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Re-implement mix option for precip #237

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Merged
arbennett merged 1 commit into from
Aug 1, 2020
Merged

Re-implement mix option for precip #237

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51 changes: 25 additions & 26 deletions metsim/disaggregate.py
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Original file line number Diff line number Diff line change
Expand Up @@ -280,40 +280,39 @@ def prec(prec: pd.Series, t_min: pd.Series, ts: float, params: dict,
prec:
A sub-daily timeseries of precipitation. [mm]
"""
#def prec_TRIANGLE(prec: pd.Series, t_min: pd.Series,
# month_of_year: int, do_mix: bool, params: dict):
def prec_TRIANGLE(daily_prec, t_min, prec_peak, prec_dur, ts, do_mix):
'''Triangular disaggregation'''
ts_per_day = int(cnst.HOURS_PER_DAY * cnst.MIN_PER_HOUR)
n_days = len(daily_prec)
# Scale to desired timestep
#prec_peak /= ts
#prec_dur /= ts
disagg_prec = np.zeros(int(n_days*ts_per_day))

# Loop over days
for i, (t, P) in enumerate(daily_prec.iteritems()):
times_day = np.arange(ts_per_day)
prec_day = np.zeros(ts_per_day)

# Look up climatology
t_pk = prec_peak[t]
t_dur = prec_dur[t]

# Rising and falling time
t_start = t_pk - 0.5 * t_dur
t_stop = t_pk + 0.5 * t_dur
t_plus = times_day[
np.logical_and(times_day <= t_pk, times_day >= t_start)]
t_minus = times_day[
np.logical_and(times_day >= t_pk, times_day <= t_stop)]

# Begin with relative intensity
prec_day[t_plus] = np.linspace(0, 1.0, len(t_plus))
prec_day[t_minus] = np.linspace(1.0, 0, len(t_minus))

# Scale to input precipitation
prec_day = (P / np.sum(prec_day)) * prec_day

if do_mix and t_min[t] < 0:
prec_day = P * np.ones(ts_per_day) / ts_per_day
else:
times_day = np.arange(ts_per_day)
prec_day = np.zeros(ts_per_day)

# Look up climatology
t_pk = prec_peak[t]
t_dur = prec_dur[t]

# Rising and falling time
t_start = t_pk - 0.5 * t_dur
t_stop = t_pk + 0.5 * t_dur
t_plus = times_day[
np.logical_and(times_day <= t_pk, times_day >= t_start)]
t_minus = times_day[
np.logical_and(times_day >= t_pk, times_day <= t_stop)]

# Begin with relative intensity
prec_day[t_plus] = np.linspace(0, 1.0, len(t_plus))
prec_day[t_minus] = np.linspace(1.0, 0, len(t_minus))

# Scale to input precipitation
prec_day = (P / np.sum(prec_day)) * prec_day
disagg_prec[i*ts_per_day:(i+1)*ts_per_day] = prec_day
return disagg_prec

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