def cal_bn_features(preprocessed_dir):
preprocessed_ANO = preprocessed_dir + "/preprocessed.ano"
bn.genLinkerFile(preprocessed_dir, preprocessed_ANO)
##batch computing generate features
feature_file = preprocessed_dir+'/features.nfb'
bn.batch_compute(preprocessed_ANO, feature_file)
### convert feature file into csv file
nfb.generateALLFeatureCSV(feature_file, preprocessed_dir + '/features_with_tags.csv')
return
def cal_bn_features(preprocessed_dir):
preprocessed_ANO = preprocessed_dir + "/preprocessed.ano"
bn.genLinkerFile(preprocessed_dir, preprocessed_ANO)
##batch computing generate features
feature_file = preprocessed_dir + '/features.nfb'
bn.batch_compute(preprocessed_ANO, feature_file)
### convert feature file into csv file
nfb.generateALLFeatureCSV(feature_file,
preprocessed_dir + '/features_with_tags.csv')
return
def main():
###############################################################################
preprocessing = 0
janelia = 0
taiwan = 1
if taiwan:
data_DIR = "/data/mat/xiaoxiaol/data/big_neuron/consensus_all/taiwan"
original_dir = data_DIR + "/consensus_0330_anisosmooth"
db_tags_csv_file = data_DIR + '/taiwan_smooth_features_with_tags.csv'
if janelia:
data_DIR = "/data/mat/xiaoxiaol/data/big_neuron/consensus_all/janelia_set1"
original_dir = data_DIR + "/consensus_0330_anisosmooth"
db_tags_csv_file = data_DIR + '/j1_smooth_features_with_tags.csv'
###############################################################################
print original_dir
preprocessed_dir = data_DIR + "/preprocessed_consensus_smooth"
if not os.path.exists(preprocessed_dir):
os.system("mkdir -p " + preprocessed_dir)
if preprocessing == 1:
#preprocssing alignment
count = 0
qsub_folder = "/data/mat/xiaoxiaol/work/qsub"
os.system("rm " + qsub_folder + "/*.qsub")
os.system("rm " + qsub_folder + "/*.o*")
os.system("rm " + qsub_folder + "/jobs.txt")
for input_swc_path in glob.glob(original_dir + "/*.eswc"):
swc_fn = input_swc_path.split('/')[-1]
preprocessed_swc_fn = preprocessed_dir + '/' + swc_fn
if not os.path.exists(preprocessed_swc_fn):
bn.pre_processing(input_swc_path, preprocessed_swc_fn, 1,
qsub_folder, count)
count = count + 1
exit() #run jobs on pstar
print "done"
preprocessed_ANO = preprocessed_dir + "/preprocessed.ano"
bn.genLinkerFile(preprocessed_dir, preprocessed_ANO)
##batch computing generate features
feature_file = preprocessed_dir + '/features.nfb'
bn.batch_compute(preprocessed_ANO, feature_file)
### convert feature file into csv file
nfb.generateALLFeatureCSV(feature_file, db_tags_csv_file)
return
def main():
###############################################################################
preprocessing =0
janelia =0
taiwan=1
if taiwan:
data_DIR = "/data/mat/xiaoxiaol/data/big_neuron/consensus_all/taiwan"
original_dir = data_DIR + "/consensus_0330_anisosmooth"
db_tags_csv_file = data_DIR + '/taiwan_smooth_features_with_tags.csv'
if janelia:
data_DIR = "/data/mat/xiaoxiaol/data/big_neuron/consensus_all/janelia_set1"
original_dir = data_DIR + "/consensus_0330_anisosmooth"
db_tags_csv_file = data_DIR + '/j1_smooth_features_with_tags.csv'
###############################################################################
print original_dir
preprocessed_dir = data_DIR + "/preprocessed_consensus_smooth"
if not os.path.exists(preprocessed_dir):
os.system("mkdir -p " + preprocessed_dir)
if preprocessing==1:
#preprocssing alignment
count=0
qsub_folder= "/data/mat/xiaoxiaol/work/qsub"
os.system("rm "+qsub_folder+"/*.qsub")
os.system("rm "+qsub_folder+"/*.o*")
os.system("rm "+qsub_folder+"/jobs.txt")
for input_swc_path in glob.glob(original_dir + "/*.eswc"):
swc_fn = input_swc_path.split('/')[-1]
preprocessed_swc_fn = preprocessed_dir+'/' + swc_fn
if not os.path.exists(preprocessed_swc_fn):
bn.pre_processing(input_swc_path, preprocessed_swc_fn,1,qsub_folder,count)
count=count+1
exit() #run jobs on pstar
print "done"
preprocessed_ANO = preprocessed_dir + "/preprocessed.ano"
bn.genLinkerFile(preprocessed_dir, preprocessed_ANO)
##batch computing generate features
feature_file = preprocessed_dir+'/features.nfb'
bn.batch_compute(preprocessed_ANO, feature_file)
### convert feature file into csv file
nfb.generateALLFeatureCSV(feature_file, db_tags_csv_file)
return
