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Hourly Gridded MBARI Mooring M1 Sea Water Temperature and Salinity Observations

These data have been processed from the original instrument netCDF files which have individual irregular time axes. The data in this file have been combined on a common hourly time axes and a common nominal depth axis. Data values outside of reasonable ranges have been flagged as bad.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:43:40

Mooring M1 Backscatterometer data from MBARI instrument id 1427 at original sampling intervals

Observational oceanographic data obtained from a moored platform. Sensors placed on the mooring record environmental parameters as time passes.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:43:58

Hourly Gridded MBARI Mooring M2 Sea Water Temperature and Salinity Observations

These data have been processed from the original instrument netCDF files which have individual irregular time axes. The data in this file have been combined on a common hourly time axes and a common nominal depth axis. Data values outside of reasonable ranges have been flagged as bad.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:44:43

Mooring M2 Scatterometer data from MBARI instrument id 1389 at original sampling intervals

Observational oceanographic data obtained from a moored platform. Sensors placed on the mooring record environmental parameters as time passes.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:44:55

AUV survey data with decimation and interpolation to 2 second intervals.

Observational oceanographic data obtained from an Autonomous Underwater Vehicle survey. Sensors record environmental parameters as the vehicle travels a programmed path through the water. Those measurements have been processed by MBARI Matlab-based dorado science data processing toolbox into this data set.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:11:01

AUV survey data with decimation and interpolation to 2 second intervals.

Observational oceanographic data obtained from an Autonomous Underwater Vehicle survey. Sensors record environmental parameters as the vehicle travels a programmed path through the water. Those measurements have been processed by MBARI Matlab-based dorado science data processing toolbox into this data set.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:22:29

AUV survey data with decimation and interpolation to 2 second intervals.

Observational oceanographic data obtained from an Autonomous Underwater Vehicle survey. Sensors record environmental parameters as the vehicle travels a programmed path through the water. Those measurements have been processed by MBARI Matlab-based dorado science data processing toolbox into this data set.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:34:35

AUV survey data with decimation and interpolation to 2 second intervals.

Observational oceanographic data obtained from an Autonomous Underwater Vehicle survey. Sensors record environmental parameters as the vehicle travels a programmed path through the water. Those measurements have been processed by MBARI Matlab-based dorado science data processing toolbox into this data set.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:40:25

Laser Optical Plankton Counter measurements from AUV mission 2011.164.05

These data have been processed from the original lopb.bin file produced by the LOPC instrument. The data in this file are to be considered as simple time series data only and are as close to the original data as possible. Further processing is required to turn the data into a time series of profiles.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:12:24

Laser Optical Plankton Counter measurements from AUV mission 2011.165.00

These data have been processed from the original lopb.bin file produced by the LOPC instrument. The data in this file are to be considered as simple time series data only and are as close to the original data as possible. Further processing is required to turn the data into a time series of profiles.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:23:55

Laser Optical Plankton Counter measurements from AUV mission 2011.166.00

These data have been processed from the original lopb.bin file produced by the LOPC instrument. The data in this file are to be considered as simple time series data only and are as close to the original data as possible. Further processing is required to turn the data into a time series of profiles.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:35:46

Laser Optical Plankton Counter measurements from AUV mission 2011.171.01

These data have been processed from the original lopb.bin file produced by the LOPC instrument. The data in this file are to be considered as simple time series data only and are as close to the original data as possible. Further processing is required to turn the data into a time series of profiles.

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T00:47:56

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Loaded into STOQS:
2018-08-04T01:16:16

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Loaded into STOQS:
2018-08-04T01:41:36

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Loaded into STOQS:
2018-08-04T01:44:01

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Loaded into STOQS:
2018-08-04T01:48:53

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Loaded into STOQS:
2018-08-04T02:05:10

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Loaded into STOQS:
2018-08-04T02:17:37

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(No summary in NetCDF file)

(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T02:23:53

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(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T02:47:43

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Loaded into STOQS:
2018-08-04T02:55:08

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Loaded into STOQS:
2018-08-04T03:26:51

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(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T03:46:22

(No title in NetCDF file)

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Loaded into STOQS:
2018-08-04T03:51:18

(No title in NetCDF file)

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(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:20:20

(No title in NetCDF file)

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(No comment in NetCDF file)

Loaded into STOQS:
2018-08-04T04:41:28
Dorado389_2011_164_05_164_05_decim.nc2columnbiolumehist_statslopcnav_adjustprof_stats
Dorado389_2011_165_00_165_00_decim.nc2columnbiolumehist_statslopcnav_adjustprof_stats
Dorado389_2011_166_00_166_00_decim.nc2columnbiolumehist_statslopcnav_adjustprof_stats
Dorado389_2011_171_01_171_01_decim.nc2columnbiolumehist_statslopcnav_adjustprof_stats
http://dods.mbari.org/opendap/data/ssdsdata/ssds/generated/netcdf/files/ssds.shore.mbari.org/auvctd/missionlogs/2011/2011164/2011.164.05/lopc.nc (stride=1)
http://dods.mbari.org/opendap/data/ssdsdata/ssds/generated/netcdf/files/ssds.shore.mbari.org/auvctd/missionlogs/2011/2011165/2011.165.00/lopc.nc (stride=1)
http://dods.mbari.org/opendap/data/ssdsdata/ssds/generated/netcdf/files/ssds.shore.mbari.org/auvctd/missionlogs/2011/2011166/2011.166.00/lopc.nc (stride=1)
http://dods.mbari.org/opendap/data/ssdsdata/ssds/generated/netcdf/files/ssds.shore.mbari.org/auvctd/missionlogs/2011/2011171/2011.171.01/lopc.nc (stride=1)

Parameter-Parameter

Linear regression Free range Samples No stride

                                    
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mp-parquet  Apache Parquet binary column format
Collect: standard_name(s) name(s)
Include: Activity names
Estimated Available
RAM (GB) ... ...
Time (min) ... ...
Rows ...
File size (MB) ...
 
 
Click "Estimate requirements" button before using this Data Access URL
https://stoqs.mbari.org/stoqs_june2011/api/measuredparameter.parquet?collect=standard_name

In addition to using the above ad hoc .parquet request generation capability you may also script up requests by activity__name, inherently reducing server-side memory and time requirements. Here is an example of reading Dorado data survey-by-survey into a Matlab session:
% File: read_parquet.m
% Read parquet files from STOQS and report the time it takes
campaign_url = "https://stoqs.mbari.org/stoqs_all_dorado";
api_call = "/api/measuredparameter.parquet?collect=name&activity__name__contains=";
surveys = ["Dorado389_2020_006_06_006_06_decim", "Dorado389_2020_035_00_035_00_decim"];
wo = weboptions('CertificateFilename', '', 'Timeout', 300);
for i=1:length(surveys),
    tic;
    disp(strcat("Reading ", surveys(i), " as .parquet"));
    parquet_url = strcat(campaign_url, api_call, surveys(i));
    file = websave(strcat(surveys(i), '.parquet'), parquet_url, wo);
    disp(strcat("Wrote ", file));
    toc;
    tic;
    info = parquetinfo(file);
    T = parquetread(file);
    disp(strcat("Read ", file));
    toc;
end
disp(info.VariableNames)

—————————————————

>> read_parquet
Reading Dorado389_2020_006_06_006_06_decim as .parquet
Wrote /u/mccann/matlab/Dorado389_2020_006_06_006_06_decim.parquet
Elapsed time is 14.278701 seconds.
Read /u/mccann/matlab/Dorado389_2020_006_06_006_06_decim.parquet
Elapsed time is 0.410685 seconds.
Reading Dorado389_2020_035_00_035_00_decim as .parquet
Wrote /u/mccann/matlab/Dorado389_2020_035_00_035_00_decim.parquet
Elapsed time is 14.070438 seconds.
Read /u/mccann/matlab/Dorado389_2020_035_00_035_00_decim.parquet
Elapsed time is 0.097718 seconds.
    Columns 1 through 11

    "adinos"    "altitude"    "bbp420 (m-1)"    "bbp700 (m-1)"    "bg_biolum (ph L^..."    "biolume"    "diatoms"    "fl700_uncorr"    "fluo"    "hdinos"    "intflash (ph s^{..."

    Columns 12 through 19

    "mepCountList (co..."    "nbflash_high (L^..."    "nbflash_low (L^{..."    "nitrate (micromo..."    "oxygen (ml/l)"    "pitch (degree)"    "profile"    "roll (degree)"

    Columns 20 through 30

    "salinity"    "sepCountList (co..."    "sigmat"    "spice"    "temperature (Cel..."    "yaw (degree)"    "platform"    "timevalue"    "depth"    "latitude"    "longitude"
                                                    
Here is visualize_parquet.ipynb which shows how to read and visualize parquet data in Python.
Here is parquet2csv.py which you can execute at the command line to convert parquet data to a comma separated value file.
mp-kml  Keyhole Markup Language - click on icon to view in Google Earth
Select a single measured parameter for data access to view your selection in Google Earth
KML colormap

                                    
                                
mp-sql  Structured Query Language for PostgreSQL

                                        
                                    
                                
mp-matlab  Matlab - Load data into Matlab structure array
Install this m-file on your system (instructions are in the comments):
https://raw.githubusercontent.com/stoqs/stoqs/master/stoqs/contrib/analysis/dbconn_jdbc_stoqs.m

Then use the 'SQL' in the adjacent tab to load data into Matlab.
mp-json  JavaScript Object Notation
https://stoqs.mbari.org/stoqs_june2011/api/measuredparameter.json
mp-csv  Comma Separated Values
https://stoqs.mbari.org/stoqs_june2011/api/measuredparameter.csv
mp-tsv  Tabbed Separated Values
https://stoqs.mbari.org/stoqs_june2011/api/measuredparameter.tsv
mp-html  Hyper Text Markup Language table
https://stoqs.mbari.org/stoqs_june2011/api/measuredparameter.html

Temporal

Click and drag to zoom
Jun 11
Jun 12
Jun 13
Jun 14
Jun 15
Jun 16
Jun 17
Jun 18
Jun 19
Jun 20
Jun 21
Jun 22
Jun 23
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Time (gmt)
Depth (m)
   
Click and drag to zoom
 
Color by: Platform Activity

Measured Parameters

Plot Data Filter & Select for data access
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Parameter Values

Show distribution of parameter values
Histogram display:

Platforms