periods=36600))
matrix_weather.loc[:, 'year'] = expected_days.dt.year.values
matrix_weather.loc[:, 'doy'] = expected_days.dt.dayofyear.values
days_harvest = _clean_harvest(days_harvest, matrix_weather)
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
def calc_past_mean(fun, pg_mode, return_norm=True, freq='month'):
site = 'oxford'
mode = 'dryland'
weather = get_vcsn_record(site=site)
rest = None
params, doy_irr = get_params_doy_irr(mode)
matrix_weather = create_matrix_weather(mode, weather, rest, fix_leap=False)
days_harvest = create_days_harvest(mode,
matrix_weather,
site,
fix_leap=False)
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
out.loc[:, 'per_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest,
mode=pg_mode,
resamp_fun=fun,
freq=freq))
out.loc[:, 'pg'] = pg.loc[:, 'pg']
out.loc[:, 'month'] = pd.Series(out.index).dt.month.values
out_norm = out.groupby('month').agg({'pg': fun})
strs = ['2011-{:02d}-15'.format(e) for e in out_norm.index]
out_norm.loc[:, 'date'] = pd.to_datetime(strs)
out_norm.set_index('date', inplace=True)
if return_norm:
return out_norm
else:
return out, out_norm
days_harvest.loc[:, 'reseed_trig'] = -1
days_harvest.loc[:, 'reseed_basal'] = 1
days_harvest.drop(columns=['percent_harvest'], inplace=True)
#---> 4.0:
# days of irrigation
doy_no_irr = [0]
doy_irr = [i for i in range(1, 120)] + [i for i in range(280, 366)]
#doy_irr = [0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31]
#---> 5.0:
# run BASGRA
outputs_no_irrig = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_no_irr,
verbose=False,
dll_path='default',
supply_pet=True)
params_irrig = copy.deepcopy(params)
params_irrig['irr_frm_paw'] = 1.0
params_irrig['IRRIGF'] = .90
outputs_irrig = run_basgra_nz(params_irrig,
matrix_weather,
days_harvest,
doy_irr,
verbose=False,
dll_path='default',
supply_pet=True)
def create_irrigation_abandomnet_data(base_name, params, reseed_trig=-1, reseed_basal=1, site='eyrewell'):
out = {}
weather = get_vcsn_record(site=site)
rest = get_restriction_record()
p, doy_irr = get_params_doy_irr(mode)
matrix_weather = create_matrix_weather(mode, weather, rest, fix_leap=False)
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / 5
matrix_weather.loc[:, 'max_irr'] = 5
days_harvest = create_days_harvest(mode, matrix_weather, site, fix_leap=False)
# set reseed days harvest
idx = days_harvest.doy == 152
days_harvest.loc[idx, 'reseed_trig'] = reseed_trig
days_harvest.loc[idx, 'reseed_basal'] = reseed_basal
# run models
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[restrict >= 0.9999, 'max_irr'] = 0
temp = run_basgra_nz(params, matrix_weather_new, days_harvest, doy_irr, verbose=False)
temp.loc[:, 'f_rest'] = 1 - matrix_weather_new.loc[:, 'max_irr'] / 5
temp.loc[:, 'pg'] = calc_pasture_growth(temp, days_harvest, 'from_yield', '1D', resamp_fun='mean')
out['{}_no_rest'.format(base_name)] = temp
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[:, 'max_irr'] *= (1 - restrict)
temp = run_basgra_nz(params, matrix_weather_new, days_harvest, doy_irr, verbose=False)
temp.loc[:, 'f_rest'] = 2 - matrix_weather_new.loc[:, 'max_irr'] / 5
temp.loc[:, 'pg'] = calc_pasture_growth(temp, days_harvest, 'from_yield', '1D', resamp_fun='mean')
out['{}_partial_rest'.format(base_name)] = temp
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[:, 'max_irr'] *= (restrict <= 0).astype(int)
temp = run_basgra_nz(params, matrix_weather_new, days_harvest, doy_irr, verbose=False)
temp.loc[:, 'f_rest'] = 3 - matrix_weather_new.loc[:, 'max_irr'] / 5
temp.loc[:, 'pg'] = calc_pasture_growth(temp, days_harvest, 'from_yield', '1D', resamp_fun='mean')
out['{}_full_rest'.format(base_name)] = temp
out['{}_mixed_rest'.format(base_name)] = (out['{}_full_rest'.format(base_name)].transpose() *
restrict.values +
out['{}_no_rest'.format(base_name)].transpose() *
(1 - restrict.values)).transpose()
# paddock level restrictions
levels = np.arange(0, 125, 25) / 100
padock_values = {}
temp_out = []
for i, (ll, lu) in enumerate(zip(levels[0:-1], levels[1:])):
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[restrict <= ll, 'max_irr'] = 5
matrix_weather_new.loc[restrict >= lu, 'max_irr'] = 0
idx = (restrict > ll) & (restrict < lu)
matrix_weather_new.loc[idx, 'max_irr'] = 5 * ((restrict.loc[idx] - ll) / 0.25)
temp = run_basgra_nz(params, matrix_weather_new, days_harvest, doy_irr, verbose=False)
temp_out.append(temp.values)
temp.loc[:, 'per_PAW'] = temp.loc[:, 'PAW'] / temp.loc[:, 'MXPAW']
temp.loc[:, 'pg'] = calc_pasture_growth(temp, days_harvest, 'from_yield', '1D', resamp_fun='mean')
temp.loc[:, 'f_rest'] = restrict
temp.loc[:, 'RESEEDED'] += i
padock_values['l:{} u:{}'.format(ll, lu)] = temp
temp = np.mean(temp_out, axis=0)
temp2 = out['{}_mixed_rest'.format(base_name)].copy(deep=True).drop(columns=['f_rest', 'pg'])
temp2.loc[:, :] = temp
temp2.loc[:, 'f_rest'] = restrict + 3
temp2.loc[:, 'pg'] = calc_pasture_growth(temp2, days_harvest, 'from_yield', '1D', resamp_fun='mean')
out['{}_paddock_rest'.format(base_name)] = temp2
for k in out:
out[k].loc[:, 'per_PAW'] = out[k].loc[:, 'PAW'] / out[k].loc[:, 'MXPAW']
return out, padock_values
def irrigation_restrictions(duration, restrict, rest, outdir, ttl_str):
"""
plot up irrigatino restriction handling
run two irritation restrictions with a rest period
:param duration: length of restriction
:param restrict: restiction 0-1
:param rest: non-restriction time in between
:param outdir: passed to plotting function
:param ttl_str: title string passed to plotting function
:return:
"""
out = {}
start_mod1, dates, params, matrix_weather, days_harvest, doy_irr = _base_restriction_data(
)
print(start_mod1)
stop_mod1 = start_mod1 + pd.DateOffset(days=duration)
start_mod2 = stop_mod1 + pd.DateOffset(days=rest)
stop_mod2 = start_mod2 + pd.DateOffset(days=duration)
idx = ((dates < stop_mod1) &
(dates >= start_mod1)) | ((dates < stop_mod2) &
(dates >= start_mod2))
out['no_rest'] = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False)
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[idx, 'max_irr'] *= (1 - restrict)
out['partial_rest'] = run_basgra_nz(params,
matrix_weather_new,
days_harvest,
doy_irr,
verbose=False)
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[idx, 'max_irr'] = 0
out['full_rest'] = run_basgra_nz(params,
matrix_weather_new,
days_harvest,
doy_irr,
verbose=False)
out['mixed_rest'] = out['full_rest'] * restrict + out['no_rest'] * (
1 - restrict)
for k in out:
out[k].loc[:,
'per_PAW'] = out[k].loc[:, 'PAW'] / out[k].loc[:, 'MXPAW']
plot_multiple_results(out,
out_vars=[
'DM', 'YIELD', 'BASAL', 'DMH_RYE', 'DM_RYE_RM',
'IRRIG', 'per_PAW'
],
outdir=outdir,
title_str=ttl_str)
def compare_oxford_irr_dry(save=False):
nsims = 100
storyline = get_baseline_storyline()
simlen = np.array([month_len[e] for e in storyline.month]).sum()
params_dry, doy_irr, all_matrix_weathers, all_days_harvests_dry = _gen_input(
storyline=storyline,
nsims=nsims,
mode='dryland',
site='oxford',
chunks=1,
current_c=1,
nperc=nsims,
simlen=simlen,
swg_dir=default_swg_dir,
fix_leap=True)
params_irr, doy_irr, all_matrix_weathers, all_days_harvests_irr = _gen_input(
storyline=storyline,
nsims=nsims,
mode='irrigated',
site='oxford',
chunks=1,
current_c=1,
nperc=nsims,
simlen=simlen,
swg_dir=default_swg_dir,
fix_leap=True)
params_irr_eyre, doy_irr_eyre, all_matrix_weathers_eyre, all_days_harvests_irr_eyre = _gen_input(
storyline=storyline,
nsims=nsims,
mode='irrigated',
site='eyrewell',
chunks=1,
current_c=1,
nperc=nsims,
simlen=simlen,
swg_dir=default_swg_dir,
fix_leap=True)
all_out_dry = np.zeros((len(out_variables), simlen, nsims)) * np.nan
all_out_dry_monk = np.zeros((len(out_variables), simlen, nsims)) * np.nan
all_out_irr = np.zeros((len(out_variables), simlen, nsims)) * np.nan
all_out_irr_eyre = np.zeros((len(out_variables), simlen, nsims)) * np.nan
for i, (matrix_weather, days_harvest_irr, days_harvest_dry,
matrix_weather_eyre, days_harvest_irr_eyre) in enumerate(
zip(all_matrix_weathers, all_days_harvests_irr,
all_days_harvests_dry, all_matrix_weathers_eyre,
all_days_harvests_irr_eyre)):
if i % 10 == 0:
print(i)
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
out = run_basgra_nz(params_irr,
matrix_weather,
days_harvest_irr,
doy_irr,
verbose=False,
run_365_calendar=True)
out.loc[:, 'PER_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest_irr,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out_irr[:, :, i] = out.loc[:, out_variables].values.transpose()
# run eyrewell
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest_irr,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out_irr[:, :, i] = out.loc[:, out_variables].values.transpose()
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
out = run_basgra_nz(params_irr_eyre,
matrix_weather_eyre,
days_harvest_irr_eyre,
doy_irr_eyre,
verbose=False,
run_365_calendar=True)
out.loc[:, 'PER_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest_irr_eyre,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out_irr_eyre[:, :, i] = out.loc[:,
out_variables].values.transpose()
# run dryland
matrix_weather.loc[:, 'max_irr'] = 0
matrix_weather.loc[:, 'irr_trig'] = 0
matrix_weather.loc[:, 'irr_targ'] = 0
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
out = run_basgra_nz(params_dry,
matrix_weather,
days_harvest_dry,
doy_irr,
verbose=False,
run_365_calendar=True)
out.loc[:, 'PER_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest_dry,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out_dry[:, :, i] = out.loc[:, out_variables].values.transpose()
# run dryland monk
for m in range(1, 13):
days_harvest_dry.loc[days_harvest_dry.index.month == m,
'weed_dm_frac'] = weed_dict_2[m]
matrix_weather.loc[:, 'max_irr'] = 0
matrix_weather.loc[:, 'irr_trig'] = 0
matrix_weather.loc[:, 'irr_targ'] = 0
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
out = run_basgra_nz(params_dry,
matrix_weather,
days_harvest_dry,
doy_irr,
verbose=False,
run_365_calendar=True)
out.loc[:, 'PER_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest_dry,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out_dry_monk[:, :, i] = out.loc[:,
out_variables].values.transpose()
all_out_dry = pd.DataFrame(np.nanmean(all_out_dry, axis=2).transpose(),
columns=out_variables,
index=matrix_weather.index)
all_out_dry_monk = pd.DataFrame(np.nanmean(all_out_dry_monk,
axis=2).transpose(),
columns=out_variables,
index=matrix_weather.index)
all_out_irr = pd.DataFrame(np.nanmean(all_out_irr, axis=2).transpose(),
columns=out_variables,
index=matrix_weather.index)
all_out_irr_eyre = pd.DataFrame(np.nanmean(all_out_irr_eyre,
axis=2).transpose(),
columns=out_variables,
index=matrix_weather.index)
if save:
all_out_irr.to_csv(
"D:\mh_unbacked\SLMACC_2020\one_off_data\irr_baseline.csv")
all_out_irr_eyre.to_csv(
"D:\mh_unbacked\SLMACC_2020\one_off_data\dry_baseline.csv")
all_out_dry.to_csv(
"D:\mh_unbacked\SLMACC_2020\one_off_data\dry_baseline.csv")
all_out_dry_monk.to_csv(
"D:\mh_unbacked\SLMACC_2020\one_off_data\dry_monk_baseline.csv")
all_out_irr.loc[:, 'pg'] = all_out_irr.loc[:, 'PGR']
all_out_irr_eyre.loc[:, 'pg'] = all_out_irr_eyre.loc[:, 'PGR']
all_out_dry.loc[:, 'pg'] = all_out_dry.loc[:, 'PGR']
all_out_dry_monk.loc[:, 'pg'] = all_out_dry_monk.loc[:, 'PGR']
data = {
'dryland_monkeying': all_out_dry_monk,
'dryland_baseline': all_out_dry,
'irrigated_oxford': all_out_irr,
'irrigated_eyrewell': all_out_irr_eyre,
}
fun = 'mean'
data2 = {e: make_mean_comparison(v, fun) for e, v in data.items()}
data2['horata'] = get_horarata_data_old()
plot_multiple_monthly_results(data=data2,
out_vars=['pg', 'pgr'],
show=True,
main_kwargs={'marker': 'o'})
def _run_paddock_rest(storyline_key, outdir, storyline, nsims, mode, site,
simlen, save_daily, description, storyline_text, swg_dir,
verbose, n_parallel, fix_leap):
"""
run storyline through paddock restrictions...
:param storyline:
:param swg_path:
:param nsims:
:param mode:
:param site:
:return:
"""
# paddock level restrictions
levels = np.arange(
0, 125, 25
) / 100 # levels already capture the extra (mean run) as levels has start stop
number_run = int(psutil.virtual_memory().available //
(memory_per_run *
(simlen / 365) * len(levels)) / n_parallel)
chunks = int(-1 * (-nsims // number_run))
if chunks == 1:
number_run = nsims
all_outpaths = []
for c in range(chunks):
if verbose:
print('running paddock pasture growth for chunk {} of {} {}-{}'.
format(c + 1, chunks, site, mode))
params, doy_irr, all_matrix_weathers, all_days_harvests = _gen_input(
storyline=storyline,
nsims=nsims,
mode=mode,
site=site,
chunks=chunks,
current_c=c,
nperc=number_run,
simlen=simlen,
swg_dir=swg_dir,
fix_leap=fix_leap)
all_out = np.zeros(
(len(out_variables), simlen, len(levels) - 1, number_run)) * np.nan
out_names = []
out_limits = []
for j, (matrix_weather, days_harvest) in enumerate(
zip(all_matrix_weathers, all_days_harvests)):
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
matrix_weather.loc[:, 'max_irr'] = abs_max_irr
for i, (ll, lu) in enumerate(zip(levels[0:-1], levels[1:])):
if j == 0:
out_name = 'paddock_{}'.format(i)
out_names.append(out_name)
out_limits.append('lower_{}_upper_{}'.format(ll, lu))
matrix_weather_new = matrix_weather.copy(deep=True)
matrix_weather_new.loc[restrict <= ll, 'max_irr'] = abs_max_irr
matrix_weather_new.loc[restrict >= lu, 'max_irr'] = 0
idx = (restrict > ll) & (restrict < lu)
matrix_weather_new.loc[idx, 'max_irr'] = abs_max_irr * (
(restrict.loc[idx] - ll) / 0.25)
temp = run_basgra_nz(params,
matrix_weather_new,
days_harvest,
doy_irr,
verbose=False,
run_365_calendar=fix_leap)
temp.loc[:,
'PER_PAW'] = temp.loc[:, 'PAW'] / temp.loc[:, 'MXPAW']
temp.loc[:, 'PGR'] = calc_pasture_growth(temp,
days_harvest,
'from_yield',
'1D',
resamp_fun='mean')
temp.loc[:, 'F_REST'] = restrict
all_out[:, :, i,
j] = temp.loc[:, out_variables].values.transpose()
# one netcdf for each mode,site, (paddock, mean)
month = temp.index.month.values
doy = temp.loc[:, 'doy'].values
year = temp.loc[:, 'year'].values
outpaths = _output_to_nc_paddock(storyline_key=storyline_key,
storyline_text=storyline_text,
outdir=outdir,
outdata=all_out,
nsims=nsims,
month=month,
doy=doy,
year=year,
chunks=chunks,
current_c=c,
nperchunk=number_run,
out_names=out_names,
out_limits=out_limits,
save_daily=save_daily,
description=description,
site=site,
mode=mode,
verbose=verbose)
all_outpaths.extend(outpaths)
for p in np.unique(all_outpaths):
add_pasture_growth_anaomoly_to_nc(p)
def _run_simple_rest(storyline, nsims, mode, site, simlen, storyline_key,
outdir, save_daily, description, storyline_text, swg_dir,
verbose, n_parallel, fix_leap):
number_run = int(psutil.virtual_memory().available // memory_per_run *
(simlen / 365) / n_parallel)
chunks = int(-1 * (-nsims // number_run))
if chunks == 1:
number_run = nsims
all_outpaths = []
for c in range(chunks):
if verbose:
print(
'starting chunk {} of {} for simple irrigation restrictions {}-{}'
.format(c + 1, chunks, site, mode))
params, doy_irr, all_matrix_weathers, all_days_harvests = _gen_input(
storyline=storyline,
nsims=nsims,
mode=mode,
site=site,
chunks=chunks,
current_c=c,
nperc=number_run,
simlen=simlen,
swg_dir=swg_dir,
fix_leap=fix_leap)
all_out = np.zeros((len(out_variables), simlen, number_run)) * np.nan
for i, (matrix_weather, days_harvest) in enumerate(
zip(all_matrix_weathers, all_days_harvests)):
restrict = 1 - matrix_weather.loc[:, 'max_irr'] / abs_max_irr
out = run_basgra_nz(params,
matrix_weather,
days_harvest,
doy_irr,
verbose=False,
run_365_calendar=fix_leap)
out.loc[:, 'PER_PAW'] = out.loc[:, 'PAW'] / out.loc[:, 'MXPAW']
pg = pd.DataFrame(
calc_pasture_growth(out,
days_harvest,
mode='from_yield',
resamp_fun='mean',
freq='1d'))
out.loc[:, 'PGR'] = pg.loc[:, 'pg']
out.loc[:, 'F_REST'] = restrict
all_out[:, :, i] = out.loc[:, out_variables].values.transpose()
# one netcdf file for each mode/site
month = out.index.month.values
doy = out.loc[:, 'doy'].values
year = out.loc[:, 'year'].values
outpath = _output_to_nc(storyline_key=storyline_key,
storyline_text=storyline_text,
outdir=outdir,
outdata=all_out,
nsims=nsims,
month=month,
doy=doy,
year=year,
chunks=chunks,
current_c=c,
nperchunk=number_run,
save_daily=save_daily,
description=description,
site=site,
mode=mode,
verbose=verbose)
add_pasture_growth_anaomoly_to_nc(outpath)
