A direct conversion of T_Tide to Python.

This is a work in progress. It is not done.

It is now mostly functional. Any help with finishing the conversion is welcome.

All credit for T_Tide goes to Rich Pawlowicz, the original creator of T_Tide. It is available at http://www2.ocgy.ubc.ca/~rich/.

This has little to no testing. Use at your own risk.

Run,

python setup.py install

from ttide.t_tide import t_tide

[name, freq, tidecon, xout]=t_tide(xin)

All other input is optional. Currently dt,stime,lat,constitnames,output,errcalc,synth,out_style, and secular can be specified. To do so use key=value (ex dt=0.5).

dt - Sampling interval (hours) default = 1

stime - Start time as number of days. using mpl.dates rather then datetime because it handles fractional days default = empty

lat - Decimal degress (+north) default = empty

errcalc - Method for calculation of confidence limits. (cboot,wboot,linear(not finished)) default = 'cboot'

constitnames - Names of constituents to use. default = empty

output - Flag to disable output. default = True

synth - Synthesis value for tidal prediction. default = 2

out_style - Output format. (classic,pandas) default='classic'

secular - Adjustment for long-term behavior. (mean,linear) default='linear'

1. The code to handle timeseries longer then 18.6 years has not been converted yet.

2. t_predic is working. Call it with [xout]=t_predic(time_in,names,freq,tidecon)

3. The code is a little messy and they are a few hacky bits that probably will need to be fixed. The most notable is in noise_realizations. It swaps eig vectors around to match Matlab's output. Also, the returned diagonal array would sometimes be a negative on the order of 10^-10. Values between (-0.00000000001,0) are forced to 0.

4. ttide_py was initially converted to python with SMOP. Available at, https://github.com/victorlei/smop.git.