Example constructed elevation not matching original

[LaurieWilkinson]

Hi,

I have tried the example from the docstring with the 'real' data, i.e.

import ttide as tt
import numpy as np

t = np.arange(1001)
m2_freq = 2 * np.pi / 12.42
elev = 5 * np.cos(m2_freq * t)
tfit_e = tt.t_tide(elev)
elev_fit = tfit_e(t)

When comparing the original elevation, 'elev', to the reconstructed one, 'elev_fit', the phases and frequencies are different, as shown in the figure below. Shouldn't these be the same or am I missing something?

[mdunphy]

The synthesis that should match "elev" is in tfit_e['xout']

The mismatch with the synthesis from calling the TTideCon object is due to the time vector that you're providing doesn't match the time vector constructed within t_tide:

import ttide as tt
import numpy as np

N=1000
t = np.arange(N+1)
m2_freq = 2 * np.pi * 0.0805114
elev = 5 * np.cos(m2_freq * t)
tfit_e = tt.t_tide(elev)
elev_fit = tfit_e( (t-N/2)/24)

print("Err1:", np.max(np.abs(elev - elev_fit)))
print("Err2:", np.max(np.abs(elev - tfit_e['xout'][:,0])))

Gives me:

Err1: 1.0786432503656629e-05
Err2: 1.0786432503656629e-05

[SusanEAllen]

I can get the example (shown above) to work but I am struggling about how to construct my time for a prediction in a real case. I am running with:
tide_dict = tt.t_tide(data.obs.values, dt=1/60, stime=data.index[0], lat= 49.1530)
where data.obs.values is an array of 227416 values and stime is 2016-01-01 00:00:00

tide_dict['xout'][:,0] gives me the observations back pretty well but I can't figure out what time to put in:
elev_fit = tide_dict(time)

[SusanEAllen]

And the answer is:

timeseries = np.arange(len(data.obs) * dt). # So an hourly time series

time = timeseries/24 + tide_dict['stime'] ). # So now hourly in day units shifted by stime

and then elev_fit = time_dict(time) matches tide_dict['xout']