def test6():
# Fail
# Large date variation and seconds granularity although
# prevent_overflow = True tries to prevent overflow by setting smallest date (01JAN2019) as julianBaseDate
pathname = "/IRREGULAR/TIMESERIES/PARAM//IR-DAY/Ex14_Test6/"
T = ['01JAN2019 01:00', '01JAN2455 00:00']
tsc = TimeSeriesContainer()
tsc.pathname = pathname
tsc.interval = -1
tsc.granularity = 1
tsc.times = T
tsc.values = [2019, 5000]
tsc.numberValues = 2
fid.put_ts(tsc, prevent_overflow=True)
#
ts = fid.read_ts(pathname, regular=False)
print(ts.pytimes)
print(ts.values)
def test7():
# Pass
# Writing one time,value pair with prevent_overflow = True is safest
pathname = "/IRREGULAR/TIMESERIES/PARAM//IR-DECADE/Ex14_Test7/"
T = ['01JAN2019 02:01:05', '01JAN5000 01:02:06']
V = [2019, 5000]
for t, v in zip(T, V):
tsc = TimeSeriesContainer()
tsc.pathname = pathname
tsc.interval = -1
tsc.granularity = 60
tsc.times = [t]
tsc.values = [v]
tsc.numberValues = 1
fid.put_ts(tsc, prevent_overflow=True)
ts = fid.read_ts(pathname, regular=False)
print(ts.times)
print(ts.pytimes)
print(ts.values)
return tsc, ts
def zstat2dss(zs_list, basin, ds, dss_file):
dates = [i.grid.date for i in zs_list]
sbasin_avg = [np.round(i.sbasin_avg, 4) for i in zs_list]
sbasin_vol = [i.sbasin_vol for i in zs_list]
basin_avg = [np.round(i.basin_avg, 4) for i in zs_list]
basin_vol = [i.basin_vol for i in zs_list]
names = [i.sbasin_names for i in zs_list]
date_rav = np.ravel(np.repeat(dates, len(zs_list[0].sbasin_names)))
sbasin_avg_rav = np.ravel(sbasin_avg)
sbasin_vol_rav = np.ravel(sbasin_vol)
names_rav = np.ravel(names)
idx = pd.MultiIndex.from_tuples(zip(date_rav, names_rav),
names=['date', 'name'])
sbasin = pd.DataFrame(index=idx,
data={
'mean_swe': sbasin_avg_rav,
'vol': sbasin_vol_rav
})
sbasin = sbasin.sort_index(level=0)
idx = pd.MultiIndex.from_tuples(zip(
dates, np.ravel(np.repeat('Total_Basin', len(dates)))),
names=['date', 'name'])
tbasin = pd.DataFrame(index=idx,
data={
'mean_swe': basin_avg,
'vol': basin_vol
})
tbasin = tbasin.sort_index(level=0)
idx = pd.date_range(
sbasin.index.get_level_values(0).min(),
sbasin.index.get_level_values(0).max())
fid = HecDss.Open(dss_file, version=6)
fid.close()
for name, group in sbasin.groupby(level=1):
#group.loc[:, 'wy'] = np.where(group.index.get_level_values(0).month>9,group.index.get_level_values(0).year+1,group.index.get_level_values(0).year)
group.index = group.index.droplevel(1)
#group.index = group.index.sort_values()
group = group.reindex(idx, fill_value=-901.0)
group.index = group.index + pd.DateOffset(hours=12)
start_date = group.index.min().strftime('%d%b%Y %H:%M:%S')
print(start_date)
pname = '/{0}/{1}/AVG_SWE//1DAY/{2}/'.format(
basin,
name.upper().replace(' ', '_'), ds)
print(pname)
tsc = TimeSeriesContainer()
tsc.granularity = 60 #seconds i.e. minute granularity
tsc.numberValues = group.mean_swe.size
tsc.startDateTime = start_date
tsc.pathname = pname
tsc.units = "M"
tsc.type = "INST-VAL"
tsc.interval = 1
#must a +ve integer for regular time-series
#actual interval implied from E part of pathname
tsc.values = group.mean_swe.values
#values may be list,array, numpy array
fid = HecDss.Open(dss_file)
fid.deletePathname(tsc.pathname)
status = fid.put(tsc)
fid.close()
pname = '/{0}/{1}/VOL//1DAY/{2}/'.format(
basin,
name.upper().replace(' ', '_'), ds)
print(pname)
tsc = TimeSeriesContainer()
tsc.granularity = 60 #seconds i.e. minute granularity
tsc.numberValues = group.index.size
tsc.startDateTime = start_date
tsc.pathname = pname
tsc.units = "CUBIC_METERS"
tsc.type = "INST-VAL"
tsc.interval = 1
#must a +ve integer for regular time-series
#actual interval implied from E part of pathname
tsc.values = group.vol.values
#values may be list,array, numpy array
fid = HecDss.Open(dss_file)
fid.deletePathname(tsc.pathname)
status = fid.put(tsc)
fid.close()
for name, group in tbasin.groupby(level=1):
#group.loc[:, 'wy'] = np.where(group.index.get_level_values(0).month>9,group.index.get_level_values(0).year+1,group.index.get_level_values(0).year)
group.index = group.index.droplevel(1)
#group.index = group.index.sort_values()
group = group.reindex(idx, fill_value=-901.0)
group.index = group.index + pd.DateOffset(hours=12)
start_date = group.index.min().strftime('%d%b%Y %H:%M:%S')
print(start_date)
pname = '/{0}/{1}/AVG_SWE//1DAY/{2}/'.format(
basin,
name.upper().replace(' ', '_'), ds)
print(pname)
tsc = TimeSeriesContainer()
tsc.granularity = 60 #seconds i.e. minute granularity
tsc.numberValues = group.mean_swe.size
tsc.startDateTime = start_date
tsc.pathname = pname
tsc.units = "M"
tsc.type = "INST-VAL"
tsc.interval = 1
#must a +ve integer for regular time-series
#actual interval implied from E part of pathname
tsc.values = group.mean_swe.values
#values may be list,array, numpy array
fid = HecDss.Open(dss_file)
fid.deletePathname(tsc.pathname)
status = fid.put(tsc)
fid.close()
pname = '/{0}/{1}/VOL//1DAY/{2}/'.format(
basin,
name.upper().replace(' ', '_'), ds)
print(pname)
tsc = TimeSeriesContainer()
tsc.granularity = 60 #seconds i.e. minute granularity
tsc.numberValues = group.index.size
tsc.startDateTime = start_date
tsc.pathname = pname
tsc.units = "CUBIC_METERS"
tsc.type = "INST-VAL"
tsc.interval = 1
#must a +ve integer for regular time-series
#actual interval implied from E part of pathname
tsc.values = group.vol.values
#values may be list,array, numpy array
fid = HecDss.Open(dss_file)
fid.deletePathname(tsc.pathname)
status = fid.put(tsc)
fid.close()
return sbasin, tbasin