def terraclimate_fia_wide():
# generate wide data w/ terraclim
df_fia = load.fia(store='az', states=states, group_repeats=True)
df = load.terraclim(
store='az',
tlim=(int(df_fia['year_0'].min()), 2020),
variables=variables,
df=df_fia,
group_repeats=True,
sampling='annual',
)
write_df(df, 'FIA-TerraClim-Wide')
def terraclimate_fia_long():
# generate long data w/ terraclim
df_fia = load.fia(store='az', states=states, clean=False)
for target in targets_terraclimate:
tlim = (str(int(target) - 5), str(int(target) + 4))
print(tlim)
df = load.terraclim(
store='az',
tlim=(int(tlim[0]), int(tlim[1])),
variables=variables,
df=df_fia,
sampling='annual',
)
write_df(df, f'FIA-TerraClim-Long-{tlim[0]}.{tlim[1]}')
def cmip_fia_long(cmip_table, method):
# generate long data w/ cmip
df_fia = load.fia(store='az', states=states, clean=False)
keep_vars = (['lat', 'lon', 'plot_cn'] +
[var + '_min' for var in variables] +
[var + '_mean'
for var in variables] + [var + '_max' for var in variables])
for i, row in cmip_table.iterrows():
if 'hist' in row.scenario:
targets = targets_historical
else:
targets = targets_future
for target in targets:
historical = target == '2015'
tlim = (str(int(target) - 5), str(int(target) + 4))
print(tlim, row)
df = load.cmip(
store='az',
tlim=(int(tlim[0]), int(tlim[1])),
variables=variables,
df=df_fia,
model=row.model,
scenario=row.scenario,
member=row.member,
historical=historical,
method=method,
sampling='annual',
)
df = df[keep_vars]
write_df(
df,
f'{method}/FIA-CMIP6-Long-{row.model}.{row.scenario}.{row.member}-{tlim[0]}.{tlim[1]}',
)
from tqdm import tqdm
from carbonplan_forest_risks import fit, load, prepare, utils
args = sys.argv
if len(args) < 2:
store = 'local'
else:
store = args[1]
data_vars = ['ppt', 'tavg']
data_aggs = ['sum', 'mean']
print('[insects] loading data')
df = load.fia(store=store, states='conus', group_repeats=True)
df = load.terraclim(
store=store,
tlim=(int(df['year_0'].min()), 2020),
data_vars=data_vars,
data_aggs=data_aggs,
df=df,
group_repeats=True,
)
print('[insects] prepare for fitting')
x, y, pf = prepare.insects(df)
x_z, x_mean, x_std = utils.zscore_2d(x)
codes = pf['type_code'].unique()
print('[insects] fit models')
import pandas as pd
from tqdm import tqdm
from carbonplan_forest_risks import fit, load
args = sys.argv
if len(args) < 2:
store = 'local'
else:
store = args[1]
variables = ['tmean', 'ppt']
print('[biomass] loading data')
df = load.fia(store=store, states='conus')
df = load.terraclim(
store=store,
tlim=(2000, 2020),
variables=variables,
remove_nans=True,
sampling='annual',
df=df,
)
type_codes = df['type_code'].unique()
print('[biomass] fitting models')
models = {}
for code in tqdm(type_codes):
df_type = df[df['type_code'] == code].reset_index()
x = df_type['age']