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ptbXL2trainval.py
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import os,sys,shutil
import pandas as pd
import numpy as np
import wfdb
import ast
import json
from tqdm import tqdm
import gzip
def mkdir_without_del(path):
if not os.path.exists(path):
os.makedirs(path)
def mkdir_with_del(path):
if os.path.exists(path):
shutil.rmtree(path)
os.makedirs(path)
def check_path(path):
main_dir = os.path.dirname(path)
mkdir_without_del(main_dir)
def process(input_dir,out_dir):
csv_path = input_dir+'ptbxl_database.csv'
Y = pd.read_csv(csv_path, index_col='ecg_id')
filename_hr = Y['filename_hr'].tolist()
# ['I', 'II', 'III', 'AVR', 'AVL', 'AVF', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6']
for filename_i in tqdm(filename_hr):
out_name = filename_i+'.json'
file_path = input_dir+filename_i
data_raw = wfdb.rdsamp(file_path)
data_json = {}
lead_names = data_raw[1]['sig_name']
for idx,lead_name in enumerate(lead_names):
data_i = data_raw[0][:,idx].tolist()
lead_name = lead_name.replace('A','a')
data_json[lead_name] = data_i
out_dir_path = out_dir+out_name
check_path(out_dir_path)
# with open(out_dir_path, 'w') as fw:
# json.dump(data_json,fw)
with gzip.open(out_dir_path, 'w') as fout:
fout.write(json.dumps(data_json).encode('utf-8'))
def clean_json(args):
file_path = args[0]
out_dir_path = args[1]
if os.path.exists(out_dir_path):
pass
else:
data_raw = wfdb.rdsamp(file_path)
data_json = {}
lead_names = data_raw[1]['sig_name']
for idx,lead_name in enumerate(lead_names):
data_i = data_raw[0][:,idx].tolist()
lead_name = lead_name.replace('A','a')
data_json[lead_name] = data_i
# out_dir_path = out_dir+out_name
check_path(out_dir_path)
with gzip.open(out_dir_path, 'w') as fout:
fout.write(json.dumps(data_json).encode('utf-8'))
def process(input_csv,name_csv,column_name,input_dir,out_dir):
df_stats = pd.read_csv(name_csv)
label_name = df_stats['names'].tolist()
Y = pd.read_csv(input_csv, index_col='ecg_id')
# filename_hr = Y['filename_hr'].tolist()
# diagnostic_superclass = Y[column_name].tolist()
# diagnostic_superclass = [ast.literal_eval(i) for i in diagnostic_superclass]
unqie_cls_dict = {i:[0,0,0] for i in label_name}
test_fold = 10
y_train_df = Y[(Y.strat_fold != test_fold)]
y_test_df = Y[Y.strat_fold == test_fold]
# for train
filename_hr_train = y_train_df['filename_hr'].tolist()
diagnostic_superclass_train = y_train_df[column_name].tolist()
diagnostic_superclass_train = [ast.literal_eval(i) for i in diagnostic_superclass_train]
train_paths,train_labels = [],[]
for i in range(len(filename_hr_train)):
filename_i = filename_hr_train[i]
diagnostic_superclass_i = diagnostic_superclass_train[i]
out_name = filename_i+'.json'
file_path = input_dir+out_name
if not os.path.exists(file_path):
assert 1>2,file_path
if len(diagnostic_superclass_i) > 0:
valid_label_i =[]
for label_i in diagnostic_superclass_i:
if label_i in unqie_cls_dict:
unqie_cls_dict[label_i][0]+=1
unqie_cls_dict[label_i][1]+=1
valid_label_i.append(label_i)
train_paths.append(filename_i)
train_labels.append(valid_label_i)
# for test
filename_hr_val = y_test_df['filename_hr'].tolist()
diagnostic_superclass_val = y_test_df[column_name].tolist()
diagnostic_superclass_val = [ast.literal_eval(i) for i in diagnostic_superclass_val]
val_paths,val_labels = [],[]
for i in range(len(filename_hr_val)):
filename_i = filename_hr_val[i]
diagnostic_superclass_i = diagnostic_superclass_val[i]
out_name = filename_i+'.json'
file_path = input_dir+out_name
if not os.path.exists(file_path):
assert 1>2,file_path
if len(diagnostic_superclass_i) > 0:
valid_label_i=[]
for label_i in diagnostic_superclass_i:
if label_i in unqie_cls_dict:
unqie_cls_dict[label_i][0]+=1
unqie_cls_dict[label_i][2]+=1
valid_label_i.append(label_i)
val_paths.append(filename_i)
val_labels.append(valid_label_i)
inter_c = list(set(train_paths).intersection(val_paths))
if len(inter_c) >0:
print('train and val intersetion',inter_c)
assert 1>2
# df = pd.DataFrame.from_dict(unqie_cls_dict)
df_stats['all']=[unqie_cls_dict[i][0] for i in label_name]
df_stats['train']=[unqie_cls_dict[i][1] for i in label_name]
df_stats['val']=[unqie_cls_dict[i][2] for i in label_name]
print(df_stats)
df_stats.to_csv(out_dir+'k{}_train_val_stats.csv'.format(test_fold),index=False,encoding='utf-8_sig')
df = pd.DataFrame()
df['paths']=train_paths
df['label_names']=train_labels
df.to_csv(out_dir+'k{}_train.csv'.format(test_fold),index=False,encoding='utf-8_sig')
df = pd.DataFrame()
df['paths']=val_paths
df['label_names']=val_labels
df.to_csv(out_dir+'k{}_val.csv'.format(test_fold),index=False,encoding='utf-8_sig')
print('train {} val {} all_valid {} all_orginal {}'.format(len(train_paths),len(val_paths),\
len(train_paths)+len(val_paths),len(Y)))
print('all done')
if __name__ == "__main__":
import multiprocessing as mp
input_csv = './ptbxl_data.csv'
data_dir = '/Users/wenjing_qiaoran/Downloads/计算机长尾方向paper/代码clone/data/raw_data/PTB_XL/clean_data/'
# column_name = '0diag_main5'
# column_name = '0diag_super23'
column_name = '0diag_sub44'
# column_name = '1form_19cls'
# column_name = '2rhythm_12cls'
name_csv = './name_csv/{}.csv'.format(column_name)
out_dir = data_dir+column_name+'/'
mkdir_without_del(out_dir)
print(column_name)
process(input_csv,name_csv,column_name,data_dir,out_dir)