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author | Niclas Dobbertin <niclas.dobbertin@stud.tu-darmstadt.de> | 2023-08-02 14:13:45 +0200 |
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committer | Niclas Dobbertin <niclas.dobbertin@stud.tu-darmstadt.de> | 2023-08-02 14:13:45 +0200 |
commit | 4d9c0dce1f5bc0bd3cde1b89875387f1a13c18c4 (patch) | |
tree | 661fe5eb571ae7dc89ea52c546f6aa5df0cc3595 /bjoern/videoanalyse/post_processing.py | |
parent | 3fdab9495310e8d29cab65d1ee0c1bfa0b26a76e (diff) |
add additional scripts
Diffstat (limited to 'bjoern/videoanalyse/post_processing.py')
-rw-r--r-- | bjoern/videoanalyse/post_processing.py | 108 |
1 files changed, 108 insertions, 0 deletions
diff --git a/bjoern/videoanalyse/post_processing.py b/bjoern/videoanalyse/post_processing.py new file mode 100644 index 0000000..d32457e --- /dev/null +++ b/bjoern/videoanalyse/post_processing.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python3 + +import argparse +from pathlib import Path +import pandas as pd +import Levenshtein +import csv +from itertools import pairwise + +argparser = argparse.ArgumentParser( + description="Distance evaluation" +) +argparser.add_argument( + "vp_dir", help="Directory containing metrics.csv" +) + +args = argparser.parse_args() + +data_path = Path(args.vp_dir ) + + +# def insertion_cost(char): +# return 1.0 + + +# def deletion_cost(char): +# return 1.0 + + +# def substitution_cost(char_a, char_b): +# if char_a == "t" and char_b == "r": +# return 0.5 +# return 1.0 + + +# weighted_levenshtein = WeightedLevenshtein( +# substitution_cost_fn=substitution_cost, +# insertion_cost_fn=insertion_cost, +# deletion_cost_fn=deletion_cost, +# ) + +# Distance threshold to define "same" url +dist_threshold = 5 + + +# Function to return all elements in candidates that are similar to original +def take_similar(original, candidates): + print(original) + print(candidates) + result = [ + x + for x in candidates + if dist_threshold >= Levenshtein.distance(original, x) + ] + return result + + +# Read results.csv +# with open(data_path / "metrics.csv", "r") as csvfile: +# reader = csv.reader(csvfile, quotechar='"') +# print(next(reader)) +# +df = pd.read_csv(data_path / "metrics.csv") +df = df.fillna('') + + +# List with only urls +all_urls = list(df["url"].values) +urls = list(df["url"].values) + +# urls = [[0, "Start"]] +# for url in all_urls: +# if len(url[1]) > 0: +# urls.append([float(url[0]), url[1]]) + + +# Iterate over list of all urls, putting similar one into a group and removing them from +# the original list +url_groups = [] +while len(all_urls) > 0: + group = take_similar(all_urls[0], all_urls) + url_groups.append([set(group), 0]) + for url in group: + all_urls.remove(url) + +# Iterate over result-elements pairwise, removing elements under distance threshold +# and always cumulating time of url-groups +new_urls = [] +cum_times = [] +for pair in pairwise(urls): + print(pair) + dist = Levenshtein.distance(pair[0], pair[1]) + if dist > dist_threshold: + new_urls.append(pair[1]) + + +with open(data_path / "grouping_post.cvs", "w") as csvfile: + writer = csv.writer(csvfile, quotechar='"', quoting=csv.QUOTE_NONNUMERIC) + writer.writerow(["url"]) + for line in new_urls: + writer.writerow(line) + +with open(data_path / "all_urls.txt", "w") as f: + for group in url_groups: + f.write("=== new group, cumulative_time: {}\n".format(group[1])) + for url in group[0]: + f.write(url) + f.write("\n") |