Completed the web tool

banyan_sigma_wrapper.py

@@ -3,34 +3,48 @@
 import numpy as np #Numpy maths
 from banyan_sigma import banyan_sigma
 import pdb #Debugging
+#/home/ipm/banyan/python_launch_banyansigma.bash 'HIP9' '0.035320833333333336' '36.58595833333334' '-6.88' '0.5799999833106995' '8.57' '1.0399999618530273' '2007A&A...474..653V' '-9999' '-9999' '2.38' '0.9300000071525574'
 
 def banyan_sigma_wrapper(name=None,ip=None,ra=None,dec=None,pmra=None,pmdec=None,epmra=None,epmdec=None,rv=None,erv=None,plx=None,eplx=None):
 
+	#Parse missing values
+	if rv == '-9999' or erv == '-9999':
+		rv = np.nan
+		erv = np.nan
+	if plx == '-9999' or eplx == '-9999':
+		plx = np.nan
+		eplx = np.nan
+
 	#Launch the regular banyan_sigma
-	output = banyan_sigma(ra=ra,dec=dec,pmra=pmra,pmdec=pmdec,epmra=epmra,epmdec=epmdec,rv=rv,erv=erv,plx=plx,eplx=eplx)
+	output = banyan_sigma(ra=float(ra),dec=float(dec),pmra=float(pmra),pmdec=float(pmdec),epmra=float(epmra),epmdec=float(epmdec),rv=float(rv),erv=float(erv),plx=float(plx),eplx=float(eplx))
 
 	#Transform LN_P to 0-1 probabilities
-	probs = np.exp(output['ALL'].values)
-	output['ALL'].loc[0] = probs
-	pdb.set_trace()
+	output_all = output['ALL']
+	probs = output_all.iloc[0].values*1e2
+	max_prob = 99.9
+	probs = np.minimum(probs,max_prob)
+	probs_formatted = np.round(np.minimum(probs,max_prob),1)
+	output_all.loc[0] = probs_formatted
+	print(output_all)
 
 	#Save output probabilities to CSV
-	outdir = '/home/gagne/www/banyansigma/answer/'
-	output['ALL'].to_csv(outdir+'_prob'+name+'.dat')
+	outdir = '/home/ipm/banyan/banyansigma/answer/'
+	output_all.to_csv(outdir+'prob_'+name+'.dat',index=False)
 
 	#Read all most probable RVs
 	rv_opt = pd.DataFrame()
 	erv_opt = pd.DataFrame()
 	d_opt = pd.DataFrame()
 	ed_opt = pd.DataFrame()
 	for keys in output['ALL'].keys():
-		rv_opt[keys] = [output[keys]['RV_OPT'][0]]
-		erv_opt[keys] = [output[keys]['ERV_OPT'][0]]
-		d_opt[keys] = [output[keys]['D_OPT'][0]]
-		ed_opt[keys] = [output[keys]['ED_OPT'][0]]
-
+		rv_opt[keys] = [np.round(output[keys]['RV_OPT'][0],1)]
+		erv_opt[keys] = [np.round(output[keys]['ERV_OPT'][0],1)]
+		d_opt[keys] = [np.round(output[keys]['D_OPT'][0],1)]
+		ed_opt[keys] = [np.round(output[keys]['ED_OPT'][0],1)]
+
+	print(rv_opt)
 	#Save optimal quantities to CSV files	
-	d_opt.to_csv(outdir+'mdist_'+name+'.dat')
-	ed_opt.to_csv(outdir+'emdist_'+name+'.dat')
-	erv_opt.to_csv(outdir+'emvrad_'+name+'.dat')
-	rv_opt.to_csv(outdir+'mvrad_'+name+'.dat')
+	d_opt.to_csv(outdir+'mdist_'+name+'.dat',index=False)
+	ed_opt.to_csv(outdir+'emdist_'+name+'.dat',index=False)
+	erv_opt.to_csv(outdir+'emvrad_'+name+'.dat',index=False)
+	rv_opt.to_csv(outdir+'mvrad_'+name+'.dat',index=False)

name_resolver_webtool.py

@@ -44,6 +44,5 @@ def name_resolver_webtool(name=None):
 	#pdb.set_trace()
 
 	#Export to CSV file
-	outfile = '/home/gagne/www/banyansigma/answer/info_'+name+'.dat'
+	outfile = '/home/ipm/banyan/banyansigma/answer/info_'+name+'.dat'
 	data[['Name','RADEG','DECDEG','PMRA','ePMRA','PMDEC','ePMDEC','REFPM','VRAD','eVRAD','PLX','ePLX']].to_csv(outfile,index=False)
-	pdb.set_trace()

python_launch_banyansigma.bash

@@ -0,0 +1,7 @@
+#!/bin/bash
+#Keywords: NAME, RA, DEC, PMRA, EPMRA, PMDEC, EPMDEC, RV, ERV, PLX, EPLX, IP
+export PATH="/usr/local/anaconda3/bin:$PATH"
+export HOME="/home/ipm/banyan/banyansigma/answer"
+wwwdir='/home/ipm/banyan'
+python3 -c "import sys; sys.path.append('$wwwdir'); from banyan_sigma_wrapper import banyan_sigma_wrapper; void = banyan_sigma_wrapper(name='$1',ra='$2',dec='$3',pmra='$4',epmra='$5',pmdec='$6',epmdec='$7',rv='$8',erv='$9',plx='${10}',eplx='${11}',ip='${12}')"
+echo "Success running python_launch_banyansigma.bash"

python_resolve_name.bash

@@ -0,0 +1,6 @@
+#!/bin/bash
+export PATH="/usr/local/anaconda3/bin:$PATH"
+export HOME="/home/ipm/banyan/banyansigma/answer"
+wwwdir='/home/ipm/banyan'
+python3 -c "import sys; sys.path.append('$wwwdir'); from name_resolver_webtool import name_resolver_webtool; void = name_resolver_webtool(name='$1')"
+echo "Success running python_resolve_name.bash"