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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Look at the data file to see what structure it has.
Step1:  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.
Step2:  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.
Step3:  Now we can call solve to obtain the coefficients.
Step4:  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.
Step5:  The output from the reconstruction is also a Bunch
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Python Code:
with open("can1998.dtf") as f:
    lines = f.readlines()
print("".join(lines[:5]))
Explanation: Look at the data file to see what structure it has.
End of explanation
names = ["seconds", "year", "month", "day", "hour", "elev", "flag"]
obs = pd.read_csv(
    "can1998.dtf",
    names=names,
    skipinitialspace=True,
    delim_whitespace=True,
    na_values="9.990",
)
date_cols = ["year", "month", "day", "hour"]
index = pd.to_datetime(obs[date_cols])
obs = obs.drop(date_cols, axis=1)
obs.index = index
obs.head(5)
Explanation: 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.
End of explanation
bad = obs["flag"] == 2
corrected = obs["flag"] == 1
obs.loc[bad, "elev"] = np.nan
obs["anomaly"] = obs["elev"] - obs["elev"].mean()
obs["anomaly"] = obs["anomaly"].interpolate()
print(f"{bad.sum()} points were flagged 'bad' and interpolated")
print(f"{corrected.sum()} points were flagged 'corrected' and left unchanged")
Explanation: 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.
End of explanation
coef = utide.solve(
    obs.index,
    obs["anomaly"],
    lat=-25,
    method="ols",
    conf_int="MC",
    verbose=False,
)
Explanation: Now we can call solve to obtain the coefficients.
End of explanation
print(coef.keys())
tide = utide.reconstruct(obs.index, coef, verbose=False)
Explanation: 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.
End of explanation
print(tide.keys())
t = obs.index.to_pydatetime()
fig, (ax0, ax1, ax2) = plt.subplots(figsize=(17, 5), nrows=3, sharey=True, sharex=True)
ax0.plot(t, obs.anomaly, label="Observations", color="C0")
ax1.plot(t, tide.h, label="Prediction", color="C1")
ax2.plot(t, obs.anomaly - tide.h, label="Residual", color="C2")
fig.legend(ncol=3, loc="upper center");
Explanation: The output from the reconstruction is also a Bunch:
End of explanation
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