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]}')
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')
models = {}
for code in tqdm(codes):
inds = pf['type_code'] == code
if (y[inds].sum() > 1) & (y[inds].sum() > 1):
model = fit.hurdle(x=x_z[inds], y=y[inds])
coarsen = 4
tlim = (1984, 2018)
variables = ["ppt", "tmean", "cwd"]
store = "az"
print('[stats] loading data')
mask = (load.nlcd(store=store, year=2001).sel(band=[41, 42, 43, 90]).sum('band') > 0.25).astype(
'float'
)
nftd = load.nftd(store=store, area_threshold=1500, coarsen=coarsen, mask=mask)
climate = load.terraclim(
store=store,
tlim=(tlim[0] - 1, tlim[1]),
coarsen=coarsen,
variables=variables,
mask=mask,
sampling="monthly",
)
mtbs = load.mtbs(store=store, coarsen=coarsen, tlim=tlim, mask=mask)
prepend = climate.sel(time=slice('1983', '1983'))
x, y = prepare.fire(
climate.sel(time=slice('1984', '2018')),
nftd,
mtbs,
add_global_climate_trends={
'tmean': {'climate_prepend': prepend, 'rolling_period': 12},
'ppt': {'climate_prepend': prepend, 'rolling_period': 12},
},
add_local_climate_trends=None,
tlim = (1983, 2018)
analysis_tlim = slice('1984', '2018')
print('[fire] loading data')
mask = (load.nlcd(store=store, year=2001).sel(
band=[41, 42, 43, 90]).sum('band') > 0.25).astype('float')
nftd = load.nftd(store=store,
groups='all',
coarsen=coarsen_fit,
mask=mask,
area_threshold=1500)
climate = load.terraclim(
store=store,
tlim=tlim,
coarsen=coarsen_fit,
variables=data_vars,
mask=mask,
sampling="monthly",
)
mtbs = load.mtbs(store=store, coarsen=coarsen_fit, tlim=tlim, mask=mask)
mtbs = mtbs.assign_coords({'x': nftd.x, 'y': nftd.y})
print('[fire] fitting model')
prepend = climate.sel(time=slice('1983', '1983'))
x, y = prepare.fire(
climate.sel(time=slice('1984', '2018')),
nftd,
mtbs,
add_global_climate_trends={
'tmean': {
'climate_prepend': prepend,
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']
y = df_type['biomass']
f = [df_type['tmean_mean'], df_type['ppt_mean']]
model = fit.growth(x=x, y=y, f=f, noise='gamma')
models[code] = model