Right after I test ttide_py a friend told me about the python version of the Unified Tidal Analysis and Prediction (UTide) written by Wesley Bowman.

UTide seems to be a significant advance over t_tide. So let's try the python version! I will use the same data from my previous t_tide posts for comparison.

In [2]:

from datetime import datetime
from pandas import read_table, DataFrame

def date_parser(year, day, month, hour):
    year, day, month, hour = map(int, (year, day, month, hour))
    return datetime(year, day, month, hour)

parse_dates = dict(datetime=['year', 'month', 'day','hour'])

names = ['year', 'month', 'day', 'hour', 'elev', 'flag']

obs = read_table('./data/can1998.dtf',
                 names=names,
                 skipinitialspace=True,
                 delim_whitespace=True,
                 index_col='datetime',
                 usecols=range(1, 7),
                 na_fvalues='9.990',
                 parse_dates=parse_dates,
                 date_parser=date_parser)
obs.head(6)

Out[2]:

	elev	flag
datetime
1998-01-01 00:00:00	1.20	0
1998-01-01 01:00:00	1.43	0
1998-01-01 02:00:00	1.73	0
1998-01-01 03:00:00	2.03	0
1998-01-01 04:00:00	2.38	0
1998-01-01 05:00:00	2.54	0

Now let's remove the mean and interpolate the bad data points.

In [3]:

import numpy as np
import matplotlib.pyplot as plt

bad = obs['flag'] == 2
corrected = obs['flag'] == 1

obs[bad] = np.NaN
obs['anomaly'] = obs['elev'] - obs['elev'].mean()
obs['anomaly'] = obs['anomaly'].interpolate()

Python UTide is still in its early development stage. There are no docs at this point.

I opened an issue regarding the input/output description.

For now I am assuming it takes a matplotlib date2num data for the time argument. Note also that we have to specify all the inputs, even when they are empty.

In [4]:

from utide import ut_solv
from matplotlib.dates import date2num

lat = -25.0147

time = date2num(obs.index.to_pydatetime())

coef = ut_solv(time, obs['anomaly'].values, np.array([]), lat, cnstit='auto',
               notrend=True, rmin=0.95, method='ols',
               nodiagn=True, linci=True, conf_int=True)

ut_solv: 
matrix prep ... 
Solution ...
conf. int vls... 
Done.

Now let's reconstruct the tidal series,

In [5]:

from utide import ut_reconstr


xout, _ = ut_reconstr(time, coef)

ut_reconstr:
prep/calcs...
Done.

and plot them.

In [6]:

fig, (ax0, ax1, ax2) = plt.subplots(nrows=3, sharey=True, sharex=True, figsize=(13, 5))

ax0.plot(obs.index, obs['anomaly'], label=u'Observations')
ax0.legend(numpoints=1, loc='lower right')

ax1.plot(obs.index, xout, alpha=0.5, label=u'Prediction')
ax1.legend(numpoints=1, loc='lower right')

ax2.plot(obs.index, obs['anomaly']-xout, alpha=0.5, label=u'Residue')
_ = ax2.legend(numpoints=1, loc='lower right')

The python version of UTide needs some help. Try it out, open issues, sent PRs, request features etc.

In [7]:

HTML(html)

Out[7]:

This post was written as an IPython notebook. It is available for download or as a static html.

python4oceanographers by Filipe Fernandes is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://ocefpaf.github.io/.

python4oceanographers

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UTide for python

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