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index 65ff68125d64c6ffd4e6511fa0ba4dc50d9b28fc..433f0b7290f50c919f9a7fde093985321d188ec6 100644
--- a/data/Level1A/Lake_Greifen_CTD_W_2020-09-14.nc
+++ b/data/Level1A/Lake_Greifen_CTD_W_2020-09-14.nc
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diff --git a/data/Level1A/Lake_Greifen_CTD_W_2020-09-21.nc b/data/Level1A/Lake_Greifen_CTD_W_2020-09-21.nc
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--- a/data/Level1A/Lake_Greifen_CTD_W_2020-09-21.nc
+++ b/data/Level1A/Lake_Greifen_CTD_W_2020-09-21.nc
@@ -1,3 +1,3 @@
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+++ b/data/Level1A/Lake_Greifen_CTD_W_2020-09-28.nc
@@ -1,3 +1,3 @@
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+++ b/data/Level1A/Lake_Greifen_CTD_W_2020-10-05.nc
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diff --git a/data/Level1A/Lake_Greifen_CTD_W_2020-10-12.nc b/data/Level1A/Lake_Greifen_CTD_W_2020-10-12.nc
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diff --git a/notebooks/functions.py b/notebooks/functions.py
index 94a1ebaaa21a27980cf230322ca67ba13064aefc..08a3fd915fc0b726d79d89cd7001bdc5319b6890 100644
--- a/notebooks/functions.py
+++ b/notebooks/functions.py
@@ -20,83 +20,84 @@ def mysql_netcdf(database, start, end, Level0, Level1A):
press = df.press.sort_values(ascending=True).unique()
press_temp = pd.DataFrame(press, columns=["press"])
profiles = []
- for index, value in enumerate(profile_ids):
- profile = df.loc[df['profile_id'] == value]
- profiles.append(pd.merge(press_temp, profile.drop_duplicates(subset=['press']), how='left', on='press'))
-
- data = {}
- data["press"] = list(press)
- data["time"] = list(df.timestamp.sort_values(ascending=True).unique().astype(int) / 10 ** 9)
-
- att = {
- "institution": "EAWAG",
- "source": "Lake Greifen CTD Data",
- "history": "See history on Renku",
- "conventions": "CF 1.7",
- "comment": "Data from CTD profiler on platform in Lake Greifen",
- "title": "Lake Greifen CTD Data"
- }
-
- dim_dict = {
- 'time': {'dim_name': 'time', 'dim_size': None},
- 'press': {'dim_name': 'press', 'dim_size': None}
- }
-
- dim_var_dict = {
- 'time': {'var_name': 'time', 'dim': ('time',), 'unit': 'seconds since 1970-01-01 00:00:00', 'longname': 'time'},
- 'press': {'var_name': 'pressure', 'dim': ('press',), 'unit': 'm', 'longname': 'pressure'}
- }
-
- var_dict = {
- 'temp': {'var_name': 'temperature', 'dim': ('press', 'time',), 'unit': 'degC',
- 'longname': 'Water Temperature'},
- 'cond': {'var_name': 'conductivity', 'dim': ('press', 'time',), 'unit': 'mS/cm',
- 'longname': 'Conductivity'},
- 'sal': {'var_name': 'conductivity_20', 'dim': ('press', 'time',), 'unit': 'mS/cm',
- 'longname': 'Conductivity 20deg'},
- 'o2_percent': {'var_name': 'o2_percent', 'dim': ('press', 'time',), 'unit': '%',
- 'longname': 'Percentage Oxygen Concentration'},
- 'o2_ppm': {'var_name': 'o2_ppm', 'dim': ('press', 'time',), 'unit': 'mg/L',
- 'longname': 'Oxygen Concentration'},
- 'ph': {'var_name': 'ph', 'dim': ('press', 'time',), 'unit': '',
- 'longname': 'pH'},
- 'chl': {'var_name': 'chla', 'dim': ('press', 'time',), 'unit': 'μg/L',
- 'longname': 'Chlorophyll A'},
- 'turb': {'var_name': 'turb', 'dim': ('press', 'time',), 'unit': 'NTU',
- 'longname': 'Turbidity'},
- 'phy': {'var_name': 'phycocyanin', 'dim': ('press', 'time',), 'unit': 'μg/L',
- 'longname': 'Phycocyanin'},
- 'par': {'var_name': 'par', 'dim': ('press', 'time',), 'unit': 'μE',
- 'longname': 'Photosynthetically Active Radiation'},
- }
-
- # Create new NetCDF file
- ncfile = netCDF4.Dataset(Level1A, mode='w', format='NETCDF4')
-
- # Add global attributes
- for key in att:
- setattr(ncfile, key, att[key])
-
- # Create Dimensions
- for key, values in dim_dict.items():
- ncfile.createDimension(values['dim_name'], values['dim_size'])
-
- # Create Dimensions Variables
- for key, values in dim_var_dict.items():
- var = ncfile.createVariable(values["var_name"], np.float64, values["dim"])
- var.units = values["unit"]
- var.long_name = values["longname"]
- var[:] = data[key]
-
- # Create Dimensions Variables
- for key, values in var_dict.items():
- var = ncfile.createVariable(values["var_name"], np.float64, values["dim"])
- var.units = values["unit"]
- var.long_name = values["longname"]
- for index, value in enumerate(data["time"]):
- var[:, index] = list(profiles[index][key])
-
- # Close NetCDF file
- ncfile.close()
+ if len(profile_ids) > 0:
+ for index, value in enumerate(profile_ids):
+ profile = df.loc[df['profile_id'] == value]
+ profiles.append(pd.merge(press_temp, profile.drop_duplicates(subset=['press']), how='left', on='press'))
+
+ data = {}
+ data["pressure"] = list(press)
+ data["time"] = list(df.timestamp.sort_values(ascending=True).unique().astype(int) / 10 ** 9)
+
+ att = {
+ "institution": "EAWAG",
+ "source": "Lake Greifen CTD Data",
+ "history": "See history on Renku",
+ "conventions": "CF 1.7",
+ "comment": "Data from CTD profiler on platform in Lake Greifen",
+ "title": "Lake Greifen CTD Data"
+ }
+
+ dim_dict = {
+ 'time': {'dim_name': 'time', 'dim_size': None},
+ 'pressure': {'dim_name': 'pressure', 'dim_size': None}
+ }
+
+ dim_var_dict = {
+ 'time': {'var_name': 'time', 'dim': ('time',), 'unit': 'seconds since 1970-01-01 00:00:00', 'longname': 'time'},
+ 'pressure': {'var_name': 'pressure', 'dim': ('pressure',), 'unit': 'm', 'longname': 'pressure'}
+ }
+
+ var_dict = {
+ 'temp': {'var_name': 'temperature', 'dim': ('pressure', 'time',), 'unit': 'degC',
+ 'longname': 'Water Temperature'},
+ 'cond': {'var_name': 'conductivity', 'dim': ('pressure', 'time',), 'unit': 'mS/cm',
+ 'longname': 'Conductivity'},
+ 'sal': {'var_name': 'conductivity_20', 'dim': ('pressure', 'time',), 'unit': 'mS/cm',
+ 'longname': 'Conductivity 20deg'},
+ 'o2_percent': {'var_name': 'o2_percent', 'dim': ('pressure', 'time',), 'unit': '%',
+ 'longname': 'Percentage Oxygen Concentration'},
+ 'o2_ppm': {'var_name': 'o2_ppm', 'dim': ('pressure', 'time',), 'unit': 'mg/L',
+ 'longname': 'Oxygen Concentration'},
+ 'ph': {'var_name': 'ph', 'dim': ('pressure', 'time',), 'unit': '',
+ 'longname': 'pH'},
+ 'chl': {'var_name': 'chla', 'dim': ('pressure', 'time',), 'unit': 'μg/L',
+ 'longname': 'Chlorophyll A'},
+ 'turb': {'var_name': 'turb', 'dim': ('pressure', 'time',), 'unit': 'NTU',
+ 'longname': 'Turbidity'},
+ 'phy': {'var_name': 'phycocyanin', 'dim': ('pressure', 'time',), 'unit': 'μg/L',
+ 'longname': 'Phycocyanin'},
+ 'par': {'var_name': 'par', 'dim': ('pressure', 'time',), 'unit': 'μE',
+ 'longname': 'Photosynthetically Active Radiation'},
+ }
+
+ # Create new NetCDF file
+ ncfile = netCDF4.Dataset(Level1A, mode='w', format='NETCDF4')
+
+ # Add global attributes
+ for key in att:
+ setattr(ncfile, key, att[key])
+
+ # Create Dimensions
+ for key, values in dim_dict.items():
+ ncfile.createDimension(values['dim_name'], values['dim_size'])
+
+ # Create Dimensions Variables
+ for key, values in dim_var_dict.items():
+ var = ncfile.createVariable(values["var_name"], np.float64, values["dim"])
+ var.units = values["unit"]
+ var.long_name = values["longname"]
+ var[:] = data[key]
+
+ # Create Dimensions Variables
+ for key, values in var_dict.items():
+ var = ncfile.createVariable(values["var_name"], np.float64, values["dim"])
+ var.units = values["unit"]
+ var.long_name = values["longname"]
+ for index, value in enumerate(data["time"]):
+ var[:, index] = list(profiles[index][key])
+
+ # Close NetCDF file
+ ncfile.close()