sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
72569094ca08fbbcd70224785f23b1f420c9fee7655f272f04ddc02ed086a5a7 | Python | 14,217 | 425 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
356f25c9e59ac8f8e2e4629e335f1bae6836c73ea4eb8042164ca0efb6950949 | Python | 14,218 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
739b20ef78fe97ec4bbdf7c7f1afe318d117c71e337ed0d22120ddc23971b429 | Python | 14,222 | 374 | import matplotlib.pyplot as plt
import pandas as pd
import os
import numpy as np
from upsetplot import UpSet, from_memberships
import warnings
from matplotlib_venn import venn3
warnings.filterwarnings("ignore", category=FutureWarning, module="upsetplot.plotting")
warnings.filterwarnings("ignore", category=UserWarning, ... |
8a651c16ef9589993fb3c6edc2dcda9907a8263c0abedd2c0eeef2743c51929d | Python | 14,222 | 365 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul 14 11:01:30 2026
@author: vbp
"""
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Nov 26 16:21:46 2025
@author: vbp
Figure S4I only: sensory preference index (senso_bias) by region. RPE value for punishment set to zero
Data can... |
ed135a96398dbe0adc2118ef9b40db4d7f26fa5f773a13dbc445ae938f04a985 | Python | 14,222 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
0b417d11b351462482cdeef8cd9866a96d41e168d681af7b2efaaa9157bb1f8d | Python | 14,225 | 436 | '''
Tools for reading ImageJ files.
Based on code originally written by Luis Pedro Coelho <luis@luispedro.org>,
2012, available at https://gist.github.com/luispedro/3437255, distributed
under the MIT License.
Modified
- 2014 by Jeffrey Zaremba (@jzaremba), https://github.com/losonczylab/sima
- 2015 by Scott L... |
80af3904a0951949c0fb2176f7bab1b2af024c2bb164c4a654cc6f5c2841dbde | Python | 14,225 | 268 | ##在线进行数据预处理，计算dR和组内叠加平均，现在第55/57行设置数据参数
# 定义视觉刺激参数设置的类
##20210928，第一次读取相机的原始数据时，归一化到[-1,1]的范围内
from PyQt5.QtWidgets import *
import os
from PyQt5.QtGui import *
import cv2 as cv
import numpy as np
import time
import threading
import gc
import sys
class Data_Preprocess(QWidget):
def __init__(self):
... |
3efb36bf2de4fa397d4eec06349a0ea519d56d07de1281c34674053dce7d07be | Python | 14,229 | 483 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
5d763751982ddbfb359da3e812f7dc3c0b1b316136e3f7a6d7d6123dc0165d12 | Python | 14,230 | 426 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
b7ee14c81b59bf9fdede6c19ec1b2f71ec4072e171d6501d04cf3ba07d849d90 | Python | 14,231 | 427 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
df5d567c9398c9c804b3e0d87c093c5bc7f2754ff2b0b873ddbdc9c199b3b0fa | Python | 14,235 | 455 | from __future__ import annotations
from datetime import datetime
from datetime import timezone
from io import BytesIO
from typing import TYPE_CHECKING
from typing import Any
import pytest
from packaging.utils import NormalizedName
from packaging.utils import canonicalize_name
from poetry.core.constraints.version imp... |
99dc70b2004fb296b8542b8a4d7eafdd7486e381fcdaf4263d7beceed51abbad | Python | 14,238 | 428 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .
# 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(
[
"#1f77b4",
... |
9fe00222b8dd9e03c05845036cfbed669fdc73ea5be91d712a38458ca6119737 | Python | 14,238 | 427 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
bdb3349fd1936ca86df36d990ae9fc1584a2c1450d96f177258733435f088936 | Python | 14,240 | 387 | # %%
# imports
# autoreload
# %load_ext autoreload
# %autoreload 2
import torch
import torch.nn as nn
from torch import Tensor
import math
from torchsummary import summary
from dataset import SzDatasetRegs
import lightning as L
import torch.optim as optim
import torch.nn.functional as F
from lightly.loss import SwaVLos... |
994ea0efc7df4949b3322c91473cf0fd76b19b23d989be9598d2ebfd6f815e19 | Python | 14,244 | 427 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
4e333d2f3ce001d526208788dd8ad22c32d3c705749fc55f6c821bf8faf1ab73 | Python | 14,245 | 427 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
# 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(
[
"#1f77b4",
"#9... |
f88fe66af6134dd620c8b93673f88c648bbe965bfb350b2620d929ec5d2a0279 | Python | 14,249 | 428 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA
# 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(
[
"#1f... |
52b344303964ef1458e520ad8cc15a71d12dc450268ebb76ffcaa7446edbc519 | Python | 14,253 | 368 | """
Copied and updated from squidpy (to not have squidpy as a dependency)
Functions for building graphs from spatial coordinates.
"""
import logging
import warnings
from collections.abc import Iterable
from enum import Enum
from functools import partial
from typing import Literal, get_args
import numpy as np
import p... |
f246b739ce387edb06083f415d7d753d5273bce34b52c45f74bc1af6d5e21147 | Python | 14,257 | 428 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA import
# 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(
[
... |
a40ee260ce55f5c064cdce9f453a53b1ceb627c77f02918078cb9a1701bb13f1 | Python | 14,262 | 428 | import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
from .paper_ANOVA import ANOVA
# 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(
[... |
e3078261e61d9198a35aa883e335fdeb3947ea61bacdfe6eb621b8d81e88f0d4 | Python | 14,267 | 428 | 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(
... |
249dc415a91aebdc87a605598964f960cba21b7882a7cf6313139e831e7e4a06 | Python | 14,268 | 483 | from scipy.spatial import KDTree
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
from matplotlib.patches import Patch
from collections import Counter
import jax
import jax.numpy as jnp
from graph_tool.all import Graph
from graph_tool.topology import max_cardinality_matching
from... |
f40a47a510e28c471186afe2db654886f6c340c2bad4fb2a61c6705f873680e0 | Python | 14,270 | 343 | """Figure 7 — Brain region classification benchmark (Allen + IBL).
Reads:
results/benchmark/brain_region_benchmark.csv
results/benchmark/benchmark_cache/hippie/{dataset}/fold_0/predictions/
transductive_predictions.csv (for region label counts)
Emits in figures/figure_7/:
allen_region_profile.{sv... |
f9478ba2de849409a8b888f9ce1b8bf5b2f367d3440abadea0e3b73263ce5b5c | Python | 14,288 | 358 | """
File adapted and modified from stevolopolis/GrTrainer_paperspace
"""
import glob
import torch
import os
import random
import math
import numpy as np
from PIL import Image
from torchvision import transforms
import yaml
import json
from types import SimpleNamespace
from tqdm import tqdm
with open('config.yaml', '... |
4937ef22d6286d99d1190515387d0ba695c945467d0ae2b4d2f2bb8d79ac4c38 | Python | 14,292 | 235 | import numpy as np
from copy import deepcopy
from typing import Union, List, Tuple
from dynamic_network_architectures.architectures.unet import ResidualEncoderUNet
from dynamic_network_architectures.building_blocks.helper import convert_dim_to_conv_op, get_matching_instancenorm
from torch import nn
from nnunetv2.expe... |
fb2c56be88cf6130a9d3fd2a9c8ab6133f8bd5e197f8ad2b2fbd4e546ff5a908 | Python | 14,297 | 380 | from pathlib import Path
import re
import math
import numpy as np
import pandas as pd
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
FIGURE_LAYOUT_VERSION = "bottom-panel-labels-v2"
BASE_DIR = Path(".")
INDEX_2016_PATH = BASE_DIR / "dataset" / "index" / "v2016" / "INDEX_general_PL_data.2016... |
ebd02054ceacb7b50c1aeea39dace1dc499f7c3bd22dd99eaeaa2cdc37953dff | Python | 14,310 | 364 | import re
import random
import numpy as np
import pandas as pd
import nibabel as nb
import matplotlib.pyplot as plt
from scipy.stats import mannwhitneyu
from statsmodels.stats.multitest import multipletests
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from meld_classifier.meld_cohort import MeldCohort, ... |
91b0f08746982c7be75eebfeb9f3036b5cd0a6488f3266e81a066709f23807cb | Python | 14,312 | 482 | import os
from pathlib import Path
import torch
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns
from torch_geometric.loader import DataLoader
from sklearn.model_selection import KFold
from sklearn.metrics import mean_absolute_error, mean_squared_error
f... |
1d7e19e31a683532c16f3fce0ac0399c75e4cd887b9066b1813dcb1362027b78 | Python | 14,321 | 436 | # flake8: noqa
# There's no way to ignore "F401 '...' imported but unused" warnings in this
# module, but to preserve other warnings. So, don't check this module at all.
__version__ = "2.5.0"
# Work around to update TensorFlow's absl.logging threshold which alters the
# default Python logging output behavior when pre... |
6a92df3520b4a4b4f311f936e47a9daf0ae895baf9b18089787b1aab5e5ed711 | Python | 14,326 | 439 | 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(
... |
08b430659a9c71b63770d9c6dedd6dd39e1f27a6c663a941e43f1329194a4766 | Python | 14,327 | 440 | 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(
... |
5e5481dc817445b241dc3b5bed301673294e0e708e666245e4caeef445a9325e | Python | 14,332 | 258 | import contextlib
import ast
import io
import json
from pathlib import Path
from unittest import mock
import tempfile
import unittest
import zipfile
import types
import nibabel as nib
import numpy as np
import sys
sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
import inferref3d_backend as infer
import t... |
1bb6410bdddeb32eae2cc8ec470f5873b5377ad40c9ce47d2ff805874601bd2a | Python | 14,333 | 354 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
import torch
from datastructures.Graphs import Graph
from utilities.Utilities import build_Rx2
class ForceModule(torch.nn.Module):
def __init__(
self,
amp,
simulator,
n_nlist=64,
pairlist_paddin... |
b283edf6ddc05f1499bb7da0d42e3ddc95ad7e0cfd6cdcd2f40c5fb7abfec372 | Python | 14,340 | 317 | #!/usr/bin/env python3
"""ppi_analysis.py — protein-protein interaction analysis for MS_GEO candidates.
Queries STRING (REST API, physical-only) and GeneMANIA (physical interaction
attributes) for the union of our high-confidence candidates:
- CO7 panel (LXN, SH3BP4, CHL1, CTSZ, RPAP2, PCNP, THRB)
- Top 30 inverse... |
1958ee751e8a2353a8ee6ecd2e71a3cea21c17a9d1acbb1f95ee07bf47c4490e | Python | 14,352 | 363 | """ Chromatophore Segmentation.
Accurate identification and isolation of individual chromatophores in video frames are crucial for detailed analysis. \
The process of "segmentation" involves creating a binarized image where each pixel is classified as either part of a \
chromatophore or the background. Chromatophore c... |
0b33fedfe42e5cd256ada8d9325900cfe747679ca1e6d6d98046ca8bf4cbb3cb | Python | 14,362 | 322 | import logging
import pydantic.v1.utils as pu
import yaml
from bigstream.configure_bigstream import default_bigstream_config_str
from bigstream.image_data import ImageData
logger = logging.getLogger(__name__)
def inttuple(arg):
if arg is not None and arg.strip():
return tuple([int(d) for d in arg.split... |
4e6540faed83fdd80b5da14e0efbdee7817d33b129dbb7e436d96323c3af7a0c | Python | 14,366 | 334 | # this file is generated by tests/repositories/fixtures/pypi.org/generate.py
from __future__ import annotations
import dataclasses
from typing import TYPE_CHECKING
import pytest
if TYPE_CHECKING:
from tests.types import DistributionHashGetter
@dataclasses.dataclass
class DistributionHash:
sha256: str = "... |
49ad8f1cdd52e35bc097bafdc3c4f47292ed1caaab064cf00758360f7c51e731 | Python | 14,375 | 337 | # Copyright (C) 2025 ETH Zurich, Moritz Thürlemann, and other AMP contributors
from utilities_calibration import scatter_sum as scatter
import torch
from datastructures_calibration import Graph
from utilities_calibration import scalar_product, ff_module, build_Rx2
from torch import Tensor
import numpy as np
import os
... |
53aa4dc6dfa32d71653721c2139fdabf2cbc32a045db392ca2a5b5a0cd31c877 | Python | 14,378 | 482 | import os
from pathlib import Path
import torch
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns
from torch_geometric.loader import DataLoader
from sklearn.model_selection import KFold
from sklearn.metrics import mean_absolute_error, mean_squared_error
f... |
0731f2cbf45873edd6564dcb0c98ea1a8726b2cbc88f945f6dfa0d6cd6672e97 | Python | 14,381 | 405 | from typing import Mapping, Any
import torch
from torch import nn
from torch.optim.lr_scheduler import LinearLR
import lightning.pytorch as pl
from lightning.pytorch.loggers.wandb import WandbLogger
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.callbacks.lr_monitor import LearningRateM... |
3175614ece58fe57195ba8f0ade4605b9cd8a3da9257bfa67569197f21fc70b9 | Python | 14,386 | 433 | from __future__ import annotations
import pandas as pd
import pytest
from src.features.telemetry_alignment.exporters.export_frames import (
build_intercluster_interval_frame,
build_native_signal_frame,
get_standardized_native_window_bounds,
)
def test_build_intercluster_interval_frame_orders_photometry_... |
25285ee198e71ef55f7bd9a3923341895d2bbd2850780eb518d184eaf01f89d8 | Python | 14,388 | 523 | """
Comprehensive benchmarking script for Garfield spatial data scalability.
This script benchmarks all major tasks:
1. Dimension reduction (PCA)
2. Graph construction (KNN, Radius, mu_std, Squidpy)
3. Model training (embedding generation)
4. Label transfer (mapping)
Runs experiments on datasets ranging from ~5k to 1... |
34eb534e1f6763d0c4cbd650a9401bf22e95dbdc4e35c5289857935adab16d38 | Python | 14,388 | 384 | # 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 typing import Any, Dict, List, Optional
import torch
import torch.nn as nn
from torch import Tensor
from fairseq import utils
from fair... |
ab1e9aa9ae3216fddaa269e68e82a71098ccbe8eb0fa31171e74809368e2769a | Python | 14,401 | 385 | import json
import os
import subprocess
import sys
from glob import glob
from absl import app, flags
from scripts import pdb_utils, pmhc_templates, seq_utils, tcr_utils
flags.DEFINE_string('output_dir', "experiments/",
'Path to output directory.')
flags.DEFINE_string('pep_seq', None, 'Peptide se... |
0238b52c8825c714094c1b4ace0b7a12cbd27ba48f1d0080aaf81d65f82f15c2 | Python | 14,405 | 432 | from __future__ import annotations
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from collections.abc import Callable
from typing import Literal
import torch
import torch.nn.functional as F
from torch import nn
from torch.distributions import Normal
from scvi.module.base import auto_move_data
def _ge... |
709e67b0058b7f6cb0aca75002fab957f0a0b09439e48a732620f144418c27ac | Python | 14,425 | 436 | #!/usr/bin/env python3
import math
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
from torch import Tensor
from .utils import batched_outer
class BaseSpikeBuffer(ABC, nn.Module):
"""
Base class for a rolling binary spike buffer.
"""
def __init__(
self,
num_n... |
d109170c639f27256221cca2c82cf03f67ee298d46945428f408ae56c3d213a9 | Python | 14,430 | 286 | """
This module contains classes that back up the state of the MM/PBSA calculation
so that future post-processing of the output files can be done without
re-supplying all of the information again.
"""
# ##############################################################################
# GPLv3 LIC... |
25baf5e506847196998591d39c9cfb670fc151ae08b96b9f589e6289ff4d202a | Python | 14,434 | 386 | """
Training Script for Vision-Language Model SFT
This script trains VisionLanguageModel with supervised fine-tuning.
Uses the ImageDataModule + VisionInstructionDataModule stack and FSDP.
"""
import warnings
warnings.simplefilter(action="ignore", category=FutureWarning)
import os
import json
import logg... |
4e97396a78bd7a943fdf9bb6771f840b449d2b361094028f664d98bc12369345 | Python | 14,435 | 415 | from __future__ import annotations
import shutil
from typing import TYPE_CHECKING
import pytest
from cleo.io.null_io import NullIO
from poetry.factory import Factory
from poetry.publishing.uploader import Uploader
from poetry.publishing.uploader import UploadError
if TYPE_CHECKING:
from pathlib import Path
... |
224eb74c4e8d29d069d5e3a01f5d1ecd69db5b9d2d8286ad4d082f74e3dee0c8 | Python | 14,457 | 337 | # encoding: utf-8
"""
@author: Jiayang Chen
@contact: yjcmydkzgj@gmail.com
"""
import logging
import torch
import torch.nn as nn
from ignite.engine import Engine, Events
from ignite.metrics import Average
import numpy as np
import os
import subprocess # for ct file graph visualization
abs_file = __file__
work_di... |
1f4c51686ef1245efebe769dc0644c8a62e46ea0387846188d44fe01dcd28895 | Python | 14,468 | 351 | import os
from collections import OrderedDict
from PyQt5 import QtCore
from PyQt5.QtCore import pyqtSlot, Qt, QObject
from PyQt5.QtGui import QBrush
from PyQt5.QtWidgets import QFileDialog, QTableWidgetItem, QAbstractItemView, QMenu, QMessageBox, \
QDialog, QDialogButtonBox
from mdt.gui.model_fit.design.ui_generat... |
be68d98a8943aa1b15056f66fa4324e4dbacb8ec084ab33f3cd321b40b21411c | Python | 14,470 | 385 | import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import pytorch_lightning as pl
from .backbones import ResNet18Enc, ResNet18Dec
from pytorch_lightning.utilities import grad_norm
from torch.nn.functional import normalize
from .optimizers import AdamWScheduleF... |
54439e7f60788fc7632cf3774244aa4330e36cfc3f1187c7726a9e0c5dc9e158 | Python | 14,484 | 308 | #!/usr/bin/env python3
"""Portao de entrada: reprova ANTES de gastar fila.
Uso:
python3 preflight_eos.py --threads 8 [--timeout 1800] [--skip-run]
Existe por uma falha real de 2026-07-26 na campanha de producao: sem
`LD_LIBRARY_PATH` o SIESTA morre em milissegundos com
`error while loading shared libraries`, a fi... |
dab7733d1fa948a0d163bfaee376f8c47f3c6771b8617625b28237390b0cd589 | Python | 14,495 | 345 | import torch
from torch import nn
import torch.nn.functional as F
import os
__all__ = ['HRNet', 'hrnetv2_48', 'hrnetv2_32']
# Checkpoint path of pre-trained backbone (edit to your path). Download backbone pretrained model hrnetv2-32 @
# https://drive.google.com/file/d/1NxCK7Zgn5PmeS7W1jYLt5J9E0RRZ2oyF/view?usp=sharin... |
e0e65533d2e2cba36223e8aa82ab78ef956b3a715217741b026017199c93446a | Python | 14,512 | 310 | import shutil
import time
from tqdm import tqdm
from dataloadR.augmentations import *
from evalR import voc_eval
from utils.utils_basic import *
from utils.visualize import *
from utils.heatmap import Show_Heatmap
current_milli_time = lambda: int(round(time.time() * 1000))
class Evaluator(object):
def __init__(s... |
f8e2fe3b4652ab5eccbf8bad38a9df56d69a01026dd7db5573569b60ed33d4e1 | Python | 14,525 | 361 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import json
import os
from collections.abc import Iterable, Sequence
from functools import cached_property
from inspect import getmembers, isclass
from pathlib import Path
from typing import Literal, Sequence, Type, TypeVar
import numpy.typing
i... |
d0134ab65edb93781320e57306b8253b6ae756e3057c3a7b2552a7ba7f62bf78 | Python | 14,532 | 420 | from neuron import h
from neuron.units import mV, ms
import cell_singlespine_randomloc as cellrl
import cell_singlespine as cellaspine
import numpy as np
# from scipy.optimize import curve_fit
# distance from soma vs somatic amplitude
# does not include dendrite information
# higher amplitude line from basal dendrit... |
cf5a4577f5a8afefe4b65b5a0b306e6f735e819fc470f28262afa4b929c02fe3 | Python | 14,542 | 361 | import torch
import numpy as np
import pandas as pd
from sklearn.linear_model import TheilSenRegressor
import torch.nn as nn
import networkx as nx
from tqdm import tqdm
import pickle
import sys, os
import requests
from torch_geometric.data import Data
from zipfile import ZipFile
import statsmodels.api as sm
from skle... |
8f53ef5f0ffb7075ac26558d80641eb5290cf3310e26ba9d7087d0f450ca1d5d | Python | 14,557 | 307 | # -*- coding: utf-8 -*-
""" Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU
Copyright (C) 2019 Andac Hamamci
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License... |
7137bfafe0919eb9a434c8266373a79753e963c8889e959a9cc9ec20a512fb8f | Python | 14,564 | 444 | #!/usr/bin/env python3
"""
Compute 3D Autocorrelograms from Spike Times for NEMO Benchmark
This script computes 3D log-scale autocorrelograms (ACGs) from raw spike times,
which are required for NEMO's bimodal embedding model.
The 3D ACG has shape (N, 10, 101):
- N: number of neurons
- 10: frequency/firing rate bins
-... |
c04a9ccc6bef2cc69741f1179eca4a2b6a0b2b2bc3e704d446a58999e9ccdaa9 | Python | 14,570 | 291 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
from st_risk.paths import current_results_dir, ensure_results_layout, project_root, results_file, set_selected_run
DEFAULT_RUN_ID = "2026-06-20-threshold-sensitivity-v2-donor-disjoint"
DEF... |
996f13c4d18a8d240b4ca2148b7ebe0f096f0082a844f725710ce9c560a41d90 | Python | 14,579 | 376 | # -*- coding: utf-8 -*-
from __future__ import print_function
import json
import os
import struct
import sys
import platform
import re
import time
import traceback
import requests
import socket
import random
import math
import numpy as np
import torch
import logging
import datetime
from torch.optim.lr_scheduler import... |
7030f8b4339c6b1a7ac7c1d54ea0172af166d9442b9d4ea1bd9246ebcaf94650 | Python | 14,607 | 423 | from __future__ import annotations
import copy
import hashlib
import io
import logging
import os
import shutil
import stat
import sys
import tarfile
import tempfile
import warnings
import zipfile
from collections.abc import Mapping
from contextlib import contextmanager
from pathlib import Path
from typing import TYPE... |
ccfa24b676fd3912bd1a4ce54d24450e19daf546627a00098646f1169518db9f | Python | 14,608 | 401 | import torch
import abc
import os
import copy
import pytorch_lightning as pl
from utils.lr_scheduler import *
from torch import distributed as dist
class AbstractModel(pl.LightningModule):
def __init__(self,
lr_scheduler_kwargs: dict = None,
optimizer_kwargs: dict = None,
... |
deea87ea5ab2e39279f95279041e964921734fddcd1dc04fc726bc0e1f0d8c48 | Python | 14,613 | 455 | from __future__ import annotations
import dataclasses
import json
import logging
import os
import re
from copy import deepcopy
from json import JSONDecodeError
from pathlib import Path
from typing import TYPE_CHECKING
from typing import Any
from typing import ClassVar
from packaging.utils import NormalizedName
from ... |
7948d9401daa03f2017bfa27d8637bb5dffb1a304cb91a41c987666ec16f1d7c | Python | 14,616 | 244 | #!/usr/bin/env python3
"""figure4_proteomics.py — Figure 5 (proteomic validation), CORRECTED.
KEY CORRECTION: the "timsTOF" dataset is CSF (Bader & Mann 2024, 2nd platform of
the Astral CSF cohort; n=1,536 MS / 2,363 HC), NOT a separate "Wang & Julien
brain" cohort. The genuine brain proteomics is Wang & Julien 2025 (... |
4f44ffcca5e95674b2450a308a3252eb23eb3cba8d1a01071e60d992fe5e1a4d | Python | 14,621 | 508 | from __future__ import annotations
import torch
import pytest
import vak.metrics.boundary_detection.functional
from .conftest import FIND_HITS_TEST_CASES, PRECISION_RECALL_FSCORE_RVAL_TEST_CASES
@pytest.mark.parametrize(
'test_case',
FIND_HITS_TEST_CASES,
)
def test_find_hits(test_case):
(preds,
t... |
8f64b9d8508fd47de09dbbafbdc6159f775e6b4cb1a4a4b93a62a206872875bf | Python | 14,622 | 351 |
import os
import zarr
import torch
import shutil
import argparse
import numpy as np
from pathlib import Path
from einops import rearrange
from torch.utils.data import Dataset, DataLoader
from PIL import Image
import torch.multiprocessing as mp
from torch.utils.data.distributed import DistributedSampler
from torch.nn.... |
60998b9fcc8082047a650364db957b3e5bb38f6647bc4f39eca31cf784a55cfa | Python | 14,625 | 404 | """LAMMPS + AIREBO runner for carbon percolation clusters.
Cheap, well-parameterised reactive force-field for pure-C clusters with
arbitrary connectivity (handles dangling bonds gracefully — AIREBO was
designed for radical hydrocarbons). Single-point and minimisation modes.
Output: total potential energy in eV and t... |
ae6341371f4cb4e021409f6f5ebcf5ad5577d9389c06867dc3913d70979cb42d | Python | 14,659 | 339 | from __future__ import annotations
from typing import ClassVar
from PySide6.QtWidgets import QGridLayout, QSizePolicy, QTabWidget, QVBoxLayout, QWidget
from src.core.app_settings_manager import AppSettingsManager
from src.features.behaviour_alignment.exporters.aligned_behaviour_exporter import (
AlignedBehaviour... |
04c0f7d92baa6e4b852f953032e88170e7b24937a0f1979fc688925e9c5ecaae | Python | 14,660 | 354 | # Copyright 2015 Google Inc. 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 at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or a... |
a199999a543d77c6e2439b6ef277eb21015e045e1584f591c1a75309251516da | Python | 14,662 | 377 | import numpy as np
import pandas as pd
import scipy.stats as stats
import scipy.io as sio
import os, sys, argparse, time, logging, getpass
import matplotlib.pyplot as plt
from GWAS_IO.summary_stats_Utils import *
def read_sum_dat(sumFile, logger, kargs):
'''
Read give summary statistics.
Input:
sumFil... |
c1fdacfa434dc91475d066f2e38ee7c631aad04bc50c7170f4a9bb15a67325f4 | Python | 14,677 | 436 | #!/usr/bin/env python3 -u
# 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.
"""
Run inference for pre-processed data with a trained model.
"""
import ast
import logging
import math
import os
... |
d2cc1d975ed32bb2f0ecfaa6470914ef846b0a9c5b0d7d1e0318a625d837e5bf | Python | 14,677 | 334 | import json
import tkinter as tk
class AppSettingsManager:
def __init__(self, app_type="default"):
"""
Initialize the settings manager with default settings.
Parameters:
- app_type (str): The type of app to initialize the settings for.
"""
self.app_type = app_type
... |
c0eb4252967de2a9368ee46f49dfab16ee2c86d5fa82b555030a9d23e29564cd | Python | 14,679 | 338 | #三个头0414
import sys
sys.path.append("..")
import utils.gpu as gpu
from modelR.backbones.mobilenetv3 import MobileNetV3
from modelR.backbones.mobilenetv2 import MobilenetV2
# from modelR.necks.conv_csa_drf_fpn_hbb import FC2_CSA_DRF_FPN
from modelR.necks.Three_Head import FC2_CSA_DRF_FPN
from modelR.head.dsc_head_hbb im... |
ced6ff89b007e705f24be1d06b906a2d949c68602f0c93ed48c34c3915b5ac4e | Python | 14,679 | 556 | # -*- coding: utf-8 -*-
"""
Created on Sun Mar 2 21:23:46 2025
@author: hanna
"""
import os
import pickle
import numpy as np
import pandas as pd
from tqdm import tqdm
import seaborn as sb
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
# from scipy.optimize import cur... |
55e4fbc65096a04f5c36b06bd315aac31bc96f9033dad105137625e6ac8ec3d1 | Python | 14,682 | 476 | # %%
# Standard imports
import re
from typing import Union
import matplotlib.pyplot as plt
import seaborn as sns
# Third party imports
import pandas as pd
from scipy.signal import iirnotch, sosfiltfilt, butter, filtfilt
import numpy as np
# %%
def _pull_iEEG(ds, start_usec, duration_usec, channel_ids):
"""
Pu... |
ec23955b1d1ce1505f2dde456df84cfa6894b0222eb15fb62d59c32637fca9f8 | Python | 14,686 | 426 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR, CosineAnnealingWarmRestarts
import numpy as np
from celltype_ibl.utils.c4_vae_util import VAEEncoder, vae_encode_model, load_acg_vae
from celltype_ibl.models.BiModalEmbedding import (
BimodalEmbeddingMode... |
c6e6c0b9ac1a4bcb05909cf45a04db8761d7f5baea9eb9544e690f77f03fdbbf | Python | 14,702 | 279 | import torch
import torch.nn as nn
from ..layers.convolutions import Convolutional, Separable_Conv_dila, Separable_Conv, Deformable_Convolutional
import torch.nn.functional as F
from ..layers.attention_blocks import SELayer
class SPP(nn.Module):
def __init__(self, depth=512):
super(SPP,self).__init__()
... |
2dc684f863eecbf496847ae21b3e0d5695382a51cc09386789573091c7fbfe20 | Python | 14,711 | 367 | import warnings
warnings.filterwarnings('ignore')
from typing import Callable, Tuple, Union
import math
import torch
from torch import Tensor
import torch.nn as nn
import torch_geometric.nn as gnn
import torch.nn.functional as F
from torch_geometric.nn.conv import MessagePassing
from torch_geometric.nn.dense.linear im... |
d235aebe768de224e87e4210c98f2a9c68f15caccb31dbd1e69c8f15614363ed | Python | 14,716 | 426 | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.optim.lr_scheduler import StepLR, CosineAnnealingWarmRestarts
import numpy as np
from celltype_ibl.utils.c4_vae_util import VAEEncoder, vae_encode_model, load_acg_vae
from celltype_ibl.models.BiModalEmbedding import (
BimodalEmbeddingMode... |
1407071e2725e8eda5b46de23a0b3b936a8e4bd9862f014b7e4bbe31a4c98884 | Python | 14,720 | 455 | import os
import warnings
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from scipy.spatial import ConvexHull
from vedo import Cylinder
from tqdm import tqdm
import NeuRosetta as nr
import HexCraft as hc
def add_compass(ax, pos=(0.9, 0.1), size=0.05):
"""
ax : matplotlib axis... |
891c8056d077ea29dc5c33472c9bcc71472de22299d1b3f788f6bcbb12625bdb | Python | 14,722 | 393 | """Building a model from a config, and loading a checkpoint back into it.
`train`, `tune` and `evaluate` each used to construct the model themselves with
`getattr(models, config.model_class)`. That resolved any attribute of the
package, checked nothing, and — because it ran after the dataset had loaded —
reported a mi... |
4bdf3a7e315d3bc34e3eca02d5125f492e2cefca8d00277c26073e40c8f970ec | Python | 14,758 | 374 | import torch, functools
from torch import nn
import torch.nn.functional as F
from torch_geometric.nn import MessagePassing
from torch_scatter import scatter_add
def tuple_sum(*args):
'''
Sums any number of tuples (s, V) elementwise.
'''
return tuple(map(sum, zip(*args)))
def tuple_cat(*args, dim=-1):
... |
634c3e512c4274ac29ce7c2ccf29e5a5603de1d4a7bffe51f3eb76fd70a993c5 | Python | 14,762 | 313 | # !/usr/bin/python
# -*- coding: utf-8 -*-
# Pytoolkit GeneSymbolUniform for hECA 2.0
# author: Yixin Chen, Haiyang Bian
# date: 2023/09/25
import scanpy as sc
import pandas as pd
import sys
import numpy as np
from loguru import logger
import scipy.sparse as sp
import os
from tqdm import tqdm
from concurrent.futures i... |
47b5aff37c10c6d93075099a665a8879a4f0b02f09b633a7c3b84b83e939a540 | Python | 14,763 | 314 | from albumentations.augmentations.transforms import HueSaturationValue
from batchgenerators.transforms.abstract_transforms import AbstractTransform
import numpy as np
from scipy import linalg
from skimage.util import dtype
from skimage.exposure import rescale_intensity
# import os
import pickle
def write_pickle(obj, fi... |
9c9dd3467d321f0bf571c878dc5636dc72edad73392c449fb20db29827d4f2e3 | Python | 14,770 | 383 | """
GLS/LVEF estimation.
"""
from __future__ import annotations
import argparse
import os
from pathlib import Path
from glob import glob
import copy
import cv2
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from core.MOAv3 import MOAFlow
from core.modeling.... |
8789f27c8f6b2ca0de75599843eb262d8ae6c38b1b4ff05a556cadcf535b6126 | Python | 14,771 | 434 | from typing import Optional, Tuple, List
from abc import ABC, abstractmethod
import torch
import torch.nn as nn
import pytorch_lightning as pl
import numpy as np
import sklearn.linear_model
from tqdm import tqdm
from dataclasses import dataclass, field
from utils.tokenization import Vocab
from utils.metrics import rna... |
3449fed93df4078b56fa04ab4267de83556004fad0852058aab0ccd44ec66132 | Python | 14,772 | 393 | #Code from: https://gist.github.com/rockt/15ee013889d65342088e9260a377dc8f
import re
import torch
import numpy as np
def einsum(equation, *inputs):
"""A generalized contraction between tensors of arbitrary dimension.
This function returns a tensor whose elements are defined by `equation`,
which is written in ... |
afc4af6bd47d87e02b5fb57457fe873c499ba9b2e217ed210c343938845bf976 | Python | 14,773 | 365 | """
Pure Excel/CSV export helpers for telemetry/photometry/opto alignment.
No UI framework dependencies.
"""
from __future__ import annotations
import re
import numpy as np
import pandas as pd
# ---------------------------------------------------------------------------
# Cluster heading generation
# -------------... |
eccdbb112ec7a31ee4d8c9e111adc1c169520cf1a904afbcaacdc3c89bf6f2fa | Python | 14,773 | 308 | #!/usr/bin/env python3
"""Some functions and classes for streaming predictions."""
import numpy as np
import h5py
import pybedtools
import pysam
from bpreveal import logUtils
from bpreveal.logUtils import wrapTqdm
from bpreveal.internal.constants import LOGIT_T, LOGCOUNT_T
import bpreveal.internal.files
class FastaRe... |
fbea609edf99261b804479618ed3d7fb691fd49fc4f81c12d4995e0b6dbbb071 | Python | 14,774 | 446 | """Neural sampling implementation (Buesing et al. 2011)."""
import time
from datetime import datetime
from typing import Any, Callable, TypeAlias
import numba
import numpy as np
import numpy.typing as npt
from .utils import (
bm_to_probs,
distr_from_states,
get_states_from_spikes,
list_of_states,
... |
611267d66ef4702b73f9ebe6ed680f54ed14069a49b5da4882bb2ff8713cc09f | Python | 14,775 | 468 | """
This module builds rna rna interaction functions.
Author: wangning(wangning.roci@gmail.com)
Date : 2022/12/7 7:41 PM
"""
import time
import os.path as osp
from collections import defaultdict
import numpy as np
import pandas as pd
from tqdm import tqdm
import paddle
import paddle.nn as nn
from paddlenlp.data im... |
45e5054697ec904112e239dbb98e05136523db45cc825ced185b469b7eaf12e8 | Python | 14,777 | 319 | # -*- coding: utf-8 -*-
""" Cellular Automata Tractography: Fast Geodesic Diffusion MR Tractography and Connectivity Based Segmentation on the GPU
Copyright (C) 2019 Andac Hamamci
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as p... |
d49d3e139dc5e4b4a0209c216b02fca850aec1c16d18eb3e4314d5847d636e62 | Python | 14,777 | 372 | # -*- coding: UTF-8 -*-
"""
@Project: iDCF
@File : utils.py
@IDE : PyCharm
@Author : hjguo
@Date : 2025/7/9 11:37
@Doc : Data processing and knowledge base loading code
"""
import os
import pandas as pd
import numpy as np
import anndata as ad
import scanpy as sc
from tqdm import tqdm
from sklearn.preprocessin... |
b66cb4bd68a759734abd0fe1c5a96bf58b221b8e3d68d6077d88b69cc8d1cf20 | Python | 14,779 | 317 | import torch
import os
import sys
from .model.model import MulanConfig, scMulanModel
from .model.model_kvcache import scMulanModel_kv
import torch.nn.functional as F
try:
import torch_npu
print('Using torch_npu')
except ImportError:
pass
from .utils.hf_tokenizer import scMulanTokenizer
import scipy.sparse
import n... |
c739a941951d7efbac71d6279a5a4e71aa6946ac36261967e3afe1a25e4149fa | Python | 14,782 | 326 | #!/usr/bin/env python3
# 12_celltype_4layer_master_py — generated from notebook spec
# ============================================================
# # 12 — Cell-type-specific scRNA validation + 4-layer master summary
#
# For every gene of interest (CO7 panel + top inverse-concordant), produce:
#
# 1. **Cell-type... |
f3d3dd2b74677266d9e5f496af4185dc7e0cce339fecce547b401a7afa362451 | Python | 14,794 | 308 | from __future__ import annotations
import argparse
import json
from pathlib import Path
import numpy as np
import pandas as pd
from st_risk.paths import current_results_dir, ensure_results_layout, project_root, results_file, set_selected_run
DEFAULT_RUN_ID = "2026-06-20-uncertainty-baseline-v1"
RELIST_SCORES = ("r... |
fd3984d6c0fe8e3ce93a40475472a4c8a227d2c88eab635cf0547fa88c2f9d0e | Python | 14,807 | 352 | # -*- coding: utf-8 -*-
"""
Created on Mon May 1 19:41:07 2023
@author: Sen
"""
import os
import sys
import subprocess
import hashlib
import warnings
import platform
import csv
import numpy as np
from tqdm import tqdm
import argparse
import torch
from transformers import AutoTokenizer, GPT2LMHeadModel
import shutil... |
b5a82cd9f72bb6f923cb0cc2772b3139f2434e16656ce72bcc67ca0f2584c055 | Python | 14,816 | 484 | from __future__ import annotations
from pathlib import Path
from typing import TYPE_CHECKING
import pytest
from packaging.tags import Tag
from poetry.core.packages.package import Package
from poetry.core.packages.utils.link import Link
from poetry.console.exceptions import PoetryRuntimeError
from poetry.installatio... |
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