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Here is an example plot from our testing environment with a limited time range.
MAT / netCDF: Time Series Scalar Gridded Data
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TimeSeriesData: structured as an 1 x 1 structure with M fields:
- sensorID: Unique identifier number for sensor.
sensorName: Name of sensor.
sensorCode: Unique string for the sensor.
sensorDescription: Description of sensor.
sensorType: Type of sensor as classified in the ONC data model.
sensorTypeID: ONC ID given to sensor type.
units: Unit of measure for the sensor data.
isEngineeringSensor: boolean (flag) to determine if sensor is an engineering sensor.
sensorDerivation: String describing the source of the sensor data: derived from calibration formula (dmas-derived), calculated on the device (instrument-derived), calculated by an external process (externally-derived), or direct from the instrument.
propertyCode: Unique string for the sensor using only lowercase letters and no unique characters
isMobilePositionSensor: boolean (flag) to determine if sensor is a mobile sensor. Note, this will only be flagged true if this data was added in addition to the requested data. For example, if the user requests a device-level mat product from a GPS device, then the latitude sensor is not flagged. Conversely, if the user requests temperature data from a mobile platform like a ship, then the latitude data from the GPS is added and interpolated to match the time stamps of the temperature sensor. See Positioning and Attitude for Mobile Devices for more information.
deviceID: Unique identifier number for the parent device.
searchDateNumFrom: Start date of the specific search in MATLAB datenum format - searches are truncated by availability and deployment dates.
searchDateNumTo: End date of the specific search MATLAB datenum format - searches are truncated by availability and deployment dates.
samplePeriod: Vector of sample periods in seconds.
samplePeriodDateFrom: Vector of the start date of each sample period (MATLAB datenum format).
samplePeriodDateTo: Vector of the end date of each of sample period (MATLAB datenum format).
sampleSize: The size of the data sample.
resampleType: Type of resampling used.
resampleDescription: Description of the resampleing used.
resamplePeriod_sec: Resample period in seconds.
resampleTypeID: Unique identifier of the subsample type used: 0/NaN - none, 1 - average, 2 - decimated (not offered), 3 - min/max, 4 - linear interpolation (VPS pressure only).
dataProductOptions: A string describing the data product options selected for this data product. This information is reflected in the file name.
qaqcFlagDescription: A string describing the flags. See the QAQC page for more information.
time: A vector of data timestamps in MATLAB datenum format.
dat: A vector of sensor values corresponding to each timestamp. When resampling by averaging, this becomes the average value. (May make a separate field for this in the future, especially if users prefer that option).
qaqcFlags: A vector indicating the quality of the data, matching the time and dat vectors. See the QAQC page for more information.
dataDateNumFrom: First time-stamp of the time series.
dataDateNumTo: Last time-stamp of the time series.
samplesExpected: The number of valid samples expected from the minimum returned data to the maximum returned data, accounting for variations in sample period.
samplesReceived: The number of raw samples received, maybe less than length(data.time) when data gaps are being filled with the NaN option.
startIndx: Start indices for each profile (index corresponding to time series)
- endIndx: Stop indices for each profile (index corresponding to time series)
- gridDat: A matrix containing the binned and gridded sensor data (see Description above for binning and gridding method)
- gridTime: Array with the time grid used in gridding data
- gridDepth: Array with the depth bins used in gridding data
- gridqaqcFlags: A matrix containing the binned and gridded sensor qaqcFlags (see Description above for binning and gridding method)
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