array_R2_corrAverageTPI = np.asarray(R2_corrAverageTPI)
array_R2_corrAverageEastENSO = np.asarray(R2_corrAverageEastENSO)
array_R2_corrAverageEastTPI = np.asarray(R2_corrAverageEastTPI)
array_R3_corrAverageENSO = np.asarray(R3_corrAverageENSO)
array_R3_corrAverageTPI = np.asarray(R3_corrAverageTPI)
array_R3_corrAverageEastENSO = np.asarray(R3_corrAverageEastENSO)
array_R3_corrAverageEastTPI = np.asarray(R3_corrAverageEastTPI)
##############################################################################
#Significant difference from Australia or eastern Australia
##############################################################################
#ENSO
awap_ENSO_Aus_EAus = unpaired_t_test(array_awap_corrAverageENSO,array_awap_corrAverageEastENSO[:,0])
awap_ENSO_EAus_Equ = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,1])
awap_ENSO_EAus_Trop = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,2])
awap_ENSO_EAus_SubTrop = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,3])
awap_ENSO_EAus_Des = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,4])
awap_ENSO_EAus_Gr = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,5])
awap_ENSO_EAus_Tem = unpaired_t_test(array_awap_corrAverageEastENSO[:,0],array_awap_corrAverageEastENSO[:,6])
awap_ENSO_regions = np.vstack((awap_ENSO_Aus_EAus,awap_ENSO_EAus_Equ,awap_ENSO_EAus_Trop,\
awap_ENSO_EAus_SubTrop,awap_ENSO_EAus_Des,awap_ENSO_EAus_Gr,\
awap_ENSO_EAus_Tem))
R1_ENSO_Aus_EAus = unpaired_t_test(array_R1_corrAverageENSO,array_R1_corrAverageEastENSO[:,0])
R1_ENSO_EAus_Equ = unpaired_t_test(array_R1_corrAverageEastENSO[:,0],array_R1_corrAverageEastENSO[:,1])
R1_ENSO_EAus_Trop = unpaired_t_test(array_R1_corrAverageEastENSO[:,0],array_R1_corrAverageEastENSO[:,2])
R1_ENSO_EAus_SubTrop = unpaired_t_test(array_R1_corrAverageEastENSO[:,0],array_R1_corrAverageEastENSO[:,3])
R3_corr[6,:,0].flatten(),\
R3_corr[6,:,1].flatten(),\
R3_corr[7,:,0].flatten(),\
R3_corr[7,:,1].flatten(),\
R3_corr[8,:,0].flatten(),\
R3_corr[8,:,1].flatten()))
np.savetxt('data/Correlation_coefficients/correlations_stratified_3SD.csv',output,delimiter=',')
###################################################################################
# Check to see if stratified correlations are statistically significantly different
###################################################################################
#Check if data is statistically significant from ENSO/IPO neutral
had_pos_pos = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[0,:,0])
had_neu_pos = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[6,:,0])
had_neg_pos = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[3,:,0])
had_pos_neu = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[2,:,0])
had_neg_neu = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[5,:,0])
had_pos_neg = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[1,:,0])
had_neu_neg = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[7,:,0])
had_neg_neg = unpaired_t_test(HadISST_corr[8,:,0],HadISST_corr[4,:,0])
hadISST_stat_sig = np.vstack((had_pos_pos,had_neu_pos,had_neg_pos,\
had_pos_neu,had_neg_neu,had_pos_neg,\
had_neu_neg,had_neg_neg))
R1_pos_pos = unpaired_t_test(R1_corr[8,:,0],R1_corr[0,:,0])
R1_neu_pos = unpaired_t_test(R1_corr[8,:,0],R1_corr[6,:,0])
R1_neg_pos = unpaired_t_test(R1_corr[8,:,0],R1_corr[3,:,0])
AccR3_November[1], AccR3_December[1], AccR3_January[1],\
AccR3_February[1], \
AccR3_March[1], AccR3_April[1], AccR3_May[1], AccR3_JJA[1],\
AccR3_SON[1], \
AccR3_DJF[1], AccR3_MAM[1], AccR3_annual[1]])
#Correlation data to CSV output
output = np.column_stack((HadISST_all,accessR1_all,accessR2_all,accessR3_all))
np.savetxt('data/Correlation_coefficients/ENSO_IPO.csv',output,delimiter=',')
################################################################################
#Test for significant difference between different datasets' correlation output
################################################################################
# p-values <0.05 indicate that there is a significant difference between datasets
#Significant difference from HadISST:
sig_diff_had_R1 = unpaired_t_test(HadISST,accessR1)
sig_diff_had_R2 = unpaired_t_test(HadISST,accessR2)
sig_diff_had_R3 = unpaired_t_test(HadISST,accessR3)
#Significant difference in ACCESS rounds
sig_diff_R1_R2 = unpaired_t_test(accessR1,accessR2)
sig_diff_R1_R3 = unpaired_t_test(accessR1,accessR3)
sig_diff_R2_R3 = unpaired_t_test(accessR2,accessR3)
output2 = np.column_stack((sig_diff_had_R1,sig_diff_had_R2,sig_diff_had_R3,\
sig_diff_R1_R2,sig_diff_R1_R3,sig_diff_R2_R3))
np.savetxt('data/Correlation_coefficients/ENSO_IPO_sig_diff.csv',output2,delimiter=',')
R3_corr[7,:,0].flatten(),\
R3_corr[7,:,1].flatten(),\
R3_corr[8,:,0].flatten(),\
R3_corr[8,:,1].flatten()))
np.savetxt('data/Correlation_coefficients/correlations_stratified_3SD.csv',
output,
delimiter=',')
###################################################################################
# Check to see if stratified correlations are statistically significantly different
###################################################################################
#Check if data is statistically significant from ENSO/IPO neutral
had_pos_pos = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[0, :, 0])
had_neu_pos = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[6, :, 0])
had_neg_pos = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[3, :, 0])
had_pos_neu = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[2, :, 0])
had_neg_neu = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[5, :, 0])
had_pos_neg = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[1, :, 0])
had_neu_neg = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[7, :, 0])
had_neg_neg = unpaired_t_test(HadISST_corr[8, :, 0], HadISST_corr[4, :, 0])
hadISST_stat_sig = np.vstack((had_pos_pos,had_neu_pos,had_neg_pos,\
had_pos_neu,had_neg_neu,had_pos_neg,\
had_neu_neg,had_neg_neg))
R1_pos_pos = unpaired_t_test(R1_corr[8, :, 0], R1_corr[0, :, 0])
R1_neu_pos = unpaired_t_test(R1_corr[8, :, 0], R1_corr[6, :, 0])
R1_neg_pos = unpaired_t_test(R1_corr[8, :, 0], R1_corr[3, :, 0])
AccR3_March[1], AccR3_April[1], AccR3_May[1], AccR3_JJA[1],\
AccR3_SON[1], \
AccR3_DJF[1], AccR3_MAM[1], AccR3_annual[1]])
#Correlation data to CSV output
output = np.column_stack(
(HadISST_all, accessR1_all, accessR2_all, accessR3_all))
np.savetxt('data/Correlation_coefficients/ENSO_IPO.csv', output, delimiter=',')
################################################################################
#Test for significant difference between different datasets' correlation output
################################################################################
# p-values <0.05 indicate that there is a significant difference between datasets
#Significant difference from HadISST:
sig_diff_had_R1 = unpaired_t_test(HadISST, accessR1)
sig_diff_had_R2 = unpaired_t_test(HadISST, accessR2)
sig_diff_had_R3 = unpaired_t_test(HadISST, accessR3)
#Significant difference in ACCESS rounds
sig_diff_R1_R2 = unpaired_t_test(accessR1, accessR2)
sig_diff_R1_R3 = unpaired_t_test(accessR1, accessR3)
sig_diff_R2_R3 = unpaired_t_test(accessR2, accessR3)
output2 = np.column_stack((sig_diff_had_R1,sig_diff_had_R2,sig_diff_had_R3,\
sig_diff_R1_R2,sig_diff_R1_R3,sig_diff_R2_R3))
np.savetxt('data/Correlation_coefficients/ENSO_IPO_sig_diff.csv',
output2,
delimiter=',')
