Add historical data to l3

.github/workflows/process_l2_test.yml → .github/workflows/process_test.yml

@@ -31,20 +31,34 @@ jobs:
         run: | 
           cd $GITHUB_WORKSPACE
           git clone --depth 1 https://oauth2:${{ env.GITLAB_TOKEN }}@geusgitlab.geus.dk/glaciology-and-climate/promice/aws-l0.git
-      - name: Run L0 to L3 processing
+      - name: Run L0 to L2 processing
         env:
           TEST_STATION: KAN_U HUM
         shell: bash
         run: |
           mkdir $GITHUB_WORKSPACE/out/
-          mkdir $GITHUB_WORKSPACE/out/L2/
+          mkdir $GITHUB_WORKSPACE/out/L0toL2/
           for i in $(echo ${{ env.TEST_STATION }} | tr ' ' '\n'); do
-            python3 $GITHUB_WORKSPACE/main/src/pypromice/process/get_l2.py -c $GITHUB_WORKSPACE/aws-l0/tx/config/$i.toml -i $GITHUB_WORKSPACE/aws-l0/tx -o $GITHUB_WORKSPACE/out/L2/
+            python3 $GITHUB_WORKSPACE/main/src/pypromice/process/get_l2.py -c $GITHUB_WORKSPACE/aws-l0/tx/config/$i.toml -i $GITHUB_WORKSPACE/aws-l0/tx -o $GITHUB_WORKSPACE/out/L0toL2/
           done
-#          mkdir $GITHUB_WORKSPACE/out/L3/            
+#      - name: Run L0 to L2 processing
+#        env:
+#          TEST_STATION: KAN_U HUM
+#        shell: bash
+#        run: |
+#          mkdir $GITHUB_WORKSPACE/out/L2toL3/            
 #          for i in $(echo ${{ env.TEST_STATION }} | tr ' ' '\n'); do           
-#            python3 $GITHUB_WORKSPACE/main/src/pypromice/process/get_l2tol3.py -i $GITHUB_WORKSPACE/out/L2/$i/$i_hour.nc -o $GITHUB_WORKSPACE/out/ -t 60min
+#            python3 $GITHUB_WORKSPACE/main/src/pypromice/process/get_l2tol3.py -i $GITHUB_WORKSPACE/out/L0toL2/$i/$i_hour.nc -o $GITHUB_WORKSPACE/out/L2toL3 -t 60min
 #          done
+      - name: Run L0 to L3 processing
+        env:
+          TEST_STATION: KAN_U HUM
+        shell: bash
+        run: |
+          mkdir $GITHUB_WORKSPACE/out/L0toL3/
+          for i in $(echo ${{ env.TEST_STATION }} | tr ' ' '\n'); do
+            python3 $GITHUB_WORKSPACE/main/src/pypromice/process/get_l2.py -c $GITHUB_WORKSPACE/aws-l0/tx/config/$i.toml -i $GITHUB_WORKSPACE/aws-l0/tx -o $GITHUB_WORKSPACE/out/L2/
+          done
       - name: Upload test output
         uses: actions/upload-artifact@v3
         with:

setup.py

@@ -31,20 +31,20 @@
     packages=setuptools.find_packages(where="src"),
     python_requires=">=3.8",
     package_data={
-    	"pypromice.process": ["metadata.csv", "variables.csv"],
+    	"pypromice.ressources": ["variables.csv", "file_attributes.csv", "variable_aliases_GC-Net.csv"],
     	"pypromice.tx": ["payload_formats.csv", "payload_types.csv"],
         "pypromice.qc.percentiles": ["thresholds.csv"],
         "pypromice.postprocess": ["station_configurations.toml", "positions_seed.csv"],
     },
-    install_requires=['numpy>=1.23.0', 'pandas>=1.5.0', 'xarray>=2022.6.0', 'toml', 'scipy>=1.9.0', 'Bottleneck', 'netcdf4', 'pyDataverse==0.3.1', 'eccodes', 'scikit-learn>=1.1.0'],
+    install_requires=['numpy>=1.23.0,<2.0.0', 'pandas>=1.5.0', 'xarray>=2022.6.0', 'toml', 'scipy>=1.9.0', 'Bottleneck', 'netcdf4', 'pyDataverse==0.3.1', 'eccodes', 'scikit-learn>=1.1.0'],
 #    extras_require={'postprocess': ['eccodes','scikit-learn>=1.1.0']},
     entry_points={
     'console_scripts': [
         'get_promice_data = pypromice.get.get_promice_data:get_promice_data',
         'get_l0tx = pypromice.tx.get_l0tx:get_l0tx',
         'join_l2 = pypromice.process.join_l2:join_l2',
+        'join_l3 = pypromice.process.join_l3:join_l3',
         'get_l2 = pypromice.process.get_l2:get_l2',
-        'get_l3 = pypromice.process.get_l3:get_l3',
         'get_l2tol3 = pypromice.process.get_l2tol3:get_l2tol3',
         'get_watsontx = pypromice.tx.get_watsontx:get_watsontx',
         'get_bufr = pypromice.postprocess.get_bufr:main',

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):
@@ -28,9 +28,10 @@ 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'
 
     for l in list(ds.keys()):
         if l not in ['time', 'msg_i', 'gps_lat', 'gps_lon', 'gps_alt', 'gps_time']:
@@ -247,7 +248,7 @@ def getPressDepth(z_pt, p, pt_antifreeze, pt_z_factor, pt_z_coef, pt_z_p_coef):
         rho_af = 1145
     else:
         rho_af = np.nan
-        print('ERROR: Incorrect metadata: "pt_antifreeze" = ' +
+        logger.info('ERROR: Incorrect metadata: "pt_antifreeze" = ' +
               f'{pt_antifreeze}. Antifreeze mix only supported at 50% or 100%')
         # assert(False)
 

src/pypromice/process/L1toL2.py

@@ -70,6 +70,7 @@ def toL2(
         Level 2 dataset
     '''
     ds = L1.copy(deep=True)                                                    # Reassign dataset
+    ds.attrs['level'] = 'L2'
     try:
         ds = adjustTime(ds)                                                    # Adjust time after a user-defined csv files
         ds = flagNAN(ds)                                                       # Flag NaNs after a user-defined csv files
@@ -85,7 +86,7 @@ def toL2(
 
     # filtering gps_lat, gps_lon and gps_alt based on the difference to a baseline elevation
     # right now baseline elevation is gapfilled monthly median elevation
-    baseline_elevation = (ds.gps_alt.to_series().resample('M').median()
+    baseline_elevation = (ds.gps_alt.to_series().resample('MS').median()
                           .reindex(ds.time.to_series().index, method='nearest')
                           .ffill().bfill())
     mask = (np.abs(ds.gps_alt - baseline_elevation) < 100) & ds.gps_alt.notnull()
@@ -326,7 +327,7 @@ def smoothTilt(da: xr.DataArray, threshold=0.2):
     # we calculate the moving standard deviation over a 3-day sliding window
     # hourly resampling is necessary to make sure the same threshold can be used 
     # for 10 min and hourly data
-    moving_std_gap_filled = da.to_series().resample('H').median().rolling(
+    moving_std_gap_filled = da.to_series().resample('h').median().rolling(
                     3*24, center=True, min_periods=2
                     ).std().reindex(da.time, method='bfill').values
     # we select the good timestamps and gapfill assuming that
@@ -353,7 +354,7 @@ def smoothRot(da: xr.DataArray, threshold=4):
     xarray.DataArray
         smoothed rotation measurements from inclinometer
     '''
-    moving_std_gap_filled = da.to_series().resample('H').median().rolling(
+    moving_std_gap_filled = da.to_series().resample('h').median().rolling(
                     3*24, center=True, min_periods=2
                     ).std().reindex(da.time, method='bfill').values
     # same as for tilt with, in addition:

src/pypromice/process/L2toL3.py

@@ -32,6 +32,7 @@ def toL3(L2, T_0=273.15, z_0=0.001, R_d=287.05, eps=0.622, es_0=6.1071,
         1013.246.
     '''
     ds = L2
+    ds.attrs['level'] = 'L3'
 
     T_100 = _getTempK(T_0)                                                     # Get steam point temperature as K 
 

src/pypromice/process/aws.py

@@ -52,9 +52,7 @@ def __init__(self, config_file, inpath, var_file=None, meta_file=None):
         L0 = self.loadL0()
         self.L0=[]
         for l in L0:
-            n = write.getColNames(self.vars, 
-                                  l.attrs['number_of_booms'], 
-                                  l.attrs['format'])
+            n = write.getColNames(self.vars, l)
             self.L0.append(utilities.popCols(l, n))
 
         self.L1 = None
@@ -106,18 +104,6 @@ def getL3(self):
         logger.info('Level 3 processing...')
         self.L3 = toL3(self.L2)
 
-    # def resample(self, dataset):       
-    #     '''Resample dataset to specific temporal resolution (based on input
-    #     data type)'''
-    #     f = [l.attrs['format'] for l in self.L0]
-    #     if 'raw' in f or 'STM' in f:
-    #         logger.info('Resampling to 10 minute')
-    #         resampled = resample_dataset(dataset, '10min')
-    #     else:
-    #         resampled = resample_dataset(dataset, '60min')
-    #         logger.info('Resampling to hour')
-    #     return resampled
-
     def writeArr(self, dataset, outpath, t=None):
         '''Write L3 data to .nc and .csv hourly and daily files
 
@@ -141,23 +127,6 @@ def writeArr(self, dataset, outpath, t=None):
             else:
                 write.prepare_and_write(dataset, outpath, self.vars, 
                                         self.meta, '60min')
-
-    # def addAttributes(self, dataset):
-    #     '''Add variable and attribute metadata
-
-    #     Parameters
-    #     ----------
-    #     dataset : xr.Dataset
-    #         Dataset (i.e. L2 or L3) object
-
-    #     Returns
-    #     -------
-    #     d2 : xr.Dataset
-    #         Data object with attributes
-    #     '''
-    #     d2 = utilities.addVars(dataset, self.vars)
-    #     d2 = utilities.addMeta(d2, self.meta)
-    #     return d2
 
     def loadConfig(self, config_file, inpath):
         '''Load configuration from .toml file

src/pypromice/process/get_l2.py

@@ -3,8 +3,8 @@
 from argparse import ArgumentParser
 import pypromice
 from pypromice.process.aws import AWS
-from pypromice.process.write import prepare_and_write
 from pypromice.process.load import getVars, getMeta
+from pypromice.process.write import prepare_and_write
 
 def parse_arguments_l2():
     parser = ArgumentParser(description="AWS L2 processor")
@@ -30,38 +30,27 @@ def get_l2():
         level=logging.INFO,
         stream=sys.stdout,
     )
-
-    # Define variables (either from file or pypromice package defaults)
-    if args.variables is None:
-        v = os.path.join(os.path.dirname(pypromice.__file__),'process/variables.csv')
-    else:
-        v = args.variables
-
-    # Define metadata (either from file or pypromice package defaults)
-    if args.variables is None:
-        m = os.path.join(os.path.dirname(pypromice.__file__),'process/metadata.csv')
-    else:
-        m = args.metadata
 
     # Define input path
     station_name = args.config_file.split('/')[-1].split('.')[0] 
     station_path = os.path.join(args.inpath, station_name)
     if os.path.exists(station_path):
-        aws = AWS(args.config_file, station_path, v, m)
+        aws = AWS(args.config_file, station_path, args.variables, args.metadata)
     else:
-        aws = AWS(args.config_file, args.inpath, v, m)
+        aws = AWS(args.config_file, args.inpath, args.variables, args.metadata)
 
     # Perform level 1 and 2 processing
     aws.getL1()
     aws.getL2() 
-
+    v = getVars(args.variables)
+    m = getMeta(args.metadata)
     # Write out level 2
     if args.outpath is not None:
         if not os.path.isdir(args.outpath):
             os.mkdir(args.outpath)
         if aws.L2.attrs['format'] == 'raw':
-            prepare_and_write(aws.L2, args.outpath, getVars(), getMeta(), '10min')
-        prepare_and_write(aws.L2, args.outpath, getVars(), getMeta(), '60min')
+            prepare_and_write(aws.L2, args.outpath, v, m, '10min')
+        prepare_and_write(aws.L2, args.outpath, v, m, '60min')
 
 
 if __name__ == "__main__":  

src/pypromice/process/get_l2tol3.py

@@ -3,9 +3,10 @@
 import xarray as xr
 from argparse import ArgumentParser
 import pypromice
-from pypromice.process.load import getVars, getMeta
 from pypromice.process.L2toL3 import toL3
+from pypromice.process.load import getVars, getMeta
 from pypromice.process.write import prepare_and_write
+logger = logging.getLogger(__name__)
 
 def parse_arguments_l2tol3(debug_args=None):
     parser = ArgumentParser(description="AWS L3 script for the processing L3 "+
@@ -34,23 +35,24 @@ def get_l2tol3():
         level=logging.INFO,
         stream=sys.stdout,
     )
-
-    # Define variables and metadata (either from file or pypromice package defaults)
-    v = getVars(args.variables)
-    m = getMeta(args.metadata)
 
     # Define Level 2 dataset from file
     with xr.open_dataset(args.inpath) as l2:
-        l2.load()    
+        l2.load()
+    for varname in l2.variables:
+        if 'encoding' in l2[varname].attrs:
+            del l2[varname].attrs['encoding']        
     if 'bedrock' in l2.attrs.keys():
         l2.attrs['bedrock'] = l2.attrs['bedrock'] == 'True'
     if 'number_of_booms' in l2.attrs.keys():
         l2.attrs['number_of_booms'] = int(l2.attrs['number_of_booms'])
 
     # Perform Level 3 processing
     l3 = toL3(l2)
-    
+
     # Write Level 3 dataset to file if output directory given
+    v = getVars(args.variables)
+    m = getMeta(args.metadata)
     if args.outpath is not None:
         prepare_and_write(l3, args.outpath, v, m, '60min')
         prepare_and_write(l3, args.outpath, v, m, '1D')