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(J 1.0)
here's all the calls to 801008C0 when creating a save file,
which in turn calls memcpy at 80092260:
to: 801EF484; from: 801C1510; size: 00000028
to: 801EF4AC; from: 801C1538; size: 00000022
to: 801EF4D0; from: 801C155C; size: 00000088
to: 801F07EE; from: 801C15E4; size: 00000002 # checksum
to: 80506D30; from: 801EF460; size: 00003F50 # copy to buffer to write to SRAM

same thing but with save context offsets and decompressed rom addresses:

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#!/usr/bin/env python3
# find duplicate images given a hamming distance threshold.
# employs dhash to do the heavy lifting.
# doesn't recurse into "./_duplicate/" so you can dump things there if you wish.
# dependencies: pillow, dhash

import sys, os, os.path, pickle
from PIL import Image
import dhash

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stuff i've plotted in desmos

in some order idk

CIELAB nonlinearities

the two nonlinearities involved in (a scalar version of) converting RGB to CIELAB.

f(x) converts RGB to linear sRGB. g(x) is used in converting linear sRGB to XYZ. the black line is the combination of the two, not quite linear.

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int validate(const char *key) {
    int magic = 3;
    int count = 0;
    for (char c; (c = *key); key++) {
        if (c < '0' || c > '9') continue;
        int v = c - '0';
        if (++count == 13) {
            // final character.
            return magic % 10 == v;
        } else {

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/* Clocks (v1)
 * Portable Snippets - https://github.com/nemequ/portable-snippets
 * Created by Evan Nemerson <[email protected]>
 *
 *   To the extent possible under law, the authors have waived all
 *   copyright and related or neighboring rights to this code.  For
 *   details, see the Creative Commons Zero 1.0 Universal license at
 *   https://creativecommons.org/publicdomain/zero/1.0/
 *
 * Modified by Connor Olding, 2017

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-- This is free and unencumbered software released into the public domain.
-- For more information, please refer to <http://unlicense.org/>

local charset = ("\
\x00☺☻♥♦♣♠•◘○◙♂♀♪♫☼\
►◄↕‼¶§▬↨↑↓→←∟↔▲▼\
 !\"#$%&'()*+,-./\
0123456789:;<=>?\
@ABCDEFGHIJKLMNO\
PQRSTUVWXYZ[\\]^_\

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# Example Huffman coding implementation
# Distributions are represented as dictionaries of { 'symbol': probability }
# Codes are dictionaries too: { 'symbol': 'codeword' }

def huffman(p):
    '''Return a Huffman code for an ensemble with distribution p.'''
    # Base case of only two symbols, assign 0 or 1 arbitrarily
    if len(p) == 2:
        return dict(zip(p.keys(), ['0', '1']))

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import autograd.numpy as np
import autograd.numpy.random as npr
from autograd.misc.flatten import flatten
from autograd.misc.optimizers import unflatten_optimizer
import itertools, types

def iterize(iterable):
    if type(iterable) in (tuple, list):
        iterator = iter(iterable)
    elif np.isscalar(iterable):

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#!/usr/bin/env bash
set -e

# set this as needed!
rom="Legend of Zelda, The - Ocarina of Time (U) (V1.0) [!].z64"

output="reconstructed.z64"

gcc_flags="-std=gnu11 -Wall -O3 -s"