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_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 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)