Hey everyone,
I found a formula on the forums for optimizing stat training and asked ChatGPT to help me turn it into a working Python script. It figures out the most efficient way to train your stats based on your current values, energy, and happiness.
You'll need to customize the script with your own values, Just edit the parts under USER INPUT before running it. All the defaults are set to None so you don’t forget.
How to Use the Script Online:
- Go to https://www.programiz.com/python-programming/online-compiler
- Delete everything in the editor.
- Copy the full script below and paste it in.
- Fill in your happiness, energy, energy per train, stat gain per train, and your current stat values.
- Click "Run" to get your training plan.
Enjoy:
python
CopyEdit
# Stat Training Calculator for Torn
# === USER INPUT ===
happiness = None # e.g. 3750
energy = None # e.g. 950
energy_per_train = None # e.g. 5
stat_gain_per_train = None # e.g. 5.5
# Your current stats
stats = {
'Strength': None,
'Defense': None,
'Speed': None,
'Dexterity': None
}
# === CALCULATION ===
if None in [happiness, energy, energy_per_train, stat_gain_per_train] or any(v is None for v in stats.values()):
print("⚠️ Please fill in all your stat values and inputs before running the script.")
else:
total_trains = energy // energy_per_train
total_stat_gain = total_trains * stat_gain_per_train
# Step 1: Equalize all stats to the highest of the current lowest
sorted_stats = sorted(stats.items(), key=lambda x: x[1])
lowest_stat = sorted_stats[0][0]
# Step 2: Calculate how many points needed to match the next lowest
sorted_stat_values = [v for _, v in sorted_stats]
target_value = sorted_stat_values[1] # second lowest
needed_gain = target_value - stats[lowest_stat]
trains_to_equalize = int(needed_gain // stat_gain_per_train)
energy_used = trains_to_equalize * energy_per_train
# Update lowest stat
stats[lowest_stat] += trains_to_equalize * stat_gain_per_train
remaining_trains = total_trains - trains_to_equalize
# Step 3: Use remaining trains on the stat with the new lowest value
new_lowest_stat = min(stats, key=stats.get)
stats[new_lowest_stat] += remaining_trains * stat_gain_per_train
# === OUTPUT ===
print("n=== Stat Training Plan ===")
print(f"Total Trains Available: {total_trains}")
print(f"Stat Gain Per Train: {stat_gain_per_train}")
print(f"Initial Equalization used {trains_to_equalize} trains on {lowest_stat}")
print(f"Remaining trains spent on {new_lowest_stat}n")
print("Estimated Stats After Training:")
for stat, value in stats.items():
print(f"{stat}: {int(value)}")
print(f"nEstimated Total Stats: {int(sum(stats.values()))}")
Let me know if you want an advanced version (with gym types, happy multipliers, diminishing returns, etc.), it’s not very difficult for me to ask, takes 2 seconds.