# 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
```
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"description": "# MonsterLab\nby Robert Sharp\n\n## Monster Class\n### Optional Inputs\nIt is recommended to pass all the optional arguments or none of them. For example,\na custom type requires a custom name.\n- Name: Compound Gaussian Distribution -> String\n - Derived from Type\n - Multidimensional distribution of types and subtypes\n- Type: Wide Flat Distribution -> String\n - Demonic\n - Devilkin\n - Dragon\n - Undead\n - Elemental\n - Fey\n - Undead\n- Level: Poisson Distribution -> Integer\n - Range: [1..20]\n - Most Common: [4..7] ~64%\n - Mean: 6.001\n - Median: 6\n- Rarity: Linear Distribution [Rank 0..Rank 5] -> String\n - Rank 0: 30.5% Very Common\n - Rank 1: 25.0% Common\n - Rank 2: 19.4% Uncommon\n - Rank 3: 13.8% Rare\n - Rank 4: 8.3% Epic\n - Rank 5: 2.7% Legendary\n\n### Derived Fields\n- Damage: Compound Geometric Distribution with Linear Noise -> String\n - Derived from Level and Rarity\n- Health: Geometric Distribution with Gaussian Noise -> Float\n - Derived from Level and Rarity\n- Energy: Geometric Distribution with Gaussian Noise -> Float\n - Derived from Level and Rarity\n- Sanity: Geometric Distribution with Gaussian Noise -> Float\n - Derived from Level and Rarity\n- Time Stamp: The Monster's Birthday -> String\n\n### Example Monster\n- Name: Revenant\n- Type: Undead\n- Level: 3\n- Rarity: Rank 0\n- Damage: 3d2+1\n- Health: 6.35\n- Energy: 5.78\n- Sanity: 6.0\n- Time Stamp: 2021-08-09 14:23:23\n\n### Code Example\n```\n$ pip install MonsterLab\n$ python3\n>>> from MonsterLab import Monster\n>>> Monster()\nName: Imp\nType: Demonic\nLevel: 10\nRarity: Rank 0\nDamage: 10d2+1\nHealth: 20.89\nEnergy: 20.55\nSanity: 20.79\nTime Stamp: 2021-08-09 14:23:23\n```\n",
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