def ce_r_ens_avg(gmt_ens,
times,
analysis_gmt,
analysis_times,
trange=(1880, 2000),
center_trange=(1900, 2000)):
start, end = trange
# Get the global mean for the analysis dataset
gmt_ens = center_to_time_range(times,
gmt_ens,
time_axis=-1,
trange=center_trange)
# Get a mask for overlapping times
analysis_tidx = (analysis_times >= start) & (analysis_times <= end)
lmr_tidx = (times >= start) & (times <= end)
ens_ce = \
np.array([utils2.coefficient_efficiency(analysis_gmt[analysis_tidx],
a_ens_gmt[lmr_tidx])
for a_ens_gmt in gmt_ens])
ens_ce[ens_ce == 1] = np.nan
ens_r = np.array([
np.corrcoef(analysis_gmt[analysis_tidx], a_ens_gmt[lmr_tidx])[0, 1]
for a_ens_gmt in gmt_ens
])
return ens_ce, ens_r
def find_ce_corr(VAR,
REF,
REF_TIME,
VAR_TIME,
START_TIME,
END_TIME,
detrend=False):
"""Finds the correlation coefficient and coefficient of efficiency between
REF and VAR between START_TIME and END_TIME.
inputs:
VAR = test data (1D in time)
REF = reference data (1D in time)
REF_time = reference data time (1D time)
VAR_TIME = test data time (1D years)
START_TIME = comparison start year to be included (float)
END_TIME = last year included in comparison (float)
"""
yr_range_var = np.where((VAR_TIME >= START_TIME)
& (VAR_TIME < END_TIME + 1))
yr_range_ref = np.where((REF_TIME >= START_TIME)
& (REF_TIME < END_TIME + 1))
if detrend is False:
ref = REF[yr_range_ref[0]]
var = VAR[yr_range_var[0]]
else:
ref = spy.detrend(REF[yr_range_ref])
var = spy.detrend(VAR[yr_range_var])
ce = lmr.coefficient_efficiency(ref, var)
corr = np.corrcoef(ref, var)[0, 1]
var_ref = np.var(ref)
var_var = np.var(var)
return ce, corr, var_ref, var_var
