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Oct 25, 2024
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deprecated debug argument, reformatting #170

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9e58131
deprecated debug argument, reformatting
veenstrajelmer Oct 25, 2024
5ddf2a1
updated changelog
veenstrajelmer Oct 25, 2024
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4 changes: 4 additions & 0 deletions docs/whats-new.md
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,10 @@
- exposed yearly tidal components with `kw.calc_getijcomponenten()` in #159
- support for timedelta `diff()` because of update to `pandas>=2.1.4` in #161
- neater handling of time in `kw.calc_havengetallen()` in #163
- automatic cropping of timeseries if required to simplify user interaction in #168

### Deprecated
- deprecated debug argument for `kw.calc_gemiddeldgetij()` in #170


## 0.3.0 (2024-10-01)
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69 changes: 21 additions & 48 deletions kenmerkendewaarden/gemiddeldgetij.py
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Expand Up @@ -34,7 +34,6 @@ def calc_gemiddeldgetij(
nf: int = 0,
scale_extremes: bool = False,
scale_period: bool = False,
debug: bool = False,
):
"""
Generate an average tidal signal for average/spring/neap tide by doing a tidal
Expand Down Expand Up @@ -64,8 +63,6 @@ def calc_gemiddeldgetij(
Whether to scale extremes with havengetallen. The default is False.
scale_period : bool, optional
Whether to scale to 12h25min (for boi). The default is False.
debug : bool, optional
Whether to generate a debug figure with selected spring/neap tidalperiods. The default is False.

Returns
-------
Expand All @@ -78,7 +75,6 @@ def calc_gemiddeldgetij(

current_station = df_meas_10y.attrs["station"]

# TODO: deprecate debug argument+plot (maybe use max HW instead of max tidalrange?)
# TODO: add correctie havengetallen HW/LW av/sp/np met slotgemiddelde uit PLSS/modelfit (HW/LW av)

if scale_period:
Expand All @@ -96,15 +92,10 @@ def calc_gemiddeldgetij(

df_ext_10y = crop_timeseries_last_nyears(df_ext, nyears=10)
df_havengetallen = calc_havengetallen(df_ext=df_ext_10y, min_coverage=min_coverage)
HW_sp, LW_sp = df_havengetallen.loc[
0, ["HW_values_median", "LW_values_median"]
] # spring
HW_np, LW_np = df_havengetallen.loc[
6, ["HW_values_median", "LW_values_median"]
] # neap
HW_av, LW_av = df_havengetallen.loc[
"mean", ["HW_values_median", "LW_values_median"]
] # mean
list_cols = ["HW_values_median", "LW_values_median"]
HW_sp, LW_sp = df_havengetallen.loc[0, list_cols] # spring
HW_np, LW_np = df_havengetallen.loc[6, list_cols] # neap
HW_av, LW_av = df_havengetallen.loc["mean", list_cols] # mean
else:
HW_av = LW_av = None
HW_sp = LW_sp = None
Expand All @@ -113,21 +104,20 @@ def calc_gemiddeldgetij(
# derive components via TA on measured waterlevels
comp_frommeasurements_avg, comp_av = get_gemgetij_components(df_meas_10y)

# start 12 hours in advance, to assure also corrected values on desired tstart
times_pred_1mnth = pd.date_range(
start=pd.Timestamp(tstop_dt.year, 1, 1, 0, 0) - pd.Timedelta(hours=12),
end=pd.Timestamp(tstop_dt.year, 2, 1, 0, 0),
freq=freq,
tz=df_meas_10y.index.tz,
) # start 12 hours in advance, to assure also corrected values on desired tstart
comp_av.attrs["nodalfactors"] = (
False # nodalfactors=False to guarantee repetative signal
)
# nodalfactors=False to guarantee repetative signal
comp_av.attrs["nodalfactors"] = False
prediction_avg = hatyan.prediction(comp_av, times=times_pred_1mnth)
prediction_avg_ext = hatyan.calc_HWLW(ts=prediction_avg, calc_HWLW345=False)

time_firstHW = prediction_avg_ext.loc[prediction_avg_ext["HWLWcode"] == 1].index[
0
] # time of first HW
bool_hw_avg = prediction_avg_ext["HWLWcode"] == 1
time_firstHW = prediction_avg_ext.loc[bool_hw_avg].index[0] # time of first HW
ia1 = prediction_avg_ext.loc[time_firstHW:].index[0] # time of first HW
ia2 = prediction_avg_ext.loc[time_firstHW:].index[2] # time of second HW
prediction_avg_one = prediction_avg.loc[ia1:ia2]
Expand Down Expand Up @@ -180,9 +170,8 @@ def calc_gemiddeldgetij(

# make prediction with springneap components with nodalfactors=False (alternative for choosing a year with a neutral nodal factor). Using 1yr instead of 1month does not make a difference in min/max tidal range and shape, also because of nodalfactors=False. (when using more components, there is a slight difference)
comp_frommeasurements_avg_sncomp = comp_frommeasurements_avg.loc[components_sn]
comp_frommeasurements_avg_sncomp.attrs["nodalfactors"] = (
False # nodalfactors=False to make independent on chosen year
)
# nodalfactors=False to make independent on chosen year
comp_frommeasurements_avg_sncomp.attrs["nodalfactors"] = False
prediction_sn = hatyan.prediction(
comp_frommeasurements_avg_sncomp, times=times_pred_1mnth
)
Expand All @@ -192,15 +181,12 @@ def calc_gemiddeldgetij(
# selecteer getijslag met minimale tidalrange en maximale tidalrange (werd geselecteerd adhv havengetallen in 1991.0 doc)
prediction_sn_ext = calc_HWLWtidalrange(prediction_sn_ext)

time_TRmax = prediction_sn_ext.loc[
prediction_sn_ext["HWLWcode"] == 1, "tidalrange"
].idxmax()
bool_hw_sn = prediction_sn_ext["HWLWcode"] == 1
time_TRmax = prediction_sn_ext.loc[bool_hw_sn, "tidalrange"].idxmax()
is1 = prediction_sn_ext.loc[time_TRmax:].index[0]
is2 = prediction_sn_ext.loc[time_TRmax:].index[2]

time_TRmin = prediction_sn_ext.loc[
prediction_sn_ext["HWLWcode"] == 1, "tidalrange"
].idxmin()
time_TRmin = prediction_sn_ext.loc[bool_hw_sn, "tidalrange"].idxmin()
in1 = prediction_sn_ext.loc[time_TRmin:].index[0]
in2 = prediction_sn_ext.loc[time_TRmin:].index[2]

Expand All @@ -209,23 +195,7 @@ def calc_gemiddeldgetij(
prediction_sp_ext_one = prediction_sn_ext.loc[is1:is2]
prediction_np_one = prediction_sn.loc[in1:in2]
prediction_np_ext_one = prediction_sn_ext.loc[in1:in2]

# plot selection of neap/spring
if debug:
fig, (ax1, ax2) = hatyan.plot_timeseries(
ts=prediction_sn, ts_ext=prediction_sn_ext
)
ax1.plot(prediction_sp_one["values"], "r")
ax1.plot(prediction_np_one["values"], "r")
ax1.legend(
labels=ax1.get_legend_handles_labels()[1]
+ ["kromme spring", "kromme neap"],
loc=4,
)
ax1.set_ylabel("waterstand [m]")
ax1.set_title(f"spring- en doodtijkromme {current_station}")
# fig.savefig(os.path.join(dir_gemgetij,f'springdoodtijkromme_{current_station}_slotgem{year_slotgem}.png'))


# timeseries for gele boekje (av/sp/np have different lengths, time is relative to HW of av and HW of sp/np are shifted there)
logger.info(f"reshape_signal GEMGETIJ: {current_station}")
prediction_av_corr_one = reshape_signal(
Expand All @@ -235,9 +205,10 @@ def calc_gemiddeldgetij(
LW_goal=LW_av,
tP_goal=tP_goal,
)
# make relative to first timestamp (=HW)
prediction_av_corr_one.index = (
prediction_av_corr_one.index - prediction_av_corr_one.index[0]
) # make relative to first timestamp (=HW)
)
if scale_period: # resampling required because of scaling
prediction_av_corr_one = prediction_av_corr_one.resample(freq).nearest()
prediction_av = repeat_signal(prediction_av_corr_one, nb=nb, nf=nf)
Expand All @@ -250,9 +221,10 @@ def calc_gemiddeldgetij(
LW_goal=LW_sp,
tP_goal=tP_goal,
)
# make relative to first timestamp (=HW)
prediction_sp_corr_one.index = (
prediction_sp_corr_one.index - prediction_sp_corr_one.index[0]
) # make relative to first timestamp (=HW)
)
if scale_period: # resampling required because of scaling
prediction_sp_corr_one = prediction_sp_corr_one.resample(freq).nearest()
prediction_sp = repeat_signal(prediction_sp_corr_one, nb=nb, nf=nf)
Expand All @@ -265,9 +237,10 @@ def calc_gemiddeldgetij(
LW_goal=LW_np,
tP_goal=tP_goal,
)
# make relative to first timestamp (=HW)
prediction_np_corr_one.index = (
prediction_np_corr_one.index - prediction_np_corr_one.index[0]
) # make relative to first timestamp (=HW)
)
if scale_period: # resampling required because of scaling
prediction_np_corr_one = prediction_np_corr_one.resample(freq).nearest()
prediction_np = repeat_signal(prediction_np_corr_one, nb=nb, nf=nf)
Expand Down
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