Progress.

plot.py

@@ -1,21 +1,66 @@
 import sqlite3
+import time
+import matplotlib.pyplot as plt
+import pathlib
+from xmlrpc.client import ServerProxy
+
+api = ServerProxy("https://api.molodetz.nl/rpc")
 
 connection = sqlite3.connect('tikker.db')
 
-def render_per_hour():
+
+weekday_sql = (
+    "CASE "
+    "WHEN strftime('%w', timestamp) = '0' THEN 'Sunday' "
+    "WHEN strftime('%w', timestamp) = '1' THEN 'Monday' "
+    "WHEN strftime('%w', timestamp) = '2' THEN 'Tuesday' "
+    "WHEN strftime('%w', timestamp) = '3' THEN 'Wednesday' "
+    "WHEN strftime('%w', timestamp) = '4' THEN 'Thursday' "
+    "WHEN strftime('%w', timestamp) = '5' THEN 'Friday' "
+    "WHEN strftime('%w', timestamp) = '6' THEN 'Saturday' "
+    "END"
+)
+
+def query(sql):
+    start = time.time()
+
     cursor = connection.cursor()
+    print(sql)
+    result = cursor.execute(sql).fetchall()
 
-    cursor.execute("SELECT count(0) as total, strftime('%H',timestamp) as hour  FROM kevent WHERE event = 'PRESSED' GROUP BY hour ORDEr BY hour")
+    cursor.close()
-    rows_presses = cursor.fetchall()
+    duration = time.time() - start
 
-    cursor.execute("SELECT count(0) as total, strftime('%H',timestamp) as hour  FROM kevent WHERE event = 'REPEATED' GROUP BY hour ORDEr BY hour")
+    print("Duration: {}\n".format(duration))
-    rows_repeated = cursor.fetchall()
+    return result
 
+def render_per_hour(week):
+    week = f"'{week}'"
 
-    cursor.execute("SELECT count(0) as total, strftime('%H',timestamp) as hour  FROM kevent WHERE event = 'RELEASED' GROUP BY hour ORDEr BY hour")
+    sql_presses = (
-    rows_released = cursor.fetchall()
+        "SELECT count(0) as total, strftime('%H', timestamp) as hour, "
+        "strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'PRESSED' AND week = {week} "
+        "GROUP BY week, hour ORDER BY hour"
+    )
+    
+    sql_repeated = (
+        "SELECT count(0) as total, strftime('%H', timestamp) as hour, "
+        "strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'REPEATED' AND week = {week} "
+        "GROUP BY week, hour ORDER BY hour"
+    )
 
-    import matplotlib.pyplot as plt
+    sql_released = (
+        "SELECT count(0) as total, strftime('%H', timestamp) as hour, "
+        "strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'RELEASED' AND week = {week} "
+        "GROUP BY week, hour ORDER BY hour"
+    )
+
+    rows_presses = query(sql_presses.format(week=week))
+    rows_repeated = query(sql_repeated.format(week=week))
+    rows_released = query(sql_released.format(week=week))
 
     totals = [row[0] for row in rows_presses]
     hours = [row[1] for row in rows_presses]
@@ -27,66 +72,139 @@ def render_per_hour():
     hours_released = [row[1] for row in rows_released]
 
     plt.figure(figsize=(8, 6))
-
     plt.plot(hours_repeated, totals_repeated, marker='o', label='Repeats per hour', color='green')
     plt.plot(hours_released, totals_released, marker='o', label='Releases per hour', color='orange')
+    plt.plot(hours, totals, marker='o', label=f'Presses per hour week {week}', color='red')
 
-    plt.plot(hours, totals, marker='o', label='Presses per hour', color='red')
     plt.xlabel('Hour')
     plt.ylabel('Event count')
     plt.title('Keyboard events')
     plt.legend()
-
     plt.grid(True)
-    plt.show()
 
-def render_per_weekday():
+    plt.savefig(f"graph_week_{week.strip("'")}_per_hour.png")
-    cursor = connection.cursor()
 
-    weekday_sql = """ CASE 
+def render_per_weekday(week):
-        WHEN strftime("%w",timestamp) = 0 THEN 'Sunday'
+    week = f"'{week}'"
-        WHEN strftime("%w",timestamp) = 1 THEN 'Monday'
-        WHEN strftime("%w",timestamp) = 2 THEN 'Tuesday'
-        WHEN strftime("%w",timestamp) = 3 THEN 'Wednesday'
-        WHEN strftime("%w",timestamp) = 4 THEN 'Thursday'
-        WHEN strftime("%w",timestamp) = 5 THEN 'Friday'
-        WHEN strftime("%w",timestamp) = 6 THEN 'Saturday'
-    END """
 
-    cursor.execute(f"SELECT count(0) as total, {weekday_sql} as weekday, strftime('%w',timestamp) as hour   FROM kevent WHERE event = 'PRESSED' GROUP BY hour ORDEr BY hour")
+    
-    rows_presses = cursor.fetchall()
 
-    cursor.execute(f"SELECT count(0) as total, {weekday_sql} as weekday, strftime('%w',timestamp) as hour  FROM kevent WHERE event = 'REPEATED' GROUP BY hour ORDEr BY hour")
+    sql_presses = (
-    rows_repeated = cursor.fetchall()
+        f"SELECT count(0) as total, {weekday_sql} as weekday, "
+        "strftime('%w', timestamp) as day, strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'PRESSED' AND week = {week} "
+        "GROUP BY week, day ORDER BY day"
+    )
 
+    sql_repeated = (
+        f"SELECT count(0) as total, {weekday_sql} as weekday, "
+        "strftime('%w', timestamp) as day, strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'REPEATED' AND week = {week} "
+        "GROUP BY week, day ORDER BY day"
+    )
 
-    cursor.execute(f"SELECT count(0) as total, {weekday_sql} as weekday, strftime('%w',timestamp) as hour  FROM kevent WHERE event = 'RELEASED' GROUP BY hour ORDEr BY hour")
+    sql_released = (
-    rows_released = cursor.fetchall()
+        f"SELECT count(0) as total, {weekday_sql} as weekday, "
+        "strftime('%w', timestamp) as day, strftime('%U', timestamp) as week "
+        "FROM kevent WHERE event = 'RELEASED' AND week = {week} "
+        "GROUP BY week, day ORDER BY day"
+    )
 
-    import matplotlib.pyplot as plt
+    rows_presses = query(sql_presses.format(week=week))
+    rows_repeated = query(sql_repeated.format(week=week))
+    rows_released = query(sql_released.format(week=week))
 
     totals = [row[0] for row in rows_presses]
-    hours = [row[2] for row in rows_presses]
+    days = [row[2] for row in rows_presses]
 
     totals_repeated = [row[0] for row in rows_repeated]
-    hours_repeated = [row[2] for row in rows_repeated]
+    days_repeated = [row[2] for row in rows_repeated]
 
     totals_released = [row[0] for row in rows_released]
-    hours_released = [row[2] for row in rows_released]
+    days_released = [row[2] for row in rows_released]
 
     plt.figure(figsize=(8, 6))
+    plt.plot(days_repeated, totals_repeated, marker='o', label='Repeats per weekday', color='green')
+    plt.plot(days_released, totals_released, marker='o', label='Releases per weekday', color='orange')
+    plt.plot(days, totals, marker='o', label=f'Presses per weekday week {week}', color='red')
 
-    plt.plot(hours_repeated, totals_repeated, marker='o', label='Repeats per weekday', color='green')
-    plt.plot(hours_released, totals_released, marker='o', label='Releases per weekday', color='orange')
-
-    plt.plot(hours, totals, marker='o', label='Presses per weekday', color='red')
     plt.xlabel('Day of week (0 = Sunday, 6 = Saturday)')
     plt.ylabel('Event count')
     plt.title('Keyboard events')
     plt.legend()
-
     plt.grid(True)
-    plt.show()
 
-render_per_hour()
+    plt.savefig(f"graph_week_{week.strip("'")}_per_weekday.png")
-render_per_weekday()
+
+def get_weeks():
+    sql = "SELECT strftime('%U', timestamp) as week FROM kevent GROUP BY week"
+    weeks = query(sql)
+    return [record[0] for record in weeks]
+
+def get_score_per_week():
+    sql = (
+        "SELECT strftime('%U', timestamp) as week, event, COUNT(0) as total "
+        "FROM kevent GROUP BY event, week"
+    )
+    return query(sql)
+
+def get_score_per_day():
+    
+    sql ="SELECT count(0) as total, CASE WHEN strftime('%w', timestamp) = 0 THEN 'Sunday' WHEN strftime('%w', timestamp) = 1 THEN 'Monday' WHEN strftime('%w', timestamp) = 2 THEN 'Tuesday' WHEN strftime('%w', timestamp) = 3 THEN 'Wednesday' WHEN strftime('%w', timestamp) = 4 THEN 'Thursday' WHEN strftime('%w', timestamp) = 5 THEN 'Friday' WHEN strftime('%w', timestamp) = 6 THEN 'Saturday' END as weekday, strftime('%w', timestamp) as day, strftime('%U', timestamp) as week FROM kevent WHERE event = 'REPEATED' AND week = '50' GROUP BY week, day ORDER BY day"
+
+    sql = (
+        f"SELECT strftime('%U',timestamp) as week, {weekday_sql} as wday, event, COUNT(0) as total "
+        f"FROM kevent WHERE event in ('PRESSED') GROUP BY week, event, wday  ORDER BY week, event, wday"
+    )
+    return query(sql)
+
+def get_totals():
+    sql = "SELECT count(0) as total, event from kevent group by event"
+    return query(sql)
+
+# Main execution
+if __name__ == "__main__":
+    time_start = time.time()
+
+    for week in get_weeks():
+        render_per_hour(week)
+        render_per_weekday(week)
+
+    print("Score per week:")
+    for record in get_score_per_week():
+        print(f"{record[0]}  \t{record[1]}  \t{record[2]}")
+
+    print("Score per day:")
+    for record in get_score_per_day():
+        print(f"{record[0]}  \t{record[1]}  \t{record[2]}")
+    
+    print("Total events:")
+    totals = 0
+    for record in get_totals():
+        print(f"{record[1]}: {record[0]}")
+        totals += record[0]
+    print(totals)
+
+    result = {}
+    rows = query("SElECT strftime('%Y-%m-%d', timestamp) as day, GROUP_CONCAT(char,'') FROM kevent WHERE event = 'PRESSED' group by day")
+    for row in rows:
+        result[row[0]] = row[1]
+    
+    with open("keylog.txt","w") as f:
+        for day in result.keys():
+            with open("logs_plain/"+day+".txt","w") as g:
+                g.write(f"**{day}**: ```{result[day]}```\n\n")
+            f.write(f"**{day}**: ```{result[day]}```\n\n")
+    for file in pathlib.Path(".").glob("logs_plain/*.txt"):
+        print("Working on: {}".format(file))
+        dest_file = file.parent.parent.joinpath("logs_summaries").joinpath(file.name)
+        print("Dest file: ", dest_file)
+        with dest_file.open("w+") as f:
+            print("Requesting...")
+            response = api.gpt4o("The following data is from my keylogger, make a summary of what i did: ```"+file.read_text().replace("@","").replace("`","")+"```")
+            print("Done")
+            f.write(response)
+            print(response)
+
+
+    print("Duration: {}".format(time.time() - time_start))

review.md

@@ -0,0 +1,36 @@
+```markdown
+# Summary of Project Reviews
+
+This project consists of various C and Python files primarily focused on handling keyboard input, visualizing data, and managing SDL graphics rendering. Below are primary insights from the code reviews and breakdowns:
+
+## Code Files
+
+### C Files
+- **keyc3.c**: Demonstrates handling input events using XIM and XIC for X11 applications. It performs resource cleanup but lacks keyboard layout validation and error handling.
+- **keyc.c**: Handles key symbols and input using `XkbKeycodeToKeysym` but has casting issues with `XLookupString` leading to potential undefined behavior.
+- **keyc2.c**: Converts keycodes to characters using X11 but doesn’t fully initialize event fields, and hardcodes values may limit functionality.
+- **graph2.c**: Utilizes SDL for creating an animated graph. Manages resources well but assumes SDL setup. Code organization and scaling could be optimized.
+- **graph.c**: A simple SDL bar graph application that could benefit from dynamic scaling and better error handling.
+
+### Python Files
+- **plot.py**: Analyzes keystrokes from an SQLite database and generates plots. Implements clear workflows but has security risks due to non-parameterized SQL queries.
+- **zipit.py**: Compresses text files using base64 and zlib; effective but lacks error handling for file operations.
+
+### Other Files
+- **tikker.c**: Monitors keyboard events on Linux, logging to a database. Needs better error handling and could benefit from using other event handling utilities for optimizations.
+
+## Common Issues Across Files
+- **Error Handling**: Many files lack sufficient error handling, particularly in database operations and system calls.
+- **Security**: Potential vulnerabilities in SQL handling due to non-parameterized queries.
+- **Code Optimization**: Common opportunities include optimizing loops, using more efficient data structures, and improving modularization.
+- **Platform Assumptions**: Assumptions in SDL and X11 setups can lead to undefined behaviors if not met.
+
+## General Recommendations
+- **Security**: Use parameterized queries to prevent SQL injection vulnerabilities.
+- **Modularization**: Break down larger functions into smaller ones for better readability and maintenance.
+- **Error Handling**: Implement robust error checks, especially for system calls and IO operations.
+- **Performance**: Optimize event handling to use latest utilities and data structures where applicable.
+
+## Grade: 6.5
+This is the average rating based on individual file reviews, reflecting both strengths in resource management and areas needing improvement in error handling and security practices.
+```