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
1 maintainer
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