Skip to main content

Python bindings for the nod library.

Project description

py-nod

Python 3.8 bindings for the NOD, a library for traversing, dumping, and authoring GameCube and Wii optical disc images.

Usage

Unpacking

import nod

def progress_callback(path, progress):
    if args.verbose:
        print("Extraction {:.0%} Complete; Current node: {}".format(progress, path))

context = nod.ExtractionContext()
context.set_progress_callback(progress_callback)

try:
    disc, is_wii = nod.open_disc_from_image("game.iso")
    data_partition = disc.get_data_partition()
    if not data_partition:
        raise RuntimeError("Could not find a data partition in the disc.")
    data_partition.extract_to_directory("dir_out", context)
except RuntimeError as e:
    raise Exception("Could not extract disc at 'game.iso' to 'dir_out': {}".format(e))

Packing

import nod

if nod.DiscBuilderGCN.calculate_total_size_required("dir_out") is None:
    raise Exception("Image built with given directory would pass the maximum size.")

def fprogress_callback(progress: float, name: str, bytes: int):
    print("\r" + " " * 100, end="")
    print("\r{:.0%} {} {} B".format(progress, name, bytes), flush=True)

disc_builder = nod.DiscBuilderGCN("game.iso", fprogress_callback)
try:
    disc_builder.build_from_directory("dir_out")    
except RuntimeError as e:
    raise Exception("Failure building the image: {}".format(e))

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

nod-1.9.4.tar.gz (1.0 MB view details)

Uploaded Source

Built Distributions

nod-1.9.4-cp313-cp313-win_amd64.whl (11.5 MB view details)

Uploaded CPython 3.13Windows x86-64

nod-1.9.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.13manylinux: glibc 2.17+ x86-64

nod-1.9.4-cp313-cp313-macosx_11_0_arm64.whl (479.6 kB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

nod-1.9.4-cp313-cp313-macosx_10_13_x86_64.whl (507.5 kB view details)

Uploaded CPython 3.13macOS 10.13+ x86-64

nod-1.9.4-cp312-cp312-win_amd64.whl (11.5 MB view details)

Uploaded CPython 3.12Windows x86-64

nod-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.12manylinux: glibc 2.17+ x86-64

nod-1.9.4-cp312-cp312-macosx_11_0_arm64.whl (480.5 kB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

nod-1.9.4-cp312-cp312-macosx_10_13_x86_64.whl (508.6 kB view details)

Uploaded CPython 3.12macOS 10.13+ x86-64

nod-1.9.4-cp311-cp311-win_amd64.whl (11.5 MB view details)

Uploaded CPython 3.11Windows x86-64

nod-1.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.11manylinux: glibc 2.17+ x86-64

nod-1.9.4-cp311-cp311-macosx_11_0_arm64.whl (480.4 kB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

nod-1.9.4-cp311-cp311-macosx_10_9_x86_64.whl (507.9 kB view details)

Uploaded CPython 3.11macOS 10.9+ x86-64

nod-1.9.4-cp310-cp310-win_amd64.whl (11.5 MB view details)

Uploaded CPython 3.10Windows x86-64

nod-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.10manylinux: glibc 2.17+ x86-64

nod-1.9.4-cp310-cp310-macosx_11_0_arm64.whl (480.0 kB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

nod-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl (507.3 kB view details)

Uploaded CPython 3.10macOS 10.9+ x86-64

nod-1.9.4-cp39-cp39-win_amd64.whl (11.5 MB view details)

Uploaded CPython 3.9Windows x86-64

nod-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

nod-1.9.4-cp39-cp39-macosx_11_0_arm64.whl (480.7 kB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

nod-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl (507.9 kB view details)

Uploaded CPython 3.9macOS 10.9+ x86-64

File details

Details for the file nod-1.9.4.tar.gz.

File metadata

  • Download URL: nod-1.9.4.tar.gz
  • Upload date:
  • Size: 1.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4.tar.gz
Algorithm Hash digest
SHA256 be223c8d99427de0337bf889434d636625e4d1bd833e1466e7cdfcfee5054a88
MD5 3ad2b49f86242e1afc3b8666e46ed45d
BLAKE2b-256 2d20bb121229c7dac68b117c22e09146ef49fe4ce271982bc660e5006bd216f7

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: nod-1.9.4-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 11.5 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 aa380754d8c3331df2e7510a3bc2a4087f10fc3010190c5cdfb602c88474343b
MD5 3c9a2f5e2ffaba8979a34ffaaa56d62a
BLAKE2b-256 d10cec1fe34a9c8b91a8ed5e4f32d9bdb64ec82dc33517d6b3785b7e9b284f74

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 a0ded7d729a4aa9ecbce46a24ec909e1d9d443d3b759d5733b70ee1aa51bc6bd
MD5 4c5ab396c3746ce431c80c0c00b54e1a
BLAKE2b-256 c9be5fa2fc394639b237024f22c4b52fa799453a25890f40d89bae1282207313

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

  • Download URL: nod-1.9.4-cp313-cp313-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 479.6 kB
  • Tags: CPython 3.13, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 76429939c0ac16d8c58133de386ae3baadb22a53922fb19c5ed9d30895b13d42
MD5 4dee873fb8ff8eadce4c9b98ebeb353e
BLAKE2b-256 7f079c542011d339f30de8c7bc733bc42cf668ebbb8b513843b38302349bc27b

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp313-cp313-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp313-cp313-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 cd6cb29e825c543b0bf086e05081dd6626ccd8e6b9f690bd1d0e35f697b75431
MD5 fc70c6c511968d6579730d5b0bea81df
BLAKE2b-256 c656eca39dc73264258b64bab75afb4d88392b420723ea81f67b71c78a686d66

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: nod-1.9.4-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 11.5 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 69bf617d23e4188ccf7878e54a6493450e12211997cfd7f84629dc385e173a8f
MD5 fe1cb603bd20c9c24def12b88c034ac7
BLAKE2b-256 93b3c4867ca5b96d8c55d5ec64414d0b379a0b9b355e33bd619f8f3c546d1750

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 e400f23840eff95835fda78998ddc722665bdbf6c7e188a98a06b65cae9fa859
MD5 dc60f3ab151cb6437a10da883474721c
BLAKE2b-256 6ce13f150d8ba5a25d744b3c79e7062a34f3396ad33b99f5f79d34271a9780fa

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

  • Download URL: nod-1.9.4-cp312-cp312-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 480.5 kB
  • Tags: CPython 3.12, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 93b965bfacc9bdecab0749fd8b3ec57cf7a169e4643335fe7f638f9fe096f4bb
MD5 fc48cbc4af2bb1158ed4397c5e26ebe9
BLAKE2b-256 ff0ea53b528962d431c120b121e7c83241ebb7edfd4a4e8fe1203cb1892fedf4

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp312-cp312-macosx_10_13_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp312-cp312-macosx_10_13_x86_64.whl
Algorithm Hash digest
SHA256 f502b2fca7a93bfac4e13d7d3ddefc42043b86ca91d99e9cbc98e23b25eff228
MD5 557a24f39b9949fc405ebadc3d4d30c8
BLAKE2b-256 3044efa3ea4db7ba4f64ab1ca727f4a6519e20460bdba222fc1b91b44b735902

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: nod-1.9.4-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 11.5 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 02ab3b9d5e24a3edaceaed86db499b5677b8c7f797a1849649f319e397a743b6
MD5 f5f4f53a24baef8069861095de5e7933
BLAKE2b-256 a1293d3a307b76803624d78b9fe05396200717402ff2e46bbc5a84c1d0c58847

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 9205df8b612bf536c826dbd327e0736a9eab5ad8e4fb35802aadf9f7912caee7
MD5 381fb47d872df8d59f456d84ac166bb6
BLAKE2b-256 62c41dffcf2a7e37df655bf5f7b56b50f637a83e6a719c66abd6f10c57b1c483

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

  • Download URL: nod-1.9.4-cp311-cp311-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 480.4 kB
  • Tags: CPython 3.11, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 447274a60d31ea748e4831ace72902c746d3772b431581c7d876ad1c7ef0e6e6
MD5 1bc2c103157558ab6b9d9a3ba73ee5f7
BLAKE2b-256 c0065b89be1fc912cf9d82288dd5a4db0e7989b5c0eab48e7b96a8490c19ea5f

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp311-cp311-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 8bdc94260469c8a5f6176a2aff037abd09cfd76a8664c412d031d8b3cbc5b113
MD5 bd96173d27d7aada728b1d452d15009d
BLAKE2b-256 7d12549e07225613dc552dae51aba780e71a952e72837b044a14b4869b086ab3

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: nod-1.9.4-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 11.5 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 0fd9d546265c38eab76233f450504fad630628e0d3b0d843e80d943f33ba1ace
MD5 91e431c7733a32a3115e211c8cf64d2b
BLAKE2b-256 20d7c713e24bcfabe28948113dd8f7fb2b7951bf9d64ac26982cf0e2337ff5f3

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 91f29b8faa21ef7a5660c0ced910f0c7d4badede1fc35a04fc251d2315e5a0ed
MD5 f4ce4a2be2c85e6917d5fe06ae5624e9
BLAKE2b-256 13d064ee824c015ca134fd9870433f00aedb94ec7bcf711f616002b58390d72b

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

  • Download URL: nod-1.9.4-cp310-cp310-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 480.0 kB
  • Tags: CPython 3.10, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 8e4e8ebb14bc98b745afb9122d4e8e7f3398a02b0f81a31b531dae4c1dbc055d
MD5 b6cc61d38176440aba4b6ee7a3535acc
BLAKE2b-256 804c595c81c3f6c0a94d79ca75268ca21fc0b336825a2c99439f3413353ee802

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 b5d55d0b96301b5cfbb00e4c8bc3e69c05332f6c1e4cd0d48e5366f1e7962972
MD5 157759c29604cf98b124226f735cabaf
BLAKE2b-256 01902f77ba0841c58edc5d4cdac427ca37da464ee74afbe2e42e9bcfac388833

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: nod-1.9.4-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 11.5 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 6d833386cb467dd2915f1a2728121e7d643dbd3d58b7c575ca4d542cc0bc4583
MD5 7e8d9d015fa6d59045fb07c7088712f1
BLAKE2b-256 36dcd6837e871c6c590486a05cd65725f6ab6b6e5363bd2e68ef7f597419c3b0

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for nod-1.9.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 fb394ebe309d181afc3134e17a53f5f1d1a159860c5a3c7d72ba7a358018025b
MD5 dba768662a6a0ea6391698d58e207701
BLAKE2b-256 c0951d3749b74b128c04a56b32066bca64a82f20d15aa9273ea5d8cc82563eb9

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

  • Download URL: nod-1.9.4-cp39-cp39-macosx_11_0_arm64.whl
  • Upload date:
  • Size: 480.7 kB
  • Tags: CPython 3.9, macOS 11.0+ ARM64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 d9a74cd17c63c22d629fd2d5a9d0af67dfbdc5ca00a67ecd91222af6b06035ac
MD5 17a088596eec427650683307d98a6056
BLAKE2b-256 17c4cf1e7b0c2b662085bd8040cd5b54e3c3acabc9a5c4f023b96619233d0ddd

See more details on using hashes here.

File details

Details for the file nod-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: nod-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 507.9 kB
  • Tags: CPython 3.9, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.12.8

File hashes

Hashes for nod-1.9.4-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 313f5e824a0eea1f9297622d59a5707d44d682904897bc9e372137c3d10bb861
MD5 91601df9b89a40524b624dc42719a1c8
BLAKE2b-256 0dd5a85b400b98c46b7f1afa5bd14b385832e3c78164c3c04d0d67b4733efd7f

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page