1.0.4 • Published 24 days ago

lsp8.app.rarity-lib v1.0.4

Weekly downloads
-
License
MIT
Repository
github
Last release
24 days ago

🌟 Unlock NFT Traits with LSP8 app Rarity Lib

Craving insights from your Non-Fungible Tokens (NFTs)? Dive deep into the characteristic traits of your collection and bring the hidden gems to the surface with our LSP8 app Rarity Lib. This powerful tool calculates rarity scores, updates a database with these scores, and assigns unique ranks for each NFT.

Usage

import { AssetBase, IEquationOptions, calculateRanking } from 'lsp8.app.rarity-lib';

const assets: AssetBase[] = [
    {
        tokenId: '1',
        tokenName: 'Warrior 1',
        tokenAttributes: [
            { key: 'Weapon', value: 'Axe', type: 'STRING' },
            { key: 'Armor', value: 'Cloth', type: 'STRING' }
        ],
        tokenImage: 'https://image.com/1',
        assetName: 'Excalibur'
    },
    {
        tokenId: '2',
        tokenName: 'Warrior 2',
        tokenAttributes: [
            { key: 'Weapon', value: 'Sword', type: 'STRING' },
            { key: 'Armor', value: 'Plate', type: 'STRING' }
        ],
        tokenImage: 'https://image.com/2',
        assetName: 'Aegis'
    },
    {
        tokenId: '3',
        tokenName: 'Warrior 3',
        tokenAttributes: [
            { key: 'Weapon', value: 'Wand', type: 'STRING' },
            { key: 'Armor', value: 'Cloth', type: 'STRING' }
        ],
        tokenImage: 'https://image.com/2',
        assetName: 'Aegis'
    },
    {
        tokenId: '4',
        tokenName: 'Warrior 4',
        tokenAttributes: [
            { key: 'Profession', value: 'Wizard', type: 'STRING' },
            { key: 'Weapon', value: 'Wand', type: 'STRING' },
            { key: 'Armor', value: 'Cloth', type: 'STRING' }
        ],
        tokenImage: 'https://image.com/2',
        assetName: 'Aegis'
    }
];

const options: IEquationOptions = {
    useTraitCount: true,
    useNormalization: true,
    algorithm: 'lsp8Generic',
    nonUniqueRanking: true
};

const ranking = calculateRanking(assets, options);
console.log(JSON.stringify(ranking, null, 2));

will result into:

{
  "traitFrequencies": {
    "Weapon": {
      "Axe": 1,
      "Sword": 1,
      "Wand": 2
    },
    "Armor": {
      "Cloth": 3,
      "Plate": 1
    },
    "TraitCount": {
      "2": 3,
      "3": 1
    },
    "Profession": {
      "Wizard": 1
    }
  },
  "enrichedAssets": [
    {
      "tokenImage": "https://image.com/1",
      "tokenId": "1",
      "tokenName": "Warrior 1",
      "assetName": "Excalibur",
      "score": 6.666666666666666,
      "rank": 3,
      "meta": {},
      "tokenAttributes": [
        {
          "key": "Weapon",
          "value": "Axe",
          "percentage": 25
        },
        {
          "key": "Armor",
          "value": "Cloth",
          "percentage": 75
        },
        {
          "key": "TraitCount",
          "value": "2",
          "percentage": 75
        }
      ]
    },
    {
      "tokenImage": "https://image.com/2",
      "tokenId": "2",
      "tokenName": "Warrior 2",
      "assetName": "Aegis",
      "score": 9.333333333333334,
      "rank": 2,
      "meta": {},
      "tokenAttributes": [
        {
          "key": "Weapon",
          "value": "Sword",
          "percentage": 25
        },
        {
          "key": "Armor",
          "value": "Plate",
          "percentage": 25
        },
        {
          "key": "TraitCount",
          "value": "2",
          "percentage": 75
        }
      ]
    },
    {
      "tokenImage": "https://image.com/2",
      "tokenId": "3",
      "tokenName": "Warrior 3",
      "assetName": "Aegis",
      "score": 4.666666666666666,
      "rank": 4,
      "meta": {},
      "tokenAttributes": [
        {
          "key": "Weapon",
          "value": "Wand",
          "percentage": 50
        },
        {
          "key": "Armor",
          "value": "Cloth",
          "percentage": 75
        },
        {
          "key": "TraitCount",
          "value": "2",
          "percentage": 75
        }
      ]
    },
    {
      "tokenImage": "https://image.com/2",
      "tokenId": "4",
      "tokenName": "Warrior 4",
      "assetName": "Aegis",
      "score": 11.333333333333332,
      "rank": 1,
      "meta": {},
      "tokenAttributes": [
        {
          "key": "Profession",
          "value": "Wizard",
          "percentage": 25
        },
        {
          "key": "Weapon",
          "value": "Wand",
          "percentage": 50
        },
        {
          "key": "Armor",
          "value": "Cloth",
          "percentage": 75
        },
        {
          "key": "TraitCount",
          "value": "3",
          "percentage": 25
        }
      ]
    }
  ]
}

Exemplifying NFT Characteristics with "TraitCount"

Adding a poignant novel trait called "TraitCount" to your NFTs is made simple. It mimics the number of traits each NFT possesses.

Amplify Your Analysis with Trait Frequencies

Enhance your analysis by studying the frequency of each trait among the NFTs.

Trait frequency equation

Rise in Ranks with Rarity Scores

Rarity scores pave the way for ranking your NFTs! This detailed ranking can be either unique or non-unique depending upon your preference.

Unique Ranks: Every NFT occupies a unique position in the rank hierarchy based solely on its score.

Non-Unique Ranks: NFTs with same scores are assigned same ranks, thereby allowing potential ties.

Understand Trait Distribution with Trait Percentages

Get a better understanding of the diversity of your collection by examining the percentage representation of each trait.

Trait frequency equation

Meet the Universal Equation of LSP8 App!

The rarity score is the summation of the rarity of each trait of an NFT. The rarity of a trait reciprocates the trait's frequency.

Rarity generic equation

Discover a Spectrum of Support with OpenRarity

Harmonic Mean

Don't let big differences in scores leave you confused. Harmonic Mean simplifies the scenario by dividing the number of scores by the sum of reciprocals of scores.

OpenRarity harmonic mean equation

Geometric Mean

With Geometric Mean, the 'average' of scores is computed through the product of the numbers.

OpenRarity geometric mean equation

Weighted Average

Weighted Average uplifts scores of higher significance, hence adding precision to your analysis.

OpenRarity weighted average equation

Information Content

Information Content projects the rarity in terms of information, transforming its basis into a logarithmic scale.

OpenRarity harmonic mean equation

Sum of Scores

Simple is often most effective. The Sum of Scores method sums up all scores for an overall rating.

OpenRarity sum of scores equation

Normalization and Collection Entropy

Normalization allows scores to be compared by bringing them within a certain range. Collection Entropy measures the diversity or unpredictability of traits within the collection.

OpenRarity collection entropy

⚠️ A Word to the Wise

lsp8-app-rarity-lib aims to offer an open-source library for https://lsp8.app webapp that is easily accessible, user-friendly, and transparent. Feel free to leverage it for your calculations and rankings as well.

🤝 Come, Join Us!

Got suggestions for lsp8-app-rarity-lib? Found a bug? We would love to hear from you! Raise an issue or submit a pull request today.

📜 License

MIT

1.0.4

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1.0.1

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