use LogParser in post_processing

2 files changed, 33 insertions, 17 deletions

diff --git a/bjoern/videoanalyse/LogParser.py b/bjoern/videoanalyse/LogParser.py
index 448b283..1104e7f 100755
--- a/bjoern/videoanalyse/LogParser.py
+++ b/bjoern/videoanalyse/LogParser.py
@@ -12,11 +12,10 @@ import codecs
 from read_sqlite import get_url_from_sqlite

 from pathlib import Path

 

-BROWSER_TITLE_SUFFIX = " - Mozilla Firefox"

-

 

 # takes the log data string and returns a list of activity titles and their time windows

 def extract_activities(log_data):

+    BROWSER_TITLE_SUFFIX = " - Mozilla Firefox"

     # regex which matches between squared brackets

     reg_titles = re.compile("(?<=\[).*?(?=\])")

     # regex for total/active time

@@ -86,6 +85,7 @@ def match_urls(history_db, log):
         entry.append(url)

     return log

 

+

 def generate_log(activities: dict):

     # For each start time in ascending order, make an entry with title and timestamp

     log = []

@@ -101,26 +101,23 @@ def generate_log(activities: dict):
                     smallest_start_time = (title, idx)

         log.append(

             [

-                activities[smallest_start_time[0]][smallest_start_time[1]][0].isoformat(),

-                activities[smallest_start_time[0]][smallest_start_time[1]][1].isoformat(),

+                activities[smallest_start_time[0]][smallest_start_time[1]][

+                    0

+                ].isoformat(),

+                activities[smallest_start_time[0]][smallest_start_time[1]][

+                    1

+                ].isoformat(),

                 smallest_start_time[0],

             ]

         )

         del activities[smallest_start_time[0]][smallest_start_time[1]]

         if not activities[smallest_start_time[0]]:

             del activities[smallest_start_time[0]]

-    return(log)

-

-

-for vp_dir in [f.name for f in os.scandir() if f.is_dir()]:

-    print(vp_dir)

-    log = extract_activities(get_log_data(vp_dir))

-    log = generate_log(log)

+    return log

 

-    history = get_history_db(vp_dir)

-    log = match_urls(history, log)

 

-    path = Path(f"{vp_dir}/{vp_dir}.csv")

+def write_logfile(vp_dir, log):

+    path = Path(f"{vp_dir}/logs.csv")

     with open(path, "w") as csvfile:

         writer = csv.writer(csvfile, delimiter=",", quoting=csv.QUOTE_NONNUMERIC)

         writer.writerow(["Starttime", "Endtime", "Title", "URL"])

@@ -128,4 +125,16 @@ for vp_dir in [f.name for f in os.scandir() if f.is_dir()]:
             writer.writerow(row)

 

 

-input("*Press enter to close*")

+def main():

+    for vp_dir in [f.name for f in os.scandir() if f.is_dir()]:

+        print(vp_dir)

+        log = extract_activities(get_log_data(vp_dir))

+        log = generate_log(log)

+

+        history = get_history_db(vp_dir)

+        log = match_urls(history, log)

+        write_logfile(vp_dir, log)

+

+

+if __name__ == "__main__":

+    main()

diff --git a/bjoern/videoanalyse/post_processing.py b/bjoern/videoanalyse/post_processing.py
index e24edcf..bdf76c3 100644
--- a/bjoern/videoanalyse/post_processing.py
+++ b/bjoern/videoanalyse/post_processing.py
@@ -6,6 +6,7 @@ from pprint import pprint
 import pandas as pd
 
 import utils
+import LogParser
 
 argparser = argparse.ArgumentParser(description="OCR-Logfile evaluation")
 argparser.add_argument("vp_dir", help="Directory with all VPs")
@@ -17,12 +18,18 @@ all_vp = [x for x in data_path.iterdir() if x.is_dir()]
 
 vp_results = []
 for vp_path in all_vp:
+    log = LogParser.extract_activities(LogParser.get_log_data(vp_path))
+    log = LogParser.generate_log(log)
+    history = LogParser.get_history_db(vp_path)
+    log = LogParser.match_urls(history, log)
+    LogParser.write_logfile(vp_path, log)
+
     video_path = next(vp_path.glob("*.mkv"))
     ocr_path = vp_path / "analysis_results.csv"
-    log_path = vp_path / f"{vp_path.stem}.csv"
+    log_path = vp_path / "logs.csv"
 
     df = utils.combine_ocr_logs(video_path, ocr_path, log_path)
-    df = df.fillna('')
+    df = df.fillna("")
     df["vp_code"] = vp_path.stem
 
     df = utils.calc_levenshtein_distance(df)