README.md

TAPPY

TAPPY is a tidal analysis package. It breaks down an hourly record of water levels into the component sine waves. It is written in Python and uses the least squares optimization and other functions in SciPy. The focus is to make the most accurate analysis possible. TAPPY only determines the constituents that are calculatable according to the length of the time series.

Authors

• Tim Cera (tim at cerazone.net)
• Pierre Cazenave (Plymouth Marine Laboratory)

Source

This repository is an import of the CVS repository found on the official site:

https://sourceforge.net/p/tappy/wiki/Main_Page

http://sourceforge.net/projects/tappy

I (Pierre Cazenave) have made minor changes to the code to allow TAPPY to be used as a module in scripts.

Installation

TAPPY requires the astronomia, filelike and pyparsing packages which are available with pip:

pip install astronomia filelike pyparsing

TAPPY itself can be installed with setup.py:

python setup.py install

or as a non-privileged user (gets installed to ~/.local):

python setup.py install --user

Examples

The official SourceForge wiki covers the general usage of TAPPY. Given the change I made to allow the code to be imported as a module, an example of its use in that manner is useful.

import tappy

if __name__ == '__main__':

    # Load a time series to analyse
    dates = ... # a datetime.datetime list of dates
    elevation = ... # a list of surface elevation values

    # Set up the bits needed for TAPPY. This is mostly lifted from
    # tappy.py in the baker function "analysis" (around line 1721).
    quiet = True
    debug = False
    outputts = False
    outputxml = False
    ephemeris = False
    rayleigh = 1.0
    print_vau_table = False
    missing_data = 'ignore'
    linear_trend = False
    remove_extreme = False
    zero_ts = None
    filter = None
    pad_filters = None
    include_inferred = True

    if rayleigh:
        ray = float(rayleigh)

    x = tappy.tappy(
        outputts = outputts,
        outputxml = outputxml,
        quiet=quiet,
        debug=debug,
        ephemeris=ephemeris,
        rayleigh=rayleigh,
        print_vau_table=print_vau_table,
        missing_data=missing_data,
        linear_trend=linear_trend,
        remove_extreme=remove_extreme,
        zero_ts=zero_ts,
        filter=filter,
        pad_filters=pad_filters,
        include_inferred=include_inferred,
        )

    x.dates = dates
    x.elevation = elevation[:, p].tolist()
    package = x.astronomic(x.dates)
    (x.zeta, x.nu, x.nup, x.nupp, x.kap_p, x.ii, x.R, x.Q, x.T, x.jd, x.s, x.h, x.N, x.p, x.p1) = package
    (x.speed_dict, x.key_list) = x.which_constituents(len(x.dates),
            package, rayleigh_comp=ray)

    x.constituents() # the analysis

    # Print the M2 amplitude and phase.
    print('M2 phase:     {}'.format(x.phase[x.key_list.index('M2')]))
    print('M2 amplitude: {}'.format(x.r[x.key_list.index('M2')]))