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 |
|---|---|---|---|---|---|---|---|---|
andylvua/bibaandboba | https://github.com/andylvua/bibaandboba/blob/b2c5ee1ad241fe3384394c7e8cf18e1feb930457/BibaAndBoba/utils/tokenizer.py | BibaAndBoba/utils/tokenizer.py | import pkgutil
from nltk.tokenize import word_tokenize
from BibaAndBoba.utils.cacher import cache_to_file
from BibaAndBoba.utils.progress_bar import progress_bar
from BibaAndBoba.utils.languages import get_supported_language
# from emoji.unicode_codes import EMOJI_UNICODE_ENGLISH
stopwords = pkgutil.get_data(__name... | python | MIT | b2c5ee1ad241fe3384394c7e8cf18e1feb930457 | 2026-01-05T07:14:26.497544Z | false |
andylvua/bibaandboba | https://github.com/andylvua/bibaandboba/blob/b2c5ee1ad241fe3384394c7e8cf18e1feb930457/BibaAndBoba/utils/nltk_punkt_downloader.py | BibaAndBoba/utils/nltk_punkt_downloader.py | """
Important! This module downloads the punkt tokenizer from NLTK.
"""
import nltk
import ssl
from BibaAndBoba.utils.logger import logger
def download_punkt():
try:
_create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
logger.warning(
"Warning, ... | python | MIT | b2c5ee1ad241fe3384394c7e8cf18e1feb930457 | 2026-01-05T07:14:26.497544Z | false |
andylvua/bibaandboba | https://github.com/andylvua/bibaandboba/blob/b2c5ee1ad241fe3384394c7e8cf18e1feb930457/BibaAndBoba/utils/progress_bar.py | BibaAndBoba/utils/progress_bar.py | def progress_bar(iterable, prefix: str = '', suffix: str = '', decimals: int = 1,
length: int = 50, fill: str = '█', print_end: str = "") -> list:
"""
Takes an iterable as input, which is the object that will be looped over.
The prefix and suffix arguments are strings that will be displayed... | python | MIT | b2c5ee1ad241fe3384394c7e8cf18e1feb930457 | 2026-01-05T07:14:26.497544Z | false |
andylvua/bibaandboba | https://github.com/andylvua/bibaandboba/blob/b2c5ee1ad241fe3384394c7e8cf18e1feb930457/BibaAndBoba/docs/source/conf.py | BibaAndBoba/docs/source/conf.py | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... | python | MIT | b2c5ee1ad241fe3384394c7e8cf18e1feb930457 | 2026-01-05T07:14:26.497544Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/prune_sparse_seq.py | prune_sparse_seq.py | from __future__ import print_function
import datetime
import time
import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.optim as optim
import codecs
import pickle
import math
from model_word_ada.LM import LM
from model_word_ada.basic import BasicRNN
from model_word_ada.densenet import Dense... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/train_lm.py | train_lm.py | from __future__ import print_function
import datetime
import time
import torch
import torch.nn as nn
import torch.optim as optim
import codecs
import pickle
import math
from model_word_ada.LM import LM
from model_word_ada.basic import BasicRNN
from model_word_ada.ldnet import LDRNN
from model_word_ada.densenet import ... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/train_seq_elmo.py | train_seq_elmo.py | from __future__ import print_function
import datetime
import time
import torch
import torch.nn as nn
import torch.optim as optim
import codecs
import pickle
import math
import numpy as np
from model_word_ada.LM import LM
from model_word_ada.basic import BasicRNN
from model_word_ada.densenet import DenseRNN
from model_... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/train_seq.py | train_seq.py | from __future__ import print_function
import datetime
import time
import torch
import torch.nn as nn
import torch.optim as optim
import codecs
import pickle
import math
from model_word_ada.LM import LM
from model_word_ada.basic import BasicRNN
from model_word_ada.densenet import DenseRNN
from model_word_ada.ldnet impo... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/pre_seq/encode_data.py | pre_seq/encode_data.py | """
.. module:: encode_data
:synopsis: encode data for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import pickle
import argparse
import os
import random
import numpy as np
from tqdm import tqdm
import itertools
import functools
def encode_dataset(input_file, flm_map, blm_map, gw_map, c_map, y_map):
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/pre_seq/gene_map.py | pre_seq/gene_map.py | """
.. module:: gene_map
:synopsis: generate map for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import pickle
import argparse
import os
import random
import numpy as np
from tqdm import tqdm
import itertools
import functools
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.ad... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/sparse_lm.py | model_seq/sparse_lm.py | """
.. module:: sparse_lm
:synopsis: sparse language model for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_seq.utils as utils
class SBUnit(nn.Module):
"""
The basic recurrent unit for the dense-RNNs wrappe... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/seqlm.py | model_seq/seqlm.py | """
.. module:: seqlm
:synopsis: language model for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_seq.utils as utils
import torch
import torch.nn as nn
import torch.nn.functional as F
class BasicSeqLM(nn.Module):
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/crf.py | model_seq/crf.py | """
.. module:: crf
:synopsis: conditional random field
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.optim as optim
import torch.sparse as sparse
import model_seq.utils as utils
class CRF(nn.Module):
"""
Conditional Random Field Module
Parameters
----------
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/dataset.py | model_seq/dataset.py | """
.. module:: dataset
:synopsis: dataset for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import pickle
import random
import functools
import itertools
from tqdm import tqdm
class SeqDataset(object):
"""
Dataset f... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/evaluator.py | model_seq/evaluator.py | """
.. module:: evaluator
:synopsis: evaluator for sequence labeling
.. moduleauthor:: Liyuan Liu
"""
import torch
import numpy as np
import itertools
import model_seq.utils as utils
from torch.autograd import Variable
class eval_batch:
"""
Base class for evaluation, provide method to calculate f1 score... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/utils.py | model_seq/utils.py | """
.. module:: utils
:synopsis: utils
.. moduleauthor:: Liyuan Liu
"""
import numpy as np
import torch
import json
import torch
import torch.nn as nn
import torch.nn.init
from torch.autograd import Variable
def log_sum_exp(vec):
"""
log sum exp function.
Parameters
----------
vec : ``torc... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/__init__.py | model_seq/__init__.py | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false | |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/elmo.py | model_seq/elmo.py | """
.. module:: elmo
:synopsis: deep contextualized representation
.. moduleauthor:: Liyuan Liu
"""
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_seq.utils as utils
import torch
import torch.nn as nn
import torch.nn.functional as F
class EBUnit(nn.Module):
"""
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_seq/seqlabel.py | model_seq/seqlabel.py | """
.. module:: seqlabel
:synopsis: sequence labeling model
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_seq.utils as utils
from model_seq.crf import CRF
class SeqLabel(nn.Module):
"""
Sequence Labeling model augumented with language mod... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/LM.py | model_word_ada/LM.py | """
.. module:: LM
:synopsis: language modeling
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_word_ada.utils as utils
class LM(nn.Module):
"""
The language model model.
Parameters
----------
rnn : ``torch.nn.Module``, req... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/dataset.py | model_word_ada/dataset.py | """
.. module:: dataset
:synopsis: dataset for language modeling
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import sys
import pickle
import random
from tqdm import tqdm
from torch.utils.data import Dataset
class EvalDataset(object):
"""
Datas... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/utils.py | model_word_ada/utils.py | """
.. module:: utils
:synopsis: utils
.. moduleauthor:: Liyuan Liu
"""
import numpy as np
import torch
import json
import torch
import torch.nn as nn
import torch.nn.init
from torch.autograd import Variable
def repackage_hidden(h):
"""
Wraps hidden states in new Variables, to detach them from their hi... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/basic.py | model_word_ada/basic.py | """
.. module:: basic
:synopsis: basic rnn
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_word_ada.utils as utils
class BasicUnit(nn.Module):
"""
The basic recurrent unit for the vanilla stacked RNNs.
Parameters
----------
uni... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/adaptive.py | model_word_ada/adaptive.py | """
.. module:: adaptive
:synopsis: adaptive softmax
.. moduleauthor:: Liyuan Liu
"""
import torch
from torch import nn
from math import sqrt
class AdaptiveSoftmax(nn.Module):
"""
The adaptive softmax layer.
Modified from: https://github.com/rosinality/adaptive-softmax-pytorch/blob/master/adasoft.py... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/__init__.py | model_word_ada/__init__.py | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false | |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/ldnet.py | model_word_ada/ldnet.py | """
.. module:: ldnet
:synopsis: LD-Net
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_word_ada.utils as utils
import random
class BasicUnit(nn.Module):
"""
The basic recurrent unit for the densely connected RNNs with layer-wise dropout.
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/model_word_ada/densenet.py | model_word_ada/densenet.py | """
.. module:: densenet
:synopsis: densernn
.. moduleauthor:: Liyuan Liu
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import model_word_ada.utils as utils
class BasicUnit(nn.Module):
"""
The basic recurrent unit for the densely connected RNNs.
Parameters
----------
... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/pre_word_ada/encode_data2folder.py | pre_word_ada/encode_data2folder.py | """
.. module:: encode_data2folder
:synopsis: encode data folder for language modeling
.. moduleauthor:: Liyuan Liu
"""
import pickle
import argparse
import os
import random
import numpy as np
from tqdm import tqdm
import itertools
import functools
def encode_dataset(input_folder, w_map, reverse):
w_eof =... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/pre_word_ada/gene_map.py | pre_word_ada/gene_map.py | """
.. module:: gene_map
:synopsis: gene map for language modeling
.. moduleauthor:: Liyuan Liu
"""
import pickle
import argparse
import os
import random
import numpy as np
from tqdm import tqdm
import itertools
import functools
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_ar... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
LiyuanLucasLiu/LD-Net | https://github.com/LiyuanLucasLiu/LD-Net/blob/f9489b6e7d436b7e3ed6447b797fb6ce9a886483/docs/source/conf.py | docs/source/conf.py | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Wrapper documentation build configuration file, created by
# sphinx-quickstart on Thu Sep 14 03:49:01 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# au... | python | Apache-2.0 | f9489b6e7d436b7e3ed6447b797fb6ce9a886483 | 2026-01-05T07:14:28.410516Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/train.py | train.py | import torch
import torch.optim as optim
from data import SMPL_DATA
from model_maxpool import NPT
import utils as utils
import numpy as np
import time
import pymesh
batch_size=8
dataset = SMPL_DATA(train=True, shuffle_point = True)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=T... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/model_maxpool.py | model_maxpool.py | from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class PoseFeature(nn.Module):
def __init__(self, num_points = 6890):
super(PoseFeature, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = ... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/model.py | model.py | from __future__ import print_function
import torch
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
class PoseFeature(nn.Module):
def __init__(self, num_points = 6890):
super(PoseFeature, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = ... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/data_generation.py | data_generation.py | import numpy as np
import pickle
import math
import random
import torch
import os
import trimesh
import torch
class SMPLModel():
def __init__(self, model_path):
"""
SMPL model.
Parameter:
---------
model_path: Path to the SMPL model parameters, pre-processed by
`preprocess.py`.
"""
... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/utils.py | utils.py | import numpy as np
import torch
def init_regul(source_vertices, source_faces):
sommet_A_source = source_vertices[source_faces[:, 0]]
sommet_B_source = source_vertices[source_faces[:, 1]]
sommet_C_source = source_vertices[source_faces[:, 2]]
target = []
target.append(np.sqrt( np.sum((sommet_A_source... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/demo.py | demo.py | import torch
from model import NPT
import numpy as np
import pymesh
net_G=NPT()
net_G.cuda()
net_G.load_state_dict(torch.load('original_169.model'))
def face_reverse(faces):
identity_faces=faces
face_dict={}
for i in range(len(random_sample)):
face_dict[random_sample[i]]=i
new_f=[]
for i ... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/evaluate.py | evaluate.py | import trimesh
import numpy as np
#make sure the order of identity points and gt points are same
#for original_model, please keep the identity and pose points in different order
ours_mesh = trimesh.load('ours.obj')
ours_vertices=ours_mesh.vertices
ours_bbox= np.array([[np.max(ours_vertices[:,0]), np.max(ours_vertice... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
jiashunwang/Neural-Pose-Transfer | https://github.com/jiashunwang/Neural-Pose-Transfer/blob/bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6/data.py | data.py | import torch.utils.data as data
import torch
import numpy as np
import pymesh
import random
class SMPL_DATA(data.Dataset):
def __init__(self, train, npoints=6890, shuffle_point = False):
self.train = train
self.shuffle_point = shuffle_point
self.npoints = npoints
self.path='./smpl... | python | Apache-2.0 | bd62eef7bad6752ae6cab7fa40bc1935e4dfeec6 | 2026-01-05T07:14:12.473684Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/setup.py | setup.py | from setuptools import setup, find_packages
from pathlib import Path
import os
if __name__ == "__main__":
with Path(Path(__file__).parent, "README.md").open(encoding="utf-8") as file:
long_description = file.read()
def _read_reqs(relpath):
fullpath = os.path.join(os.path.dirname(__file__), rel... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/tests/test_similarity.py | tests/test_similarity.py | import os
import numpy as np
import pytest
import tempfile
import torch
import open_clip
from clip_video_encode import clip_video_encode
def test_similarity():
test_path = "tests/test_videos"
with tempfile.TemporaryDirectory() as tmpdir:
clip_video_encode(
["tests/test_videos/vid1.mp4", "... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/tests/test_encode.py | tests/test_encode.py | import os
import numpy as np
import pytest
import tempfile
from clip_video_encode import clip_video_encode
FRAME_COUNTS = {
"vid1.mp4": 56,
"vid2.mp4": 134,
"https://www.youtube.com/watch?v=a8DM-tD9w2I": 20,
}
def test_encode():
test_path = "tests/test_videos"
with tempfile.TemporaryDirectory() ... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/tests/test_modules.py | tests/test_modules.py | import os
import glob
import pytest
import tempfile
import open_clip
import multiprocessing
import numpy as np
import tarfile
import torch
from torchvision.transforms import Compose, Normalize, ToPILImage, ToTensor
from clip_video_encode.utils import block2dl
from clip_video_encode.simplemapper import FrameMapper
fr... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/writer.py | clip_video_encode/writer.py | """save embeddings."""
import os
import json
import fsspec
import numpy as np
import webdataset as wds
from io import BytesIO
write_fmt = {
"mp4": lambda data: data, # pylint: disable=unnecessary-lambda
"txt": lambda data: str(data), # pylint: disable=unnecessary-lambda
"json": lambda data: json.dumps... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/cli.py | clip_video_encode/cli.py | """cli entry point"""
import fire
from clip_video_encode import clip_video_encode
def main():
"""Main entry point"""
fire.Fire(clip_video_encode)
if __name__ == "__main__":
main()
| python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/reader.py | clip_video_encode/reader.py | """handles input parsing."""
import os
import json
import glob
import pyarrow.parquet as pq
import pyarrow.csv as csv_pq
import pyarrow as pa
class Reader:
"""Parses input into required data.
Necessary columns (reader will always look for these columns in parquet and csv):
* videoLoc - location of video... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/simplemapper.py | clip_video_encode/simplemapper.py | """simplemapper - simple frame -> embedding mapper."""
import torch
import numpy as np
import open_clip
from torchvision.transforms import ToPILImage
try:
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel, GumbelVQ
except ImportError as e:
print("Missing imports")
def load_config(c... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/distributed.py | clip_video_encode/distributed.py | """functions for distributing computation"""
import os
def world_info_from_env():
"""get info from dist env"""
local_rank = 0
for v in ("LOCAL_RANK", "MPI_LOCALRANKID", "SLURM_LOCALID", "OMPI_COMM_WORLD_LOCAL_RANK"):
if v in os.environ:
local_rank = int(os.environ[v])
break... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/utils.py | clip_video_encode/utils.py | """clip-video-encode utils."""
from torch.utils.data import Dataset, DataLoader
class HelperDataset(Dataset):
"""Helper dataset that preprocesses frames"""
def __init__(self, imgs, preprocess):
super().__init__()
self.imgs = imgs
self.preprocess = preprocess
def __len__(self):
... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/__init__.py | clip_video_encode/__init__.py | """clip video encode"""
from .clip_video_encode import clip_video_encode
| python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/clip_video_encode.py | clip_video_encode/clip_video_encode.py | """encode video with CLIP"""
import re
import sys
import time
import math
import torch
from video2numpy.frame_reader import FrameReader
from .reader import Reader, read_shard
from .simplemapper import FrameMapper
from .writer import FileWriter, WebDatasetWriter
from .distributed import world_info_from_env
from .hand... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/handle_chunk.py | clip_video_encode/handle_chunk.py | """encode chunk with CLIP"""
import numpy as np
import torch
from .utils import block2dl
# BATCH_SIZE = 256
BATCH_SIZE = 128
N_DATASET_WORKERS = 6
def encode_chunk(
frames,
ind_dict,
writer,
mapper,
meta,
ids,
use_dst_name,
device,
input_format="table",
captioning_strategy="... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/live_numpy_encoder.py | clip_video_encode/live_numpy_encoder.py | """encode numpy video frame arrays with CLIP from directory as they come in from other processes."""
import os
import time
import numpy as np
from .utils import block2dl
from .writer import FileWriter
N_DATASET_WORKERS = 6
BATCH_SIZE = 256
class LiveNumpyEncoder:
"""class that watches directory for set of nump... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/dataset/dataset_reader.py | clip_video_encode/dataset/dataset_reader.py | """
utils for processing datasets of format described in https://github.com/iejMac/clip-video-encode/pull/13
used https://github.com/rom1504/laion-prepro/blob/main/laion5B/usage_guide/dataloader_pytorch.py as template
"""
import io
import json
import numpy as np
import open_clip
import torch
import webdataset as wds... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/dataset/create_shards.py | clip_video_encode/dataset/create_shards.py | """creates EmbeddingWebDataset from Processed Dataset format"""
import os
import os.path
import random
import argparse
import json
from csv import writer
from pathlib import Path
import numpy as np
import webdataset as wds
parser = argparse.ArgumentParser("""Generate Embedding WebDataset from Processed Dataset.""")
... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/dataset/kinetics700_example_process.py | clip_video_encode/dataset/kinetics700_example_process.py | """
processes s3://s-datasets/kinetics-700/kinetics700_embeddings into processed dataset format.
run from kinetics700_embeddings directory (so train/val/test at the same level)
"""
import os
import glob
import json
import shutil
from tqdm import tqdm
SPLITS = ["train", "val", "test"]
PROCESSED_DIR = "processed"
sa... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/clip_video_encode/dataset/__init__.py | clip_video_encode/dataset/__init__.py | """clip-video-encode dataset."""
from .dataset_reader import EmbeddingWebDatasetReader
| python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/examples/reader.py | examples/reader.py | """
The example below is showcasing how to read an Embedding WebDataset uing the
EmbeddingWebDatasetReader object.
In order to follow along, below are instructions to download the dataset used
in this example.
In a directory of your choice, from the command line call:
git clone https://huggingface.co/datasets/iejMa... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/examples/live_encoding.py | examples/live_encoding.py | import os
import glob
import clip
import torch
import time
from torchvision.transforms import ToPILImage, Compose, ToTensor, Normalize
from clip_video_encode.live_numpy_encoder import LiveNumpyEncoder
from clip_video_encode.simplemapper import FrameMapper
def _convert_image_to_rgb(image):
return image.convert("... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iejMac/clip-video-encode | https://github.com/iejMac/clip-video-encode/blob/b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7/examples/thing_detector/thing_detector.py | examples/thing_detector/thing_detector.py | """thing detector script using clip-video-encode."""
import clip
import numpy as np
import sys
import torch
from matplotlib import pyplot as plt
def conv_filter(probs, width=10):
padded_probs = np.pad(probs, width // 2)
prob_filt = np.zeros(probs.shape)
for i in range(len(probs)):
prob_filt[i] =... | python | MIT | b5df2c7e116d937ac1e67b14c1516f2f07cfe5b7 | 2026-01-05T07:14:33.720079Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/src/T-Bot.py | Python/src/T-Bot.py | import sys
import json
import os
import requests
import telegram
from datetime import datetime, timedelta
from pathlib import Path
from typing import (Optional, Dict, Any, List, Tuple, DefaultDict, BinaryIO, IO)
from collections import defaultdict
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.paren... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | true |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/src/INI-XT-Bot.py | Python/src/INI-XT-Bot.py | import sys
import json
import os
import subprocess
import telegram
from datetime import datetime
from pathlib import Path
from typing import List, Dict
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(_project_root))
from utils.log_utils import LogUtils
# -----------------... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/src/X-Bot.py | Python/src/X-Bot.py | import sys
import json
import os
from datetime import datetime, timedelta
from pathlib import Path
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(_project_root))
from utils.log_utils import LogUtils
# --------------------
# 配置区
# --------------------
class Config:
# ... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/utils/encrypt_7z.py | Python/utils/encrypt_7z.py | import sys
import py7zr
from pathlib import Path
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(_project_root))
from utils.log_utils import LogUtils
logger = LogUtils().get_logger()
logger.info("🔄 Encrypt_7z 初始化完成")
def compress_folders(dirs, output_file, password):
... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/utils/get_redis_config.py | Python/utils/get_redis_config.py | import os
import json
import sys
import redis
from redis.exceptions import RedisError
from pathlib import Path
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(_project_root))
from utils.log_utils import LogUtils
logger = LogUtils().get_logger()
logger.info("🔄 Get_Redis_Co... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/utils/log_utils.py | Python/utils/log_utils.py | import sys
import json
import logging
from datetime import datetime
from pathlib import Path
from dotenv import load_dotenv
# 获取python根目录（向上找两级）
python_root = Path(__file__).resolve().parent.parent
# 获取项目根目录（向上找三级）
project_root = python_root.parent
# 定位到项目根目录的 .env
env_path = project_root / '.env'
load_dotenv(dotenv_... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
iniwym/XT-Bot | https://github.com/iniwym/XT-Bot/blob/f577d2a3161a5209b059b07aada4c087a2aa2894/Python/utils/sync_data.py | Python/utils/sync_data.py | import sys
import os
import shutil
import argparse
from pathlib import Path
# 将项目根目录添加到模块搜索路径
_project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(_project_root))
from utils.log_utils import LogUtils
logger = LogUtils().get_logger()
logger.info("🔄 Sync_Data 初始化完成")
def sync_dirs(source, dest)... | python | MIT | f577d2a3161a5209b059b07aada4c087a2aa2894 | 2026-01-05T07:14:36.260795Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/__init__.py | src/cellcharter/__init__.py | from importlib.metadata import version
from . import datasets, gr, pl, tl
__all__ = ["gr", "pl", "tl", "datasets"]
__version__ = version("cellcharter")
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/_utils.py | src/cellcharter/_utils.py | from __future__ import annotations
from typing import Union
import numpy as np
AnyRandom = Union[int, np.random.RandomState, None]
def str2list(value: Union[str, list]) -> list:
"""Check whether value is a string. If so, converts into a list containing value."""
return [value] if isinstance(value, str) els... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/_constants/_pkg_constants.py | src/cellcharter/_constants/_pkg_constants.py | """Internal constants not exposed to the user."""
class Key:
class obs:
@classmethod
@property
def sample(cls) -> str:
return "sample"
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/datasets/_dataset.py | src/cellcharter/datasets/_dataset.py | from copy import copy
from squidpy.datasets._utils import AMetadata
_codex_mouse_spleen = AMetadata(
name="codex_mouse_spleen",
doc_header="Pre-processed CODEX dataset of mouse spleen from `Goltsev et al "
"<https://doi.org/10.1016/j.cell.2018.07.010>`__.",
shape=(707474, 29),
url="https://figshar... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/datasets/__init__.py | src/cellcharter/datasets/__init__.py | from ._dataset import * # noqa: F403
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/_shape.py | src/cellcharter/pl/_shape.py | from __future__ import annotations
import warnings
from pathlib import Path
import anndata as ad
import geopandas
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from anndata import AnnData
from squidpy._docs import d
from ._utils import adjust_box_widths
def plot_bound... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/_autok.py | src/cellcharter/pl/_autok.py | from __future__ import annotations
from pathlib import Path
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from cellcharter.tl import ClusterAutoK
def autok_stability(autok: ClusterAutoK, save: str | Path | None = None, return_ax: bool = False) -> None:
"""
Plot the clustering st... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/_nhood.py | src/cellcharter/pl/_nhood.py | from __future__ import annotations
import warnings
from itertools import combinations
from pathlib import Path
from types import MappingProxyType
from typing import Any, Mapping
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from anndata import AnnData
from matplotlib import rcParams
from matp... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/__init__.py | src/cellcharter/pl/__init__.py | from ._autok import autok_stability
from ._group import enrichment, proportion
from ._nhood import diff_nhood_enrichment, nhood_enrichment
from ._shape import boundaries, shape_metrics
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/_group.py | src/cellcharter/pl/_group.py | from __future__ import annotations
import warnings
from pathlib import Path
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from anndata import AnnData
from matplotlib.colors import LogNorm, Normalize
from matplotlib.legend_handler import HandlerTuple
fro... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/pl/_utils.py | src/cellcharter/pl/_utils.py | from __future__ import annotations
from copy import copy
from types import MappingProxyType
from typing import Any, Mapping
import matplotlib as mpl
import numpy as np
import seaborn as sns
import squidpy as sq
from anndata import AnnData
from matplotlib import colors as mcolors
from matplotlib import pyplot as plt
f... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/_build.py | src/cellcharter/gr/_build.py | from __future__ import annotations
import numpy as np
import pandas as pd
import scipy.sparse as sps
from anndata import AnnData
from scipy.sparse import csr_matrix
from squidpy._constants._pkg_constants import Key
from squidpy._docs import d
from squidpy.gr._utils import _assert_connectivity_key
@d.dedent
def remov... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/_nhood.py | src/cellcharter/gr/_nhood.py | from __future__ import annotations
import warnings
from concurrent.futures import ProcessPoolExecutor, as_completed
from functools import partial
from itertools import combinations
import numpy as np
import pandas as pd
import scipy.sparse as sp
from anndata import AnnData
from squidpy._constants._pkg_constants impor... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/__init__.py | src/cellcharter/gr/__init__.py | from ._aggr import aggregate_neighbors
from ._build import connected_components, remove_intra_cluster_links, remove_long_links
from ._group import enrichment
from ._nhood import diff_nhood_enrichment, nhood_enrichment
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/_group.py | src/cellcharter/gr/_group.py | from __future__ import annotations
import numpy as np
import pandas as pd
from anndata import AnnData
from squidpy._docs import d
from tqdm import tqdm
def _proportion(obs, id_key, val_key, normalize=True):
df = pd.pivot(obs[[id_key, val_key]].value_counts().reset_index(), index=id_key, columns=val_key)
df[d... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/_aggr.py | src/cellcharter/gr/_aggr.py | from __future__ import annotations
import warnings
from typing import Optional, Union
import numpy as np
import scipy.sparse as sps
from anndata import AnnData
from scipy.sparse import spdiags
from squidpy._constants._pkg_constants import Key as sqKey
from squidpy._docs import d
from tqdm.auto import tqdm
from cellc... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/gr/_utils.py | src/cellcharter/gr/_utils.py | """Graph utilities."""
from __future__ import annotations
from anndata import AnnData
def _assert_distances_key(adata: AnnData, key: str) -> None:
if key not in adata.obsp:
key_added = key.replace("_distances", "")
raise KeyError(
f"Spatial distances key `{key}` not found in `adata.o... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/_shape.py | src/cellcharter/tl/_shape.py | from __future__ import annotations
import warnings
from collections import deque
from concurrent.futures import ProcessPoolExecutor, as_completed
import h5py
import networkx as nx
import numpy as np
import pandas as pd
import shapely
import sknw
from anndata import AnnData
from anndata._io.specs.registry import _REGI... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/_autok.py | src/cellcharter/tl/_autok.py | from __future__ import annotations
import concurrent.futures
import inspect
import json
import logging
import os
import pickle
import warnings
from collections import defaultdict
from copy import deepcopy
from pathlib import Path
from typing import Any, Dict, List
import anndata as ad
import numpy as np
import pandas... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/_trvae.py | src/cellcharter/tl/_trvae.py | from __future__ import annotations
import os
from typing import Optional
from anndata import AnnData, read_h5ad
from torch import nn
try:
from scarches.models import TRVAE as scaTRVAE
from scarches.models import trVAE
from scarches.models.base._utils import _validate_var_names
except ImportError:
cl... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/__init__.py | src/cellcharter/tl/__init__.py | from ._autok import ClusterAutoK
from ._gmm import Cluster, GaussianMixture
from ._shape import (
boundaries,
curl,
elongation,
linearity,
purity,
relative_component_size_metric,
)
from ._trvae import TRVAE
| python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/_gmm.py | src/cellcharter/tl/_gmm.py | from __future__ import annotations
import logging
from typing import List, Tuple, cast
import anndata as ad
import numpy as np
import pandas as pd
import scipy.sparse as sps
import torch
from pytorch_lightning import Trainer
from torchgmm.base.data import (
DataLoader,
TensorLike,
collate_tensor,
data... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/src/cellcharter/tl/_utils.py | src/cellcharter/tl/_utils.py | from itertools import combinations
import numpy as np
from joblib import Parallel, delayed
from sklearn.metrics import adjusted_rand_score
def _stability(labels, similarity_function=adjusted_rand_score, n_jobs=-1):
clusters = list(labels.keys())
max_runs = len(labels[clusters[0]])
num_combinations = max_... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/conftest.py | tests/conftest.py | import time
from urllib.error import HTTPError
import anndata as ad
import numpy as np
import pytest
import scanpy as sc
from squidpy._constants._pkg_constants import Key
_adata = sc.read("tests/_data/test_data.h5ad")
_adata.raw = _adata.copy()
@pytest.fixture()
def non_visium_adata() -> ad.AnnData:
non_visium_... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/tools/test_gmm.py | tests/tools/test_gmm.py | import pytest
import scipy.sparse as sps
import squidpy as sq
import cellcharter as cc
class TestCluster:
@pytest.mark.parametrize("dataset_name", ["mibitof"])
def test_sparse(self, dataset_name: str):
download_dataset = getattr(sq.datasets, dataset_name)
adata = download_dataset()
ad... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/tools/test_autok.py | tests/tools/test_autok.py | import numpy as np
import pytest
import scipy.sparse as sps
import squidpy as sq
import cellcharter as cc
class TestClusterAutoK:
@pytest.mark.parametrize("dataset_name", ["mibitof"])
def test_spatial_proteomics(self, dataset_name: str):
download_dataset = getattr(sq.datasets, dataset_name)
a... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/tools/test_shape.py | tests/tools/test_shape.py | import numpy as np
import pandas as pd
from anndata import AnnData
from shapely import Polygon
import cellcharter as cc
# Test for cc.tl.boundaries, that computes the topological boundaries of sets of cells.
class TestBoundaries:
def test_boundaries(self, codex_adata: AnnData):
cc.gr.connected_components... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/graph/test_build.py | tests/graph/test_build.py | import numpy as np
import pandas as pd
import scipy.sparse as sps
import squidpy as sq
from anndata import AnnData
from squidpy._constants._pkg_constants import Key
import cellcharter as cc
class TestRemoveLongLinks:
def test_remove_long_links(self, non_visium_adata: AnnData):
# ground-truth removing con... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/graph/test_aggregate_neighbors.py | tests/graph/test_aggregate_neighbors.py | import numpy as np
import scipy.sparse as sps
import squidpy as sq
from anndata import AnnData
from cellcharter.gr import aggregate_neighbors
class TestAggregateNeighbors:
def test_aggregate_neighbors(self):
n_layers = 2
aggregations = ["mean", "var"]
G = sps.csr_matrix(
np.a... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/graph/test_diff_nhood.py | tests/graph/test_diff_nhood.py | import numpy as np
import pytest
from anndata import AnnData
import cellcharter as cc
_CLUSTER_KEY = "cell_type"
_CONDITION_KEY = "sample"
key = f"{_CLUSTER_KEY}_{_CONDITION_KEY}_diff_nhood_enrichment"
class TestDiffNhoodEnrichment:
def test_enrichment(self, codex_adata: AnnData):
n_conditions = codex_a... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/graph/test_group.py | tests/graph/test_group.py | import numpy as np
import squidpy as sq
import cellcharter as cc
GROUP_KEY = "batch"
LABEL_KEY = "Cell_class"
key = f"{GROUP_KEY}_{LABEL_KEY}_enrichment"
adata = sq.datasets.merfish()
class TestEnrichment:
def test_enrichment(self):
cc.gr.enrichment(adata, group_key=GROUP_KEY, label_key=LABEL_KEY)
... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/graph/test_nhood.py | tests/graph/test_nhood.py | import numpy as np
import scipy
import squidpy as sq
from squidpy._constants._pkg_constants import Key
import cellcharter as cc
_CK = "cell type"
key = Key.uns.nhood_enrichment(_CK)
adata = sq.datasets.imc()
sq.gr.spatial_neighbors(adata, coord_type="generic", delaunay=True)
cc.gr.remove_long_links(adata)
class Te... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/plotting/test_plot_stability.py | tests/plotting/test_plot_stability.py | import pytest
import scipy.sparse as sps
import squidpy as sq
import cellcharter as cc
class TestPlotStability:
@pytest.mark.parametrize("dataset_name", ["mibitof"])
def test_spatial_proteomics(self, dataset_name: str):
download_dataset = getattr(sq.datasets, dataset_name)
adata = download_da... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
CSOgroup/cellcharter | https://github.com/CSOgroup/cellcharter/blob/461165f57ee9ac5614f550d99a82bf01fb086948/tests/plotting/test_group.py | tests/plotting/test_group.py | import matplotlib.pyplot as plt
import pytest
import squidpy as sq
try:
from matplotlib.colormaps import get_cmap
except ImportError:
from matplotlib.pyplot import get_cmap
import cellcharter as cc
GROUP_KEY = "batch"
LABEL_KEY = "Cell_class"
key = f"{GROUP_KEY}_{LABEL_KEY}_enrichment"
adata = sq.datasets.m... | python | BSD-3-Clause | 461165f57ee9ac5614f550d99a82bf01fb086948 | 2026-01-05T07:13:12.201168Z | false |
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