Baysian Update to environmental data
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Baysian Update to environmental data
update to atleast .25
Lat_effect_temp = pm.Normal('Lat_effect_temp', mu=0, sigma=.25)
Long_effect_temp = pm.Normal('Long_effect_temp', mu=0, sigma=.25)
need to save off trace and model for reload and predict
Need to kinds.
What is the best way to get variable values out of x-ray for an array of ordered pair coordinates?
Where are the coordinate values are not exact
quadrent plots:
for i, ((N_S_label,N_S_idx), (E_W_label,E_W_idx)) in enumerate([[i,j]
for i in [('North',idx_north),('South',idx_south)]
for j in [('West',idx_west), ('East',idx_east)]]):
print(N_S_label,E_W_label)
ax[i].set_title(f'Lat: {N_S_label} Long: {E_W_label}')
idx = np.intersect1d(N_S_idx,E_W_idx)
az.plot_forest(idata2.sel(alt_lat_long_time=[x for x in idx_sortby_alt if np.any(np.isin(idx,x))]),
var_names=['mu_t'],
kind='ridgeplot',
combined=True, ax= ax[i],
labeller=labeller
)
all forest:
az.plot_forest(idata2.sel( alt_lat_long_time=idx_sortby_alt), var_names=['mu_t'],kind='ridgeplot', combined=True,labeller=labeller)
az.plot_forest(idata2.sel( alt_lat_long_time=idx_sortby_alt), var_names=['mu_p'],kind='ridgeplot', combined=True,labeller=labeller)
comparison:
az.plot_dist_comparison(idata2, kind='observed', labeller=labeller)
Check that lat (and long) dimension is carried correctly through our code.
Can you make new dimensions with group y using construct
Does the prediction interval contain 'actual' temp and pressure
https://ncar.github.io/esds/posts/2022/xarray-groupby-vs-geocat-climatology/
more:
https://ncar.github.io/esds/
ds = xr.open_dataset('603321.sst.sst.mnmean.nc')
ds = ds.sst # Pull out the sea surface temperature data
ds = ds.isel(
time=range(1, 49)
) # Remove the first data point so that we have an equal number of data points from each month
try out plotly bokeh and holoviz
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