In [ ]:
%matplotlib notebook

import datetime

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates

import pandas as pd

import utide

print(utide.__version__)


Look at the data file to see what structure it has.

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with open('can1998.dtf') as f:
print(''.join(lines[:30]))


It looks like the fields are seconds, year, month, day, hour, elevation, flag. We need a date parser function to combine the date and time fields into a single value to be used as the datetime index.

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

# Names of the columns that will be used to make a "datetime" column:
parse_dates = dict(datetime=['year', 'month', 'day','hour'])

# Names of the original columns in the file, including only
# the ones we will use; we are skipping the first, which appears
# to be seconds from the beginning.
names = ['year', 'month', 'day', 'hour', 'elev', 'flag']

names=names,
skipinitialspace=True,
delim_whitespace=True,
index_col='datetime',
usecols=range(1, 7),
na_values='9.990',
parse_dates=parse_dates,
date_parser=date_parser,
)


Although there are no elevations marked bad via special value, which should be nan after reading the file, the flag value of 2 indicates the values are unreliable, so we will mark them with nan, calculate the deviations of the elevations from their mean (stored in a new column called "anomaly"), and then interpolate to fill in the nan values in the anomaly.

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

obs['anomaly'] = obs['elev'] - obs['elev'].mean()
obs['anomaly'] = obs['anomaly'].interpolate()
print('{} points were flagged "corrected" and left unchanged'.format(corrected.sum()))


The utide package works with ordinary numpy arrays, not with Pandas Series or Dataframes, so we need to make a time variable in floating point days since a given epoch, and use the values attribute of the elevation anomaly (a Pandas Series) to extract the underlying numpy ndarray.

In [ ]:
time = mdates.date2num(obs.index.to_pydatetime())

coef = utide.solve(time, obs['anomaly'].values,
lat=-25,
method='ols',
conf_int='MC')


The amplitudes and phases from the fit are now in the coef data structure (a Bunch), which can be used directly in the reconstruct function to generate a hindcast or forecast of the tides at the times specified in the time array.

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print(coef.keys())

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tide = utide.reconstruct(time, coef)


The output from the reconstruction is also a Bunch:

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print(tide.keys())

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#t = obs.index.values  # dtype is '<M8[ns]' (numpy datetime64)
# It is more efficient to supply the time directly as matplotlib
# datenum floats:
t = tide.t_mpl

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

ax0.plot(t, obs.anomaly, label=u'Observations', color='C0')
ax1.plot(t, tide.h, label=u'Tide Fit', color='C1')
ax2.plot(t, obs.anomaly - tide.h, label=u'Residual', color='C2')
ax2.xaxis_date()
fig.legend(ncol=3, loc='upper center')
fig.autofmt_xdate()