for i in range(numSims):
# loop through variable
for var in ['Non-breeding','Breeding','Total']:
# Time-series
series=df_traj.loc[i+1][var]
# Find time value at which population goes extinct
tExt=series[series<=0].index[0]
ews_dic = ewstools.ews_compute(df_traj.loc[i+1][var],
roll_window = rw,
smooth='Lowess',
span=span,
lag_times = lags,
ews = ews,
ham_length = ham_length,
ham_offset = ham_offset,
pspec_roll_offset = pspec_roll_offset,
upto=tExt-5,
sweep=False)
# The DataFrame of EWS
df_ews_temp = ews_dic['EWS metrics']
# The DataFrame of power spectra
df_pspec_temp = ews_dic['Power spectrum']
# The DataFrame of kendall tau values
df_ktau_temp = ews_dic['Kendall tau']
# Compute cross-correlation
df_cross_corr = cross_corr(df_traj_filt.loc[i+1][['Non-breeding','Breeding']],
print('Compute EWS')
#--------------------------------
# Compute EWS without bootstrapping
#-------------------------------------
# Time-series data as a pandas Series
series = df_traj['x']
# Put into ews_compute
ews_dic = ewstools.ews_compute(series,
smooth='Lowess',
span=span,
roll_window=rw,
upto=tcrit,
ews=ews,
lag_times=lags,
sweep=sweep,
ham_length=ham_length,
ham_offset=ham_offset)
# DataFrame of EWS
df_ews = ews_dic['EWS metrics']
# DataFrame of Power spectra
df_pspec = ews_dic['Power spectrum']
print('Do bootstrapping')
#-------------------------------------
# Compute EWS using bootstrapping
appended_pspec = []
# loop through realisation number
print('\nBegin EWS computation\n')
for r in rVals:
# If time-series goes extinct at any point, do not compute EWS
if any(df_traj.loc[r]['State variable'] == 0):
continue
ews_dic = ewstools.ews_compute(df_traj.loc[r]['State variable'],
roll_window = rw,
smooth = 'Lowess',
span = span,
lag_times = lags,
ews = ews,
ham_length = ham_length,
ham_offset = ham_offset,
sweep = False
)
# The DataFrame of EWS
df_ews_temp = ews_dic['EWS metrics']
# The DataFrame of power spectra
df_pspec_temp = ews_dic['Power spectrum']
# Include a column in the DataFrames for r value and variable
df_ews_temp['r'] = r
df_pspec_temp['r'] = r
# Add DataFrames to list
# Filter time-series to have time-spacing dt2
df_traj_filt = df_traj.loc[::int(dt2 / dt)]
# set up a list to store output dataframes from ews_compute- we will concatenate them at the end
appended_ews = []
appended_ktau = []
# loop through realisation number
print('\nBegin EWS computation\n')
for i in range(numSims):
# loop through variable
for var in ['x']:
ews_dic = ewstools.ews_compute(df_traj_filt.loc[i + 1][var],
roll_window=rw,
span=span,
lag_times=lags,
ews=ews,
upto=tcrit)
# The DataFrame of EWS
df_ews_temp = ews_dic['EWS metrics']
# The DataFrame of ktau values
df_ktau_temp = ews_dic['Kendall tau']
# Include a column in the DataFrames for realisation number and variable
df_ews_temp['Realisation number'] = i + 1
df_ews_temp['Variable'] = var
df_ktau_temp['Realisation number'] = i + 1
df_ktau_temp['Variable'] = var
