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Cookbook
John Kerfoot edited this page May 11, 2021
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Wiki ▸ Cookbook
Creation of an instance of the GliderDataModel Class is the same for all glider platforms:
> from gdm import GliderDataModel
> config_path = '/Users/kerfoot/data/gliders/rucool/deployments/2020/ru29-20200908T1623/config/gmvc'
> gdm = GliderDataModel(config_path)
The config_path is searched for one or more (preferably all) of the following YAML configuration files:
- deployment.yml: contains information on the glider deployment.
- global_attributes.yml: contains global attributes to be assigned to the resulting xarray Dataset.
- instruments.yml: contains information on the scientific payload including instrument serial numbers, calibration dates, etc.
- sensor_defs.yml: contains descriptions of the sensors and optional variable mapping names.
Examples of each of these files can be found here and are discussed here.
Once configured, we can look at the instance:
> gdm
<GliderNetCDF(cfg=True, data=False, profiles=False)>
In this case, we can see that the configuration files were found and successfully parsed (cfg=True), but that no pandas DataFrame containing the raw data or indexed profile metadata have been specified. Now that the instance has been configured, let's parse a raw Slocum data file, index the profiles and add these 2 DataFrames to the instance so that we can export the dataset to xarray Dataset objects.
The functions used below to parse and raw Slocum glider dba file are available here. Let's walk through an example by doing the following:
Let's get a time-sorted list of the available dba files in a specified directory:
> from gdm.gliders.slocum import get_dbas
> dba_path = '/Users/kerfoot/data/gliders/rucool/deployments/2020/ru29-20200908T1623/data/in/ascii/sbd'
> dba_files = get_dbas(dba_path)
The get_dbas() method parses the headers of all the slocum data files dba_path and returns a pandas DataFrame containing columns that correspond to the headers in each dba file:
> dba_files.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 1081 entries, 2020-09-08 16:24:45+00:00 to 2020-11-16 12:34:07+00:00
Data columns (total 18 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 file 1081 non-null object
1 dbd_label 1081 non-null object
2 encoding_ver 1081 non-null object
3 num_ascii_tags 1081 non-null object
4 all_sensors 1081 non-null object
5 filename 1081 non-null object
6 the8x3_filename 1081 non-null object
7 filename_extension 1081 non-null object
8 filename_label 1081 non-null object
9 mission_name 1081 non-null object
10 fileopen_time 1081 non-null object
11 sensors_per_cycle 1081 non-null object
12 num_label_lines 1081 non-null object
13 num_segments 1081 non-null object
14 segment_filename_0 1081 non-null object
15 path 1081 non-null object
16 bytes 1081 non-null int64
17 glider 1081 non-null object
dtypes: int64(1), object(17)
memory usage: 160.5+ KB
The index is the parsed fileopen_time and the data frame is sorted by increasing values. Let's look at the first file:
> dba_files.iloc[0]
file ru29_2020_251_4_0_sbd.dat
dbd_label DBD_ASC(dinkum_binary_data_ascii)file
encoding_ver 2
num_ascii_tags 14
all_sensors 0
filename ru29-2020-251-4-0
the8x3_filename 04270000
filename_extension sbd
filename_label ru29-2020-251-4-0-sbd(04270000)
mission_name 1K_N.MI
fileopen_time Tue_Sep__8_16:24:45_2020
sensors_per_cycle 112
num_label_lines 3
num_segments 1
segment_filename_0 ru29-2020-251-4-0
path /Users/kerfoot/data/gliders/rucool/deployments...
bytes 103178
glider ru29
Name: 2020-09-08 16:24:45+00:00, dtype: object
Let's parse the first file and see if it contains any profiles:
> dba_file = os.path.join(dba_files.iloc[0].path, dba_files.iloc[0].file)
> dba, pro_meta = load_slocum_dba(dba_file)
Here's the data frame containing the file contents: > dba.info() <class 'pandas.core.frame.DataFrame'> DatetimeIndex: 202 entries, 2020-09-08 16:23:40.157530069 to 2020-09-08 16:53:37.990449905 Columns: 121 entries, c_alt_time to sound_speed dtypes: datetime64ns, float64(113), object(2) memory usage: 202.5+ KB
and here are the sensor columns:
array(['c_alt_time', 'c_climb_target_depth', 'c_de_oil_vol',
'c_dive_target_depth', 'c_fin', 'c_heading', 'c_science_send_all',
'c_thruster_on', 'c_weight_drop', 'c_wpt_lat', 'c_wpt_lon',
'c_wpt_x_lmc', 'c_wpt_y_lmc', 'f_fin_offset',
'f_ocean_pressure_max', 'f_ocean_pressure_min', 'm_avg_speed',
'm_battery_inst', 'm_battpos', 'm_bms_aft_current',
'm_bms_ebay_current', 'm_bms_emergency_battery_voltage',
'm_bms_pitch_current', 'm_comms_tickle_timestamp',
'm_cop_tickle_timestamp', 'm_coulomb_amphr',
'm_coulomb_amphr_total', 'm_depth', 'm_depth_rate_avg_final',
'm_de_oil_vol', 'm_digifin_leakdetect_reading', 'm_dip_angle',
'm_fin', 'm_final_water_vx', 'm_final_water_vy', 'm_gps_lat',
'm_gps_lon', 'm_gps_mag_var', 'm_gps_status', 'm_hdg_derror',
'm_hdg_error', 'm_hdg_ierror', 'm_heading',
'm_iridium_attempt_num', 'm_iridium_call_num',
'm_iridium_dialed_num', 'm_lat', 'm_leakdetect_voltage',
'm_leakdetect_voltage_forward', 'm_leakdetect_voltage_science',
'm_lon', 'm_magnetic_field', 'm_pitch', 'm_present_time',
'm_pressure', 'm_raw_altitude', 'm_roll',
'm_science_clothesline_lag', 'm_thruster_power',
'm_tot_num_inflections', 'm_vacuum', 'm_vehicle_temp',
'm_veh_temp', 'm_water_depth', 'm_water_vx', 'm_water_vy',
'm_weight_drop', 'm_x_lmc', 'm_y_lmc', 'u_alt_filter_enabled',
'u_alt_min_depth', 'u_hd_fin_ap_deadband_reset',
'u_hd_fin_ap_dgain', 'u_hd_fin_ap_gain',
'u_hd_fin_ap_hardover_holdoff', 'u_hd_fin_ap_igain',
'u_hd_fin_ap_inflection_holdoff', 'u_heading_deadband',
'u_heading_rate_deadband', 'u_low_power_cycle_time',
'u_low_power_hd_fin_ap_dgain', 'u_low_power_hd_fin_ap_gain',
'u_low_power_hd_fin_ap_igain', 'u_min_water_depth',
'u_pitch_ap_deadband', 'u_pitch_ap_dgain', 'u_pitch_ap_gain',
'u_pitch_max_delta_battpos', 'u_reqd_depth_at_surface',
'u_science_low_power', 'x_current_target_altitude',
'x_cycle_overrun_in_ms', 'x_fin_deadband', 'x_fin_max',
'x_lmc_xy_source', 'x_low_power_status', 'x_thruster_state',
'sci_ad2cp_bottom_track_signal', 'sci_ad2cp_file_state',
'sci_ad2cp_run_state', 'sci_ad2cp_surface_state',
'sci_ctd41cp_timestamp', 'sci_flbbcd_bb_units',
'sci_flbbcd_cdom_units', 'sci_flbbcd_chlor_units',
'sci_flbbcd_timestamp', 'sci_m_disk_free', 'sci_m_present_time',
'sci_water_cond', 'sci_water_pressure', 'sci_water_temp',
'segment', 'the8x3_filename', 'latitude', 'longitude', 'ilatitude',
'ilongitude', 'depth', 'salinity', 'density', 'sound_speed'],
dtype=object)
In addition to the 112 native sensors contained in the raw data file, there are 10 additional columns added:
- segment: the dba segment name, in this case 'ru29-2020-251-4-0'
- the8x3_filename: the 8.3 dba segment name, in this case '04270000'
- latitude: m_gps_lat converted to decimal degrees
- longitude: m_gps_lon converted to decimal degrees
- ilatitude: linearly interpolated latitude to provide an estimate of latitude for each record
- ilongitude: linearly interpolated longitude to provide an estimate of longitude for each record
- depth: depth calculated from sci_water_pressure (converted to decibars) and ilatitude
- salinity: calculated salinity
- density: calculated density
- sound_speed: calculated sound_speed
load_slocum_dba.py also returns a data frame that contains meta data from all of the indexed profiles contained in the data file:
> pro_meta.info()
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 2 entries, 2020-09-08 16:34:03.054290056 to 2020-09-08 16:45:40.181445121
Data columns (total 10 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 total_seconds 2 non-null float64
1 num_points 2 non-null int64
2 direction 2 non-null object
3 start_time 2 non-null datetime64[ns]
4 end_time 2 non-null datetime64[ns]
5 start_depth 2 non-null float64
6 end_depth 2 non-null float64
7 segment 2 non-null object
8 depth_resolution 2 non-null float64
9 sampling_frequency 2 non-null float64
dtypes: datetime64[ns](2), float64(5), int64(1), object(2)
memory usage: 176.0+ bytes
and the first profile:
> pro_meta.iloc[0]
total_seconds 654.977
num_points 54
direction d
start_time 2020-09-08 16:28:35.565610170
end_time 2020-09-08 16:39:30.542969942
start_depth 8.37
end_depth 124.19
segment ru29-2020-251-4-0
depth_resolution 0.176831
sampling_frequency 12.1292
Name: 2020-09-08 16:34:03.054290056, dtype: object
These 2 DataFrames are then added to the GliderDataModel instance:
> gdm.data = dba
> gdm.profiles = pro_meta
> gdm
<GliderNetCDF(cfg=True, data=True, profiles=2)>
Now we are ready to export the data to and xarray Dataset.
Once we have our GliderDataModel instance configured and have specified the dataset and indexed profiles, we can export all or one or more profiles. There are 2 methods that export the data to an xarray Dataset:
- to_timeseries_dataset(): return an xarray trajectory dataset containing the all data from the raw data file. Global attributes, instrument variables and variable metadata are taken from the instance configuration.
- slice_profile_dataset(): return an xarray trajectoryProfile dataset containing the data from the profile specified by the profile midpoint timestamp. Global attributes, instrument variables and variable metadata are taken from the instance configuration.
The objects returned by any of these methods are xarray Datasets which can be used for downstream data processing, analysis or exported to CF, ACDD or IOOS Metadata Profile compliant NetCDF files.
NOTE: The following global attributes are calculated from the exported dataset and added during export:
- geospatial_bounds
- geospatial_bounds_crs
- geospatial_bounds_vertical_crs
- geospatial_lat_max
- geospatial_lat_min
- geospatial_lat_resolution
- geospatial_lat_units
- geospatial_lon_max
- geospatial_lon_min
- geospatial_lon_resolution
- geospatial_lon_units
- geospatial_vertical_max
- geospatial_vertical_min
- geospatial_vertical_positive
- geospatial_vertical_resolution
- geospatial_vertical_units
- time_coverage_duration
- time_coverage_end
- time_coverage_resolution
- time_coverage_start
and must be changed after export in the event any changes are necessary.
To export the dataset as a trajectory:
> ds = gdm.to_timeseries_dataset()
> ds
<xarray.Dataset>
Dimensions: (time: 202)
Coordinates:
* time (time) datetime64[ns] 2020-09-08T...
Data variables:
commanded_alt_time (time) float64 nan nan ... nan nan
commanded_climb_target_depth (time) float64 nan nan ... nan nan
commanded_de_oil_vol (time) float64 nan nan ... nan nan
commanded_dive_target_depth (time) float64 nan nan ... nan nan
commanded_fin (time) float64 nan nan ... nan nan
commanded_heading (time) float64 nan nan ... nan nan
commanded_science_send_all (time) float64 nan nan ... nan nan
commanded_thruster_on (time) float64 nan nan ... nan nan
commanded_weight_drop (time) float64 nan nan ... nan nan
commanded_wpt_lat (time) float64 nan nan ... nan nan
commanded_wpt_lon (time) float64 nan nan ... nan nan
commanded_wpt_x_lmc (time) float64 nan nan ... nan nan
commanded_wpt_y_lmc (time) float64 nan nan ... nan nan
f_fin_offset (time) float64 nan nan ... nan nan
f_ocean_pressure_max (time) float64 nan nan ... nan nan
f_ocean_pressure_min (time) float64 nan nan ... nan nan
measured_avg_speed (time) float64 nan nan ... nan nan
measured_battery_inst (time) float64 nan nan ... nan nan
measured_battpos (time) float64 nan nan ... nan nan
measured_bms_aft_current (time) float64 nan nan ... nan nan
measured_bms_ebay_current (time) float64 nan nan ... nan nan
measured_bms_emergency_battery_voltage (time) float64 nan nan ... nan nan
measured_bms_pitch_current (time) float64 nan nan ... nan nan
...
ctd41cp_timestamp (time) datetime64[ns] 1970-01-01 ...
beta_700nm (time) float64 0.0 nan ... nan nan
cdom (time) float64 0.0 nan ... nan nan
chlorophyll_a (time) float64 0.0 nan ... nan nan
flbbcd_timestamp (time) datetime64[ns] 1970-01-01 ...
sci_m_disk_free (time) float64 1.988e+03 ... 1.98...
science_timestamp (time) datetime64[ns] 2020-09-08T...
conductivity (time) float64 0.0 nan ... nan nan
pressure (time) float64 nan nan ... nan nan
temperature (time) float64 0.0 nan ... nan nan
segment (time) object 'ru29-2020-251-4-0'...
the8x3_filename (time) object '04270000' ... '042...
measured_lat (time) float64 nan nan ... 18.18 nan
measured_lon (time) float64 nan nan ... -64.8 nan
lat (time) float64 nan nan ... 18.18
lon (time) float64 nan nan ... -64.8
depth (time) float64 nan nan ... nan nan
salinity (time) float64 nan nan ... nan nan
density (time) float64 nan nan ... nan nan
sound_speed (time) float64 nan nan ... nan nan
platform float64 nan
instrument_ctd float64 nan
instrument_flbbcd float64 nan
instrument_ad2cp float64 nan
trajectory <U18 'ru29-20200908T1623'
Attributes:
Conventions: CF-1.6, ACDD-1.3, IOOS-1.2
acknowledgment: Funding and support provided by the G. U...
cdm_data_type:
comment: Deployed by Antonio Farchetti, Kenique L...
contributor_name: Scott Glenn,Oscar Schofield,Travis Miles...
contributor_role: principalInvestigator,principalInvestiga...
contributor_role_vocabulary: https://vocab.nerc.ac.uk/collection/G04/...
creator_country: USA
creator_email: kerfoot@marine.rutgers.edu
creator_institution: Rutgers University
creator_name: John Kerfoot
creator_sector: academic
creator_type: person
creator_url: https://rucool.marine.rutgers.edu
date_created: 2020-12-08T18:14:22Z
date_issued: 2020-12-08T18:14:22Z
date_metadata_modified:
date_modified:
featureType: trajectory
geospatial_bounds: POLYGON ((18.17716666666667 -64.80589000...
geospatial_bounds_crs: EPSG:4326
geospatial_bounds_vertical_crs: EPSG:5831
geospatial_lat_max: 18.17716666666667
geospatial_lat_min: 18.17529
geospatial_lat_resolution: 0.00001 degree
geospatial_lat_units: degrees_north
geospatial_lon_max: -64.8028
geospatial_lon_min: -64.80589
geospatial_lon_resolution: 0.00001 degree
geospatial_lon_units: degrees_east
geospatial_vertical_max: 123.4073257374073
geospatial_vertical_min: 6.043413831651445
geospatial_vertical_positive: down
geospatial_vertical_resolution: 0.58
geospatial_vertical_units: m
gts_ingest: True
history:
id: ru29-20200908T1623
infoUrl: https://rucool.marine.rutgers.edu
institution: Rutgers University
instrument: In Situ/Laboratory Instruments > Profile...
instrument_vocabulary: NASA/GCMD Instrument Keywords Version 8.5
ioos_regional_association: MARACOOS
keywords: Oceans > Ocean Pressure > Water Pressure...
keywords_vocabulary: NASA/GCMD Earth Sciences Keywords Versio...
license: This data may be redistributed and used ...
metadata_link:
naming_authority: edu.rutgers.rucool
ncei_template_version: NCEI_NetCDF_Trajectory_Template_v2.0
platform: In Situ Ocean-based Platforms > AUVS > A...
platform_type: Slocum Glider
platform_vocabulary: NASA/GCMD Platforms Keywords Version 8.5
processing_level: Delayed-mode/recovered raw Slocum glider...
product_version: 1.0
program: Challenger
project: Challenger
publisher_country: USA
publisher_email: kerfoot@marine.rutgers.edu
publisher_institution: Rutgers University
publisher_name: John Kerfoot
publisher_type: person
publisher_url: https://rucool.marine.rutgers.edu
references: https://ioos.noaa.gov/wp-content/uploads...
sea_name: Caribbean Sea
source: local file
standard_name_vocabulary: CF Standard Name Table v27
summary: The Challenger Glider Mission is a re-cr...
time_coverage_duration: PT29M57.83292S
time_coverage_end: 2020-09-08T16:53:37Z
time_coverage_resolution: PT08S
time_coverage_start: 2020-09-08T16:23:40Z
title: ru29 20200908T162340Z trajectory
wmo_platform_code: 4802960
uuid: b56c3c0e-e2f4-4a63-ae0f-4300fc8bf17e
To export the first profile in the dataset:
> pro_ds = gdm.slice_profile_dataset(gdm.profiles.index[0])
> pro_ds
<xarray.Dataset>
Dimensions: (time: 73)
Coordinates:
* time (time) datetime64[ns] 2020-09-08T...
Data variables:
commanded_alt_time (time) float64 nan nan ... nan nan
commanded_climb_target_depth (time) float64 nan nan ... nan nan
commanded_de_oil_vol (time) float64 nan nan ... nan nan
commanded_dive_target_depth (time) float64 nan nan ... nan nan
commanded_fin (time) float64 nan nan ... nan nan
commanded_heading (time) float64 nan nan ... nan nan
commanded_science_send_all (time) float64 nan nan ... nan nan
commanded_thruster_on (time) float64 nan nan ... nan nan
commanded_weight_drop (time) float64 nan nan ... nan nan
commanded_wpt_lat (time) float64 nan nan ... nan nan
commanded_wpt_lon (time) float64 nan nan ... nan nan
commanded_wpt_x_lmc (time) float64 nan nan ... nan nan
commanded_wpt_y_lmc (time) float64 nan nan ... nan nan
f_fin_offset (time) float64 nan nan ... nan nan
f_ocean_pressure_max (time) float64 nan nan ... nan nan
f_ocean_pressure_min (time) float64 nan nan ... nan nan
measured_avg_speed (time) float64 nan nan ... nan nan
measured_battery_inst (time) float64 nan nan ... nan nan
measured_battpos (time) float64 nan nan ... nan nan
measured_bms_aft_current (time) float64 nan nan ... nan nan
measured_bms_ebay_current (time) float64 nan nan ... nan nan
measured_bms_emergency_battery_voltage (time) float64 nan nan ... nan nan
measured_bms_pitch_current (time) float64 nan nan ... nan nan
...
ctd41cp_timestamp (time) datetime64[ns] 1970-01-01 ...
beta_700nm (time) float64 0.0 nan ... nan nan
cdom (time) float64 0.0 nan ... nan nan
chlorophyll_a (time) float64 0.0 nan ... nan nan
flbbcd_timestamp (time) datetime64[ns] 1970-01-01 ...
sci_m_disk_free (time) float64 1.988e+03 ... 1.98...
science_timestamp (time) datetime64[ns] 2020-09-08T...
conductivity (time) float64 0.0 nan ... nan nan
pressure (time) float64 nan nan ... nan nan
temperature (time) float64 0.0 nan ... nan nan
segment (time) object 'ru29-2020-251-4-0'...
the8x3_filename (time) object '04270000' ... '042...
measured_lat (time) float64 nan nan ... 18.18 nan
measured_lon (time) float64 nan nan ... -64.8 nan
lat (time) float64 nan nan ... 18.18
lon (time) float64 nan nan ... -64.8
depth (time) float64 nan nan ... nan nan
salinity (time) float64 nan nan ... nan nan
density (time) float64 nan nan ... nan nan
sound_speed (time) float64 nan nan ... nan nan
platform float64 nan
instrument_ctd float64 nan
instrument_flbbcd float64 nan
instrument_ad2cp float64 nan
trajectory <U18 'ru29-20200908T1623'
profile_lat float64 18.18
profile_lon float64 -64.81
profile_time datetime64[ns] 2020-09-08T16:34:0...
Attributes:
Conventions: CF-1.6, ACDD-1.3, IOOS-1.2
acknowledgment: Funding and support provided by the G. U...
cdm_data_type:
comment: Deployed by Antonio Farchetti, Kenique L...
contributor_name: Scott Glenn,Oscar Schofield,Travis Miles...
contributor_role: principalInvestigator,principalInvestiga...
contributor_role_vocabulary: https://vocab.nerc.ac.uk/collection/G04/...
creator_country: USA
creator_email: kerfoot@marine.rutgers.edu
creator_institution: Rutgers University
creator_name: John Kerfoot
creator_sector: academic
creator_type: person
creator_url: https://rucool.marine.rutgers.edu
date_created:
date_issued:
date_metadata_modified:
date_modified:
featureType: trajectoryProfile
geospatial_bounds: POLYGON ((18.1771450617284 -64.805832777...
geospatial_bounds_crs: EPSG:4326
geospatial_bounds_vertical_crs: EPSG:5831
geospatial_lat_max: 18.177145061728396
geospatial_lat_min: 18.176626543209878
geospatial_lat_resolution: 0.00001 degree
geospatial_lat_units: degrees_north
geospatial_lon_max: -64.80445944444445
geospatial_lon_min: -64.80583277777778
geospatial_lon_resolution: 0.00001 degree
geospatial_lon_units: degrees_east
geospatial_vertical_max: 123.4073257374073
geospatial_vertical_min: 8.319587076348384
geospatial_vertical_positive: down
geospatial_vertical_resolution: 1.58
geospatial_vertical_units: m
gts_ingest: True
history:
id:
infoUrl: https://rucool.marine.rutgers.edu
institution: Rutgers University
instrument: In Situ/Laboratory Instruments > Profile...
instrument_vocabulary: NASA/GCMD Instrument Keywords Version 8.5
ioos_regional_association: MARACOOS
keywords: Oceans > Ocean Pressure > Water Pressure...
keywords_vocabulary: NASA/GCMD Earth Sciences Keywords Versio...
license: This data may be redistributed and used ...
metadata_link:
naming_authority: edu.rutgers.rucool
ncei_template_version: NCEI_NetCDF_Trajectory_Template_v2.0
platform: In Situ Ocean-based Platforms > AUVS > A...
platform_type: Slocum Glider
platform_vocabulary: NASA/GCMD Platforms Keywords Version 8.5
processing_level: Delayed-mode/recovered raw Slocum glider...
product_version: 1.0
program: Challenger
project: Challenger
publisher_country: USA
publisher_email: kerfoot@marine.rutgers.edu
publisher_institution: Rutgers University
publisher_name: John Kerfoot
publisher_type: person
publisher_url: https://rucool.marine.rutgers.edu
references: https://ioos.noaa.gov/wp-content/uploads...
sea_name: Caribbean Sea
source:
standard_name_vocabulary: CF Standard Name Table v27
summary: The Challenger Glider Mission is a re-cr...
time_coverage_duration: PT10M42.82041S
time_coverage_end: 2020-09-08T16:39:30Z
time_coverage_resolution: PT08S
time_coverage_start: 2020-09-08T16:28:47Z
title: ru29 20200908T163403Z trajectoryProfile
wmo_platform_code: 4802960
Another method:
returns an iterator used to loop through all the profiles, provided via the indexed profile metadata DataFrame, and return an xarray Dataset for each profile.
The iter_profiles() method is a quick way to iterate through all of the profiles contained in a dataset and write them to disk as NetCDF files:
> dba_files.iloc[0].file.split('_')[0]
> out_nc_path = '/Users/kerfoot/data/gliders/rucool/deployments/2020/ru29-20200908T1623/data/out/nc/sandbox'
> for profile_time, pro_ds in gdm.iter_profiles():
nc_path = os.path.join(out_nc_path, '{:}_{:}_rt.nc'.format(glider, profile_time.strftime('%Y%m%dT%H%M%S')))
logging.info('Writing {:}'.format(nc_path))
pro_ds.to_netcdf(nc_path)
2020-12-08 15:23:54,949<input>:INFO:Writing /Users/kerfoot/data/gliders/rucool/deployments/2020/ru29-20200908T1623/data/out/nc/sandbox/ru29_20200908T163403_rt.nc [line 3]
2020-12-08 15:23:56,472<input>:INFO:Writing /Users/kerfoot/data/gliders/rucool/deployments/2020/ru29-20200908T1623/data/out/nc/sandbox/ru29_20200908T164540_rt.nc [line 3]