sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
1f2cb2307c6f993d53a82627aacd82da09c2cc70746e9a115c6e9ba9374fac5a | Python | 8,217 | 265 | """
Wrappers for VTK classes needed for rendering.
"""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
from .base import BSVTKObjectWrapper, wrap_vtk
from .actor import BSActor, BSActor2D, BSScalarBarActor, BSTextActor
###############################################################
# ... |
5593e4f0a6cd910ecadd23083419ee6769dc909c1470b03104c128e217f27e25 | Python | 8,219 | 230 | import os
import zarr
import numpy as np
import tskit
import yaml
import ray
import argparse
parser = argparse.ArgumentParser("Plot observed and posterior summary statistics across windows")
parser.add_argument("--configfile", type=str, help="Path to config file", default="npe-config/DroMel_CO_FR_rnn.yaml")
parser.add... |
c632319fcd3615383401b7373972de532fa968f29c2f49b03dd82d824655f9a7 | Python | 8,219 | 184 | import os
import random
import logging
import time
import numpy as np
import torch
import skimage.io as skio
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
from src.utils.dataset import gen_train_dataloader, random_transform
from src.utils.util import parse_arguments
from model.SUPPORT import ... |
326de16211fb929fa462d6d7507a90df411cb12a520de2ecc85caddf7ee8942d | Python | 8,222 | 268 | import math
import anndata as ad
from anndata import AnnData
from scvi.utils import dependencies
from ._preprocessing import subsample
from ._utils import validate_layer_key, validate_marker, validate_obs_keys
@dependencies("seaborn", "matplotlib")
def plot_histogram(
adata: ad.AnnData,
marker: str | list[... |
47cdc8e167101e2d133a6cf6dc50338dd720cb11ae1bd4dd70a8f029b248afe0 | Python | 8,222 | 168 |
#20240109 将帧率修改为25，对应的baseline和imgnumber
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
import time
import threading
import gc
import numpy as np
import os
import cv2 as cv
class Lightstability_Check(QWidget):
def __init__(self, MainUI, Serialnumber,Datatype):
super(Lightstability_... |
513d4db5c86277c31a25010c645779bd33bb3bbf2cc1a042ee698e34e9b085c0 | Python | 8,222 | 214 | #!/usr/bin/env python3
# Written by Lorena Pantano with subsequent reworking by Jonathan Manning. Released under the MIT license.
import glob
import logging
import os
import platform
import re
from collections import Counter, OrderedDict
from collections.abc import Set
# Configure logging
logging.basicConfig(format=... |
6fd2a1c9ff6ffe7d9e1c71186421ad0b42328e690add07225ec1c71dc81129d7 | Python | 8,225 | 241 | """Expasy ENZYME: EC numbers for enzyme names, assigned outside BRENDA.
enzymeNER marks spans and no identifiers, so this nomenclature is what turns a
span into gold. It is a dictionary, which makes the enzyme evaluation weaker
than a corpus of hand-assigned identifiers — its own resolution errors are
indistinguishabl... |
aa32d54c2e4051d18afa6a9032e4bd51e6a60b9e2c7376fe27c26c61262b03d8 | Python | 8,225 | 201 | """
Created on Fri Apr 1 13:04:14 2016
@author: artur
"""
from RecognitionModel import *
import pickle
import time
import sys
from data_funcs import *
from perf_funcs import *
class TrainingAlgo(object):
"""
Class that controls the training process.
"""
def __init__(self, rec_params, rec_model,... |
ab7e3a23f5cc675cf1cffcf3287f9d7df692331e469ae1f861e9b484e47e807b | Python | 8,229 | 165 | import sys
sys.path.append("..")
from modelR.backbones.mobilenetv2 import MobilenetV2
from modelR.necks.conv_csa_drf_fpn_hbb import Conv_CSA_DRF_FPN,FC2_CSA_DRF_FPN,Cat_Conv_CSA_DRF_FPN,M_CSA_DRF_FPN
from modelR.head.dsc_head_hbb import Ordinary_Head
from modelR.loss.loss_hbb import Loss_s_l,Loss
import config.... |
0ec69fc866f60198b895acd8023a66c5f42c60f61cf6c599d180d754bd20a521 | Python | 8,231 | 221 | import scanpy as sc
import sys
sys.path.append('./scctools/scctools')
from scctools import *
# # Load Data
ad = sc.read('./FM27_cell_133454_wk.h5')
import matplotlib.patheffects as pe
matplotlib.rcParams.update({'font.size': 12})
fig = plt.figure()
gs = GridSpec(1,3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 ... |
20c0d789350a88e3544f9f27c8bd161399e1b86ce0b1acf2c469783de32b0cf8 | Python | 8,231 | 226 | """Figure 2 panel D primitive — Hausser β × z_dim hyperparameter sweep.
Uses the locked 5-fold sweep over the 42-config grid (6 z_dim × 7 β) that
mirrors NEMO's 42-config sweep to keep the two tools at parity. Highlights
the production HIPPIE config (z=30, β=1.0) selected by the stability
tiebreaker described in the M... |
f7828c5ed322f8df5df23473f6ee33148d80bcbf4b9ef5b0046bd2bc9954bf6f | Python | 8,231 | 167 | import sys
sys.path.append("..")
from modelR.backbones.mobilenetv2 import MobilenetV2
from modelR.necks.conv_csa_drf_fpn_hbb import Conv_CSA_DRF_FPN,FC_CSA_DRF_FPN,Cat_Conv_CSA_DRF_FPN,M_CSA_DRF_FPN
from modelR.head.dsc_head_hbb import Ordinary_Head
from modelR.loss.loss_hbb import Loss_s_l,Loss
import config.c... |
fa6cbe07c5c5d1c22f6b25473ae57d43df378d777d20294b6f4e4661f97664cb | Python | 8,232 | 204 | """Approximate volume estimators for relaxed carbon clusters.
For a 2D-like cluster relaxed in 3D the convex hull may be degenerate
(all atoms in a plane). We provide three robust estimators ordered by
sophistication, all in ų:
V_atomic = N · (4π/3) r_C³ (purely extensive, sanity ref)
V_inflate... |
1baf80395f6f3dfe19f666e7f94dbf4b9041498e4e2aab648a7554cac4180f6e | Python | 8,236 | 42 | import os
import gensim
def read_corpus(pmids, abstracts):
tagged_corpus = []
for pmid, abstract in zip(pmids, abstracts):
tokens = gensim.utils.simple_preprocess(abstract[0])
tagged_corpus.append(gensim.models.doc2vec.TaggedDocument(tokens, [pmid]))
return tagged_corpus
def train_model(... |
6ea55420a246c25b95a0ef83a5ca76fa6e399da18ca12e9f9f208811657c786d | Python | 8,239 | 234 | # Original work Copyright 2018 The Google AI Language Team Authors.
# Modified work Copyright 2019 Rowan Zellers
#
# 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/... |
52b99ebf28f7f4a41012db85baa0a85a993d4beb03fc7ae505dfbe40b3aaa657 | Python | 8,241 | 285 | #!/usr/bin/env python
# ENCODE DCC common functions
# Author: Jin Lee (leepc12@gmail.com)
import sys
import os
import re
import csv
import gzip
import logging
import subprocess
import math
import signal
import time
logging.basicConfig(
format='[%(asctime)s %(levelname)s] %(message)s',
stream=sys.stdout)
log ... |
58d11d316dbaf9b38c11b44506293619d6f97ac99babd84b540689eec0092169 | Python | 8,242 | 218 | from typing import List, Union, Optional
import math
import torch
from nequip.data import AtomicDataDict
from nequip.nn import GraphModuleMixin, scatter
from nequip.utils.global_dtype import _GLOBAL_DTYPE
from nequip.data._key_registry import get_field_type
class PerTypeScaleShiftE(GraphModuleMixin, torch.nn.Module)... |
768ed74db83be46cbaf93c57157cf6d7ece6141f55adcdcb4de824ad5f0b691f | Python | 8,242 | 152 | import numpy as np
import logging
import collections
import pytest
from .utils import UnivariateParametrization
from .utils import UnivariateParametrization_natural_axis
from .utils import UnivariateParametrization_constPI
from .utils import UnivariateParametrization_constPI_constSIG2BETA
from .utils import Univariate... |
7df4dc8df2487e3c1b7ff4c0ebf4efeeb40d60be18688bb61a09a7da56e982ce | Python | 8,244 | 197 | #!/usr/bin/env python3
"""
Two-way Venn diagram comparison of transcript models from Bambu and IsoSeq.
Transcripts are compared by intron chain: two transcripts are considered equivalent
if they share the same set of internal splice junctions (defined by the positions of
all internal exons, excluding the terminal 5' a... |
ccf31fc9ed833565be8deb5ce7fbd5ed4fd6bd9308c73cdf38c56bc6878265ea | Python | 8,244 | 205 | """Tests for the CV-based k selector in hippie.inference.select_k_via_cv.
The selector mirrors the procedure in
``hippie_benchmarking_release/scripts/cross_dataset_script.py``:
* L2-normalize reference embeddings
* cv = min(5, n_classes)
* scoring = "balanced_accuracy"
* try k = 1..20, return argmax
"""
from ... |
5fa25c4c3bd27fe8ab4e5f326205555fffbc1d7a891e5c335885e8310e59bccb | Python | 8,245 | 176 | #!/usr/bin/env python3
"""Cell-level Wilcoxon scan over the 25-gene candidate panel (Figure 5B, Methods 4.7 and 4.10).
WHY THIS EXISTS. Results reports 793 gene x cohort x cell-type comparisons of which 111 are
significant, and Figure 5B is drawn from them, but no script in the release produced the table:
Poster_v2/fi... |
67c3041677fe99780bce9427757420a4461321545bdcfc53786a50f0c68ec508 | Python | 8,245 | 141 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'main_gui.ui'
#
# Created by: PyQt5 UI code generator 5.10.1
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
MainWin... |
05d7e3a55923eced834ab61a169e87ab8f40c5c8a5a0b49f77b15834f0327224 | Python | 8,250 | 264 | #!/usr/bin/env python3
"""
Unified comparison tool for analyzing posteriors from multiple models.
This script provides a complete workflow for simulating data once and comparing
posterior distributions from different trained models.
"""
import argparse
import subprocess
import os
import sys
import yaml
def run_comm... |
32a2f8323c905f8c1e5ff7a44d0278ee20f5ada77369a626012167fbebcf7727 | Python | 8,250 | 143 | #!/usr/bin/env python3
"""Numeros e figuras que faltam no protocolo final da R1 (sem copias + CV interna agrupada).
Consome r1_bootstrap_oof.npz (predicoes fora da dobra do protocolo final) e
r1_cv_interno_agrupado.json (curva de aprendizado agrupada). Produz:
* Tabela S3 (por haleto) e Tabela 8 (residuo por haleto)... |
ffb464b65efaee04a3a717504a35e0a6fb17284b10441bc4fc0f39f9482c9b78 | Python | 8,252 | 236 | import xml.etree.ElementTree as ET
import os
import pickle
import numpy as np
from utils.utils_basic import *
def parse_rec(filename):
""" Parse a PASCAL VOC xml file """
tree = ET.parse(filename)
objects = []
for obj in tree.findall('object'):
obj_struct = {}
obj_struct['name'] = obj.f... |
a67e98592510547be53823b9d7ab890910bd75170890434a296cb03d1313257c | Python | 8,255 | 229 | from itertools import combinations
from typing import Dict, List, Set, Tuple
import pandas as pd
from BLEval.evaluator import Evaluator
from BLEval.data import EvaluationData, DatasetGroup
def _top_k_edges(ranked_edges: pd.DataFrame, k: int) -> Set[Tuple[str, str]]:
"""
Return the set of top-k predicted edg... |
a7c9e062e757ee3446942f593cbb7ab3b8c1f207dfaee819735952ec22020f66 | Python | 8,259 | 145 | import os
from copy import deepcopy
from typing import Union, List
import numpy as np
import torch
from acvl_utils.cropping_and_padding.bounding_boxes import bounding_box_to_slice
from batchgenerators.utilities.file_and_folder_operations import load_json, isfile, save_pickle
from nnunetv2.configuration import default... |
53c2b8feda71ae0ea56a0ac99195e4d6250ae560d483ee8267ac85141a4213f4 | Python | 8,265 | 244 | #!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from concurrent.futures import ThreadPoolExecutor
import logging
from omegaconf import MISSING
import os
import torch
... |
bb4dcebe89ea2972b63afeeb57829edbe2212be9dfee444d261569e5435c226d | Python | 8,266 | 175 | #!/usr/bin/env python3
import pandas as pd
import argparse
def classify_protein_splice_fsm(row):
if row.pr_splice_cat=='full-splice_match' and row.pr_splice_subcat=='multi-exon':
## if nterm and cterm match
if row.pr_nterm_diff==0 and row.pr_cterm_diff==0:
return 'pFSM,known_nterm_know... |
b9848d3b19e9a4332a4c4007e01826854cfd984ed0ae0a7884786bd2a3a011da | Python | 8,272 | 220 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
1e4bfbed9f7833e16440c7774da9f0bd886912067da57aa316c6050e9d91f4ba | Python | 8,273 | 244 | import os
from typing import Dict, List
import sacrebleu
import pandas as pd
from glob import glob
from pathlib import Path
from utils import retrieve_asr_config, ASRGenerator
from tqdm import tqdm
from argparse import ArgumentParser
def merge_tailo_init_final(text):
"""
Hokkien ASR hypothesis post-processing... |
8349e3337827299e39e7bf72c955a5e6f1b08123b3b220886d4904c412401484 | Python | 8,278 | 246 | # Copyright (c) Facebook, Inc. All Rights Reserved
import torch
import os
import numpy as np
import pickle
from . import retri
from ..utils import get_local_rank
class VectorPool(object):
"""
Base class of retrieval space.
"""
def __init__(self, config):
from transformers import AutoConfig
... |
5151a646aaf2f3a9707c61cb0075369ac87382e9aa2a1c085c378dd4fc11e47e | Python | 8,279 | 211 | import os
import json
import numpy as np
from warnings import warn
from voluseg._tools.get_volume_name import get_volume_name
from voluseg._tools.parameters import save_parameters
from voluseg._tools.parameters_models import ParametersModel
from voluseg._tools.nwb import open_nwbfile, find_nwbfile_volume_object_name
... |
edbf3b18dd5a4db7a22772e6de4c020b0b5f315b5a607d4b5aafb4eae1d37dfa | Python | 8,279 | 219 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
5c6b091cc2d564f29287fe5111edbaa2949634ab5528d3b75a862ba7bc488427 | Python | 8,280 | 234 | import os
# custom_loadmat.py must be importable (same directory as this script, or on PYTHONPATH).
from custom_loadmat import custom_loadmat
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import StratifiedKFold
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Sta... |
386bf0a301f0b16bd448cecc7c114118bf67b2dfe6f094db962bd8d5f49f7dce | Python | 8,285 | 227 | from typing import Sequence, Tuple, List, Union
import torch
import numpy as np
RawMSA = Sequence[Tuple[str, str]]
def collate_tokens(
values,
pad_idx,
eos_idx=None,
left_pad=False,
move_eos_to_beginning=False,
pad_to_length=None,
pad_to_multiple=1,
pad_to_bsz=None,
):
"""Convert... |
ed17d37658b81a2ee73674eb5b76c3d3a3fdeadaa08a319d5f7056bdc78fc2d9 | Python | 8,286 | 220 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
332b261b65bc1e7a5b53dc224e5902072c0c96e9294bdd5cf3dc6aa8be175bf8 | Python | 8,287 | 220 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
ae7954e1a5240f28252a7e2523df426d0450b5ea3b8336e5a85c679854dacc53 | Python | 8,287 | 252 | """`NERClassificationModel` — entity class detection without linking."""
import logging
from collections.abc import Sequence
from typing import cast
import torch
import torch.nn as nn
from d3text import tracking
from d3text.constraints import FREQUENCY_CLAMP_EPS, UnitInterval
from d3text.progress import batch_progres... |
3f0c6b58023f56571dbab4d28b3f5ded5407c11912ffaf177d576b3371965832 | Python | 8,288 | 146 | from typing import Optional
import anndata
import lightning as L
import torch
from torch.utils.data import random_split
from aestetik.dataloader import CustomDataset
from aestetik.utils.utils_data import prepare_input_for_model
class AESTETIKDataModule(L.LightningDataModule):
"""Internal Lightning DataModule fo... |
982d2462986ecfbcfb0e735f15bd488d7683d36eee84f87d06c404c38b96de08 | Python | 8,294 | 208 | import unittest
from torch.optim import Adam, SGD
from simulation_encoder.dataclass.config_schemas import (
HyperparameterDiscreteConfig,
HyperparameterRangeConfig,
)
from simulation_encoder.utils.generate_hyperparams import (
_get_continuous_values,
_get_discrete_values,
_get_optimizer_values,
... |
f28bf3b882ffc2886bced2408b4da00c94a9698b6997765a7c8da2569fa64f80 | Python | 8,294 | 250 | #!/usr/bin/env python
"""Example of display interactive flood-filling "inference" results.
shift+mousedown0 triggers the simulated flood filling to start with an initial
seed at the mouse position. The computed mask values are displayed as an image,
while the seed points chosen are displayed as point annotations.
k... |
1d8b5d33516bbd2e3b9331eff5706be9e03b27f28717ea79446372fa150f2219 | Python | 8,296 | 195 | """Writing and reading a checkpoint that carries its own provenance.
A bare `state_dict` is not self-describing: its class head is a matrix of the
right *width* and nothing more, and nothing in it says which dictionary its
token-level targets were matched against, which tokenization produced its
inputs, or which entit... |
d99833c10b319b0623e8509a97d05c0613ebb20ab679950b01d224c7fdf57029 | Python | 8,298 | 243 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import logging
import os
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
from omegaconf import II, MI... |
15b5de7fc63cfb422d3f3c2a116b9dde675f4861f2a1a02a43e49ca99d008401 | Python | 8,299 | 262 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA import ANOVAModel
# Colours and types
Types = np.array(["T4", "T5"])
Type_colours = np.array(["#17becf", "#ff7f0e"])
Subtypes = np.array(["T4a", "T4b", "T4c", "T4d", "T5a", "T5b", "T5c", "T5d"])
Subtype_colours = np.array(
... |
7eaf84d0cbd79d227d70363cc12c4115ce139918abe02536552476e8e8b810fc | Python | 8,303 | 194 | import os, gc
from pathlib import Path
import pandas as pd
import torch.utils.data as data
import pytorch_lightning as pl
from pytorch_lightning import Trainer
from modules import NFTDetector
from nft_datasets import TileDataset
def main(args):
pl.seed_everything(42)
#set the stride and model names, used to... |
80e802524bd98eaff9da6ccc72c8bd66f5a02a688b4658757de4e86ec1a89e35 | Python | 8,304 | 221 | from typing import Callable, List, Union, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from torch.nn.parameter import Parameter
import kornia.filters as KF
from . import base
from .base import Conv2d, Linear, ChAttn2d, SpAttn2d
_DEBUG_IMAGES_: Uni... |
8f680842a790e744476736eb95567da7ba562e72c0357601423944632fd101a1 | Python | 8,304 | 209 | """Building a model from a config, and loading a checkpoint back into it.
The seam between a `ModelConfig` and a ready-to-train `Model`, plus the run
context a tracking run records about the built splits. Lives above
`d3text.models` rather than inside it because `dataset_metrics` reads an
`EntityRelationDataset`, and ... |
573860479758c967e4d1c84d771925e78b2ea362b13e8c174332de2c3cec61a8 | Python | 8,309 | 215 | from tabpfn import TabPFNClassifier, TabPFNRegressor
from tabpfn_extensions import unsupervised
import pandas as pd
import numpy as np
import random
import torch
import os
from huggingface_hub import login
from main_ml import frame2arrays
from train_svm import impute_missing_values
import argparse
def parse_config(... |
28bf8ca3cc40a3f6b7b3d8f12fe597d66ff3574cce6217dc28871f4238f2bfb1 | Python | 8,311 | 197 | import os
from pathlib import Path
import numpy as np
import pandas as pd
from analysis.metrics import dPrime_monkey, get_dprime
classes = {0: 'bear', 1: 'elephant', 2: 'face', 3: 'car' ,4: 'dog', 5: 'apple', 6: 'chair', 7: 'plane'}
train_classes = {1: 'bear', 2: 'elephant', 3: 'table', 4:'face', 5: 'car' ,6: 'dog',... |
d0b715d4e09da01e749f661da781e8b67073f78bad408087245e203a39fc1a78 | Python | 8,311 | 293 | import os
import nipype.pipeline.engine as pe
from fetpype.pipelines.full_pipeline import (
create_full_pipeline,
)
import nipype.interfaces.utility as niu
from fetpype.utils.utils_bids import (
create_datasource,
create_bids_datasink,
create_description_file,
)
from fetpype.workflows.utils import ( #... |
51fee6f62e7285bb3cad4ed7037b884c10c16d9e21eff483d98f8bc8cd856bb7 | Python | 8,312 | 175 | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... |
f80e1ce047343dcc436489b0405b46d38109308287a99bb8489871f5953015f6 | Python | 8,312 | 229 | #!/usr/bin/env python3
# classify 5utr status for pb proteins
# classification focuses on monoexonic and multiexonic 5utrs
#%%
from collections import defaultdict
import re
import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--gencode_exons_bed',action='store',dest='gencode_exons_bed')... |
573dc6b88b4a63e817aa19999afaca05600d4df430487e9a072ff536ad8089f6 | Python | 8,313 | 176 | # coding=utf-8
# Copyright 2018 The OpenAI Team Authors and HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
#
# 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... |
35540b26d5911d911f40f03ced78cbb83188176315d045455c161d64d7150399 | Python | 8,315 | 226 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
import warnings
def get_init(myelin_data, highest_order, init_para):
'''
Th... |
402a1fe4a177cc5425d9ebf143ee99cf880f0787761f01526dfde4058a972d55 | Python | 8,318 | 220 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
cd610dae62e045d0bb0b9940b3df0d98ad4630da89cd39494fa8a7dbd12a3344 | Python | 8,318 | 267 | from __future__ import annotations
import logging
from collections import defaultdict
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Iterable
from anndata import AnnData
from mudata import MuData
import numpy as np
import sparse
import torch
from scvi import REGISTRY_KEY... |
e8ca2d580cbe3683adc3f0fad73d927b61d464e735effd63f071dcae3e973ff1 | Python | 8,320 | 139 | from typing import Union, Tuple
from batchgenerators.dataloading.data_loader import DataLoader
import numpy as np
from batchgenerators.utilities.file_and_folder_operations import *
from nnunetv2.training.dataloading.nnunet_dataset import nnUNetDataset
from nnunetv2.utilities.label_handling.label_handling import LabelM... |
d4338a3b03ff4355381f914991673800f8d5f544dd00e4734362acbf00cec917 | Python | 8,322 | 242 | # Copyright Howto100M authors.
# Copyright (c) Facebook, Inc. All Rights Reserved
import torch as th
import pandas as pd
import os
import numpy as np
import ffmpeg
import random
from torch.utils.data import Dataset
class VideoLoader(Dataset):
"""modified from how2's video_feature_extractor."""
def __init__(... |
2fe9f94213d685a611c83c881f028013ff8c8c33f47bf769e7342d281572ca43 | Python | 8,326 | 246 | import math
import numpy as np
import torch
from torch.nn import functional as F
def sum_except_batch(x: torch.Tensor) -> torch.Tensor:
"""
Sum the elements of the input tensor along all dimensions except the batch dimension.
Parameters:
- x (torch.Tensor): Input tensor.
Returns:
to... |
a701eb695f56fa457395635e910f9f2b959f3be92bd4848136d66b53800c733b | Python | 8,329 | 225 | from __future__ import annotations
from PySide6.QtCore import QCoreApplication, QEvent, QEventLoop, Qt
from PySide6.QtWidgets import (
QApplication,
QFrame,
QHBoxLayout,
QLabel,
QMainWindow,
QPushButton,
QSizePolicy,
QToolButton,
QVBoxLayout,
QWidget,
)
from src.dashboard.catal... |
080dfd0bd9acefce3d72b76b481ebd0b96fa4419429aff9ced1bb0547c9333f6 | Python | 8,330 | 193 | import unittest
import networkx as nx
import numpy as np
import pandas as pd
from pgmpy.independencies import Independencies
from pgmpy.models import NaiveBayes
class TestBaseModelCreation(unittest.TestCase):
def setUp(self):
self.G = NaiveBayes()
def test_class_init_without_data(self):
sel... |
4e28fdbe35ced1581b1119fc1d54b0357cd930c806a876ce2f6ad02040cc2692 | Python | 8,334 | 266 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
Signal processing-based evaluation using waveforms
"""
import numpy as np
import os.path as op
import torchaudio
import tqdm
from tabulat... |
f1206e8b4e378492d6674e0bbb9fbd727303b810eb5cb08571dd1e36180c01db | Python | 8,334 | 203 | from __future__ import annotations
import logging
import pathlib
import attrs
import crowsetta
import pandas as pd
from ...common import annotation, constants
from ...common.converters import expanded_user_path, labelset_to_set
from ...config.spect_params import SpectParamsConfig
from . import audio_helper, spect_he... |
5ce9259ce544cca768fdc4c53f2196f614367df788a02a797cb5d5940acb6f9f | Python | 8,335 | 213 | import pdb
import sys
import argparse
import numpy as np
import os
import pickle
import scipy.io as si
from sklearn.decomposition import PCA
import h5py
import matplotlib.pyplot as plt
rootpath = os.path.join(os.getcwd(), '..')
sys.path.append(rootpath)
from load_RNN_model import load_RNN_model
PATH_TO_FIXED_POINT_FIND... |
7c1a0351505f47d70f9250fef5b9e5298cd79a788aaea9af8ea899939fa38732 | Python | 8,336 | 164 | #!/usr/bin/env python3
"""Regularizacao por bloco no gap das perovskitas, com o canal HYB.
O que se testa, e contra o que:
A. AFERICAO -- com alphas iguais em todos os blocos, o solver dual novo tem de
reproduzir o ridge uniforme do projeto. Se nao reproduzir, e bug.
B. wave15 SOZINHO, uniforme x por bloco --... |
82e239684ff0de5260be804f476a54b32d873672b8562e4112998b0a45bca5af | Python | 8,339 | 234 | """Characterization of image complexity based on https://github.com/tinglyfeng/IC9600."""
import io
import json
import os
from typing import Dict, Optional, Union
import click
import cv2 # type: ignore
import numpy as np
import torch
import torch.nn.functional as F # noqa
from datasets import Dataset, load_from_dis... |
c75ca739bb3deb839a68dbea94aeb2f064c584b153e02fd7ab778f571006835a | Python | 8,343 | 233 | #!/usr/bin/env python3
"""Validate gmx_MMPBSA_test manifest against example READMEs and docs."""
from __future__ import annotations
import argparse
from collections.abc import Sequence
import re
import shlex
import sys
from pathlib import Path
REPO = Path(__file__).resolve().parents[1]
if str(REPO) not in sys.path:
... |
18bf5f09c22036a0032dd046d30bdea5ca8306036b3b8919897a61d669206670 | Python | 8,347 | 228 | from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union
import inspect
import math
from loguru import logger
import os
import torch
import torch.nn as nn
from torch.nn import functional as F
from transformers.generation.utils import SampleDecoderOnlyOutput
... |
60234d6dd73183cc74f5478c3003deaceec3e6d403596ad98aae985be2d9cb90 | Python | 8,347 | 259 | """`d3text.logs`: where the library's console output goes, and who decides.
Two guarantees. The library must not decide — importing `d3text` installs no
handler and touches no level, so an application that embeds it keeps its own
logging — and the handler an entry point *does* install must not smear a live
progress ba... |
c65da0b1362b8fd4f1b854101f90234a0493384a192d66e33e04487ad095a776 | Python | 8,351 | 265 | from __future__ import annotations
import contextlib
import itertools
import sys
from importlib import metadata
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import Literal
from typing import overload
from poetry.utils._compat import WINDOWS
from poetry.utils.helpers im... |
0559355a7766a2804c1d810a7228a4af44fb4c18fda77b406863ce88f5289a92 | Python | 8,352 | 241 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import json
from functools import lru_cache
def convert_sentence_to_json(sentence):
if "_" in sentence:
prefix, rest = sentence.... |
15c09a0ca006415cdc12620482bced8cbeff8e9dbb3acfa1413d9b868daf08e6 | Python | 8,354 | 190 | from yacs.config import CfgNode as CN
# -----------------------------------------------------------------------------
# Convention about Training / Test specific parameters
# -----------------------------------------------------------------------------
# Whenever an argument can be either used for training or for test... |
21f567be3105e152e530c24482a1da7b5113c2f953de74851cd898b238134a56 | Python | 8,354 | 216 | # This extension template provides instructions to add new metrics to pgmpy.
#
# Please follow the following steps:
# 1. Copy this file to `pgmpy/metrics` and rename the file as `your_metric_name.py` (e.g., `my_metric.py`).
# Note: Do NOT start the filename with an underscore `_`, otherwise it won't be discovered.
#... |
28f6f363f2ffe37ad210d1364788e494d6c8eef0f7c9ad4f17211a5001595a46 | Python | 8,357 | 227 | # Copyright (c) 2017-present, Facebook, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the LICENSE file in
# the root directory of this source tree. An additional grant of patent rights
# can be found in the PATENTS file in the same directory.
import logging
from dataclasses im... |
3c81da703416a24ff14444249e5035b063ced3ea9b86732b865bae62e547e798 | Python | 8,360 | 198 | #!/usr/bin/env python3
"""G0 — Latent-space classification accuracy (sanity gate).
Encodes each labeled dataset, fits a leave-one-out cross-validated
LogisticRegression on the μ vectors, and reports balanced accuracy and
per-cell-type F1. Run this immediately after training as a quality gate
before the generative exp... |
da018da72f414183afa3cab78076996efd20a4f659f06c4bdcf07289518d3f04 | Python | 8,368 | 241 | import logging
import community
import leidenalg
import networkx as nx
import igraph as ig
import numpy as np
from scipy.stats import mode
from sklearn.cluster import DBSCAN
from sklearn.decomposition import PCA
from sklearn.neighbors import NearestNeighbors
from pynndescent import NNDescent
from scipy.sparse.csgraph ... |
5048fe08ffc87d092be75a75f40fad905f9a23460ef7ac3340acec7be4f8272b | Python | 8,370 | 220 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import os
import scipy.io as sio
import numpy as np
import time
import torch
import CBIG_pMFM_basic_functions as fc
def get_init(gradient_data, highest_order, init_para):
'... |
35a46bc1eca9936b2b2182071be46636c607c293a9a7fd4695939e8f0b1af98b | Python | 8,374 | 231 | import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
from functools import partial
from .performer_module import default, Chunk, FeedForward, PreLayerNorm, Gene2VecPositionalEmbedding, RandomPositionalEmbedding
class FullAttention(nn.Module):
def __init__(self,
... |
c4806bea6bb3afaebf394c32dc524dade036f3142738fadd67a0aae83bbc6e87 | Python | 8,376 | 165 | from typing import Optional, Tuple
import torch
from torch import Tensor
from torch.nn import Module
from torch.nn import functional as F
from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
from torch.nn.parameter import Parameter
class MultiheadAttention(Module):
r"""Allows the model to jointly... |
78217e88e378d474122ce6ca9e6c9d038046d9633415ffcbfbf97a652e4c0f04 | Python | 8,377 | 275 | # -*- coding: utf-8 -*-
"""
Created on Tue Feb 11 22:25:00 2025
@author: hanna
"""
"""
Supplemental Figure 1
session-average changes for ROTATION and SHUFFLE sessions
averages are over all neurons (including non-significant)
"""
import os
import pickle
import numpy as np
import pandas as pd
from tq... |
22ccc323dc8da9a6395ee4eda05fb98bbe393714e2ee48b3ff08ccbd60c4c316 | Python | 8,379 | 230 | import os
import zarr
import torch
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from torch import Tensor
from torch.utils.data import DataLoader
from lightning import LightningModule, Trainer
from sbi.inference import DirectPosterior
from sbi.utils import BoxUniform
import ts_simulators
fr... |
5d5fcffa2cf28fc42bf50dff556c2d540858b1a8c811ecd980e38dce9c759f8c | Python | 8,386 | 221 | # 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
#
# Unless required by applicable law or agr... |
1cdf6ee9e28c29909b8e50bbda74ea8984ffc34a75e26ff5cbd0fb5219324487 | Python | 8,391 | 259 | import copy
import os
import lightning as L
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
class MALDICNN(nn.Module):
def __init__(self, num_classes, out_dir=None, skip_connections=False, binary=False):
super().__init__()
if binary:
num_units = 1
els... |
646012a2d70644c3618ad4ce84bd807109778c1ddf7a597826e0cac453c76b99 | Python | 8,393 | 267 | import os
from shutil import rmtree
import pytest
import torch
from lightning.pytorch.callbacks import ModelCheckpoint
import scvi
from scvi.data import synthetic_iid
from scvi.model import MULTIVI, SCANVI, SCVI, TOTALVI
from scvi.train._callbacks import SaveCheckpoint, ScibCallback
@pytest.mark.optional
@pytest.ma... |
a849a58d99f86207331ca609b54b3a0097995e8aa633889549dfc652a7d56a6d | Python | 8,399 | 257 | #!/usr/bin/env python3
"""
Cancer signaling network analysis with RAG-GNN.
This example demonstrates the full workflow used in the paper:
1. Load protein interaction network
2. Create functional knowledge base
3. Train RAG-GNN and baseline methods
4. Benchmark comparison
5. Case study: DDR1 analysis
"""
import numpy ... |
68cba2378b13361dbdb3ec3b254ae21f66499f6c6e181f5df05331f2ae60bb9f | Python | 8,401 | 232 | from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from jax.experimental import pallas as pl
try:
from jax.experimental.pallas import triton as plgpu
except ImportError:
from jax.experimental.pallas import gpu as plgpu
from packaging.version import Version
def sum_columns(x: jax.Array) ... |
68217a5fefad8f8fd968c30d93af37b2ad7539d4b3813cdb4056694e34347a7c | Python | 8,403 | 217 | import networkx as nx
from pgmpy.base import UndirectedGraph
from pgmpy.causal_discovery import ExpertKnowledge, HillClimbSearch
from pgmpy.ci_tests import ChiSquare
from pgmpy.estimators import StructureEstimator
from pgmpy.structure_score import BDeu
from pgmpy.utils.mathext import powerset
class MmhcEstimator(Str... |
25bb1ca5763dfbd4bd798efabcd84e3305cb2c33bd8fe9e1e15034d90e948ffe | Python | 8,406 | 214 | from typing import Literal
from pgmpy.base import ADMG, DAG
from pgmpy.identification.probability_expression import ProbabilityExpressionTree
class BaseGraphicalIdentification:
"""Base class for identification methods that return annotated graphs.
Graph-returning identification methods inherit `BaseGraphica... |
2ce022e5a3e01f027b53e8e1f97375021eac744ff9624f80c4f83447b01c0a8d | Python | 8,411 | 217 | import streamlit as st
import glob
import os
import json
import sys
import numpy as np
import random
import cv2
import time
import joblib
from collections import defaultdict
from pathlib import Path
from argparse import Namespace
from PIL import Image
from st_pages import show_pages_from_config, add_page_... |
be968f7105a80d51c3c621cd72b7ecd5d4cd64b227f9e26ba33ffaa08c9e8ff6 | Python | 8,411 | 128 | from msi_visual.saliency_opt import SaliencyOptimization
from msi_visual.nmf_3d import NMF3D
from msi_visual.nonparametric_umap import MSINonParametricUMAP
from msi_visual.percentile_ratio import TOP3, PercentileRatio
from msi_visual.metrics import MSIVisualizationMetrics
from msi_visual.normalization import t... |
3860870b95aea8acd8c0944fe975d85a3346b43dd6537bddff77bab0eea281cc | Python | 8,412 | 207 | import argparse
import contextlib
import dataclasses
import gzip
import io
import os
import tempfile
import unittest
import medaka.medaka
import medaka.options
class ParseDictArgTest(unittest.TestCase):
def test_001_basic_counting(self):
parser = argparse.ArgumentParser()
parser.add_argument('--... |
2cd11b5164d409eaf25fdf629dc7732b50f92872527e8d57b0fdb5a28dc612bd | Python | 8,413 | 253 | # Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import torch
import pickle
import random
from tqdm import tqdm
from torch.utils.data import DataLoader
from torch.utils.data.distrib... |
213c912c1ff625d1898dba63f01c5a6612173a75fa8ab2c5d06984f0386982fe | Python | 8,416 | 226 | """
=======================================
Receiver Operating Characteristic (ROC)
=======================================
Example of Receiver Operating Characteristic (ROC) metric to evaluate
seqwise_predictor output quality.
ROC curves typically feature true positive rate on the Y axis, and false
positive rate on ... |
470be36f9362c49d2bf42cd301853f6fdaa97e3705703426d281f7707ca84e9e | Python | 8,416 | 196 | import RPi.GPIO as GPIO
import time
from VariableList import var_list
class Steppercontrol:
def __init__(self,enablepin,steppin,directionpin,limitpin,Axis,Plusdir,Minusdir,Stepcon_sendtoUI):
self.enable = enablepin
self.step = steppin
self.direction = directionpin
self.limit = lim... |
a45a6c3891f025befc92ce657d9fc223a6963a6a2d26fc4b4d33cb299edd0657 | Python | 8,417 | 226 | """Tests for vak.train.frame_classification module"""
import pathlib
import pytest
import vak.config
import vak.common.constants
import vak.common.paths
import vak.train
def assert_train_output_matches_expected(cfg: vak.config.config.Config, model_name: str,
results_path: pa... |
7c26667ee6ea4e8da9451c1346cf2aaf6e5c249f67a549378811176580c9aba6 | Python | 8,418 | 261 | # source: https://github.com/eccv2020-4574/DCANet/blob/master/models/resnet/resnet_se_dca.py
# arxiv: https://arxiv.org/abs/2007.05099
import torch.nn as nn
import torch.utils.model_zoo as model_zoo
from torch.nn.parameter import Parameter
import torch
import torch.nn.functional as F
from torch.nn import init
from tor... |
c6b7f892b5059be15df126c68cbac37aea21226ff3db047e8d66a0b01ee454d5 | Python | 8,418 | 249 | """
Base Fitter class for fitting linear readout layer
(highly inspired by/credit to: https://github.com/neuroailab/VisualCheese)
"""
import os
import pickle
import sys
from collections import defaultdict
from sklearn.decomposition import PCA
from spacestream.core.feature_extractor import get_features_from_layer
fr... |
77a6d927b7d9c2ce74b803a05cd8e30fa078678bd2f430694229a71bfe4e081f | Python | 8,427 | 196 | import RPi.GPIO as GPIO
import time
from VariableList import var_list
class Steppercontrol:
def __init__(self,enablepin,steppin,directionpin,limitpin,Axis,Plusdir,Minusdir,Stepcon_sendtoUI):
self.enable = enablepin
self.step = steppin
self.direction = directionpin
self.limit = lim... |
911852785b9a2d21f611ce88b6bc27b7b556957f6f875f7dbbc5e21e9e5807fe | Python | 8,429 | 227 | from __future__ import annotations
from typing import TYPE_CHECKING
from typing import ClassVar
from cleo.helpers import option
from poetry.console.commands.installer_command import InstallerCommand
from poetry.plugins.plugin_manager import PluginManager
if TYPE_CHECKING:
from cleo.io.inputs.option import Opti... |
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