Per #2781, added function to convert MET NetCDF point observation dat…

…a to pandas so it can be read and modified in a python embedding script. Added example python embedding script

scripts/python/examples/pyembed_met_point_nc.py

@@ -0,0 +1,43 @@
+import os
+import sys
+
+from met.point_nc import nc_point_obs
+
+print(f"Python Script:\t{sys.argv[0]}")
+
+arg_cnt = len(sys.argv)
+if len(sys.argv) != 2:
+    print("ERROR: read_met_point.py -> Specify only 1 input file")
+    sys.exit(1)
+
+# Read the input file as the first argument
+input_file = os.path.expandvars(sys.argv[1])
+try:
+    print("Input File:\t" + repr(input_file))
+
+    # Read and format the input 11-column observations:
+    #   (1)  string:  Message_Type
+    #   (2)  string:  Station_ID
+    #   (3)  string:  Valid_Time(YYYYMMDD_HHMMSS)
+    #   (4)  numeric: Lat(Deg North)
+    #   (5)  numeric: Lon(Deg East)
+    #   (6)  numeric: Elevation(msl)
+    #   (7)  string:  Var_Name(or GRIB_Code)
+    #   (8)  numeric: Level
+    #   (9)  numeric: Height(msl or agl)
+    #   (10) string:  QC_String
+    #   (11) numeric: Observation_Value
+
+    # Read 11 column text input data by using pandas package
+    point_obs = nc_point_obs()
+    if not point_obs.read_data(input_file):
+        print(f"ERROR: Could not read MET point data file {input_file}")
+        sys.exit(1)
+
+    df = point_obs.to_pandas()
+    point_data = df.values.tolist()
+    print(f"     point_data: Data Length:\t{len(point_data)}")
+    print(f"     point_data: Data Type:\t{type(point_data)}")
+except FileNotFoundError:
+    print(f"The input file {input_file} does not exist")
+    sys.exit(1)

scripts/python/met/point_nc.py

@@ -8,10 +8,12 @@
 
 '''
 
+import sys
 import os
 
 import numpy as np
 import netCDF4 as nc
+import pandas as pd
 
 from met.point import met_point_obs, met_point_tools
 
@@ -274,6 +276,22 @@ def write_nc_data(nc_dataset, point_obs):
          print(f'  === ERROR at {method_name} type(nc_dataset)={type(nc_dataset)} type(point_obs)={type(point_obs)}')
          raise
 
+   def to_pandas(self):
+       return pd.DataFrame({
+           'typ': [self.hdr_typ_table[i] for i in self.hdr_typ],
+           'sid': [self.hdr_sid_table[i] for i in self.hdr_sid],
+           'vld': [self.hdr_vld_table[i] for i in self.hdr_vld],
+           'lat': self.hdr_lat,
+           'lon': self.hdr_lon,
+           'elv': self.hdr_elv,
+           'var': [self.obs_var_table[i] if self.use_var_id else f'{i}'
+                   for i in self.obs_vid],
+           'lvl': self.obs_lvl,
+           'hgt': self.obs_hgt,
+           'qc': [np.nan if np.ma.is_masked(i) else self.obs_qty_table[i]
+                  for i in self.obs_qty],
+           'obs': self.obs_val,
+       })
 
 def main(argv):
    if len(argv) != 1 and argv[1] != ARG_PRINT_DATA: