TDRPhases class¶
Class handling all TDR phase operations
Phase identification methods take depth as input.
Class and Methods Summary¶
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Detect wet/dry activity phases |
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Identify dive events |
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Detect dive phases |
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Accessor for the |
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Retrieve depth spline derivative for a given dive |
Accessor for the |
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Return parameters used for identifying wet/dry or diving phases. |
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Summary of wet/dry activities at the broadest time scale |
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Identify the wet activity phase corresponding to each dive |
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class
tdrphases.
TDRPhases
[source]¶ Core TDR phase identification routines
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wet_dry
¶ - Type
pandas.DataFrame
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dives
¶ Dictionary of dive activity data {‘row_ids’: pandas.DataFrame, ‘model’: str, ‘splines’: dict, ‘spline_derivs’: pandas.DataFrame, ‘crit_vals’: pandas.DataFrame}.
- Type
dict
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params
¶ Dictionary with parameters used for detection of wet/dry and dive phases. {‘wet_dry’: {‘dry_thr’: float, ‘wet_thr’: float}, ‘dives’: {‘dive_thr’: float, ‘dive_model’: str, ‘smooth_par’: float, ‘knot_factor’: int, ‘descent_crit_q’: float, ‘ascent_crit_q’: float}}
- Type
dict
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detect_dive_phases
(depth, dive_model, smooth_par=0.1, knot_factor=3, descent_crit_q=0, ascent_crit_q=0)[source]¶ Detect dive phases
Complete filling the
dives
attribute.- Parameters
depth (xarray.DataArray) – DataArray with zero-offset corrected depth measurements.
dive_model ({"unimodal", "smooth.spline"}) –
smooth_par (float, optional) –
knot_factor (int, optional) –
descent_crit_q (float, optional) –
ascent_crit_q (float, optional) –
Notes
See details for arguments in diveMove’s
calibrateDepth
.
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detect_dives
(depth, dive_thr)[source]¶ Identify dive events
Set the
dives
attribute’s “row_ids” dictionary element, and update thewet_act
attribute’s “phases” dictionary element.- Parameters
depth (xarray.DataArray) – DataArray with zero-offset corrected depth measurements.
dive_thr (float) –
Notes
See details for arguments in diveMove’s
calibrateDepth
.
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detect_wet
(depth, dry_thr=70, wet_cond=None, wet_thr=3610, interp_wet=False)[source]¶ Detect wet/dry activity phases
Set the
wet_dry
attribute.- Parameters
depth (xarray.DataArray) – DataArray with zero-offset corrected depth measurements.
dry_thr (float, optional) –
wet_cond (bool mask, optional) –
wet_thr (float, optional) –
interp_wet (bool, optional) –
Notes
See details for arguments in diveMove’s
calibrateDepth
. Unlike diveMove, the beginning/ending times for each phase are not stored with the class instance, as this information can be retrieved via the .time_budget method.
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get_dive_deriv
(diveNo, phase)[source]¶ Retrieve depth spline derivative for a given dive
- Parameters
diveNo (int) – Dive number to retrieve derivative for.
phase ({"descent", "bottom", "ascent"}) – If provided, the dive phase to retrieve data for.
- Returns
out
- Return type
pandas.Series
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get_dives_details
(key, columns=None)[source]¶ Accessor for the
dives
attribute- Parameters
key ({"row_ids", "model", "splines", "spline_derivs", crit_vals}) – Name of the key to retrieve.
columns (array_like, optional) – Names of the columns of the dataframe in key, when applicable.
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get_params
(key)[source]¶ Return parameters used for identifying wet/dry or diving phases.
- Parameters
key ({'wet_dry', 'dives'}) –
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stamp_dives
(ignore_z=True)[source]¶ Identify the wet activity phase corresponding to each dive
- Parameters
ignore_z (bool, optional) – Whether to ignore trivial aquatic periods.
- Returns
out – DataFrame indexed by dive ID, and three columns identifying which phase thy are in, and the beginning and ending time stamps.
- Return type
pandas.DataFrame
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time_budget
(ignore_z=True, ignore_du=True)[source]¶ Summary of wet/dry activities at the broadest time scale
- Parameters
ignore_z (bool, optional) – Whether to ignore trivial aquatic periods.
ignore_du (bool, optional) – Whether to ignore diving and underwater periods.
- Returns
out – DataFrame indexed by phase id, with categorical activity label for each phase, and beginning and ending times.
- Return type
pandas.DataFrame
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