gravitools.aqg.dataset

Handling of processed AQG data

AQGDataset(ds)

Processed AQG dataset

Parameters:

• ds (Dataset) –

  Dataset of processed AQG data

metadata property

Metadata attributes

time_span property

Measurement start and end time

duration property

Total duration of measurement

num_drops property

Total number of drops

num_outliers property

Number of drops marked as outliers

h property

Measurement reference height, in meters

name property

Dataset identifier

comment property

Comment text

columns property

List of data columns

temperature_columns property

List of temperature data columns

meter property

Instrument identifier

point property

Measurement point identifier

vgg property

Vertical gravity gradient used for height transfer

syst_uncertainty property

Systematic uncertainty of the instrument

get(column)

Retrieve a data column as Pandas series

Parameters:

• column (str) –

  Column name.

Returns:

• Series –

  Time series.

full_g(h=STANDARD_HEIGHT, incl_outliers=False, **kwargs)

Full gravity signal

Parameters:

• h (float, default: STANDARD_HEIGHT ) –

  Transfer height. Pass None to use original height.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

Returns:

• GravityTimeSeries –

  Time series of gravity values

g(h=STANDARD_HEIGHT, incl_outliers=False, min_count=0.5, **kwargs)

Gravity signal resampled to 10 sec intervals

Parameters:

• h (float, default: STANDARD_HEIGHT ) –

  Height (in meters) to transfer gravity signal to using gravity gradient.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

• min_count (bool, default: 0.5 ) –

  Minimum number of drops required in an interval to be valid.

Returns:

• GravityTimeSeries –

  Time series of gravity values.

mean(t0=None, t1=None, h=STANDARD_HEIGHT, incl_outliers=False)

Mean gravity value.

Note

Estimating the AQG's statistical error is not trivial because the signal does not follow a normal distribution. Moreover, the noise characteristic can depend on location. This procedure is intended as a standard workflow that errs on the side of overestimating the uncertainty.

The mean value is calculated from the unweighted mean of all accepted drops.

For the calculation of the statistical error, the full signal is first downsampled to 10 second intervals (see method g()) because the AQG signal does not show a normal distribution below this time scale.

For short measurements (< 2 hours), the statistical error is then calculated by the standard error of the mean (SEM) of the 10s-interval means.

For longer (> 2 hours) measurements, the AQG signal typically does not show a normal distribution anymore (flicker noise), so the SEM would be an underestimation. Instead, the statistical error is estimated from the standard deviation of 1 hour interval means, i. e. the error will not decrease with measurement duration.

If t0 and t1 are unspecified (None), the entire time series is used.

Parameters:

• t0 ((str, Timestamp), default: None ) –

  Start of time range.

• t1 ((str, Timestamp), default: None ) –

  End of time range.

• h (float, default: STANDARD_HEIGHT ) –

  Transfer height, in meters. Pass None for original height.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

Returns:

• AbsGValue –

  Mean absolute gravity value.

adev(t0=None, t1=None, incl_outliers=False)

Calculate the Allan deviation

Note

The Allan deviation assumes a fixed-interval time series. AQG data is nominally in 540 ms intervals, but can have gaps due to calibrations. This calculation assumes an interval of 540 ms, i. e. the gaps are pulled together.

Parameters:

• t0 (str | Timestamp, default: None ) –

  Start of time range for calculation.

• t1 (str | Timestamp, default: None ) –

  End of time range for calculation.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

Returns:

• Series –

  Allan deviation.

compare(g_ref, h=STANDARD_HEIGHT, incl_outliers=False)

Compare gravity time series to a reference function

Parameters:

• g_ref (Series) –

  Gravity reference function.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

Returns:

• DataFrame –

  Dataframe of AQG signal, reference and difference.

to_nc(path)

Export dataset as NETCDF4 file

Parameters:

• path (str | Path) –

  Path of .nc file to write to.

plot_adev(t0=None, t1=None, incl_outliers=False, spec=True, ax=None, **kwargs)

Plot of the Allan deviation

Parameters:

• t0 (str | Timestamp, default: None ) –

  Start of time range to select for calculation.

• t1 (str | Timestamp, default: None ) –

  End of time range to select for calculation.

• incl_outliers (bool, default: False ) –

  Include drops marked as outliers.

• spec (bool, default: True ) –

  Plot sensitivity specification, to serve as visual reference.

• ax (Axes, default: None ) –

  Plot axes.

• **kwargs (dict, default: {} ) –

  Additional arguments to pass to matplotlib.pyplot.plot.

Returns:

• Line2D –

  Plot line as returned by matplotlib.pyplot.loglog.

plot_full(t0=None, t1=None, height=None, incl_outliers=True, density=True, g0=None, ylim=(-2000, 2000), ax=None, **kwargs)

Plot full gravity time series

Parameters:

• t0 (str | Timestamp, default: None ) –

  Start of time range to select.

• t1 (str | Timestamp, default: None ) –

  End of time range to select.

• height (float, default: None ) –

  Transfer height, in meters. Pass None for original measurement height.

• incl_outliers (bool, default: True ) –

  Include drops marked as outliers.

• density (bool, default: True ) –

  Visualize sample density.

• g0 (float, default: None ) –

  Offset on y-axis. Pass None for mean value.

• ylim (tuple, default: (-2000, 2000) ) –

  Y-axis plot range, in nm/s².

• ax (Axes, default: None ) –

  Plot axes.

• **kwargs (dict, default: {} ) –

  Additional arguments to pass to matplotlib.pyplot.plot.

Returns:

• Line2D –

  Plot line as returned by matplotlib.pyplot.plot.

save_report(path, reportclass=None, **kwargs)

Generate a PDF report of the processed dataset

AQGDataCollection(datasets)

Bases: AQGDataset

A sequence of processed AQG datasets one one location

Attributes:

• datasets –

  list of AQGDataset List of processed datasets that make up the collection

metadata property

Dataset metadata

columns property

List of data columns

get(column)

Get a combined data column

read_aqg_dataset(path)

Read processed AQG dataset from an .nc-file

Parameters:

• path ((str, Path)) –

  Path to .nc file.

Returns:

• AQGDataset –