repo stringlengths 7 90 | file_url stringlengths 81 315 | file_path stringlengths 4 228 | content stringlengths 0 32.8k | language stringclasses 1
value | license stringclasses 7
values | commit_sha stringlengths 40 40 | retrieved_at stringdate 2026-01-04 14:38:15 2026-01-05 02:33:18 | truncated bool 2
classes |
|---|---|---|---|---|---|---|---|---|
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/imagedreamfusion.py | threestudio/systems/imagedreamfusion.py | import os
import random
import shutil
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
from torchmetrics import PearsonCorrCoef
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.uti... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/prolificdreamer.py | threestudio/systems/prolificdreamer.py | import os
from dataclasses import dataclass, field
import torch
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.misc import cleanup, get_device
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.typing import *
@threestudio.register("pr... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/optimizers.py | threestudio/systems/optimizers.py | # Copyright 2022 Garena Online Private Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agre... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/control4d_multiview.py | threestudio/systems/control4d_multiview.py | import os
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.systems.utils import parse_optimizer
from threestudio.utils.GAN.loss import discriminator_loss, generator_loss
from threestudio.util... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/instructnerf2nerf.py | threestudio/systems/instructnerf2nerf.py | import os
from dataclasses import dataclass, field
import torch
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.misc import cleanup, get_device
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.perceptual import PerceptualLoss
from three... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/zero123.py | threestudio/systems/zero123.py | import os
import random
import shutil
from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
from PIL import Image, ImageDraw
from torchmetrics import PearsonCorrCoef
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.ops import binary_cross_... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/utils.py | threestudio/systems/utils.py | import sys
import warnings
from bisect import bisect_right
import torch
import torch.nn as nn
from torch.optim import lr_scheduler
import threestudio
def get_scheduler(name):
if hasattr(lr_scheduler, name):
return getattr(lr_scheduler, name)
else:
raise NotImplementedError
def getattr_recu... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/dreamfusion.py | threestudio/systems/dreamfusion.py | from dataclasses import dataclass, field
import torch
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.typing import *
@threestudio.register("dreamfusion-system")
class DreamFusion(BaseLift3DSystem):
@data... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/__init__.py | threestudio/systems/__init__.py | from . import (
control4d_multiview,
dreamfusion,
fantasia3d,
imagedreamfusion,
instructnerf2nerf,
latentnerf,
magic3d,
magic123,
mvdream,
prolificdreamer,
sjc,
textmesh,
zero123,
zero123_simple,
)
| python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/fantasia3d.py | threestudio/systems/fantasia3d.py | from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.typing import *
@threestudio.register("fantasia3d-system")
class Fantasia3... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/base.py | threestudio/systems/base.py | import os
from dataclasses import dataclass, field
import pytorch_lightning as pl
import torch.nn.functional as F
import threestudio
from threestudio.models.exporters.base import Exporter, ExporterOutput
from threestudio.systems.utils import parse_optimizer, parse_scheduler
from threestudio.utils.base import (
Up... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/magic123.py | threestudio/systems/magic123.py | from dataclasses import dataclass, field
import torch
import torch.nn.functional as F
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.typing import *
@threestudio.register("magic123-system")
class Magic123(Ba... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/magic3d.py | threestudio/systems/magic3d.py | import os
from dataclasses import dataclass, field
import torch
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.misc import cleanup, get_device
from threestudio.utils.ops import binary_cross_entropy, dot
from threestudio.utils.typing import *
@threestudio.register("ma... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/systems/sjc.py | threestudio/systems/sjc.py | from dataclasses import dataclass, field
import numpy as np
import torch
import threestudio
from threestudio.systems.base import BaseLift3DSystem
from threestudio.utils.typing import *
@threestudio.register("sjc-system")
class ScoreJacobianChaining(BaseLift3DSystem):
@dataclass
class Config(BaseLift3DSystem... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/image.py | threestudio/data/image.py | import bisect
import math
import os
from dataclasses import dataclass, field
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
import threestudio
from threestudio import register
from threestudio.... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/uncond.py | threestudio/data/uncond.py | import bisect
import math
import random
from dataclasses import dataclass, field
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
import threestudio
from threestudio import register
from threestudio.utils.base import Updateab... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/uncond_multiview.py | threestudio/data/uncond_multiview.py | import math
import os
import random
from dataclasses import dataclass, field
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset, IterableDataset
from threestudio import register
from threestudio.data.uncond import ... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/multiview.py | threestudio/data/multiview.py | import json
import math
import os
import random
from dataclasses import dataclass
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torch.nn.functional as F
from scipy.spatial.transform import Rotation as Rot
from scipy.spatial.transform import Slerp
from torch.utils.data import DataLoad... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/__init__.py | threestudio/data/__init__.py | from . import co3d, image, multiview, uncond, uncond_multiview
| python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/threestudio/data/co3d.py | threestudio/data/co3d.py | import gzip
import json
import os
import warnings
from dataclasses import dataclass, field
from typing import List
import cv2
import numpy as np
import pytorch_lightning as pl
import torch
import torchvision.transforms.functional as TF
from PIL import Image
from torch.utils.data import DataLoader, Dataset, IterableDat... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/load/make_prompt_library.py | load/make_prompt_library.py | import json
dreamfusion_gallery_video_names = [
"a_20-sided_die_made_out_of_glass.mp4",
"a_bald_eagle_carved_out_of_wood.mp4",
"a_banana_peeling_itself.mp4",
"a_beagle_in_a_detective's_outfit.mp4",
"a_beautiful_dress_made_out_of_fruit,_on_a_mannequin._Studio_lighting,_high_quality,_high_resolution.... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
liuff19/DreamReward | https://github.com/liuff19/DreamReward/blob/eeb5c648e6c2a25c8f6f8038edfe75d73c811614/load/tets/generate_tets.py | load/tets/generate_tets.py | # Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distributi... | python | MIT | eeb5c648e6c2a25c8f6f8038edfe75d73c811614 | 2026-01-05T07:14:33.752935Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/setup.py | setup.py | from setuptools import setup, find_packages
if __name__ == "__main__":
setup(
name="cheap-proteins",
version="1.0.0",
author="Amy X. Lu",
license="MIT",
author_email="amyxlu@berkeley.edu",
packages=find_packages(where="src"),
package_dir={"": "src"},
)
| python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/run_benchmark.py | run_benchmark.py | import os
import hydra
import sys
import math
import pprint
import shutil
import logging
import argparse
import numpy as np
import time
import yaml
import easydict
from pathlib import Path
import uuid
import torch
from torch import distributed as dist
from omegaconf import OmegaConf, DictConfig
import torchdrug
from ... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/train_sequence_decoder.py | train_sequence_decoder.py | import typing as T
from pathlib import Path
import os
import hydra
import lightning as L
from lightning.pytorch.loggers import WandbLogger
from lightning.pytorch.utilities import rank_zero_only
from omegaconf import DictConfig, OmegaConf
import torch
import time
from plaid.utils import get_model_device
from plaid.tr... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/benchmarking/ours.py | src/benchmarking/ours.py | import os
from pathlib import Path
from einops import reduce
import torch
from torch import nn
from torchdrug import core, layers, utils, data
from torchdrug.layers import functional
from torchdrug.core import Registry as R
import numpy as np
import string
# disassembled to maintain consistency with
# https://gith... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/benchmarking/flip.py | src/benchmarking/flip.py | import os
import csv
import math
from collections import defaultdict
from tqdm import tqdm
from torch.utils import data as torch_data
from torchdrug import data, utils
from torchdrug.core import Registry as R
class FLIPDataset(data.ProteinDataset):
def load_csv(self, csv_file, sequence_field="sequence", targe... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/proteins.py | src/cheap/proteins.py | import os
import re
import typing as T
from pathlib import Path
from tqdm import trange
import numpy as np
import torch
import torch.nn.functional as F
import pandas as pd
import torch
import typing as T
import numpy as np
import re
from openfold.np import residue_constants
from lightning.pytorch.utilities import ran... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/pretrained.py | src/cheap/pretrained.py | import os
from pathlib import Path
import torch
from torch.hub import load_state_dict_from_url
from .esmfold import esmfold_v1_embed_only
from .model import HourglassProteinCompressionTransformer
from .pipeline import Pipeline
from .constants import CATH_COMPRESS_LEVEL_TO_ID, CHECKPOINT_DIR_PATH, HF_HUB_PREFIX
from ... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/constants.py | src/cheap/constants.py | import os
from pathlib import Path
# defaults to ~/.cache/cheap, but can be overridden by setting the CHEAP_CACHE as an environment variable
DEFAULT_CACHE = Path(os.environ.get("CHEAP_CACHE", Path.home() / ".cache/cheap"))
if not DEFAULT_CACHE.exists():
DEFAULT_CACHE.mkdir(parents=True)
HF_HUB_PREFIX = "https:... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/decoder.py | src/cheap/decoder.py | from pathlib import Path
import torch.nn as nn
import torch
from torch.hub import load_state_dict_from_url
from .esmfold import batch_encode_sequences
from .constants import DECODER_CKPT_PATH, HF_HUB_PREFIX
class FullyConnectedNetwork(nn.Module):
def __init__(
self,
n_classes: int = 21,
... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/typed.py | src/cheap/typed.py | from typing import Union, List
from pathlib import Path
import torch
import numpy as np
ArrayLike = Union[np.ndarray, torch.Tensor, List]
PathLike = Union[str, Path]
DeviceLike = Union[str, torch.device] | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/pipeline.py | src/cheap/pipeline.py | """
CHEAP model pipeline wrapper around ESMFold embedding, normalization module, and hourglass compression.
"""
from typing import Optional, Union, List, Tuple
import torch
from .model import HourglassProteinCompressionTransformer
from .esmfold import ESMFoldEmbed, esmfold_v1_embed_only
from .utils import LatentSca... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/__init__.py | src/cheap/__init__.py | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false | |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/model/_hourglass.py | src/cheap/model/_hourglass.py | import typing as T
import torch
from torch import nn
import numpy as np
from . import HourglassDecoder, HourglassEncoder, VectorQuantizer, FiniteScalarQuantizer
from ..utils import (
LatentScaler,
trim_or_pad_batch_first,
get_lr_scheduler,
get_model_device,
)
from ..esmfold._misc import batch_encode_s... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/model/__init__.py | src/cheap/model/__init__.py | from ._modules import (
HourglassDecoder,
HourglassEncoder,
VectorQuantizer,
FiniteScalarQuantizer,
)
from ._hourglass import HourglassProteinCompressionTransformer
| python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/model/_modules.py | src/cheap/model/_modules.py | import typing as T
import math
import torch
from torch import nn, einsum
import torch.nn.functional as F
from einops import rearrange, reduce, repeat
import einops
import numpy as np
from omegaconf import ListConfig
# helpers
def exists(val):
return val is not None
def default(val, d):
return val if exis... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_default_config.py | src/cheap/openfold_utils/_default_config.py | import re
import copy
import importlib
import ml_collections as mlc
def set_inf(c, inf):
for k, v in c.items():
if isinstance(v, mlc.ConfigDict):
set_inf(v, inf)
elif k == "inf":
c[k] = inf
def enforce_config_constraints(config):
def string_to_setting(s):
path... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | true |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_rigids.py | src/cheap/openfold_utils/_rigids.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | true |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_protein.py | src/cheap/openfold_utils/_protein.py | import dataclasses
import numpy as np
from typing import Optional, Sequence
import io
from Bio.PDB import PDBParser
import warnings
import string
from transformers.models.esm.openfold_utils import residue_constants
@dataclasses.dataclass(frozen=True)
class Protein:
"""Protein structure representation."""
# ... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_data_transforms.py | src/cheap/openfold_utils/_data_transforms.py | # Adapted from https://github.com/aqlaboratory/openfold/blob/main/openfold/data/data_transforms.py
# to remove non-MSA and non-template dependencies.
# To enable multimer mode, add necessary functions from
# https://github.com/aqlaboratory/openfold/tree/main/openfold/utils/geometry
# Copyright 2021 AlQuraishi Laborato... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_losses.py | src/cheap/openfold_utils/_losses.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | true |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_feats.py | src/cheap/openfold_utils/_feats.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_tensor_utils.py | src/cheap/openfold_utils/_tensor_utils.py | import torch
from typing import List
from functools import partial
def batched_gather(data, inds, dim=0, no_batch_dims=0):
ranges = []
for i, s in enumerate(data.shape[:no_batch_dims]):
r = torch.arange(s)
r = r.view(*(*((1,) * i), -1, *((1,) * (len(inds.shape) - i - 1))))
ranges.appen... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/__init__.py | src/cheap/openfold_utils/__init__.py | from ._protein import Protein as OFProtein
from ._protein import from_pdb_string as protein_from_pdb_string
from ._protein import to_pdb as protein_to_pdb
from ._rigids import Rigid, Rotation
from ._tensor_utils import (
batched_gather,
permute_final_dims,
masked_mean,
tree_map,
tensor_tree_map,
)
f... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_data_pipeline.py | src/cheap/openfold_utils/_data_pipeline.py | from typing import Mapping
import numpy as np
from transformers.models.esm.openfold_utils import residue_constants
from . import OFProtein
FeatureDict = Mapping[str, np.ndarray]
def _aatype_to_str_sequence(aatype):
return "".join(
[residue_constants.restypes_with_x[aatype[i]] for i in range(len(aatype))... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_fape.py | src/cheap/openfold_utils/_fape.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | true |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/openfold_utils/_residue_constants.py | src/cheap/openfold_utils/_residue_constants.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | true |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_tri_self_attn_block.py | src/cheap/esmfold/_tri_self_attn_block.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
from openfold.model.triangular_attention import (
TriangleAttentionEndingNode,
TriangleAttentionStartingNode,
)
from ope... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_trunk.py | src/cheap/esmfold/_trunk.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import typing as T
from contextlib import ExitStack
from dataclasses import dataclass, field
import torch
import torch.nn as nn
# from open... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_esmfold.py | src/cheap/esmfold/_esmfold.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import typing as T
from dataclasses import dataclass, field
from functools import partial
import pathlib as Path
import time
import torch
im... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_pretrained.py | src/cheap/esmfold/_pretrained.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from pathlib import Path
import torch
from ._esmfold import ESMFold
# https://github.com/facebookresearch/esm/blob/main/esm/esmfold/v1/pr... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_structure_module.py | src/cheap/esmfold/_structure_module.py | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# U... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/__init__.py | src/cheap/esmfold/__init__.py | ESMFOLD_S_DIM = 1024 # dimension of the s_s_0 tensor input to ESMFold folding trunk
ESMFOLD_Z_DIM = 128 # dimension of the paired representation s_z_0 input
from ._trunk import RelativePosition, FoldingTrunk, FoldingTrunkConfig
from ._misc import batch_encode_sequences, output_to_pdb, make_s_z_0
from ._pretrained imp... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_misc.py | src/cheap/esmfold/_misc.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import typing as T
from pathlib import Path
import numpy as np
import torch
import torch.nn.functional as F
from einops import rearrange, re... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_categorical_mixture.py | src/cheap/esmfold/_categorical_mixture.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import torch
class CategoricalMixture:
def __init__(self, param, bins=50, start=0, end=1):
# All tensors are of shape ..., bins... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/esmfold/_esmfold_embed_only.py | src/cheap/esmfold/_esmfold_embed_only.py | # Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import typing as T
import time
from functools import partial
import torch
import torch.nn as nn
from torch import nn
from torch.nn import La... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/losses/__init__.py | src/cheap/losses/__init__.py | from ._functions import (
masked_huber_loss,
masked_l1_loss,
masked_mse_loss,
masked_token_accuracy,
masked_token_cross_entropy_loss,
)
from ._modules import SequenceAuxiliaryLoss, BackboneAuxiliaryLoss
| python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/losses/_modules.py | src/cheap/losses/_modules.py | import typing as T
from openfold.utils.loss import backbone_loss
import pandas as pd
import torch
import wandb
from . import masked_token_cross_entropy_loss, masked_token_accuracy
from ..esmfold._misc import batch_encode_sequences
from ..proteins import LatentToSequence, LatentToStructure
from ..utils import outputs_... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/losses/_functions.py | src/cheap/losses/_functions.py | import typing as T
import torch
import torch.nn.functional as F
import numpy as np
import einops
import torch
def make_mask(broadcast_shape, mask):
while len(mask.shape) < len(broadcast_shape):
mask = mask[..., None]
return mask.expand(broadcast_shape)
def masked_mse_loss(pred: torch.Tensor, target:... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/datasets/__precomputed.py | src/cheap/datasets/__precomputed.py | from pathlib import Path
import typing as T
import torch
from torch.utils.data import Dataset, DataLoader
import numpy as np
import h5py
from ..utils import StructureFeaturizer
from ..typed import PathLike
ACCEPTED_LM_EMBEDDER_TYPES = [
"esmfold", # 1024 -- i.e. t36_3B with projection layers, used for final mo... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/datasets/_datamodules.py | src/cheap/datasets/_datamodules.py | import typing as T
import torch
from torch.utils.data import DataLoader
from ..typed import PathLike
from .__precomputed import H5Dataset, StructureH5Dataset
from .__fasta import FastaDataset
class H5DataModule:
def __init__(
self,
shard_dir: PathLike,
embedder: str = "esmfold",
... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/datasets/__fasta.py | src/cheap/datasets/__fasta.py | from typing import (
Any,
TypeVar,
Callable,
Dict,
Union,
)
import threading
from pathlib import Path
from operator import methodcaller
import subprocess
import torch
from torch.utils.data import DataLoader
import numpy as np
T = TypeVar("T")
PathLike = Union[str, Path]
"""
Adapted from
https:/... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/datasets/__init__.py | src/cheap/datasets/__init__.py | from ._datamodules import H5DataModule, StructureH5DataModule, FastaDataModule
| python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_latent_scaler.py | src/cheap/utils/_latent_scaler.py | from pathlib import Path
import os
import typing as T
import numpy as np
import torch
from torch.hub import download_url_to_file
from ._nn_utils import npy
from ..constants import TENSOR_STATS_DIR, HF_HUB_PREFIX
from ..typed import PathLike
ArrayLike = T.Union[np.ndarray, T.List[float], torch.Tensor]
GLOBAL_SEQEM... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_structure_featurizer.py | src/cheap/utils/_structure_featurizer.py | import typing as T
from pathlib import Path
import numpy as np
import torch
from ..openfold_utils import (
make_pdb_features,
make_all_atom_aatype,
make_seq_mask,
make_atom14_masks,
make_atom14_positions,
atom37_to_frames,
get_backbone_frames,
OFProtein,
protein_from_pdb_string,
)
... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_nn_utils.py | src/cheap/utils/_nn_utils.py | from typing import Union
import torch
import numpy as np
ArrayLike = Union[np.ndarray, torch.Tensor]
def npy(x: ArrayLike):
if isinstance(x, torch.Tensor):
return x.detach().cpu().numpy()
else:
return np.array(x)
def to_tensor(x, device=None, dtype=None):
if isinstance(x, torch.Tensor):... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/__init__.py | src/cheap/utils/__init__.py | from ._latent_scaler import LatentScaler
from ._scheduler import get_lr_scheduler
from ._nn_utils import (
npy,
to_tensor,
count_parameters,
get_model_device,
outputs_to_avg_metric,
)
from ._transforms import (
trim_or_pad_batch_first,
trim_or_pad_length_first,
get_random_sequence_crop,
... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_scheduler.py | src/cheap/utils/_scheduler.py | from transformers import (
get_scheduler,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
Adafactor,
)
import torch
def get_lr_scheduler(
optimizer: torch.optim.Optimizer,
sched_type: str = "constant",
num_warmup_steps: int = 0,
num_training_steps: ... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_transforms.py | src/cheap/utils/_transforms.py | from typing import List, Tuple
import torch
import random
import einops
def mask_from_seq_lens(x: torch.Tensor, seqlen: torch.Tensor):
mask = torch.arange(x.shape[1], device=x.device)
mask = einops.repeat(mask[None, :], "1 L -> N L", N=x.shape[0]) < seqlen[:, None]
return mask.long()
def get_random_seq... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
amyxlu/cheap-proteins | https://github.com/amyxlu/cheap-proteins/blob/cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e/src/cheap/utils/_analysis.py | src/cheap/utils/_analysis.py | import typing as T
import numpy as np
from ._nn_utils import npy
from ..typed import ArrayLike
def calc_sequence_recovery(
pred_seq: ArrayLike, orig_seq: ArrayLike, mask: T.Optional[ArrayLike] = None
):
if isinstance(pred_seq[0], str):
assert isinstance(orig_seq[0], str)
pred_seq = np.array(... | python | MIT | cb8a4ac36e9a44c779d31aacfe5f80c5072d7e7e | 2026-01-05T07:14:48.094150Z | false |
taketwo/glasbey | https://github.com/taketwo/glasbey/blob/a0607959fe671f012599a7dd6031904340eaf99c/glasbey.py | glasbey.py | #!/usr/bin/env python
# encoding: utf-8
import os
import sys
import ast
import argparse
import numpy as np
from colorspacious import cspace_convert
from view_palette import palette_to_image
try:
from progressbar import Bar, ETA, Percentage, ProgressBar
except ImportError:
class Bar:
pass
cla... | python | MIT | a0607959fe671f012599a7dd6031904340eaf99c | 2026-01-05T07:14:48.850040Z | false |
taketwo/glasbey | https://github.com/taketwo/glasbey/blob/a0607959fe671f012599a7dd6031904340eaf99c/view_palette.py | view_palette.py | #!/usr/bin/env python
# encoding: utf-8
import argparse
import numpy as np
def palette_to_image(palette):
from PIL import Image
WIDTH = 180
HEIGHT_SEGMENT = 20
img = Image.new("RGB", (WIDTH, HEIGHT_SEGMENT * len(palette)), "black")
pixels = img.load()
for i, color in enumerate(palette):
... | python | MIT | a0607959fe671f012599a7dd6031904340eaf99c | 2026-01-05T07:14:48.850040Z | false |
taketwo/glasbey | https://github.com/taketwo/glasbey/blob/a0607959fe671f012599a7dd6031904340eaf99c/test/test_glasbey.py | test/test_glasbey.py | import os
from shutil import copyfile, move
from unittest import TestCase
from glasbey import Glasbey
import numpy
class TestGlasbey(TestCase):
def setUp(self) -> None:
file_path = os.path.dirname(os.path.realpath(__file__))
self.test_palette = file_path + "/../palettes/set1.txt"
self.test... | python | MIT | a0607959fe671f012599a7dd6031904340eaf99c | 2026-01-05T07:14:48.850040Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/setup.py | setup.py | #!/usr/bin/env python
# This is a shim to allow Github to detect the package, build is done with poetry
import setuptools
if __name__ == "__main__":
setuptools.setup(name="nbtlib")
| python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_tag.py | tests/test_tag.py | from io import BytesIO
import pytest
from nbtlib import (
End,
Int,
String,
List,
EndInstantiation,
OutOfRange,
IncompatibleItemType,
CastError,
)
from .inputs import (
bytes_for_valid_tags,
out_of_range_numeric_tags,
unsigned_values_for_integer_tags,
)
@pytest.mark.param... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_minecraft.py | tests/test_minecraft.py | import pytest
from nbtlib.contrib.minecraft import StructureFile
def test_structure_file(tmp_path):
structure = StructureFile(
{
"DataVersion": 1139,
"author": "dinnerbone",
"size": [1, 2, 1],
"palette": [
{
"Name": "mine... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_path.py | tests/test_path.py | import pytest
from nbtlib import Path, load, parse_nbt
path_strings_to_keys = [
("", ()),
("hello", ("hello",)),
("hello.world", ("hello", "world")),
("with.trailing.dot.", ("with", "trailing", "dot")),
('using."quoted.keys"', ("using", "quoted.keys")),
('"escape \\"quotes\\""."in.quoted".key',... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/inputs.py | tests/inputs.py | from nbtlib import (
Byte,
ByteArray,
Compound,
Double,
File,
Float,
Int,
IntArray,
List,
Long,
LongArray,
Short,
String,
)
__all__ = [
"bytes_for_valid_tags",
"out_of_range_numeric_tags",
"literal_values_for_tags",
"invalid_literals",
"nbt_files"... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_literal.py | tests/test_literal.py | import pytest
from nbtlib import parse_nbt, InvalidLiteral
from .inputs import literal_values_for_tags, invalid_literals, nbt_files
@pytest.mark.parametrize("literal, expected_tag", literal_values_for_tags)
def test_literal_parsing(literal, expected_tag):
assert parse_nbt(literal) == expected_tag
@pytest.mark... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_nbt.py | tests/test_nbt.py | import pytest
from nbtlib import nbt, Compound
from .inputs import nbt_files
def validate_types(tag, expected):
return isinstance(tag, type(expected)) and (
not isinstance(tag, Compound)
or all(validate_types(val, expected[key]) for key, val in tag.items())
)
@pytest.mark.parametrize("file... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/__init__.py | tests/__init__.py | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false | |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_schema.py | tests/test_schema.py | import pytest
from nbtlib import schema, String, Int, List, CastError
@pytest.fixture
def LooseSchema():
return schema("Thing", {"foo": String, "bar": List[schema("Bar", {"value": Int})]})
@pytest.fixture
def StrictSchema():
return schema(
"Thing",
{"foo": String, "bar": List[schema("Bar", ... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/tests/test_benchmark.py | tests/test_benchmark.py | from io import BytesIO
import pytest
from nbtlib import nbt
def write_parse(nbt_tag):
data = BytesIO()
nbt_tag.write(data)
data.seek(0)
return nbt_tag.parse(data)
@pytest.mark.parametrize(
"filename",
[
"byte.nbt",
"short.nbt",
"int.nbt",
"long.nbt",
... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/path.py | nbtlib/path.py | """This module defines utilities for accessing deeply nested properties.
Exported items:
Path -- Class representing an nbt path, inherits from `tuple`
InvalidPath -- Exception raised when creating an invalid nbt path
"""
__all__ = ["Path", "InvalidPath", "NamedKey", "ListIndex", "CompoundMatch"]
imp... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/cli.py | nbtlib/cli.py | from argparse import ArgumentParser, ArgumentTypeError
from json import dumps as json_dumps
from pprint import pprint
from nbtlib import InvalidLiteral, Path, nbt, parse_nbt, serialize_tag
from nbtlib.tag import Compound, find_tag
# Validation helper
def nbt_data(literal):
try:
nbt_data = parse_nbt(lite... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/schema.py | nbtlib/schema.py | """This module defines tools for creating tag schemas.
Exported items:
schema -- Helper function to define compound schemas
CompoundSchema -- `Compound` subclass that enforces a tag schema
"""
__all__ = ["schema", "CompoundSchema"]
from itertools import chain
from .tag import Compound, CastError
... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/tag.py | nbtlib/tag.py | r"""
.. testsetup::
import io
import struct
from pprint import pprint
from nbtlib import *
All the tag classes have a :meth:`Base.parse` classmethod that reads
nbt data from a file-like object and returns a tag instance. Tag
instances can then write their binary representation back to file-like
object... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | true |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/nbt.py | nbtlib/nbt.py | """
.. testsetup::
import io
import nbtlib
from nbtlib import *
The library supports reading and writing nbt data in all its forms and
treats everything as uncompressed big-endian nbt by default.
You can load nbt files with the :func:`load` function.
.. doctest::
>>> nbtlib.load("docs/hello_world.n... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/__main__.py | nbtlib/__main__.py | from .cli import main
if __name__ == "__main__":
main()
| python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/__init__.py | nbtlib/__init__.py | from .tag import *
from .nbt import *
from .path import *
from .schema import *
from .literal.parser import *
from .literal.serializer import *
__version__ = "2.0.4"
| python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/contrib/__init__.py | nbtlib/contrib/__init__.py | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false | |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/contrib/minecraft/structure.py | nbtlib/contrib/minecraft/structure.py | __all__ = ["StructureFile", "StructureFileData"]
from nbtlib import File, CompoundSchema, tag
class StructureFileData(CompoundSchema):
"""Schema that matches the Minecraft structure file format."""
class BlockState(CompoundSchema):
schema = {
"Name": tag.String,
"Properties"... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/contrib/minecraft/__init__.py | nbtlib/contrib/minecraft/__init__.py | from .structure import *
| python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/literal/serializer.py | nbtlib/literal/serializer.py | """This module exposes utilities for serializing nbt tags to snbt.
Exported functions:
serialize_tag -- Helper function that serializes nbt tags
Exported classes:
Serializer -- Class that can turn nbt tags into their literal representation
Exported objects:
STRING_QUOTES -- Maps the two types of quote... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/literal/parser.py | nbtlib/literal/parser.py | """This module exposes utilities for parsing snbt.
Exported functions:
parse_nbt -- Helper function that parses nbt literals
tokenize -- Generator that lazily yields tokens from a string
Exported classes:
Parser -- Class that can parse nbt tags from a literal token stream
Exported exceptions:
Invali... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/nbtlib/literal/__init__.py | nbtlib/literal/__init__.py | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false | |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/examples/level_dat_bedrock.py | examples/level_dat_bedrock.py | from io import BytesIO
from nbtlib import CompoundSchema, File, schema
from nbtlib.tag import (
INT,
Byte,
Float,
Int,
List,
Long,
String,
read_numeric,
write_numeric,
)
# fmt: off
BedrockLevelData = schema("BedrockLevelData", {
"CenterMapsToOrigin": Byte,
"Difficulty": Int... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/examples/uuid.py | examples/uuid.py | from uuid import UUID
from nbtlib import Long
def combine_uuid(uuid_most_tag, uuid_least_tag):
uuid_most = uuid_most_tag.as_unsigned
uuid_least = uuid_least_tag.as_unsigned
return UUID(int=uuid_most << Long.bits | uuid_least)
def split_uuid(uuid):
uuid_most = uuid.int >> Long.bits & Long.mask
u... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
vberlier/nbtlib | https://github.com/vberlier/nbtlib/blob/a6c0cd949e10f189581ca11026a0c600ee298e11/examples/level_dat.py | examples/level_dat.py | from nbtlib import *
# fmt: off
LevelData = schema("LevelData", {
"DataVersion": Int,
"DimensionData": schema("DimensionData", {
"1": schema("EndData", {
"DragonFight": schema("DragonFight", {
"ExitPortalLocation": schema("ExitPortalLocation", {
"X": Byte... | python | MIT | a6c0cd949e10f189581ca11026a0c600ee298e11 | 2026-01-05T07:14:49.302206Z | false |
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