def read_run_results(gdir, window=36, filesuffix=None):
"""Reads the output diagnostics of a simulation
and puts the data in a pandas dataframe.
Parameters
----------
gdir : oggm.GlacierDirectory
the glacier directory defined by oggm
window : int
size of the moving window: number of observations used
to smooth the glacier length timeseries
filesuffix : str
the file identifier
Returns
-------
odf : pd.DataFrame
dataframe containing glacier length, volume and water storage
"""
# read model output
with xr.open_dataset(
gdir.get_filepath('model_diagnostics',
filesuffix=filesuffix)) as ds:
ds = ds.load()
# length needs filtering
ts = ds.length_m.to_series()
ts = ts.rolling(window).min()
ts.iloc[0:window] = ts.iloc[window]
# volume change
delta_vol = np.append(ds.volume_m3.data[1:] - ds.volume_m3.data[0:-1], [0])
if ds.calendar_month[0] == 10 and gdir.cenlat < 0:
# this is to cover up a bug in OGGM
_, m = utils.hydrodate_to_calendardate(ds.hydro_year.data,
ds.hydro_month.data,
start_month=4)
ds.calendar_month[:] = m
odf = pd.DataFrame()
odf['length_m'] = ts
odf['volume_m3'] = ds.volume_m3
odf['delta_water_m3'] = delta_vol * 0.9
odf['month'] = ds.calendar_month
return odf
def read_run_results(gdir, filesuffix=None):
"""Reads the output diagnostics of a simulation and puts the data in a pandas dataframe.
Parameters
----------
gdir : the glacier directory
filesuffix : the file identifier
Returns
-------
a pandas Dataframe with monthly temp and precip
"""
with xr.open_dataset(
gdir.get_filepath('model_diagnostics',
filesuffix=filesuffix)) as ds:
ds = ds.load()
# Lemgth needs filtering
ts = ds.length_m.to_series()
ts = ts.rolling(12 * 3).min()
ts.iloc[0:12 * 3] = ts.iloc[12 * 3]
# Volume change
delta_vol = np.append(ds.volume_m3.data[1:] - ds.volume_m3.data[0:-1], [0])
if ds.calendar_month[0] == 10 and gdir.cenlat < 0:
# this is to cover up a bug in OGGM
_, m = utils.hydrodate_to_calendardate(ds.hydro_year.data,
ds.hydro_month.data,
start_month=4)
ds.calendar_month[:] = m
odf = pd.DataFrame()
odf['length_m'] = ts
odf['volume_m3'] = ds.volume_m3
odf['delta_water_m3'] = delta_vol * 0.9
odf['month'] = ds.calendar_month
return odf
def test_hydro_convertion(self):
# October
y, m = utils.hydrodate_to_calendardate(1, 1)
assert (y, m) == (0, 10)
y, m = utils.hydrodate_to_calendardate(1, 4)
assert (y, m) == (1, 1)
y, m = utils.hydrodate_to_calendardate(1, 12)
assert (y, m) == (1, 9)
y, m = utils.hydrodate_to_calendardate([1, 1, 1], [1, 4, 12])
np.testing.assert_array_equal(y, [0, 1, 1])
np.testing.assert_array_equal(m, [10, 1, 9])
y, m = utils.calendardate_to_hydrodate(1, 1)
assert (y, m) == (1, 4)
y, m = utils.calendardate_to_hydrodate(1, 9)
assert (y, m) == (1, 12)
y, m = utils.calendardate_to_hydrodate(1, 10)
assert (y, m) == (2, 1)
y, m = utils.calendardate_to_hydrodate([1, 1, 1], [1, 9, 10])
np.testing.assert_array_equal(y, [1, 1, 2])
np.testing.assert_array_equal(m, [4, 12, 1])
# Roundtrip
time = pd.period_range('0001-01', '1000-12', freq='M')
y, m = utils.calendardate_to_hydrodate(time.year, time.month)
y, m = utils.hydrodate_to_calendardate(y, m)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
# April
y, m = utils.hydrodate_to_calendardate(1, 1, start_month=4)
assert (y, m) == (0, 4)
y, m = utils.hydrodate_to_calendardate(1, 4, start_month=4)
assert (y, m) == (0, 7)
y, m = utils.hydrodate_to_calendardate(1, 9, start_month=4)
assert (y, m) == (0, 12)
y, m = utils.hydrodate_to_calendardate(1, 10, start_month=4)
assert (y, m) == (1, 1)
y, m = utils.hydrodate_to_calendardate(1, 12, start_month=4)
assert (y, m) == (1, 3)
y, m = utils.hydrodate_to_calendardate([1, 1, 1], [1, 4, 12],
start_month=4)
np.testing.assert_array_equal(y, [0, 0, 1])
np.testing.assert_array_equal(m, [4, 7, 3])
# Roundtrip
time = pd.period_range('0001-01', '1000-12', freq='M')
y, m = utils.calendardate_to_hydrodate(time.year,
time.month,
start_month=4)
y, m = utils.hydrodate_to_calendardate(y, m, start_month=4)
np.testing.assert_array_equal(y, time.year)
np.testing.assert_array_equal(m, time.month)
