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