fixed logging & some attributes

src/pypromice/process/L0toL1.py

@@ -5,9 +5,9 @@
 import numpy as np
 import pandas as pd
 import xarray as xr
-import re
-
+import re, logging
 from pypromice.process.value_clipping import clip_values
+logger = logging.getLogger(__name__)
 
 
 def toL1(L0, vars_df, T_0=273.15, tilt_threshold=-100):
@@ -28,7 +28,7 @@ def toL1(L0, vars_df, T_0=273.15, tilt_threshold=-100):
     -------
     ds : xarray.Dataset
         Level 1 dataset
-    '''
+    '''    
     assert(type(L0) == xr.Dataset)
     ds = L0
     ds.attrs['level'] = 'L1'

src/pypromice/process/join_l2.py

@@ -97,6 +97,7 @@ def join_l2():
         logger.info(f'Invalid files {args.file1}, {args.file2}')
         exit()
 
+    all_ds.attrs['format'] = 'merged RAW and TX'
 
     # Resample to hourly, daily and monthly datasets and write to file
     prepare_and_write(all_ds, args.outpath, args.variables, args.metadata, resample = False)

src/pypromice/process/load.py

@@ -98,7 +98,7 @@ def getL0(infile, nodata, cols, skiprows, file_version,
         try:
             df.index = pd.to_datetime(df.index)
         except  ValueError as e:
-            logger.info("\n", infile)
+            logger.info("\n"+ infile)
             logger.info("\nValueError:")
             logger.info(e)
             logger.info('\t\t> Trying pd.to_datetime with format=mixed')

src/pypromice/process/resample.py

@@ -40,7 +40,7 @@ def resample_dataset(ds_h, t):
                 df_d[var] = _calcWindDir(df_d['wspd_x_'+var.split('_')[1]],
                                    df_d['wspd_y_'+var.split('_')[1]])
             else:
-                logger.info(var,'in dataframe but not','wspd_x_'+var.split('_')[1],'wspd_x_'+var.split('_')[1])
+                logger.info(var+' in dataframe but not wspd_x_'+var.split('_')[1]+' nor wspd_x_'+var.split('_')[1])
 
     # recalculating relative humidity from average vapour pressure and average
     # saturation vapor pressure
@@ -53,8 +53,9 @@ def resample_dataset(ds_h, t):
 
                 df_d[var] = (p_vap.to_series().resample(t).mean() \
                            / es_wtr.to_series().resample(t).mean())*100
-                df_d[var+'_cor'] = (p_vap.to_series().resample(t).mean() \
-                           / es_cor.to_series().resample(t).mean())*100
+                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
 
     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]

src/pypromice/process/utilities.py

@@ -154,14 +154,15 @@ def addMeta(ds, meta):
     ds : xarray.Dataset
         Dataset with metadata
    '''
-    ds['lon'] = ds['gps_lon'].mean()
-    ds['lon'].attrs = ds['gps_lon'].attrs
+    if 'gps_lon' in ds.keys():
+        ds['lon'] = ds['gps_lon'].mean()
+        ds['lon'].attrs = ds['gps_lon'].attrs
 
-    ds['lat'] = ds['gps_lat'].mean()
-    ds['lat'].attrs = ds['gps_lat'].attrs
+        ds['lat'] = ds['gps_lat'].mean()
+        ds['lat'].attrs = ds['gps_lat'].attrs
 
-    ds['alt'] = ds['gps_alt'].mean()
-    ds['alt'].attrs = ds['gps_alt'].attrs
+        ds['alt'] = ds['gps_alt'].mean()
+        ds['alt'].attrs = ds['gps_alt'].attrs
 
     # for k in ds.keys(): # for each var
     #     if 'units' in ds[k].attrs:

src/pypromice/process/write.py

@@ -168,6 +168,8 @@ def writeNC(outfile, Lx, col_names=None):
         names = [c for c in col_names if c in list(Lx.keys())]
     else:
         names = list(Lx.keys())
+    for var in names:
+        Lx[var].encoding = {}
     Lx[names].to_netcdf(outfile, mode='w', format='NETCDF4', compute=True)
 
 def getColNames(vars_df, ds, remove_nan_fields=False):