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_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 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
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
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
if __name__ == '__main__':
save = False
params, doy = get_params_doy_irr(mode)
outdir = ksl_env.shared_drives(r"Z2003_SLMACC\pasture_growth_modelling\irrigation_tuning")
if not os.path.exists(outdir):
os.makedirs(outdir)
out, paddock_values = create_irrigation_abandomnet_data('b', params, reseed_trig=0.691, reseed_basal=0.722,
site='eyrewell')
# run some stats
outkeys = ['BASAL', 'f_rest', 'IRRIG', 'per_PAW', 'pg']
for k, v in out.items():
v.loc[:, 'month'] = v.index.month
rest = get_restriction_record()
rest = rest.groupby(['year', 'month']).sum()
idxs = pd.MultiIndex.from_product([pd.unique(out['b_paddock_rest'].loc[:, 'year']),
pd.unique(out['b_paddock_rest'].loc[:, 'month'])], names=['year', 'month'])
def _gen_input(storyline, nsims, mode, site, chunks, current_c, nperc, simlen,
swg_dir, fix_leap):
"""
:param storyline: loaded storyline
:param SWG_path: path to the directory with contining the files from the SWG
:param nsims: number of sims to run
:param mode: one of ['irrigated', 'dryland']
:param site: one of ['eyrewell', 'oxford']
:param chunks: the number of chunks
:param current_c: the current chunk (from range(chunks)
:param nperc: number of simulations that can be run per chunk
:return:
"""
# manage chunks
if chunks == 1:
num_to_pull = nsims
elif chunks > 1:
num_to_pull = nperc
if current_c + 1 == chunks:
# manage last chunk
num_to_pull = nsims - (current_c * nperc)
else:
raise ValueError('shouldnt get here')
params, doy_irr = get_params_doy_irr(mode, site)
matrix_weathers = []
days_harvests = []
# get restriction data
if mode == 'dryland':
rest_data = np.repeat([None], num_to_pull)
elif mode == 'irrigated':
rest_data = get_irr_data(num_to_pull, storyline, simlen)
else:
raise ValueError('weird arg for mode: {}'.format(mode))
# get weather data
weather_data = _get_weather_data(storyline=storyline,
nsims=num_to_pull,
simlen=simlen,
swg_dir=swg_dir,
site=site,
fix_leap=fix_leap)
# make all the other data
for rest, weather in zip(rest_data, weather_data):
if rest is None:
rest_temp = None
else:
rest_temp = pd.DataFrame(data=rest,
index=weather.index,
columns=['frest'])
matrix_weather = create_matrix_weather(mode,
weather_data=weather,
restriction_data=rest_temp,
rest_key='frest',
fix_leap=fix_leap)
matrix_weathers.append(matrix_weather)
days_harvests.append(
create_days_harvest(mode, matrix_weather, site, fix_leap=fix_leap))
return params, doy_irr, matrix_weathers, days_harvests