print(r'Gaussian Fit $V$: '+str(round(10.**paramy[0],2))+'(+'+str(round((10.**(paramy[0]+paramy[1])-10.**paramy[0]),2))+' '+str(round((10.**(paramy[0]-paramy[1])-10.**paramy[0]),2))+')')
print 'Eta_nu threshold='+str(10.**sigcutx)+', V_nu threshold='+str(10.**sigcuty)
data=[[variables[n][0],np.log10(float(variables[n][1])),np.log10(float(variables[n][2])),variables[n][5],float(variables[n][-1])] for n in range(len(variables)) if float(variables[n][1]) > 0 if float(variables[n][2]) > 0]
# Get the different frequencies in the dataset
frequencies = generic_tools.get_frequencies(data)
# Create the scatter_hist plot
plotting_tools.create_scatter_hist(data,0,0,paramx,paramy,range_x,range_y,'',frequencies)
# make second array for the diagnostic plot: [eta_nu, V_nu, maxflx_nu, flxrat_nu, nu]
data2=[[variables[n][0],float(variables[n][1]),float(variables[n][2]),float(variables[n][3]),float(variables[n][4]),variables[n][5]] for n in range(len(variables)) if float(variables[n][1]) > 0 if float(variables[n][2]) > 0]
# Create the diagnostic plot
plotting_tools.create_diagnostic(data2,0,0,frequencies,'')
# Setup data to make TP/FP/TN/FN plots
# Create arrays containing the data to plot
fp=[[z[0],np.log10(float(z[1])),np.log10(float(z[2])),'FP'] for z in stable if (float(z[1])>=10.**sigcutx and float(z[2])>=10.**sigcuty)] # False Positive
tn=[[z[0],np.log10(float(z[1])),np.log10(float(z[2])),'TN'] for z in stable if (float(z[1])<10.**sigcutx or float(z[2])<10.**sigcuty)] # True Negative
tp=[[z[0],np.log10(float(z[1])),np.log10(float(z[2])),'TP'] for z in variable if (float(z[1])>=10.**sigcutx and float(z[2])>=10.**sigcuty)] # True Positive
fn=[[z[0],np.log10(float(z[1])),np.log10(float(z[2])),'FN'] for z in variable if (float(z[1])<10.**sigcutx or float(z[2])<10.**sigcuty)] # False Negative
data3=fp+tn+tp+fn
# Print out the actual precision and recall using the training data.
precision, recall = generic_tools.precision_and_recall(len(tp),len(fp),len(fn))
print "Precision: "+str(precision)+", Recall: "+str(recall)
# Get the different frequencies in the dataset
frequencies = generic_tools.get_frequencies(data3)
print 'Identified Transient Candidates (no margin)'
print np.sort(list(set([int(x[0]) for x in trans_data if x[-4]!='2' if float(x[-2])>=float(x[-1]) if float(x[-3])<float(x[-1])])))
print 'Identified Transients (no margin)'
print np.sort(list(set([int(x[0]) for x in trans_data if x[-4]!='2' if float(x[-3])>=float(x[-1])])))
# Find the thresholds for a given sigma (in log space)
sigcutx,paramx,range_x = generic_tools.get_sigcut([a[1] for a in data],sigma1)
sigcuty,paramy,range_y = generic_tools.get_sigcut([a[2] for a in data],sigma2)
if sigma1 == 0:
sigcutx=0
if sigma2 == 0:
sigcuty=0
print(r'Gaussian Fit $\eta$: '+str(round(10.**paramx[0],2))+'(+'+str(round((10.**(paramx[0]+paramx[1])-10.**paramx[0]),2))+' '+str(round((10.**(paramx[0]-paramx[1])-10.**paramx[0]),2))+')')
print(r'Gaussian Fit $V$: '+str(round(10.**paramy[0],2))+'(+'+str(round((10.**(paramy[0]+paramy[1])-10.**paramy[0]),2))+' '+str(round((10.**(paramy[0]-paramy[1])-10.**paramy[0]),2))+')')
print 'Eta_nu threshold='+str(10.**sigcutx)+', V_nu threshold='+str(10.**sigcuty)
# Get the different frequencies in the dataset
frequencies = generic_tools.get_frequencies(data)
# Create the scatter_hist plot
IdTrans = plotting_tools.create_scatter_hist(data,sigcutx,sigcuty,paramx,paramy,range_x,range_y,dataset_id,frequencies)
print 'Identified variables:'
print np.sort(list(set(IdTrans)))
# make second array for the diagnostic plot: [eta_nu, V_nu, maxflx_nu, flxrat_nu, nu, trans_type]
data2=[[trans_data[n][0],float(trans_data[n][1]),float(trans_data[n][2]),float(trans_data[n][3]),float(trans_data[n][4]),trans_data[n][5], trans_data[n][-1]] for n in range(len(trans_data)) if float(trans_data[n][1]) > 0 if float(trans_data[n][2]) > 0 if trans_data[n][-4]=='2']
# Create the diagnostic plot
plotting_tools.create_diagnostic(data2,sigcutx,sigcuty,frequencies,dataset_id)