sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e5fed2031747db8fdd070877293eafd26c5b76b067f1e0870b420c0456be8f25 | Python | 32,041 | 713 | #!/usr/bin/env python3
"""Extrai as propriedades de cada execucao SIESTA para um unico CSV.
Este script e a fronteira entre o HPC e a maquina local: ele reduz o
diretorio `run/` (milhares de pastas) a um `results/results.csv` de poucas
centenas de kB, que e o unico artefato que precisa voltar para o treino OCE.
Propr... |
d52bb90402001c80ac54532b699681ac6647f150c90401216a9aa541cf144948 | Python | 32,042 | 867 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import csv
import math
import time
import numpy as np
import torch
import scipy.io as sio
'''********** Functions for computing simulated BOLD signals ************'''
def CB... |
256383b7058b0c9fafe1a9463e9a1e0ab20628c03a407a0e2e3473b1d879df53 | Python | 32,083 | 693 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The 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 cop... |
5d83059d9ce93c9f5dd6fafe96e0508ef49195f72028d179011c0ea47eff56e8 | Python | 32,084 | 694 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The 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 cop... |
81fb82cf8a5f01f649dc98211f8019a65078d8de9b843806cc07ec11c982df76 | Python | 32,091 | 1,181 | import pytest
from scvi import settings
from scvi.data import synthetic_iid
from scvi.model import MULTIVI, SCVI
@pytest.mark.autotune
def test_experiment_init_adata(save_path: str):
from scvi.autotune import AutotuneExperiment
settings.logging_dir = save_path
adata = synthetic_iid()
SCVI.setup_annd... |
124f1e04f96da5c714beb68c4f75a4513b2c799e18f320180a4552af6d897015 | Python | 32,122 | 834 | # Copyright (c) 2025, Guipeng Li
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
HydraRNA
"""
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from einops import rearrange, repeat
from fair... |
fe9b147d308804232e608acb143fb772d50ad67e4b812fa02bf1f7141efe0f81 | Python | 32,128 | 854 | import logging
from functools import partial
from typing import Any, Dict, Tuple
import jax
import jax.numpy as jnp
from jax.experimental import pallas as pl
from folx import forward_laplacian
from folx.api import FwdJacobian, FwdLaplArray
from .mhsa import reference_mhsa_kernel
from .mhsea import reference_mhsea_ke... |
31ecaac39f28a61dbc8e4c9b395e7cef20cc487e2a15d2db862d224c2c43ce4b | Python | 32,165 | 678 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The 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 cop... |
64d59781fc2e4082e37b646d37f291bbb46900280719c814b349e063d15418ba | Python | 32,172 | 802 | import numpy as np
import matplotlib.pyplot as plt
#import figurefirst as fifi
import utils
from utils import wrapToPi
class PFN:
""" Model of PFN neural responses to air speed/direction & optic flow speed/direction.
Will generate sinusoidal bumps for the compass/EB and in the PB and FB (for PFNs).
... |
e40316bea7e486e5bfae431fdc1207e1e38ec6119c6ce3381df74f9e1a0907ac | Python | 32,193 | 842 | """
Utility functions to pre- and post-process data for Tangram.
"""
import numpy as np
import pandas as pd
from collections import defaultdict
import gzip
import pickle
import scanpy as sc
from tqdm import tqdm
from sklearn.model_selection import LeaveOneOut
from sklearn.model_selection import KFold
from . import... |
e5f06d89420c1594d24342a605a6dc24c06001851b2eb63fc49c731e00be2133 | Python | 32,198 | 838 | import numpy as np
import pandas as pd
from scipy import integrate
import scipy
import matplotlib.pyplot as plt
from matplotlib import cm, colors
from matplotlib.colors import ListedColormap
from utils import list_of_dicts_to_dict_of_lists, cart2polar
from setdict import SetDict
import figurefirst as fifi
i... |
6a707a1bddb963262b657c63ae54cb3d72677b82f4a373c92354c908ffef8000 | Python | 32,226 | 910 | """Tests for `d3text.runtime`.
Two halves. The first pins the *negative* guarantee — importing the library
leaves the process exactly as it found it — which can only be checked in a
fresh interpreter, since pytest has long since imported these modules. The
second pins what `configure()` applies when a script does opt ... |
98006979e9a89efb215669088404e6cc55116071b4955a98239f77ea334233ed | Python | 32,229 | 886 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import csv
import math
import time
import numpy as np
import torch
import scipy.io as sio
'''******************** Functions for computing simulated BOLD signals
***************... |
9d600a4e8823a337c26c62961e3878dc6fe4f252e486cd7f0daa2f0bdb9b5815 | Python | 32,236 | 971 | import contextlib
import os
import argparse
import gc
from typing import Optional, Tuple
from numpy.typing import NDArray
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
with contextlib.suppress(ImportError):
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
d60eb7ec37d413d864b20f7f7abb0271eeaa20b66fa7068419a62321a33c301b | Python | 32,266 | 971 | import contextlib
import os
import argparse
import gc
from typing import Optional, Tuple
from numpy.typing import NDArray
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
with contextlib.suppress(ImportError):
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
7001586fea9a2e2b1758dea3a6faccef860de9c21d2225b22ee554debbf0fc6e | Python | 32,267 | 961 | import warnings
import io
import pybedtools
import pkgutil
import numpy as np
import pandas as pd
import anndata as ad
from anndata import AnnData
import scipy.sparse
from scipy.sparse import (
csr_matrix,
coo_matrix,
)
from collections import defaultdict, Counter
from pandas.api.types import is_n... |
e44202d85b3447e0c43b25f2ae1994b5da1912df02f821d4d54ac84fb5287799 | Python | 32,274 | 829 | """
@file pc.py
@author Simon Yu
@date 02/07/2024
@brief PC classes.
"""
import abc
import itertools
import logger
import numpy
import os
import torch
import tqdm
class PCLearningRateScheduler:
def __init__(self, optimizer, factor = 0.8, patience = 2, threshold = 1e-4, cooldown = 2, min_learning_rate = 1e-6)... |
6043da68bedb3bc299f7e752e5c5240ffe7fbff1ea794176ae864d07c3eeb98f | Python | 32,277 | 886 | # /usr/bin/env python
'''
Written by Kong Xiaolu and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
'''
import csv
import math
import time
import numpy as np
import torch
import scipy.io as sio
'''******************** Functions for computing simulated BOLD signals
***************... |
11e9dbbce411e3ab14d9a32e0bd966aa55212436381219d43576c87db9b74174 | Python | 32,302 | 887 | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The 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 cop... |
d9d035a1c47c9f4557f14603a2e44b7a070bff3e2e92b20ad23a5942b69b6fd2 | Python | 32,312 | 850 | """
Visualization script: Compares original samples with counterfactual samples, and optionally compares specific protein expressions.
Example (Batch processing results, automatically generating visualizations for each slice):
python scripts/visualize_results.py \
--config configs/config.yaml \
--original path... |
3e5cb43610c4be268c357b39e2e4f25b62bc989ce43c4f0e9ac5f9fbf6de7052 | Python | 32,338 | 820 | """
Inference script for T2+CNN conditional diffusion model.
Pipeline:
1. Load CNN (MageUltra) for Stage 1 distortion correction
2. Load Diffusion model (DiffusionUNetT2AndCNN) for Stage 2 refinement
3. For each slice:
a. Run CNN to get initial correction (b50_cnn, adc_cnn)
b. Add noise to CNN output (... |
c09974e014c6a33c90df2c50536a7ffd6599c1cf7dd2da29d8b9d36da92654b8 | Python | 32,353 | 626 | import numpy as np
import numdifftools as nd
import scipy
from scipy.sparse import coo_matrix
from scipy.sparse import csr_matrix
from common.libbgmg import _auxoption_ezvec2, _auxoption_gradients, _auxoption_none, _auxoption_tagpdf, _auxoption_tagpdferr
from common.utils import _log_exp_converter
from common.utils i... |
3961dafb40ef03344db38de4b83f50f7528f11a04b2d8c8a20cebf36cfec3aea | Python | 32,394 | 805 | from typing import Optional, Any, Literal
from itertools import product
from logging import getLogger
import random
import math
import torch
import numpy as np
logger = getLogger(__name__)
class SpecialTokensMixin():
r"""
A mixin to handle special tokens,
"""
SPECIAL_TOKENS_ATTRIBUTES = [
"p... |
c16a53a044e611459a189ca62f90475962f7e507f176ecbb7ad04c27614e7e39 | Python | 32,401 | 767 | """
taichi_extraction_lbm.py
====================================
Taichi 2D Grayscale-LBM Extractor — Streamlit Web UI Edition (Ultimate Version)
"""
import streamlit as st
import numpy as np
import time
import matplotlib.pyplot as plt
import pandas as pd
import scipy.ndimage
st.set_page_config(
page_title="超临界甲烷... |
8938cbd414417ec74780362e868c6ea91c20a17bff994d69caa6a67a15e374b3 | Python | 32,440 | 804 | # code adapted from
# https://nipype.readthedocs.io/en/latest/users/examples/fmri_fsl.html
#
# This is supposed to simulate a FEAT run, but it doesn't actually use
# FEAT, it uses direct calls to all the constituent functions FEAT otherwise
# calls. Constructing this requires careful comparison with feat output
# logs ... |
b6fd2a2d3c96a8745663a41e9795006064f013a5b94d88773674a965c1c1a4d6 | Python | 32,440 | 809 | from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import font_manager
from matplotlib.lines import Line2D
from matplotlib.patches import FancyArrowPatch, FancyB... |
65a0bf9e9f306bff284e38f05f245dd28bac0e1e9b674069aeb0e2d6f8f6f356 | Python | 32,490 | 936 | """GUI-side plot mode orchestration for photometry-behaviour app."""
from __future__ import annotations
import copy
import logging
from PySide6.QtWidgets import QMessageBox
from src.gui.framework.figure_export import (
apply_figure_size_and_fonts,
build_save_path,
save_figure,
)
from src.gui.framework.g... |
296a3e8e72dae1f844c505fdedb1adb4b6b717fffae3d005e753f29ed7f0a50a | Python | 32,497 | 693 | # 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... |
3a1005b17a0c1c088381b6d8b41166e3ad42bef93d950cd01d4f34621254ba72 | Python | 32,497 | 693 | # 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... |
3b4acaccd109b95647fa3c31c4f8389d2161aa14dbfe72112b5341ab069afdb7 | Python | 32,522 | 777 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
# Set random seed
import random
import numpy as np
import torch
# Fix random seed
seed = 2025
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual... |
e70ef0add58a9d3595a6276e40302092b99da6b784e1d6eb06101401e087ee11 | Python | 32,583 | 776 | import numpy as np
import pandas as pd
import configparser
import sys
import time
import datetime
import os
import re
import c.cmmcost_omp as cmmcost_omp
from scipy.optimize import minimize, basinhopping, differential_evolution
import scipy.stats as sstats
import random
def process_input(template_dir, annot_f, sumsta... |
4ef8d1786c5ab0e6c83cab74a9c49923e9219d11584c3ac6d0a7d82e3884cfb1 | Python | 32,636 | 777 | import sys
import os
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
# Set random seed
import random
import numpy as np
import torch
# Fix random seed
seed = 2025
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
t... |
ed1d4887ee909862aec03f1c10e1f3705b973695d4e1335da87c002d35649bdd | Python | 32,708 | 733 | from __future__ import annotations
import logging
from pathlib import Path
from typing import Literal
import lightning as L
import numpy as np
import torch
from anndata import AnnData
from huggingface_hub import PyTorchModelHubMixin
from lightning.pytorch.callbacks import Callback
from lightning.pytorch.loggers.logge... |
3584d88006c33b8849d7daa42c60f8459c5ba2a7be1fce47bd7e772c474e7a4f | Python | 32,717 | 847 | """
dynamic_stripping_sim.py
====================================
Dynamic Gas Stripping Simulation — Helium and Nitrogen Extraction
by Supercritical Methane in Deep Formation Water
Thermodynamic basis (from fugacity equilibrium):
K_i = y_i / x_i (i = He, N2)
f_aq,i = f_gas,i -->
x_i · γ_i · K_H,i · ... |
952d1e8843e0b0c82f8de90591d1dfc7dc8ccd2493a0fd0b1774c8c1aa68aba9 | Python | 32,755 | 740 | import gzip
import pytest
from rnalysis.utils.genome_annotation import *
@pytest.mark.parametrize(
'feature_type,truth',
[
(
'gene',
{
'ENSG00000168671': 'protein_coding',
'ENSG00000249641': 'antisense_RNA',
'ENSG00000123364': '... |
56092110107991a375faa9f7fca6060a92e4018ffdbda1312397d498fbc2e47d | Python | 32,878 | 1,019 | import contextlib
import os
import argparse
import gc
from typing import Optional, Tuple
from numpy.typing import NDArray
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
with contextlib.suppress(ImportError):
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
781e97f2d5416dbefcc926a6425b8d3d995e56c51afef98a11e7500705d77b3d | Python | 32,908 | 1,019 | import contextlib
import os
import argparse
import gc
from typing import Optional, Tuple
from numpy.typing import NDArray
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
with contextlib.suppress(ImportError):
import torch
import torch.nn as nn
import torch.nn.functional as F
imp... |
d361d8c050d74bd6265b8ee0df0c8407d8c36ab371aaf1f94dcb3bc825720074 | Python | 32,915 | 693 | # 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... |
462ff7ab9b39908e4ad22656d578b0a511933771819f9915db03b8f9b761a8eb | Python | 32,916 | 694 | # 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... |
2e1445f0bfc916c228333084f21c7fefaaafa370ba3ba5a09ff3a54ecd3f7c57 | Python | 32,942 | 655 | import os
import sys
import numpy as np
import torch
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from batchgenerators.utilities.file_and_folder_operations import join, load_json
# Whole slide data dataloader
if os.name == 'nt':
os.add_dll_directory(r"C:\Program Files\openslide\bin") # w... |
a94eae5c7833d23da6858b48bbea45dfafe96a34da75a009efb3d040e5fa0e65 | Python | 32,958 | 601 | from cProfile import label
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import os
import sys
import pandas as pd
import seaborn as sns
from scipy.stats import ttest_ind
from HierarchiaPy import Hierarchia
abspath = os.path.abspath(__file__)
dname = os.path.dirname(abspath)
os.chdir(dname)
os.c... |
f37465fda15a6f1ee2d56beeddfab26b67cc8fb601533a948c5f0426ddf80abc | Python | 32,994 | 702 | # ##############################################################################
# GPLv3 LICENSE INFO #
# #
# Copyright (C) 2020 Mario S. Valdés-Tresanco and Mario E. Valdés-Tresanco ... |
8a4961004e4ec760a2f94cd1457ea7e11ef5f6207597b4828fdeedee2d7a2bd3 | Python | 33,081 | 766 | import warnings
from collections import deque
from collections.abc import Callable, Generator, Hashable
from itertools import combinations, permutations
import networkx as nx
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
from sklearn.base import BaseEstimator, clone
from sklearn.linear_mo... |
86fc356a4515c8ab88c89fc3dc97495b7752b133b380d2d0cbaf8a857b720349 | Python | 33,124 | 694 | import json
import os
from functools import partial
from pathlib import Path
from typing import Tuple, Union
import click
import cv2
import dask.array as da
import numpy as np
import scipy.ndimage
import xarray as xr
import zarr
from matplotlib import pyplot as plt
from scipy.ndimage import binary_dilation, binary_ero... |
0da836736d084efa463cf80b31b5f060b04f4ad3eba2e74cda3938e26dec8b71 | Python | 33,137 | 774 | #!/usr/bin/env python3
"""
hippie_nwb_classify.py
======================
End-to-end pipeline: NWB file → HIPPIE embeddings → classified neurons.
Two input paths are supported (auto-detected unless overridden with --path):
Path A – precomputed / pre-sorted (NWB has a ``units`` table with spike_times)
The pi... |
0d239bae46b894cc529711a9d3ae4697dbb3267ca7f5c30411292153072358b0 | Python | 33,150 | 635 | #
# Copyright 2017-2023 Sandia Corporation. Under the terms of Contract DE-AC04-94AL85000 with
# Sandia Corporation, the U.S. Government retains certain rights in this software.
#
# See LICENSE for full license details
#
import sys, os
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load... |
23f6e8276d17b09aa1e1b76bdf5ddb372123b93163a2742d03c1e6bdd4694b62 | Python | 33,227 | 919 | """Module for generating consensus sequences from reads."""
from concurrent.futures import (
as_completed,
ProcessPoolExecutor,
TimeoutError as FuturesTimeoutError,
)
from concurrent.futures.process import BrokenProcessPool
from contextlib import ExitStack
import dataclasses
import faulthandler
import math... |
77f9ca0a1584145192d01d98d00c5fb8a91dedeb3bffe359c00ca79d1b77c8f8 | Python | 33,229 | 779 | # 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 torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq import utils
from fairseq.model_parallel.model... |
65338e59e35fa0ef94e485eb51d8ca7693e6bcdf1b1fa0c8dd72409f70581ed5 | Python | 33,240 | 615 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tractseg.libs import img_utils
from tractseg.data.subjects import get_all_subjects
from tractseg.libs import utils
def get_bundle_names(CLASSES):
if CLASSES == "All":
# ... |
3a75b80adc8c4bc272484b37cc8ef8c3ca1b831f6e52b0eac3810495554e78ca | Python | 33,355 | 797 | import sys
import os
sys.path.append((os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))
# Set random seed
import random
import numpy as np
import torch
# Fix random seed
seed = 2025
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manu... |
1f6a2ccd01283bab479a9ac704a7c419384ae31b6059ae8f70f8493b0d1d46ae | Python | 33,361 | 551 | # -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'TabGeneral.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_TabGeneral(object):
def setupUi(self, TabGeneral):
TabGe... |
39c21485fe0540c45d4fbad25334ac6fc02a1af58d46e9a5cd65afd776b7e388 | Python | 33,373 | 947 | """Functions on PointData and CellData."""
# Author: Oualid Benkarim <oualid.benkarim@mcgill.ca>
# License: BSD 3 clause
import warnings
import numpy as np
from scipy.stats import mode
from scipy.spatial import cKDTree
from scipy.sparse.csgraph import laplacian
from sklearn.utils.extmath import weighted_mode
from... |
e1da92955e19678f132dc4039b9f70b0e688980e687d4c38d7fdd027bf15e0ce | Python | 33,419 | 704 | #!/usr/bin/env python3
"""Generates test, train, and validation splits and optionally performs some filtering.
BNF
---
.. highlight:: none
.. literalinclude:: ../../doc/bnf/prepareBed.bnf
Parameter Notes
---------------
bigwig-names
A list of the data bigwigs that correspond to this head.
For example, the... |
3fe1466bf74c2020fd04b2d1bbe19b82c89e2172e58c9855e880e19bf91bd5cb | Python | 33,464 | 722 | from __future__ import annotations
import inspect
import itertools
import logging
from typing import TYPE_CHECKING
import numpy as np
import pandas as pd
import torch
from scipy import sparse
from torch.nn import functional as F
import scvi
from scvi import REGISTRY_KEYS
from scvi.external.drvi._constants import DRV... |
24f599dc7641a12eb25a9af8a7a34b46e75c9326a6361176dcbd8ff589bcfbe0 | Python | 33,474 | 566 | import os
import sys
import ctypes
import six
import logging
import numpy as np
_auxoption_none = 0
_auxoption_ezvec2 = 1
_auxoption_tagpdf = 2
_auxoption_tagpdferr = 3
_auxoption_gradients = 4
_cost_calculator_sampling = 0
_cost_calculator_gaussian = 1
_cost_calculator_convolve = 2
_cost_calculator_smplfast = 3
def... |
734706fb802dcaad8f6a3a514749874ca204689fd1d4b3af33c0efafb63b2793 | Python | 33,476 | 872 | import dask.array as da
import logging
import nrrd
import numcodecs as codecs
import numpy as np
import os
import re
import zarr
import traceback
from ome_zarr_models.v04.image import Dataset
from tifffile import TiffFile
logger = logging.getLogger(__name__)
def create_dataset(
container_path,
container_su... |
4c8b055a4e9a44b4350f6c5efada9682838b71867cb2746f2cdacdf848e2b85a | Python | 33,506 | 670 | import os
import sys
import numpy as np
import torch
from nnunetv2.training.nnUNetTrainer.nnUNetTrainer import nnUNetTrainer
from batchgenerators.utilities.file_and_folder_operations import join, load_json
# Whole slide data dataloader
if os.name == 'nt':
os.add_dll_directory(r"C:\Program Files\openslide\bin") # w... |
d9f165dc4fea52689cffaa19e29b7d731d7cb1da0430dfaa34510279bfb0eeba | Python | 33,527 | 584 | # -*- coding: utf-8 -*-
"""
This module contains classes and such that are responsible for parsing
command-line arguments for gmx_MMPBSA. All of the files specified for use
in gmx_MMPBSA will be assigned as attributes to the returned class.
"""
# ######################################################################... |
914a5b23157fdfabf5e90b4ffc6968a76a21686d56397e34c2704865032e3f45 | Python | 33,528 | 902 | # -*- mode: doctest -*-
"""scikit-fmm is a Python extension module which implements the fast
marching method.
https://github.com/scikit-fmm/scikit-fmm
The fast marching method is used to model the evolution of boundaries
and interfaces in a variety of application areas. More specifically,
the fast marching method is ... |
c2a92fca4c82a346a81809636ddfd664959b10776449171a23496c2780dd9676 | Python | 33,535 | 863 | #!/usr/bin/env python3
"""Contains the different formats of CPDs used in PGM"""
import csv
import numbers
import os
from collections.abc import Hashable
from itertools import chain, product
from shutil import get_terminal_size
import numpy as np
import pandas as pd
from pgmpy import config
from pgmpy.extern import t... |
0786df4bc9f433fdfe9a804d67bfb73493d04e803555827aaf18570b139d086c | Python | 33,538 | 681 | """ProteinMPNN sequence-recovery validation on mirror-image MDM2 / D-peptide complexes.
This script demonstrates that ProteinMPNN can recover the native D-peptide
sequence once a known L-MDM2 / D-peptide complex is reflected to the
mirror-image D-MDM2 / L-peptide frame. It reproduces that protocol on the
two deposited... |
d679b91be97c4a83ab2a32eb45fddb7c20ac95444d29fc09efec8699e23fd83f | Python | 33,576 | 593 | import shutil
from copy import deepcopy
from typing import List, Union, Tuple
import numpy as np
import torch
from batchgenerators.utilities.file_and_folder_operations import load_json, join, save_json, isfile, maybe_mkdir_p
from dynamic_network_architectures.architectures.unet import PlainConvUNet
from dynamic_networ... |
187c0d0f29406bed535cf91df9afc9d830c9a05b40aa633167124fd077500f80 | Python | 33,685 | 629 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
d5ea1cb47d7be1198104701f6a7b04c125b3986f09cb681b5e566dc22c43128c | Python | 33,685 | 628 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
2d8ca73f24920956a9d43fda23d799d16cb5bf8715e8f1a975681d57f2ec0780 | Python | 33,688 | 627 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
e801165aa186d6a31012af8f94ec4a8cd3ea79eda663a33fcfb90277d30d6f86 | Python | 33,748 | 1,062 | import os
import sys
import Bio.PDB.Polypeptide
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy import stats
from sklearn.metrics import mean_absolute_error, mean_squared_error
plt.rcParams["figure.dpi"] = 300
plt.rcParams["figure.figsize"] = [9.0, 9.0]
plt.rcParams... |
d950265774c5f6c50738636c99703130ef5987c4d83e182e4913b14357565ad6 | Python | 33,784 | 1,301 | """ECO ID to Evidence code group dictionary from evidenceontology"""
# pylint: disable=too-many-lines
# 1295 ECO IDs
ECO2GRP = {
'ECO:0000030': 'ISA',
'ECO:0000031': 'ISA',
'ECO:0000032': 'ISA',
'ECO:0000053': 'IEA',
'ECO:0000209': 'IEA',
'ECO:0000210': 'IEA',
'ECO:0000211': 'IEA',
'ECO... |
997b663f05cc6cd21e98f0e4f75edde44d25e86dccc192239e115de516ad0896 | Python | 33,789 | 762 | """
自定义 pytorch 层，实现一维、二维、三维张量的 DWT 和 IDWT，未考虑边界延拓
只有当图像行列数都是偶数，且重构滤波器组低频分量长度为 2 时，才能精确重构，否则在边界处有误差。
"""
import numpy as np
import math
from torch.nn import Module
from .DWT_IDWT_Functions import *
import pywt
__all__ = ['DWT_1D', 'IDWT_1D', 'DWT_2D', 'IDWT_2D', 'DWT_3D', 'IDWT_3D', 'DWT_2D_tiny']
class DWT_1D(Module)... |
298d8cb5cc6c4c6df894ba186539942eeb5d70b766da7f364ef0c12dfeded91b | Python | 33,795 | 1,431 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
02_fetch_rcsb_annotations.py
第二步：根据第一步输出的 PDB_ID + Primary_Chain，联网批量查询
RCSB PDB Data API，并将作者链ID（auth_asym_id）映射到聚合物实体和
UniProt accession。
输入：
processed_data/target_class_analysis/
└── 01_filter_status_and_target_chains.csv
输出：
processed_data/target_class_analysis/... |
019615ed4d3bf94bfe396b0bc4940567f6fea50f33cfb46621410ea971433ba1 | Python | 33,825 | 631 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
54c1974665cea5180fa8f3ccdf02723d3ee6366d3f83152585202b0a1d56706e | Python | 33,831 | 866 | # utils/window_utils.py – PyQt6 dialog windows
import os
from PyQt6.QtWidgets import (
QDialog, QVBoxLayout, QHBoxLayout, QGridLayout, QLabel, QLineEdit,
QComboBox, QPushButton, QCheckBox, QRadioButton, QButtonGroup,
QProgressBar, QWidget, QSizePolicy, QMessageBox,
)
from PyQt6.QtCore import Qt
fr... |
149a7441fa4a8b1d00c91db53d644402a107e77fab00bcea691ef5f11cf2e2ae | Python | 33,840 | 860 | # -*- coding: UTF-8 -*-
"""
goatools find_enrichment study.file population.file gene-association.file
This program returns P-values for functional enrichment in a cluster of study
genes using Fisher's exact test, and corrected for multiple testing (including
Bonferroni, Holm, Sidak, and false discovery rate).
About s... |
6b9ce305c19b0af8c5e1deef4a2b85f526b71faee5ed3d4775272fbf521d6304 | Python | 33,856 | 869 | from math import e
import pickle
import time
from tokenize import group
import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict
import pandas as pd
import os
import pingouin as pg
from statsmodels.stats.multitest import multipletests
from torch import cond
from scipy.stats import levene
f... |
03d5a20ac1778d2876242bc22d5bfecf3f66ae683407301ef800d8dca6ce0ada | Python | 33,878 | 879 | """
Permutation functions
"""
import os
import nibabel as nb
import numpy as np
import pandas as pd
import warnings
from ..datasets import load_fsa5
from sklearn.utils import check_random_state
from scipy.spatial.distance import cdist
from sklearn.base import BaseEstimator
from ..mesh import mesh_elements as me
import... |
69923e488ae1bf677907a8e5794125817ed1c08473da0cf67ba312d2c0636419 | Python | 33,915 | 849 | import functools
import logging
from collections.abc import Sequence
import jax
import jax.numpy as jnp
import jax.tree_util as jtu
import numpy as np
from jax.core import Tracer
from .ad import vjp
from .api import (
JAC_DIM,
Array,
Axes,
ExtraArgs,
ForwardFn,
FunctionFlags,
FwdJacobian,
... |
cf5c20ef92b1c0d3b1054bb30cf695b3f3e7a5cf88068b7d50afadc604abadcd | Python | 33,949 | 800 | #!/usr/bin/env python3
"""
Learnable RAG-GNN v3: Effective End-to-End Training
====================================================
Key changes from v2:
1. Pre-train GNN on link prediction first (Phase 1)
2. Train retrieval projection with explicit protein-document matching (Phase 2)
3. Fine-tune fusion with functional... |
8f867bc67ff5d7396ffc1a79e50a8240786222285b712953ae9b25d8c89f29c6 | Python | 33,960 | 632 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
0a1117b7ca7a90a8f3e717508018f37eb23ea141f923a81bc7203a46b495af55 | Python | 34,007 | 632 | import argparse
import math
import os
import sys
import time
import shutil
import torch
from experiments.exp_forecast import Exp_Forecast
import random
import numpy as np
from datetime import datetime
from utils.tools import send_email, find_most_recently_modified_subfolder
def get_setting(args_, iter_=0):
setti... |
331d5987934e8c60f65ad16b32972f9bfd44d1b250f5137907059bc701dd7931 | Python | 34,012 | 857 | """
Vision Language Model for 3D Medical Imaging
Implements a LLaVA-style multimodal model for radiology report generation.
Combines a pretrained vision encoder with a language model via a perceiver-based connector.
Generation follows a JSON schema via outlines/pydantic.
"""
import logging
from typing import Any, Di... |
8bbb475e77059f8123d1049bf8752c31fd9d3e415c69aa7938fd667187604a20 | Python | 34,069 | 1,014 | """Annbatch disk-based dataloader tests for all supported scvi-tools models."""
from __future__ import annotations
import json
import os
import subprocess
import sys
import numpy as np
import pandas as pd
import pytest
from scipy.sparse import csr_matrix
import scvi
# ----------------------------------------------... |
003a9ec97b85940d954e6841c1131907a40100b119ba18537c78ddd9ed8db2f3 | Python | 34,213 | 926 | """Pure unit tests for `d3text.models.ete.ETEBrendaModel`.
The relation-loss ramp, config wiring, relation alignment and its bookkeeping
of gold no candidate pair covers, the reported metrics, relation-loss class
weighting, and the relation half of `ground_truth`. Candidate proposal itself is
`test_detected_candidates... |
610e6c81b0f69ad9d89c3acfdc728d355b154132efd128afa9891d81d83b8c9e | Python | 34,232 | 788 | """
Training of Neural Network based Segmentation (U-Net excluded)
==============================================================
This module provides tools and functionalities to train and evaluate deep learning
models for chromatophore segmentation of cephalopod images.
It includes methods for data preparation, mode... |
82f95fd22d9a895ae0e665951365c59425cead8e2e90a5456e8a627633b9ab9b | Python | 34,314 | 807 | import logging
import numbers
import numpy as np
from mdt.lib.exceptions import NoiseStdEstimationNotPossible
from mdt.utils import is_scalar, create_roi, estimate_noise_std, load_nifti, restore_volumes, load_protocol, load_brain_mask
from mot.lib.utils import all_elements_equal, get_single_value
__author__ = 'Robbert... |
1ebaefcc4a8166052fe73f4277a61265c02db95ce77e2d23b6fdad5247ace5e5 | Python | 34,345 | 777 | from DataSynthesizer.DataDescriber import DataDescriber
from DataSynthesizer.DataGenerator import DataGenerator
from DataSynthesizer.ModelInspector import ModelInspector
from DataSynthesizer.lib.utils import read_json_file, display_bayesian_network
from synthesize import synthesize
import os
import json
import argpar... |
fde2b7d3ccfd263be2042ed76111b44aa5b2c692ac98b0e3a387fe555ed9d048 | Python | 34,361 | 895 | """The codec for the precomputed-embeddings LMDB.
`tensor_to_bytes` and `bytes_to_tensor` are the two halves of that store's
contract; keeping them in one place is what makes it a contract rather than two
independent guesses at a byte layout. Nothing else may reach for `blosc2`
directly — `unpack_array` segfaults on a... |
ca62b9805d0fa016c706614f2342a56a411830e3bd62ada652824a9261e657f5 | Python | 34,410 | 833 | import numpy as np
import os, json, tempfile, inspect
from ClusterWrap.decorator import cluster
import dask.array as da
from dask.distributed import as_completed
import bigstream.utility as ut
from bigstream.configure_irm import configure_irm
from bigstream.align import alignment_pipeline
from bigstream.transform impor... |
4763531c42688b47f516f1e198d99e03d8c0925972e762bb0ac816b625e51a5b | Python | 34,502 | 839 | import copy
import os
import pickle
import tempfile
import unittest
import numpy as np
from .mock_data import simple_data, create_simple_bam
import libmedaka
import medaka.features
from medaka.features import __enforce_read_matrix_chunk_contiguity as ermcc
from medaka.common import Region, Sample
import medaka.labels... |
4c97ca2deabfb9babf79e5bde125b742dd9f6e64fa6188b9bb62aa3a99aa129b | Python | 34,533 | 581 | """TrainRef3D v1.0: validated case archives -> binary MONAI baseline -> model ZIP.
No network calls, automatic uploads or medical performance claims. Importing this
module needs numpy/nibabel only; torch/MONAI are imported by training functions.
"""
from __future__ import annotations
import csv
import gzip
import has... |
32fb78ee9270c4555f00e15f59674d4b8f0af82a7b8ea4c5cfaeaa373f62a04d | Python | 34,541 | 821 | import functools
import io as builtin_io
import queue
import re
import warnings
from functools import lru_cache
from pathlib import Path
from typing import Dict, Iterable, List, Literal, Set, Tuple, Union
import graphviz
import matplotlib.image as mpimg
import matplotlib.pyplot as plt
import numpy as np
import polars ... |
0a44f67ba4a79196538ff91e9a2e43f90661bf0eb1c1bdc166a925846e78887b | Python | 34,561 | 701 | #!/usr/bin/env python3
from typing import Union, List, Optional, Literal
import pyro
import torch
import pyro.distributions as dist
from pyro.infer import SVI, Trace_ELBO
from pyro.infer import Predictive
import numpy as np
from pyro.optim import Adam
import torch.nn as nn
from tqdm import tqdm
import muon as mu
from m... |
d11e99c9fbc7ab0da957d1c4ac9771972b79e7df362d3c43a784fbe74d772ef3 | Python | 34,580 | 693 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
# vim:fenc=utf-8
#
# Name: Robert Kim
# Date: Oct. 11, 2019
# Email: robert.f.kim@gmail.com
# Description: main script for training continuous-variable rate RNN models
# For more info, refer to
# Kim R., Li Y., & Sejnowski TJ. Simple Framework for Constructing Functional... |
1f43f672ad56b2f3242492f5fc4f8a3268976d4d338d2940404d91cbcb2b320e | Python | 34,617 | 1,294 | from __future__ import annotations
import os
import shutil
import sys
import textwrap
from pathlib import Path
from typing import TYPE_CHECKING
import pytest
from cleo.testers.command_tester import CommandTester
from packaging.utils import canonicalize_name
from poetry.core.utils.helpers import module_name
from po... |
a24cd9ead45980272392e0f236b1489358aabb618d39aa31eed847cac2b352a0 | Python | 34,654 | 854 | def extract_object_properties(segmented_image_path, intensity_image_path, image_name, xy_scale, z_scale):
"""
Takes a segmented image and the corresponding intensity image for that segmentation
Measures minimum, mean, max, and total intensity in addition to total # of pixels per object
Returns a list ... |
fc0b24d94ea5087c08246090fd937b610dfd1d3ea981fcc0d96ff7517c856249 | Python | 34,683 | 735 | # 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... |
2ad6f3426fa381f0688a32782055dd9ca988231ee3d851ba30e23826a4011be3 | Python | 34,690 | 858 | import numpy as np
from scipy.stats import ttest_ind, pearsonr
from sklearn.model_selection import StratifiedKFold
# General packages
import numpy as np
import seaborn as sns
import pandas as pd
# Bunch of scikit-learn stuff
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sk... |
42fb50e47e08d026bfb2cd1e807d9b79b86153e1cd25cc8bad982b71179bda0f | Python | 34,735 | 739 | # 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... |
0556e5e9db73e2e75bea6ad583d69e9f62d2405c1e3cc7824be2806e97d1ddbb | Python | 34,755 | 879 | """Preprocess pipeline for Simulation spatial transcriptomics dataset.
Reads raw data from ``{data_dir}/`` (``image_*.png``, ``label_*.png``,
``spots_*.csv``), writes NPZ patches to ``{data_dir}/Simulation/{train,val,test}/``
and visualization images to ``{data_dir}/Simulation_image/``.
Four conditions are trea... |
a76580ae6fa1d78c3782c32726a6aa56440c7d83a590330abf47e8d6e29ffeba | Python | 34,769 | 827 | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 Lice... |
1d82a3ab9784ed8d9fa8ba90beb9073077f92db180cac73ad6205338bc7adb30 | Python | 34,770 | 828 | # coding=utf-8
# Copyright 2018 Google AI, Google Brain and Carnegie Mellon University Authors and the 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 Lice... |
e06d7fbc127592a4015638ace9b6a3d504d4bd6005c6aa485772bc7e72900147 | Python | 34,878 | 711 | """This module contains various standard post-processing routines for use after optimization or sample."""
import numpy as np
from mdt.utils import tensor_spherical_to_cartesian, voxelwise_vector_matrix_vector_product, create_covariance_matrix, \
compute_noddi_dti
from mot.lib.utils import split_in_batches, parse_c... |
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