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Feature/adding 10min values in hourly files #300

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Sep 11, 2024
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Feature/adding 10min values in hourly files #300

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c3b99b9
update resample so that it copies the 10 min data into the hourly files
BaptisteVandecrux Sep 10, 2024
ad05dbc
minor edit in surface height processing
BaptisteVandecrux Sep 10, 2024
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1 change: 1 addition & 0 deletions src/pypromice/process/L2toL3.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,6 +254,7 @@ def process_surface_height(ds, data_adjustments_dir, station_config={}):

ds['z_surf_combined'] = np.maximum(ds['z_surf_combined'], ds['z_ice_surf'])
ds['snow_height'] = np.maximum(0, ds['z_surf_combined'] - ds['z_ice_surf'])
ds['z_ice_surf'] = ds['z_ice_surf'].where(ds.snow_height.notnull())
elif ds.attrs['site_type'] in ['accumulation', 'bedrock']:
# Handle accumulation and bedrock site types
ds['z_ice_surf'] = ('time', ds['z_surf_1'].data * np.nan)
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23 changes: 21 additions & 2 deletions src/pypromice/process/resample.py
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Expand Up @@ -34,6 +34,15 @@ def resample_dataset(ds_h, t):
'''
df_d = ds_h.to_dataframe().resample(t).mean()

# taking the 10 min data and using it as instantaneous values:
if (t == '60min') and (ds_h.time.diff(dim='time').isel(time=0).dt.total_seconds() == 600):
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cols_to_update = ['p_i', 't_i', 'rh_i', 'rh_i_cor', 'wspd_i', 'wdir_i','wspd_x_i','wspd_y_i']
for col in cols_to_update:
df_d[col] = ds_h.reindex(time=df_d.index)[col.replace('_i','_u')].values
if col == 'p_i':
df_d[col] = df_d[col].values-1000


# recalculating wind direction from averaged directional wind speeds
for var in ['wdir_u','wdir_l']:
boom = var.split('_')[1]
Expand All @@ -60,9 +69,19 @@ def resample_dataset(ds_h, t):
if var+'_cor' in df_d.keys():
df_d[var+'_cor'] = (p_vap.to_series().resample(t).mean() \
/ es_cor.to_series().resample(t).mean())*100

# passing each variable attribute to the ressample dataset
vals = []
for c in df_d.columns:
if c in ds_h.data_vars:
vals.append(xr.DataArray(
data=df_d[c], dims=['time'],
coords={'time':df_d.index}, attrs=ds_h[c].attrs))
else:
vals.append(xr.DataArray(
data=df_d[c], dims=['time'],
coords={'time':df_d.index}, attrs=None))

vals = [xr.DataArray(data=df_d[c], dims=['time'],
coords={'time':df_d.index}, attrs=ds_h[c].attrs) for c in df_d.columns]
ds_d = xr.Dataset(dict(zip(df_d.columns,vals)), attrs=ds_h.attrs)
return ds_d

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