MonsterLab 1.2.1

Monster Generator

MonsterLab

by Robert Sharp

Monster Class

Optional Inputs

It is recommended to pass all the optional arguments or none of them. For example, a custom type requires a custom name.

• Name: Compound Gaussian Distribution -> String
  • Derived from Type
  • Multidimensional distribution of types and subtypes
• Type: Wide Flat Distribution -> String
  • Demonic
  • Devilkin
  • Dragon
  • Undead
  • Elemental
  • Fey
  • Undead
• Level: Poisson Distribution -> Integer
  • Range: [1..20]
  • Most Common: [4..7] ~64%
  • Mean: 6.001
  • Median: 6
• Rarity: Linear Distribution [Rank 0..Rank 5] -> String
  • Rank 0: 30.5% Very Common
  • Rank 1: 25.0% Common
  • Rank 2: 19.4% Uncommon
  • Rank 3: 13.8% Rare
  • Rank 4: 8.3% Epic
  • Rank 5: 2.7% Legendary

Derived Fields

• Damage: Compound Geometric Distribution with Linear Noise -> String
  • Derived from Level and Rarity
• Health: Geometric Distribution with Gaussian Noise -> Float
  • Derived from Level and Rarity
• Energy: Geometric Distribution with Gaussian Noise -> Float
  • Derived from Level and Rarity
• Sanity: Geometric Distribution with Gaussian Noise -> Float
  • Derived from Level and Rarity
• Time Stamp: The Monster's Birthday -> String

Example Monster

• Name: Revenant
• Type: Undead
• Level: 3
• Rarity: Rank 0
• Damage: 3d2+1
• Health: 6.35
• Energy: 5.78
• Sanity: 6.0
• Time Stamp: 2021-08-09 14:23:23

Code Example

$ pip install MonsterLab
$ python3
>>> from MonsterLab import Monster
>>> Monster()
Name: Imp
Type: Demonic
Level: 10
Rarity: Rank 0
Damage: 10d2+1
Health: 20.89
Energy: 20.55
Sanity: 20.79
Time Stamp: 2021-08-09 14:23:23