def run_hi(obs_fname, train_fname, predict_fname):
varnames = {'tmax': 'tasmax', 'tmin': 'tasmin', 'pcp': 'pr'}
chunks = None
if 'hist' in predict_fname:
predict_time_bounds = slice('1950', '2006')
else:
predict_time_bounds = slice('2006', '2099')
anoms = xr.Dataset()
out = xr.Dataset()
# get variables from the obs/training/prediction datasets
ds_obs = xr.open_mfdataset(obs_fname, chunks=chunks, concat_dim='time',
data_vars='minimal')
time_bounds = slice(ds_obs.indexes['time'][0], ds_obs.indexes['time'][-1])
for obs_var, gcm_var in varnames.items():
obs_keep_vars = [obs_var, 'lon', 'lat']
ds_obs_daily = ds_obs[obs_keep_vars]
ds_obs_daily[obs_var] = ds_obs_daily[obs_var].astype('f4')
times = pd.date_range('1990-01-01', '2014-12-31', freq='D')
ds_obs_daily = ds_obs_daily.reindex(time=times, method='nearest')
ds_obs_1var = ds_obs_daily.resample(
time='MS', keep_attrs=True).mean('time').load()
for i, v in enumerate(obs_keep_vars):
ds_obs_1var[v].attrs = ds_obs[v].attrs
if i:
ds_obs_1var[v].encoding['_FillValue'] = None
print('ds_obs_1var')
ds_obs_1var.info()
da_train = xr.open_mfdataset(
train_fname.format(gcm_var=gcm_var), chunks=chunks,
concat_dim='time', data_vars='minimal')[gcm_var].sel(
time=time_bounds).astype('f4').resample(
time='MS').mean('time').load()
da_predict = xr.open_mfdataset(
predict_fname.format(gcm_var=gcm_var), chunks=chunks,
concat_dim='time', data_vars='minimal')[gcm_var].sel(
time=predict_time_bounds).astype('f4').resample(
time='MS').mean('time').load()
anoms[obs_var] = bcsd(ds_obs_1var, da_train.to_dataset(name=obs_var),
da_predict.to_dataset(name=obs_var),
var=obs_var)
out[obs_var] = disagg(ds_obs_daily[obs_var], anoms[obs_var],
var=obs_var)
gc.collect()
return anoms, out
def run_ak(obs_fname, train_fname, predict_fname):
# Alaska
varnames = {'tmax': 'tasmax', 'tmin': 'tasmin', 'pcp': 'pr'}
chunks = None
if 'hist' in predict_fname:
predict_time_bounds = slice('1950', '2006')
else:
predict_time_bounds = slice('2006', '2099')
anoms = xr.Dataset()
out = xr.Dataset()
# get variables from the obs/training/prediction datasets
ds_obs = xr.open_mfdataset(obs_fname,
chunks=chunks,
concat_dim='time',
data_vars='minimal')
time_bounds = slice(ds_obs.indexes['time'][0], ds_obs.indexes['time'][-1])
ds_obs.coords['xc'] = ds_obs['xc'].where(ds_obs['xc'] >= 0,
ds_obs.coords['xc'] + 360)
attrs_to_delete = [
'grid_mapping',
'cell_methods',
'remap',
'FieldType',
'MemoryOrder',
'stagger',
'sr_x',
'sr_y',
]
for obs_var, gcm_var in varnames.items():
obs_keep_vars = [obs_var, 'xc', 'yc', 'xv', 'yv']
ds_obs_daily = ds_obs[obs_keep_vars]
ds_obs_daily[obs_var] = ds_obs_daily[obs_var].astype('f4')
times = pd.date_range('1980-01-01', '2017-12-31', freq='D')
ds_obs_daily = ds_obs_daily.reindex(time=times, method='nearest')
ds_obs_1var = ds_obs_daily.resample(
time='MS', keep_attrs=True).mean('time').load()
for i, v in enumerate(obs_keep_vars):
ds_obs_1var[v].attrs = ds_obs[v].attrs
if i:
ds_obs_1var[v].encoding['_FillValue'] = None
for v in ds_obs_1var:
for attr in attrs_to_delete:
if attr in ds_obs_1var[v].attrs:
del ds_obs_1var[v].attrs[attr]
if 'time' in ds_obs_1var['xv'].dims:
ds_obs_1var['xv'] = ds_obs_1var['xv'].isel(time=0)
ds_obs_1var['yv'] = ds_obs_1var['yv'].isel(time=0)
print('ds_obs_1var')
ds_obs_1var.info()
da_train = (xr.open_mfdataset(
train_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim='time',
data_vars='minimal',
)[gcm_var].sel(time=time_bounds).astype('f4').resample(
time='MS').mean('time').load())
da_predict = (xr.open_mfdataset(
predict_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim='time',
data_vars='minimal',
)[gcm_var].sel(time=predict_time_bounds).astype('f4').resample(
time='MS').mean('time').load())
anoms[obs_var] = bcsd(
ds_obs_1var,
da_train.to_dataset(name=obs_var),
da_predict.to_dataset(name=obs_var),
var=obs_var,
)
out[obs_var] = disagg(ds_obs_daily[obs_var],
anoms[obs_var],
var=obs_var)
out['xv'] = ds_obs_1var['xv']
out['yv'] = ds_obs_1var['yv']
anoms['xv'] = ds_obs_1var['xv']
anoms['yv'] = ds_obs_1var['yv']
gc.collect()
return anoms, out
def run_ak(obs_fname, train_fname, predict_fname):
# Alaska
varnames = {"tmax": "tasmax", "tmin": "tasmin", "pcp": "pr"}
chunks = None
if "hist" in predict_fname:
predict_time_bounds = slice("1950", "2006")
else:
predict_time_bounds = slice("2006", "2099")
anoms = xr.Dataset()
out = xr.Dataset()
# get variables from the obs/training/prediction datasets
ds_obs = xr.open_mfdataset(obs_fname, chunks=chunks, concat_dim="time", data_vars="minimal")
time_bounds = slice(ds_obs.indexes["time"][0], ds_obs.indexes["time"][-1])
ds_obs.coords["xc"] = ds_obs["xc"].where(ds_obs["xc"] >= 0, ds_obs.coords["xc"] + 360)
attrs_to_delete = [
"grid_mapping",
"cell_methods",
"remap",
"FieldType",
"MemoryOrder",
"stagger",
"sr_x",
"sr_y",
]
for obs_var, gcm_var in varnames.items():
obs_keep_vars = [obs_var, "xc", "yc", "xv", "yv"]
ds_obs_daily = ds_obs[obs_keep_vars]
ds_obs_daily[obs_var] = ds_obs_daily[obs_var].astype("f4")
times = pd.date_range("1980-01-01", "2017-12-31", freq="D")
ds_obs_daily = ds_obs_daily.reindex(time=times, method="nearest")
ds_obs_1var = ds_obs_daily.resample(time="MS", keep_attrs=True).mean("time").load()
for i, v in enumerate(obs_keep_vars):
ds_obs_1var[v].attrs = ds_obs[v].attrs
if i:
ds_obs_1var[v].encoding["_FillValue"] = None
for v in ds_obs_1var:
for attr in attrs_to_delete:
if attr in ds_obs_1var[v].attrs:
del ds_obs_1var[v].attrs[attr]
if "time" in ds_obs_1var["xv"].dims:
ds_obs_1var["xv"] = ds_obs_1var["xv"].isel(time=0)
ds_obs_1var["yv"] = ds_obs_1var["yv"].isel(time=0)
print("ds_obs_1var")
ds_obs_1var.info()
da_train = (
xr.open_mfdataset(
train_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim="time",
data_vars="minimal",
)[gcm_var]
.sel(time=time_bounds)
.astype("f4")
.resample(time="MS")
.mean("time")
.load()
)
da_predict = (
xr.open_mfdataset(
predict_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim="time",
data_vars="minimal",
)[gcm_var]
.sel(time=predict_time_bounds)
.astype("f4")
.resample(time="MS")
.mean("time")
.load()
)
anoms[obs_var] = bcsd(
ds_obs_1var,
da_train.to_dataset(name=obs_var),
da_predict.to_dataset(name=obs_var),
var=obs_var,
)
out[obs_var] = disagg(ds_obs_daily[obs_var], anoms[obs_var], var=obs_var)
out["xv"] = ds_obs_1var["xv"]
out["yv"] = ds_obs_1var["yv"]
anoms["xv"] = ds_obs_1var["xv"]
anoms["yv"] = ds_obs_1var["yv"]
gc.collect()
return anoms, out
def run_hi(obs_fname, train_fname, predict_fname):
varnames = {"tmax": "tasmax", "tmin": "tasmin", "pcp": "pr"}
chunks = None
if "hist" in predict_fname:
predict_time_bounds = slice("1950", "2006")
else:
predict_time_bounds = slice("2006", "2099")
anoms = xr.Dataset()
out = xr.Dataset()
# get variables from the obs/training/prediction datasets
ds_obs = xr.open_mfdataset(obs_fname, chunks=chunks, concat_dim="time", data_vars="minimal")
time_bounds = slice(ds_obs.indexes["time"][0], ds_obs.indexes["time"][-1])
for obs_var, gcm_var in varnames.items():
obs_keep_vars = [obs_var, "lon", "lat"]
ds_obs_daily = ds_obs[obs_keep_vars]
ds_obs_daily[obs_var] = ds_obs_daily[obs_var].astype("f4")
times = pd.date_range("1990-01-01", "2014-12-31", freq="D")
ds_obs_daily = ds_obs_daily.reindex(time=times, method="nearest")
ds_obs_1var = ds_obs_daily.resample(time="MS", keep_attrs=True).mean("time").load()
for i, v in enumerate(obs_keep_vars):
ds_obs_1var[v].attrs = ds_obs[v].attrs
if i:
ds_obs_1var[v].encoding["_FillValue"] = None
print("ds_obs_1var")
ds_obs_1var.info()
da_train = (
xr.open_mfdataset(
train_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim="time",
data_vars="minimal",
)[gcm_var]
.sel(time=time_bounds)
.astype("f4")
.resample(time="MS")
.mean("time")
.load()
)
da_predict = (
xr.open_mfdataset(
predict_fname.format(gcm_var=gcm_var),
chunks=chunks,
concat_dim="time",
data_vars="minimal",
)[gcm_var]
.sel(time=predict_time_bounds)
.astype("f4")
.resample(time="MS")
.mean("time")
.load()
)
anoms[obs_var] = bcsd(
ds_obs_1var,
da_train.to_dataset(name=obs_var),
da_predict.to_dataset(name=obs_var),
var=obs_var,
)
out[obs_var] = disagg(ds_obs_daily[obs_var], anoms[obs_var], var=obs_var)
gc.collect()
return anoms, out