def evaluate_all_combinations(patfile, trainedforestbase, trainedforesttend, config):
original_usertag = config.get("general", "usertag") ### remember what the tag started as
original_variables = config.get("mvsc", "z") ### remember the variables
print (original_variables)
for tstart in ["no_tstart", "yes_tstart"]: ### iterate over all possible combinations
for tend in ["no_tend", "yes_tend"]:
config.set("general", "usertag", "%s-%s-%s"%(original_usertag, tstart, tend) ) ### change the usertag as needed
if(tstart == "no_tstart" and tend == "no_tend"):
config.set("mvsc", "z", "%s,%s,%s"%(original_variables, "time_locked", "time_end"))
elif(tstart == "no_tstart"):
config.set("mvsc", "z", "%s,%s"%(original_variables, "time_locked"))
elif(tend == "no_tend"):
config.set("mvsc", "z", "%s,%s"%(original_variables, "time_end"))
else:
config.set("mvsc","z", original_variables)
trainedforest = trainedforestbase + "%s-%s/"%(tstart,tend) + trainedforestend
ranked_file = trainedforest[:-3] + "dat"
gps_start_time = -np.infty
gps_end_time = np.infty
dir = "."
classifiersD, mla, ovl = idq.config_to_classifiersD( config )
classD = classifiersD['mvsc']
miniconfig = classD['config']
print(ranked_file)
print(trainedforest)
print(config.get("mvsc", "z"))
idq.forest_evaluate(patfile, trainedforest, ranked_file, miniconfig, gps_start_time, gps_end_time, dir)
config.set("general", "usertag", original_usertag)
config.set("mvsc", "z", original_variables)
### read global configuration file
config = ConfigParser.SafeConfigParser()
config.read(opts.config)
ifo = config.get('general', 'ifo')
usertag = config.get('general', 'usertag')
if usertag:
usertag = "_%s"%usertag
#========================
# which classifiers
#========================
### ensure we have a section for each classifier and fill out dictionary of options
classifiersD, mla, ovl = idq.config_to_classifiersD( config )
classifiers = sorted(classifiersD.keys())
if mla:
### reading parameters from config file needed for mla
# auxmvc_coinc_window = config.getfloat('build_auxmvc_vectors','time-window')
# auxmc_gw_signif_thr = config.getfloat('build_auxmvc_vectors','signif-threshold')
auxmvc_coinc_window = config.getfloat('realtime', 'padding')
auxmc_gw_signif_thr = config.getfloat('general', 'gw_kwsignif_thr')
auxmvc_selected_channels = config.get('general','selected-channels')
auxmvc_unsafe_channels = config.get('general','unsafe-channels')
#min_samples = config.getint('train', 'min_samples') ### minimum number of samples a training set should have
#min_svm_samples = config.getint('idq_train', 'min_svm_samples')
config = ConfigParser.SafeConfigParser()
config.read(opts.config)
#mainidqdir = config.get('general', 'idqdir') ### get the main directory where idq pipeline is going to be running.
ifo = config.get('general', 'ifo')
usertag = config.get('general', 'usertag')
if usertag:
usertag = "_%s" % usertag
#========================
# which classifiers
#========================
### ensure we have a section for each classifier and fill out dictionary of options
classifiersD, mla, ovl = idq.config_to_classifiersD(config)
### get combiners information and add these to classifiersD
combinersD, referenced_classifiers = idq.config_to_combinersD(config)
for combiner, value in combinersD.items():
classifiersD[combiner] = value
classifiers = sorted(classifiersD.keys())
#if mla:
# ### reading parameters from config file needed for mla
# auxmvc_coinc_window = config.getfloat('build_auxmvc_vectors','time-window')
# auxmc_gw_signif_thr = config.getfloat('build_auxmvc_vectors','signif-threshold')
# auxmvc_selected_channels = config.get('general','selected-channels')
# auxmvc_unsafe_channels = config.get('general','unsafe-channels')
