#DATA='/home/xiaoxiaol/work/data' #DATA='/data/mat/xiaoxiaol/data/big_neuron' DATA='/mnt/BigNeuron/data' test = 0 smooth = 1 data_DIR= DATA+"/taiwan16k" fn_list = '~/work/data/taiwan_image_file_name_list.csv' df_i = pd.read_csv(fn_list) imageIDs = df_i['image_file_name'] if test: ids = np.random.randint(1,15921,100) ids = range(1,100,1) imageIDs = imageIDs[ids] # imageIDs= imageIDs.astype('str') # for im in images[random_ids]: subfolder = "consensus_0330" if smooth>0: subfolder=subfolder+"_anisosmooth" output_dir = data_DIR+'/'+subfolder+"/analysis_results" mc2c.pipe(data_DIR+'/'+subfolder, output_dir, imageIDs,'median_distances.csv',COLLECT_FROM_DISTANCE_MATRIX=1,EXTRACT_MEDIAN_CONSENSUS=1)
import bigneuron.median_compare_to_consensus as mc2c #DATA='/home/xiaoxiaol/work/data' #DATA='/data/mat/xiaoxiaol/data/big_neuron' DATA = '/mnt/BigNeuron/data' test = 0 smooth = 1 data_DIR = DATA + "/taiwan16k" fn_list = '~/work/data/taiwan_image_file_name_list.csv' df_i = pd.read_csv(fn_list) imageIDs = df_i['image_file_name'] if test: ids = np.random.randint(1, 15921, 100) ids = range(1, 100, 1) imageIDs = imageIDs[ids] # imageIDs= imageIDs.astype('str') # for im in images[random_ids]: subfolder = "consensus_0330" if smooth > 0: subfolder = subfolder + "_anisosmooth" output_dir = data_DIR + '/' + subfolder + "/analysis_results" mc2c.pipe(data_DIR + '/' + subfolder, output_dir, imageIDs, 'median_distances.csv', COLLECT_FROM_DISTANCE_MATRIX=1, EXTRACT_MEDIAN_CONSENSUS=1)
if (platform.system() == "Linux"): WORK_PATH = "/local1/xiaoxiaol/work" else: WORK_PATH = "/Users/xiaoxiaoliu/work" p = WORK_PATH + '/src/morphology_analysis' sys.path.append(p) import bigneuron.median_compare_to_consensus as mc2c import pandas as pd subfolder="0401_gold163_all_soma_sort" #subfolder="gold_163_all_soma_sort_0328" data_DIR="/data/mat/xiaoxiaol/data/big_neuron/silver/"+subfolder df_nd = pd.read_csv(data_DIR+'/list.txt') imageIDs = df_nd['image_id'].apply(str) mc2c.pipe(input_data_dir=data_DIR, output_dir=data_DIR+"/analysis_results", imageIDs=imageIDs,distance_file_postfix='median_distances.csv', COLLECT_FROM_DISTANCE_MATRIX=1,EXTRACT_MEDIAN_CONSENSUS=1, DISPLAY=0) print "\n\n\n"
import os import platform if (platform.system() == "Linux"): WORK_PATH = "/local1/xiaoxiaol/work" else: WORK_PATH = "/Users/xiaoxiaoliu/work" p = WORK_PATH + '/src/morphology_analysis' sys.path.append(p) import bigneuron.median_compare_to_consensus as mc2c import pandas as pd subfolder = "0401_gold163_all_soma_sort" #subfolder="gold_163_all_soma_sort_0328" data_DIR = "/data/mat/xiaoxiaol/data/big_neuron/silver/" + subfolder df_nd = pd.read_csv(data_DIR + '/list.txt') imageIDs = df_nd['image_id'].apply(str) mc2c.pipe(input_data_dir=data_DIR, output_dir=data_DIR + "/analysis_results", imageIDs=imageIDs, distance_file_postfix='median_distances.csv', COLLECT_FROM_DISTANCE_MATRIX=1, EXTRACT_MEDIAN_CONSENSUS=1, DISPLAY=0) print "\n\n\n"