# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1744228670 14400
# Node ID c4bef099d0a233e0009e8f9f4a8b4d62e23b7ef3
# Parent  f4d4bb9ec34fab427722d9b12eb85f8943961f9e
adding script to extract users counts

diff -r f4d4bb9ec34f -r c4bef099d0a2 scripts/extract-user-counts.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scripts/extract-user-counts.py	Wed Apr 09 15:57:50 2025 -0400
@@ -0,0 +1,27 @@
+#! /usr/bin/env python
+
+#import numpy as np
+import argparse # os
+import pandas as pd
+import sqlite3
+
+from trafficintelligence import utils, moving
+
+parser = argparse.ArgumentParser(description='The program generates a csv of the user counts per SQLite database in the directory ')
+parser.add_argument('-i', dest = 'dirname', help = 'name of the directory containing all the databases', default = '.')
+args = parser.parse_args()
+
+out = open('user-counts.csv', 'w')
+out.write('filename')
+for i in range(1,7):
+    out.write(','+moving.userTypeNames[i])
+out.write('\n')
+for fn in utils.listfiles(args.dirname, 'sqlite'):
+    with sqlite3.connect(fn) as connection:
+        data = pd.read_sql('SELECT road_user_type, count(*) AS n FROM objects GROUP BY road_user_type', connection)
+        counts = {i:'0' for i in range(1,7)}
+        for i,r in data.iterrows():
+            counts[r['road_user_type']]=str(r['n'])
+        fn2 = fn.split('/')[-1].rsplit('.',1)[0]
+        out.write(fn2+','+','.join(counts.values())+'\n')
+out.close()