From 6f30f5bc860dc8de7b0dfa34c94fcadb182a8418 Mon Sep 17 00:00:00 2001
From: "SEDModS (Marco)" <marco.molinaro@inaf.it>
Date: Mon, 17 Feb 2025 07:42:07 -0500
Subject: [PATCH] uncommited changes found on production server
(vlkb.ia2.inaf.it)
---
Dockerfile | 5 +-
wsgi-scripts/hdf_query.py | 12 +-
wsgi-scripts/query-server_d.py | 359 +++++++++++++++++++++++++++++++--
wsgi-scripts/wsgi.py | 11 +-
wsgi-scripts/wsgid.py | 17 +-
5 files changed, 358 insertions(+), 46 deletions(-)
diff --git a/Dockerfile b/Dockerfile
index cc9c2c7..64a0fc6 100644
--- a/Dockerfile
+++ b/Dockerfile
@@ -1,10 +1,11 @@
FROM registry.fedoraproject.org/fedora:latest
RUN dnf -y update &&\
- dnf -y install httpd python3-mod_wsgi pip &&\
+ dnf -y install systemd httpd python3-mod_wsgi pip &&\
dnf clean all &&\
pip install pandas tables Pyro4
COPY wsgi.conf /etc/httpd/conf.d/
EXPOSE 80
-ENTRYPOINT /usr/sbin/httpd -DFOREGROUND
+# ENTRYPOINT /usr/sbin/httpd -DFOREGROUND
+ENTRYPOINT /bin/bash
diff --git a/wsgi-scripts/hdf_query.py b/wsgi-scripts/hdf_query.py
index 10db9f4..ad0353e 100644
--- a/wsgi-scripts/hdf_query.py
+++ b/wsgi-scripts/hdf_query.py
@@ -1,14 +1,16 @@
#!/usr/bin/env python3
-
from urllib.parse import unquote
import pandas as pd
-
+import time
def query_out(parameters):
+ t1=time.time()
parsequery=parameters.replace(' ', '')
query1=parsequery.replace('%27', '')
-
+
query_final=unquote(query1)
- table=pd.read_hdf('/sed-data/sedmodels.h5')
- myquery=table.query(query_final)
+ dataset=pd.read_csv('/var/www/wsgi-scripts/sim_total.dat', sep =' ')
+ myquery=dataset.query(query_final)
+ t2=time.time()
+ print(t2-t1)
return myquery
diff --git a/wsgi-scripts/query-server_d.py b/wsgi-scripts/query-server_d.py
index f48e4b4..80f5efc 100644
--- a/wsgi-scripts/query-server_d.py
+++ b/wsgi-scripts/query-server_d.py
@@ -5,42 +5,357 @@ Created on Fri Mar 4 15:06:40 2022
@author: smordini
"""
-
import Pyro4
-
+import Pyro4.util
+import socket
from urllib.parse import unquote
-
import pandas as pd
+import logging
+from idl_to_py_lib import *
+import numpy as np
+import sys
+
+sys.excepthook = Pyro4.util.excepthook
+
+root_logger= logging.getLogger()
+root_logger.setLevel(logging.DEBUG) # or whatever
+handler = logging.FileHandler('/home/sedmods/vlkb-sedmods/sedfit_error.log', 'a', 'utf-8') # or whatever
+formatter = logging.Formatter('%(asctime)s %(levelname)-8s %(message)s') # or whatever
+handler.setFormatter(formatter) # Pass handler as a parameter, not assign
+root_logger.addHandler(handler)
+#logging.basicConfig(filename='/home/sedmods/vlkb-sedmods/sedfit_error.log', encoding='utf-8', level=logging.DEBUG, format='%(asctime)s %(levelname)-8s %(message)s', datefmt='%Y-%m-%d %H:%M:%S')
+
+
+
+
+if socket.gethostname().find('.')>=0:
+ hostname=socket.gethostname()
+else:
+ hostname=socket.gethostbyaddr(socket.gethostname())[0] + ".local"
+
+
+
@Pyro4.expose
class QueryMaker(object):
-
-
- dataset=pd.read_hdf('/sed-data/sedmodels.h5')
-
+ dataset=pd.read_csv('/var/www/wsgi-scripts/sim_total.dat', sep =' ')
def query_out(self, parameters):
- dataset=pd.read_hdf('/sed-data/sedmodels.h5')
- parsequery=parameters.replace(' ', '')
- query1=parsequery.replace('%27', '')
-
- query_final=unquote(query1)
- myquery=QueryMaker.dataset.query(query1)
+ try:
+ parsequery=parameters.replace(' ', '')
+ query1=parsequery.replace('%27', '')
+ query_final=unquote(query1)
+ query_log=query_final.replace('_', ' ')
+ query_list=query_final.split('_')
+ if len(query_list)==0:
+ output='Deamon service running correctly'
+ return output
+ w_in=eval(query_list[0])
+ f_in=eval(query_list[1])
+ df_in=eval(query_list[2])
+ fflag_in=eval(query_list[3])
+ distance=eval(query_list[4])
+ prefilter_thresh=eval(query_list[5])
+ sed_weights=eval(query_list[6])
+ local=eval(query_list[7])
+ use_wl=eval(query_list[8])
+ delta_chi2=eval(query_list[9])
+ except:
+ logging.exception('Error occurred in reading/importing function parameters')
+ logging.info(query_log)
+ if distance<0:
+ logging.error('Negative value for distance; program interrupted')
+ mystr='The distance is set to a negative value. Please provide a positive value.'
+ return mystr
+
+ phys_par=[tt.upper() for tt in ['clump_mass','compact_mass_fraction','clump_upper_age','dust_temp','bolometric_luminosity','random_sample','n_star_tot','m_star_tot','n_star_zams','m_star_zams','l_star_tot','l_star_zams','zams_luminosity_1','zams_mass_1','zams_temperature_1','zams_luminosity_2','zams_mass_2','zams_temperature_2','zams_luminosity_3','zams_mass_3','zams_temperature_3']]
+ jy2mjy=1000.
+ d_ref=1000.
+ ref_wave=[3.4,3.6,4.5,4.6,5.8,8.0,12.,22.,24.0,70.,100.,160.,250.,350.,500.,870.,1100.]
+ col_names=['WISE1','I1','I2','WISE2','I3','I4','WISE3','WISE4','M1','PACS1','PACS2','PACS3','SPIR1','SPIR2','SPIR3','LABOC','BOL11']
+
+ fac_resc=(distance/d_ref)**2.
+ delta=1-(prefilter_thresh)
+ q12,q21=match(w_in, ref_wave)
+
+ w=[w_in[i] for i in q12]
+ f=[f_in[i] for i in q12]
+ d=[df_in[i] for i in q12]
+ ff=[fflag_in[i] for i in q12]
+
+ wwll=[i for i in w]
+ w.sort()
+ wl=[]
+ fl=[]
+ df=[]
+ fflag=[]
+ use_wave=[]
+ for ww in w:
+ q=wwll.index(ww)
+ wl.append(wwll[q])
+ fl.append(f[q])
+ df.append(d[q])
+ fflag.append(ff[q])
+ use_wave.append(use_wl[q])
+ par_str=''
+ par_str_arr=[]
+ ret_par_str=''
+ phys_str=''
+ phys_par_arr=[]
+ ret_phys_par=''
+ for t in range(len(ref_wave)):
+ for k in wl:
+ if ref_wave[t]-0.05<k and ref_wave[t]+0.05>k:
+ par_str=par_str+col_names[t]+','
+ ret_par_str=ret_par_str+col_names[t]+','
+ par_str_arr.append(col_names[t].lower())
+
+ par_str=par_str[:-1].upper()
+ ret_par_str=ret_par_str[:-1].lower()
+ for k in range(len(phys_par)):
+ phys_str=phys_str+phys_par[k]+','
+ ret_phys_par=ret_phys_par+phys_par[k]+','
+ phys_par_arr.append(phys_par[k].lower())
+ phys_str=phys_str[:-1].upper()
+ ret_phys_par=ret_phys_par[:-1].lower()
+ if use_wave!=0:
+ query=""
+ for bb, bb_count in zip(use_wave, range(len(use_wave))):
+ if bb in wl:
+ qband=wl.index(bb)
+ else:
+ logging.error('Reference wavelength required: '+str(wl.index(bb))+' not found in data file; wavelength excluded from fit procedure')
+ continue
+ if bb in ref_wave:
+ qrefband=ref_wave.index(bb)
+ else:
+ qrefband=-1
+ qqueryband,qdummy=match(ref_wave, wl)
+ ul_str=''
+ if 0 in fflag:
+ qulband=[i for i,e in enumerate(fflag) if e==0 ]
+ nqulband=fflag.count(0)
+ ul_str=' and '
+ for t in range(nqulband):
+ ul_str=ul_str+'('+col_names(qqueryband(qulband(t)))+"<'"+tostring(fl(qulband(t))*jy2mjy*fac_resc)+"') and "
+ if fflag[qband]==1:
+ ul_str=ul_str[0:len(ul_str)-4]
+ if fflag[qband]==0:
+ ul_str=ul_str[4:len(ul_str)-4]
+ nreq_par=1+len(phys_par_arr)+len(par_str_arr)
+ if fflag[qband]==1:
+ query=query+(str(remove_char(pad0_num(tostring(float(fl[qband]*jy2mjy*fac_resc*(1-(delta**2.))))),'+')+"<"+col_names[qrefband]+"<"+remove_char(pad0_num(tostring(float(fl[qband]*jy2mjy*fac_resc*(1+(delta**2.))))),'+')+' or ') )
+ if fflag[qband]==0:
+ query=query+(str(col_names[qrefband]+"<"+remove_char(pad0_num(tostring(float(fl[qband]*jy2mjy*fac_resc))),'+'))+' or ')#fluxes are mutliplied by 1000 because model fluxes are in milliJy
+ query_final=query[:-4]
+ try:
+ dmodels=QueryMaker.dataset.query(query_final)
+ except:
+ logging.exception('Error occurred while querying dataset with band intervals')
+ else:
+ #compute object luminosity from observed SED
+ lum=lbol(wl,fl,distance)
+ #rescaling factor has to stay at 1 if luminosity is used-----WHYYYY ??????
+ fac_resc=1.
+ query_fianl=remove_char(pad0_num(tostring(float(lum*fac_resc*(1-(delta**2.))))),'+')+"< bolometric_luminosity <"+remove_char(pad0_num(tostring(float(lum*fac_resc*(1+(delta**2.))))),'+')
+ try:
+ dmodels=QueryMaker.dataset.query(query_final)
+ except:
+ logging.exception('Error occurred while querying dataset with bolometricl uminosity interval')
+
+ nlines=len(dmodels)
+ if nlines<1:
+ logging.info('Model selection not performed. Program interrupted')
+ output='Model selection not performed. Retry with different paramenters'
+ return output
+ else:
+ logging.info('Model selection completed, obtained '+str(nlines)+' models.')
+ n_sel=len(dmodels['cluster_id'])
- test1=myquery.to_json(orient='split')
+ flag=[int(ff) for ff in fflag]
+ ul_flag=np.zeros(len(flag))
+ ll_flag=np.zeros(len(flag))
+ dyd=np.zeros(len(flag))
+ if sed_weights==0:
+ sed_weights=[3./7.,1./7.,3./7.]
+ renorm=sum(sed_weights)
+ w1=np.sqrt(sed_weights[0]/renorm)
+ w2=np.sqrt(sed_weights[1]/renorm)
+ w3=np.sqrt(sed_weights[2]/renorm)
+ qmir=[]
+ qfir=[]
+ qmm=[]
+ for i in range(len(wl)):
+ if wl[i]<25:
+ qmir.append(i)
+ if wl[i]>=25 and wl[i]<=250:
+ qfir.append(i)
+ if wl[i]>250:
+ qmm.append(i)
+ if len(qmir)>0:
+ q1=[]
+ q1neg=[]
+ for qq in qmir:
+ if flag[qq]==1:
+ q1.append(qq)
+ else:
+ q1neg.append(qq)
+ nq1neg=len(q1neg)
+ nq1=len(q1)
+ if nq1>0:
+ for qq in qmir:
+ dyd[qq]=np.sqrt(nq1)/w1
+ if nq1neg>0:
+ for qq in q1neg:
+ dyd[qq]=9999. #i.e. it's an upper/lower limit
+ ul_flag[qq]=1
+ if len(qfir)>0:
+ q2=[]
+ q2neg=[]
+ for qq in qfir:
+ if flag[qq]==1:
+ q2.append(qq)
+ else:
+ q2neg.append(qq)
+ nq2neg=len(q2neg)
+ nq2=len(q2)
+ if nq2>0:
+ for qq in qfir:
+ dyd[qq]=np.sqrt(nq2)/w2
+ if nq2neg>0:
+ for qq in q2neg:
+ dyd[qq]=9999. #i.e. it's an upper/lower limit
+ ul_flag[qq]=1
+ if len(qmm)>0:
+ q3=[]
+ q3neg=[]
+ for qq in qmm:
+ if flag[qq]==1:
+ q3.append(qq)
+ else:
+ q3neg.append(qq)
+ nq3neg=len(q3neg)
+ nq3=len(q3)
+ if nq3>0:
+ for qq in qmm:
+ dyd[qq]=np.sqrt(nq3)/w3
+ if nq3neg>0:
+ for qq in q3neg:
+ dyd[qq]=9999. #i.e. it's an upper/lower limit
+ ul_flag[qq]=1
+ good_flag=[1-qq for qq in ul_flag]
+ dyd=[dd/min(dyd) for dd in dyd]
+ dyd=[dd*ff/ll for dd,ff,ll in zip( dyd, df,fl)]
+ dyd=[dd*ff for dd,ff in zip(dyd, fl)]
+ nstep=10
+ dist_arr=np.arange(10)
+ dist_arr=[distance*(1-(delta**2.))+dd*(distance*(1+(delta**2.))-distance*(1-(delta**2.)))/nstep for dd in dist_arr]
+ nw=len(wl)
+ invalid_chi2=-999
+ matrix_models=np.zeros([n_sel,nstep,nw])
+ matrix_chi2=np.zeros([n_sel,nstep])
+ matrix_chi2[:]=invalid_chi2
+ matrix_fluxes=np.zeros([n_sel,nstep,nw])
+ matrix_dfluxes=np.zeros([n_sel,nstep,nw])
+ for i in range(nstep):
+ for k in range(nw):
+ matrix_models[:,i,k]=dmodels[par_str_arr[k].upper()]/1000*((d_ref/dist_arr[i])**2.)
+ for k in range(nw):
+ matrix_fluxes[:,:,k]=fl[k]
+ matrix_dfluxes[:,:,k]=dyd[k]
+ dmat=np.zeros([n_sel,nstep,nw])
+ for j in range(nstep):
+ for k in range(nw):
+ dmat[:,j,k]=((matrix_models[:,j,k]-matrix_fluxes[:,j,k])**2)/(matrix_dfluxes[:,j,k]**2)
+ matrix_chi2=np.sum(dmat, 2)
+ if delta_chi2!=0:
+ dchi2=delta_chi2
+ else:
+ dchi2=1
+ chi2min=np.min(matrix_chi2)
+ qchi2=np.argwhere(matrix_chi2<=chi2min+dchi2)
+ nqchi2=len(qchi2)
+ par={'cluster_id':[], 'clump_mass':[], 'compact_mass_fraction':[], 'clump_upper_age':[], 'dust_temp':[], 'bolometric_luminosity':[], 'random_sample':[], 'n_star_tot':[], 'm_star_tot':[], 'n_star_zams':[], 'm_star_zams':[], 'l_star_tot':[], 'l_star_zams':[], 'zams_luminosity_1':[], 'zams_mass_1':[], 'zams_temperature_1':[], 'zams_luminosity_2':[], 'zams_mass_2':[], 'zams_temperature_2':[], 'zams_luminosity_3':[], 'zams_mass_3':[], 'zams_temperature_3':[], 'd':[], 'chi2':[], 'wmod':[], 'fmod':[]}
+ logging.info('Selected '+str(nqchi2)+' models befor dubplicate removal.')
+ for i in range(nqchi2):
+ if dmodels['cluster_id'].iloc[qchi2[i][0]] not in par['cluster_id']:
+ par['cluster_id'].append(dmodels['cluster_id'].iloc[qchi2[i][0]])
+ par['clump_mass'].append(dmodels['clump_mass'].iloc[qchi2[i][0]])
+ par['compact_mass_fraction'].append(dmodels['compact_mass_fraction'].iloc[qchi2[i][0]])
+ par['clump_upper_age'].append(dmodels['clump_upper_age'].iloc[qchi2[i][0]])
+ par['bolometric_luminosity'].append(dmodels['bolometric_luminosity'].iloc[qchi2[i][0]])
+ par['random_sample'].append(dmodels['random_sample'].iloc[qchi2[i][0]])
+ par['dust_temp'].append(dmodels['dust_temp'].iloc[qchi2[i][0]])
+ par['n_star_tot'].append(dmodels['n_star_tot'].iloc[qchi2[i][0]])
+ par['m_star_tot'].append(dmodels['m_star_tot'].iloc[qchi2[i][0]])
+ par['n_star_zams'].append(dmodels['n_star_zams'].iloc[qchi2[i][0]])
+ par['m_star_zams'].append(dmodels['m_star_zams'].iloc[qchi2[i][0]])
+ par['l_star_tot'].append(dmodels['l_star_tot'].iloc[qchi2[i][0]])
+ par['l_star_zams'].append(dmodels['l_star_zams'].iloc[qchi2[i][0]])
+ par['zams_luminosity_1'].append(dmodels['zams_luminosity_1'].iloc[qchi2[i][0]])
+ par['zams_mass_1'].append(dmodels['zams_mass_1'].iloc[qchi2[i][0]])
+ par['zams_temperature_1'].append(dmodels['zams_temperature_1'].iloc[qchi2[i][0]])
+ par['zams_luminosity_2'].append(dmodels['zams_luminosity_2'].iloc[qchi2[i][0]])
+ par['zams_mass_2'].append(dmodels['zams_mass_2'].iloc[qchi2[i][0]])
+ par['zams_temperature_2'].append(dmodels['zams_temperature_2'].iloc[qchi2[i][0]])
+ par['zams_luminosity_3'].append(dmodels['zams_luminosity_3'].iloc[qchi2[i][0]])
+ par['zams_mass_3'].append(dmodels['zams_mass_3'].iloc[qchi2[i][0]])
+ par['zams_temperature_3'].append(dmodels['zams_temperature_3'].iloc[qchi2[i][0]])
+ par['d'].append(dist_arr[qchi2[i][1]])
+ final_dist=dist_arr[qchi2[i][1]]
+ par['chi2'].append(matrix_chi2[tuple(qchi2[i])])
+ par['wmod'].append(list([3.4,3.6,4.5,4.6,5.8,8.0,12.,22.,24.0,70.,100.,160.,250.,350.,500.,870.,1100.]))
+ fluxes=[]
+ for ff in col_names:
+ myflux=dmodels[ff].iloc[qchi2[i][0]]/1000*((d_ref/final_dist)**2)
+ fluxes.append(myflux)
+ par['fmod'].append(list(fluxes))
+ else:
+ j=par['cluster_id'].index(dmodels['cluster_id'].iloc[qchi2[i][0]])
+ if par['chi2'][j]>matrix_chi2[tuple(qchi2[i])]:
+ par['cluster_id'][j]=dmodels['cluster_id'].iloc[qchi2[i][0]]
+ par['clump_mass'][j]=dmodels['clump_mass'].iloc[qchi2[i][0]]
+ par['compact_mass_fraction'][j]=dmodels['compact_mass_fraction'].iloc[qchi2[i][0]]
+ par['clump_upper_age'][j]=dmodels['clump_upper_age'].iloc[qchi2[i][0]]
+ par['bolometric_luminosity'][j]=dmodels['bolometric_luminosity'].iloc[qchi2[i][0]]
+ par['random_sample'][j]=dmodels['random_sample'].iloc[qchi2[i][0]]
+ par['dust_temp'][j]=dmodels['dust_temp'].iloc[qchi2[i][0]]
+ par['n_star_tot'][j]=dmodels['n_star_tot'].iloc[qchi2[i][0]]
+ par['m_star_tot'][j]=dmodels['m_star_tot'].iloc[qchi2[i][0]]
+ par['n_star_zams'][j]=dmodels['n_star_zams'].iloc[qchi2[i][0]]
+ par['m_star_zams'][j]=dmodels['m_star_zams'].iloc[qchi2[i][0]]
+ par['l_star_tot'][j]=dmodels['l_star_tot'].iloc[qchi2[i][0]]
+ par['l_star_zams'][j]=dmodels['l_star_zams'].iloc[qchi2[i][0]]
+ par['zams_luminosity_1'][j]=dmodels['zams_luminosity_1'].iloc[qchi2[i][0]]
+ par['zams_mass_1'][j]=dmodels['zams_mass_1'].iloc[qchi2[i][0]]
+ par['zams_temperature_1'][j]=dmodels['zams_temperature_1'].iloc[qchi2[i][0]]
+ par['zams_luminosity_2'][j]=dmodels['zams_luminosity_2'].iloc[qchi2[i][0]]
+ par['zams_mass_2'][j]=dmodels['zams_mass_2'].iloc[qchi2[i][0]]
+ par['zams_temperature_2'][j]=dmodels['zams_temperature_2'].iloc[qchi2[i][0]]
+ par['zams_luminosity_3'][j]=dmodels['zams_luminosity_3'].iloc[qchi2[i][0]]
+ par['zams_mass_3'][j]=dmodels['zams_mass_3'].iloc[qchi2[i][0]]
+ par['zams_temperature_3'][j]=dmodels['zams_temperature_3'].iloc[qchi2[i][0]]
+ par['d'][j]=dist_arr[qchi2[i][1]]
+ final_dist=dist_arr[qchi2[i][1]]
+ par['chi2'][j]=matrix_chi2[tuple(qchi2[i])]
+ par['wmod'][j]=list([3.4,3.6,4.5,4.6,5.8,8.0,12.,22.,24.0,70.,100.,160.,250.,350.,500.,870.,1100.])
+ fluxes=[]
+ for ff in col_names:
+ myflux=dmodels[ff].iloc[qchi2[i][0]]/1000*((d_ref/final_dist)**2)
+ fluxes.append(myflux)
+ par['fmod'][j]=(list(fluxes))
+ logging.info('Sedfit procedure completed. Obtained '+ str(len(par['cluster_id']))+' models after duplicate removal.')
+ pd_dict=pd.DataFrame.from_dict(par)
+ test1=pd_dict.to_json(orient='split')
test2=str(test1)
- output=bytes(test2,'utf-8')
- return output
-
+ logging.info('Procedur completed.')
+ return test2
-
-daemon=Pyro4.Daemon()
+daemon=Pyro4.Daemon(hostname)
ns=Pyro4.locateNS()
uri=daemon.register(QueryMaker)
ns.register("test.query", uri)
print("Ready. Object uri=", uri)
-daemon.requestLoop()
-
-
+daemon.requestLoop()
\ No newline at end of file
diff --git a/wsgi-scripts/wsgi.py b/wsgi-scripts/wsgi.py
index fcdb9c0..71d9f8d 100644
--- a/wsgi-scripts/wsgi.py
+++ b/wsgi-scripts/wsgi.py
@@ -1,5 +1,6 @@
#!/usr/bin/env python3
import sys
+print('python version', sys.version)
import pandas
sys.path.insert(0,"/var/www/wsgi-scripts/")
from hdf_query import query_out
@@ -10,18 +11,16 @@ def application(environ, start_response):
test=query_out(var1)
test1=test.to_json(orient='split')
test2=str(test1)
-# test1=str(test)
+
output=bytes(test2,'utf-8')
- output1 = b'Hello beautiful World!'
+
getstring = environ['QUERY_STRING']
-# test += getstring.encode('utf-8')
+
response_headers = [('Content-type', 'text/plain'),
('Content-Length', str(len(output)))]
-# response_headers = [('Content-Disposition', 'attachment; filename= export.csv')]
-# test.headers['Content-Disposition']='attachment; filename= export.csv'
-# test.headers['Content-type']= 'text/csv'
+
start_response(status, response_headers)
return [output]
diff --git a/wsgi-scripts/wsgid.py b/wsgi-scripts/wsgid.py
index 8c30b05..ff5d669 100644
--- a/wsgi-scripts/wsgid.py
+++ b/wsgi-scripts/wsgid.py
@@ -1,23 +1,18 @@
#!/usr/bin/env python3
import sys
-
-import pandas
-sys.path.insert(0,"/var/www/html/")
-#from parquet_query import query_out
import Pyro4
+import pandas
+
def application(environ, start_response):
+
status = '200 OK'
query_in =str( environ['QUERY_STRING'])
query_maker=Pyro4.Proxy("PYRONAME:test.query")
- output=query_maker.query_out(query_in)
-
-
+ test2=query_maker.query_out(query_in)
+ output=bytes(test2,'utf-8')
+ getstring = environ['QUERY_STRING']
response_headers = [('Content-type', 'text/plain'),
('Content-Length', str(len(output)))]
-# response_headers = [('Content-Disposition', 'attachment; filename= export.csv')]
-# test.headers['Content-Disposition']='attachment; filename= export.csv'
-# test.headers['Content-type']= 'text/csv'
start_response(status, response_headers)
-
return [output]
--
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