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- import pandas as pd
- import os
- import requests
-
- def get_data():
- link="https://bitbucket.org/gsudmlab/mvtsdata_toolkit/downloads/petdataset_01.zip"
- r = requests.get(link)
- with open('./swan_sf.zip', 'wb') as f:
- f.write(r.content)
- os.system("unzip swan_sf.zip")
-
- def read_labeled_data():
- dir_path = "./petdataset_01"
- files = os.listdir(dir_path)
- inlier = []
- label = {}
- for f in files:
- #if "csv" not in f and "NF" not in f:
- if "csv" not in f:
- continue
- label = f.split("lab[")[1].split("]")[0]
- #print(label)
- f_path = os.path.join(dir_path, f)
- df = pd.read_csv(f_path, header=0, sep='\t')
- df['label'] = label
- inlier.append(df)
- df = pd.concat(inlier, axis=0, ignore_index=True)
- df = df.sort_values(by=['Timestamp'])
- drop_cols = [col for col in df.columns if "label" in col or "loc" in col or "Timestamp" in col][:-1]
- df = df.drop(columns=drop_cols)
- df.reset_index(drop=True, inplace=True)
- df = df.fillna(method='ffill')
- df = df.dropna(axis="columns")
- df['label'].replace({"NF":0, "C":1, "B":1, "M":1, "X":1}, inplace=True)
- df['IS_TMFI'].replace({True:1, False:0}, inplace=True)
- cols = df.columns.tolist()
- cols = cols[-1:] + cols[:-1]
- df = df[cols]
- df.to_csv("../swan_sf.csv", index=False)
-
- if __name__ == "__main__":
- get_data()
- read_labeled_data()
- os.system("rm -rf swan_sf.zip petdataset_01")
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