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Showing content from https://pypi.python.org/pypi/scikit-diveMove below:

scikit-diveMove·PyPI

scikit-diveMove is a Python interface to R package diveMove for scientific data analysis, with a focus on diving behaviour analysis. It has utilities to represent, visualize, filter, analyse, and summarize time-depth recorder (TDR) data. Miscellaneous functions for handling position and 3D kinematics data are also provided. scikit-diveMove communicates with a single R instance for access to low-level tools of package diveMove.

The table below shows which features of diveMove are accessible from scikit-diveMove:

diveMove

scikit-diveMove

Notes

Functionality

Functions/Methods

Movement

austFilter rmsDistFilter grpSpeedFilter distSpeed readLocs

Under consideration.

Bout analysis

boutfreqs boutinit bouts2.nlsFUN bouts2.nls bouts3.nlsFUN bouts3.nls bouts2.mleFUN bouts2.ll bouts2.LL bouts.mle labelBouts plotBouts plotBouts2.cdf bec2 bec3

BoutsNLS BoutsMLE

Fully implemented in Python.

Dive analysis

readTDR createTDR

TDR.__init__ TDRSource.__init__

Fully implemented. Single TDR class for data with or without speed measurements.

calibrateDepth

TDR.calibrate TDR.zoc TDR.detect_wet TDR.detect_dives TDR.detect_dive_phases

Fully implemented

calibrateSpeed rqPlot

TDR.calibrate_speed

New implementation of the algorithm entirely in Python. The procedure generates the plot concurrently.

diveStats stampDive timeBudget

TDR.dive_stats TDR.time_budget TDR.stamp_dives

Fully implemented

plotTDR plotDiveModel plotZOC

TDR.plot TDR.plot_zoc_filters TDR.plot_phases TDR.plot_dive_model

Fully implemented. Interactivity is the default, as standard matplotlib.

getTDR getDepth getSpeed getTime getCCData getDtime getFileName

TDR.tdr TDR.get_depth TDR.get_speed TDR.tdr.index TDR.src_file TDR.dtime

Fully implemented. getCCData deemed redundant, as the columns can be accessed directly from the TDR.tdr attribute.

getDAct getDPhaseLab getDiveDeriv getDiveModel getGAct

TDR.get_wet_activity TDR.get_dives_details TDR.get_dive_deriv

Fully implemented

extractDive

Fully implemented

scikit-diveMove also provides useful tools for processing signals from tri-axial Inertial Measurement Units (IMU), such as thermal calibration, corrections for shifts in coordinate frames, as well as computation of orientation using a variety of current methods. Analyses are fully tractable by encouraging the use of xarray data structures that can be read from and written to NetCDF file format. Using these data structures, meta-data attributes can be easily appended at all layers as analyses progress.

Type the following at a terminal command line:

pip install scikit-diveMove

Or install from source tree by typing the following at the command line:

python setup.py install

The documentation can also be installed as described in Documentation.

Once installed, skdiveMove can be easily imported as:

import skdiveMove as skdive

skdiveMove depends primarily on R package diveMove, which must be installed and available to the user running Python. If needed, install diveMove at the R prompt:

install.packages("diveMove")

Available at: https://spluque.github.io/scikit-diveMove

Alternatively, installing the package as follows:

pip install -e .["dev"]

allows the documentation to be built locally (choosing the desired target {“html”, “pdf”, etc.}):

make -C docs/ html

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