woctezuma

import json
from pathlib import Path

import requests

BASE_URL = (
    "https://library-service.live.use1a.on.epicgames.com/library/api/public/items"
)
STATIC_PARAMS = "includeMetadata=true"
OUTPUT_FNAME = "library-items.json"

woctezuma

Web Browsers

• [Firefox][firefox]✨
  • about:config -> browser.cache.disk.smart_size.enabled : false
  • [uBlock Origin][firefox-adblock]✨
  • [SteamDB][firefox-steamdb]✨
  • [Augmented Steam][firefox-augmented-steam]✨
  • [Translate Web Pages][firefox-translate]
  • [Firefox Multi-Account Containers][firefox-containers]
  • [PopUpOFF][firefox-popup-off]
• [TunnelBear VPN][firefox-tunnelbear]

woctezuma

files: \.(py|json)$
default_stages: [commit, manual]
repos:
  - repo: https://github.com/astral-sh/ruff-pre-commit
    rev: v0.3.4
    hooks:
      - id: ruff
        args:
          [
            --fix,

woctezuma

Required softwares

• GIMP
• ImageMagick
• Microsoft Edge
• Ghostscript

How to binarize the JPG file with GIMP

1. Open the JPG file with GIMP.

woctezuma

Get pretrained weights:

wget https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth

Remove class weights

checkpoint = torch.load("detr-r50-e632da11.pth", map_location='cpu')
del checkpoint["model"]["class_embed.weight"]
del checkpoint["model"]["class_embed.bias"]
torch.save(checkpoint,"detr-r50_no-class-head.pth")

woctezuma

import json
from pathlib import Path

BIO_FIELD = ["bio"]
PRONOUNS_FIELD = ["pronouns"]

fname = "profiles.json"
with Path(fname).open() as fn:
    d = json.load(fn)

woctezuma

import json
from pathlib import Path

import requests

BASE_URL = "https://egs-platform-service.store.epicgames.com/api/v1/egs/publisher-index"
OUTPUT_FNAME = "publisher-index.json"

LOCALE = "fr"
STORE_ID = "EGS"

woctezuma

[
  {
    "contact": null,
    "offers": [
      {
        "id": "3ceadb8101fb4950a9de85b31ed12f29",
        "namespace": "188fb42582e24d20b0a876ed817efd69",
        "title": "SOULVARS"
      }
    ],

woctezuma

import json
from pathlib import Path

# https://store.steampowered.com/dynamicstore/userdata/
fname = "store.steampowered.com.json"

with Path(fname).open() as f:
    data = json.load(f)

owned_apps = data["rgOwnedApps"]

woctezuma

This post contains a ranking of Steam games, based on a score intended to favor "hidden gems". A "hidden gem" is defined as a high-quality game (hence the "gem") which only got little attention (hence "hidden"). Therefore, the score of a game is defined as the product of a quality measure (its Wilson score) and a decreasing function of a popularity measure (its players total forever). The quality measure comes from SteamDB and the popularity measure comes from SteamSpy API. Finally, here is a reference to the NeoGAF post explaining the method, and the NeoGAF post explaining the idea behind the optimization of the only free parameter. The Python source code can be found on Github.

Reproducibility

To reproduce the results, use data downloaded betwe