Spaces:
Running
Running
File size: 3,655 Bytes
e60e568 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
'''
This file is part of PM4Py (More Info: https://pm4py.fit.fraunhofer.de).
PM4Py is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
PM4Py is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with PM4Py. If not, see <https://www.gnu.org/licenses/>.
'''
import os
import sqlite3
from datetime import datetime
import pandas as pd
from enum import Enum
from typing import Optional, Dict, Any
from pm4py.util import exec_utils, pandas_utils
class Parameters(Enum):
HISTORY_DB_PATH = "history_db_path"
def apply(parameters: Optional[Dict[Any, str]] = None) -> pd.DataFrame:
"""
Extracts a dataframe containing the navigation history of Google Chrome.
Please keep Google Chrome history closed when extracting.
CASE ID (case:concept:name) => an identifier of the profile that has been extracted
ACTIVITY (concept:name) => the complete path of the website, minus the GET arguments
TIMESTAMP (time:timestamp) => the timestamp of visit
Parameters
--------------
Parameters.HISTORY_DB_PATH
Path to the history DB path of Google Chrome (default: position of the Windows folder)
Returns
--------------
dataframe
Pandas dataframe
"""
if parameters is None:
parameters = {}
history_db_path = exec_utils.get_param_value(Parameters.HISTORY_DB_PATH, parameters, "C:\\Users\\" + os.getenv(
'USERNAME') + "\\AppData\\Local\\Google\\Chrome\\User Data")
print(history_db_path)
if os.path.isdir(history_db_path):
profiles = [(os.path.join(history_db_path, x, "History"), x) for x in os.listdir(history_db_path) if
x.startswith("Profile ")]
else:
profiles = [(history_db_path, "DEFAULT")]
profiles = [x for x in profiles if os.path.exists(x[0])]
events = []
for prof in profiles:
conn = sqlite3.connect(prof[0])
curs = conn.cursor()
curs.execute(
"SELECT b.url, datetime(a.visit_time / 1000000 + (strftime('%s', '1601-01-01')), 'unixepoch', 'localtime') FROM (SELECT url, visit_time FROM visits) a JOIN (SELECT id, url FROM urls) b ON a.url = b.id")
res = curs.fetchall()
for r in res:
ev = {"case:concept:name": prof[1], "concept:name": r[0].split("//")[-1].split("?")[0].replace(",", ""),
"complete_url": r[0],
"domain": r[0].split("//")[-1].split("/")[0], "url_wo_parameters": r[0].split("//")[-1].split("?")[0],
"time:timestamp": datetime.strptime(r[1], "%Y-%m-%d %H:%M:%S")}
if len(ev["case:concept:name"].strip()) > 0 and len(ev["concept:name"].strip()) > 0:
events.append(ev)
curs.close()
conn.close()
dataframe = pandas_utils.instantiate_dataframe(events)
if len(dataframe) > 0:
dataframe = pandas_utils.insert_index(dataframe, "@@index", copy_dataframe=False, reset_index=False)
dataframe = dataframe.sort_values(["time:timestamp", "@@index"])
dataframe["@@case_index"] = dataframe.groupby("case:concept:name", sort=False).ngroup()
dataframe = dataframe.sort_values(["@@case_index", "time:timestamp", "@@index"])
return dataframe
|