sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
61308b1c31f691d108cda200017e936f09b42e54a10633a08a3276283fcb33c3 | Python | 7,335 | 205 | import genomepy
from pybdm import BDM
import pdb
import os
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings("ignore")
warnings.simplefilter("ignore")
'''
Annotate off-target dataframe with physical features
- GC content
- Nucleotide BDM
- NuPoP Affinity and Occupancy scores
... |
75038d5b2a5bcdfbf31820ed769dccfcd25ac12709bccd838876f1d64fb0c3b8 | Python | 7,335 | 188 | """
pygments.formatters.svg
~~~~~~~~~~~~~~~~~~~~~~~
Formatter for SVG output.
:copyright: Copyright 2006-2023 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pip._vendor.pygments.formatter import Formatter
from pip._vendor.pygments.token import Comment
from pip... |
328ff114203504a6e657ed959bfcafdafb87f7403d32d6ba714e38c58eaf8a41 | Python | 7,336 | 219 | """The midsagittal-plane sidecar cache."""
import json
import os
import pytest
def _c():
from regtools.hemisphere import cache
return cache
def _plane():
from regtools.hemisphere.plane import MidsagittalPlane
# A 3-D schema-2 plane always records the voxel frame its ml_axis inde... |
429a61d89a522d339e8b9d378d67af1eb95c64654bbd22bff6612c4c31551fa3 | Python | 7,336 | 168 | from typing import Dict, Union
import io
import pywt
import numpy as np
import matplotlib.pyplot as plt
from ..base_transform import EEGTransform
class CWTSpectrum(EEGTransform):
r'''
A transform method to convert EEG signals of each channel into spectrograms using wavelet transform.
.. code-bl... |
6be5c0218a86a1392a2e3ad4204e0587342b5ea678ffc400162fcad694cf17a9 | Python | 7,339 | 201 | """Test the full PREP pipeline."""
# Authors: The PyPREP developers
# SPDX-License-Identifier: MIT
import warnings
from unittest import mock
import numpy as np
import pytest
from pyprep.prep_pipeline import PrepPipeline
from .conftest import make_random_mne_object
@pytest.mark.usefixtures("raw", "montage")
def t... |
dce726c19dab39e4419be4b598d7243d5d96b1e729a2aef87aa7c80223f3ecdf | Python | 7,341 | 228 | from scipy.integrate import solve_ivp
import matplotlib.pyplot as plt
import numpy as np
import math as m
from mpl_toolkits.mplot3d import Axes3D
import torch
import torch.optim as optim
import matplotlib.ticker as ticker
from Rosenbrock import Rosenbrock_func
from Quadratic import Quadratic_func
from Ras... |
b582e673fabba032f7671d49bfe194930b6d2168951875f55112d8f9607077cf | Python | 7,343 | 201 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Written by Tong He and CBIG under MIT license:
https://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md
"""
import os
import time
import random
import argparse
import numpy as np
from keras import backend as K
from keras.optimizers import SGD
from config import co... |
48157d8b2e68128648cc6cfd0209a99f302d5301dbb8fee47779ac7755fae559 | Python | 7,344 | 189 | import pandas as pd
import subprocess
import sys
import shutil
from tqdm import tqdm
from pathlib import Path
from argparse import ArgumentParser
from functools import partial
from multiprocessing import Pool
from saturation_mutagenesis import renumber_pdb_residues
from build_dataset_from_mutant_structures import bac... |
ada549ea2ed0046025c627cc017b3fc9bca4b35637a349e99f0489124ab2acf0 | Python | 7,344 | 181 | import pytest
import numpy as np
import pandas as pd
import nibabel as nib
from pyrelimri.masked_timeseries import (trlocked_events, extract_time_series, extract_postcue_trs_for_conditions)
import warnings
warnings.filterwarnings("ignore", message="The nilearn.glm module is experimental.", category=FutureWarning)
from ... |
dc17f3b997ef172b267acaad62ea6e8ebe8ea5de8825b23792997b965cfe6078 | Python | 7,344 | 262 | from typing import Callable, Tuple
from functools import partial
import jax.numpy as jnp
import jax
from netket.utils.types import PyTree
import netket.jax as nkjax
from netket.stats import statistics as mpi_statistics, Stats
import numpy as np
import matplotlib.pyplot as plt
from tre_tvmc.driver.utils import mpi_logm... |
f834a17bdb4119b8934fabf483188bd603e17f505703c616d3588b20782d72b9 | Python | 7,344 | 154 | """
This script summarizes the significant results for global efficiency and participation
coefficients, organizing them and matching the results with the node coordinates for H7
"""
import sys
import numpy as np
from scipy.io import loadmat
import seaborn as sns
import matplotlib as mpl
import matplotlib.pyplot as p... |
0f3725c013f769a4324dc08781a73090858edc5eb0e49d1e4fa16ff75ed3fb22 | Python | 7,348 | 204 | """Unit tests for regtools.registration.core.transform_utils.halve_and_pad.
Covers:
- left / right / whole modes on 2D arrays (axis=1, the default)
- pad size = round(0.06 * width)
- output shape correctness
- column selection (left keeps [0:mid], right keeps [mid:])
- zero-padding side (left pads right edge; r... |
3c456b61aaf879d0afaa94e623c732c569fddcc49463d52b2803fee76f9c7341 | Python | 7,348 | 196 | """
Introduction to the datasets Module
===================================
Welcome to the guide on TorchEEG's ``datasets`` Module! This module provides you with various benchmark datasets for EEG-based emotion recognition, such as DEAP, DREAMER, SEED, MAHNOB, AMIGOS, and MPED. These datasets use a range of stimuli li... |
34447627aac837470b9e35497f685dfae4d41cd4a51c95b49c90148313049cc0 | Python | 7,349 | 225 | #!/usr/bin/env python
# Copyright (c) 2018-2020 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provides the ScenarioManager implementations.
It must not be modified and is for reference only!
"""
from __future__... |
051f6d1a80a2f39987f7bc2b7cd38c1e20558a2d12f7c18726edb82dcf86e162 | Python | 7,350 | 194 | '''
Description:
Run MIOFlow model.
Reference:
[1] https://github.com/KrishnaswamyLab/MIOFlow/tree/main
'''
from MIOFlow.utils import generate_steps, set_seeds, config_criterion
from MIOFlow.models import make_model, Autoencoder
from MIOFlow.plots import plot_comparision, plot_losses
from MIOFlow.train import ... |
c850df46ab59b8b88be8c6332588d655994356c6edaa6b38dafca323a8807164 | Python | 7,350 | 196 | from typing import List, Union
import numpy as np
import schnetpack
import torch
from ase import Atoms
from ase.calculators.calculator import Calculator, all_changes
from schnetpack.units import convert_units
import spainn
from spainn.properties import SPAINN
__all__ = ["NacCalculator"]
class AtomsConverterError(E... |
06798f7a9c2b7a4d4e6d7cdd259ed89803a3d3bc30e0a7b2e91ab9adb65749d4 | Python | 7,351 | 189 | #! /usr/bin/python
import argparse
import os
class DNAReference:
def __init__(self, reffile):
self._contignames = []
self._contigs = {} # contigname 2 contigseq
with open(reffile, 'r') as rf:
contigname = ''
contigseq = ''
for line in rf:
... |
39f80fdad72077ee87c617ace1bee6c15ecef9d1f5ec2b6e1ce4124395ac444e | Python | 7,351 | 206 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
66fb05b11efa47cab09f0d6c55ab457c995bb8388b2b3e1cfbc0a95b2e199cc8 | Python | 7,351 | 175 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright 2024 Clinic for Diagnositic and Interventional Radiology, University Hospital Bonn, Germany
#
# 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 ... |
95d59f3cf385710bc1bdfd8f60175a126fbcff50b33e02af658be27c873795f9 | Python | 7,352 | 187 | """
Unit tests for regtools/registration/core/transform_utils.py
Covers:
- applyAffineTransform
- applyDeformationField
- affineTransformPointCloud
"""
import numpy as np
import pytest
# ===========================================================================
# Fixtures
# ===================... |
7c909c906500a3673ecf5a1edcd850a78ab0127bddcdaba79def4d2535df4c1e | Python | 7,353 | 231 | import torch.nn as nn
import torch.utils.model_zoo as model_zoo
# -----------------------input size>=32---------------------------------
__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
'resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde... |
a89bb3eb65e24943eb6b829d73f9399ee9c9a6df3912d8b24e01e541c3870de0 | Python | 7,353 | 194 | """Cross-platform process-tree termination + PID-file helpers (Qt-free).
Why this exists
---------------
The registration GUI launches a run in a **detached** terminal
(``wt new-tab`` / ``cmd /c start``): the ``wt``/``cmd`` *launcher* the GUI holds
a handle to exits immediately, while the real ``python pipeline.... |
157e9fefa21e64bd1e1c15899c54030f97507b216ad5ce5d5933301ee28f969e | Python | 7,355 | 178 | """
VGG16 with BN
Difference from vgg16 in torchvision for higher resolution:
1. Layer config changes from [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'] (torchvision)
to [64, 64, 128, 128, 'M', 256, 256, 256, 512, 512, 512, 'M', 512, 512, 512] (acscon... |
754c4ac55c679440a3ab3b8b4890a95b675994b2b698c1f03889a0ff9a1482d7 | Python | 7,355 | 191 | """
Unit tests for regtools/stitching/launch_helpers.py
Covers:
- get_python_exe: pythonw.exe -> python.exe substitution
- validate_mosaic_folder: no file / exactly one / multiple files
- build_batch_script: key lines present in output
- build_napari_preview_script: path and section number embedded correctly
-... |
cf4f6339a994983f25107aa5372ffe2d4d584aa79dd53b6342dc16beef5f13ba | Python | 7,355 | 217 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
47a9a6aa93c7b9a9d768d59b7cd09ceb2bcb51e67507bc3395dca064f649499a | Python | 7,358 | 244 | import numpy as np
import matplotlib.pyplot as plt
from utils.params import ParamsPack
param_pack = ParamsPack()
from math import cos, sin, atan2, asin, sqrt
import cv2
def write_obj(obj_name, vertices, triangles):
triangles = triangles.copy() # meshlab start with 1
if obj_name.split('.')[-1] != 'obj':
... |
a7824254c131e209d81cd585687bf638a6dbf4cb3c46a9850c33aeb3e2404b67 | Python | 7,359 | 187 | """Out-of-core (dask, sharded) OME-Zarr writer for large TIFF channels.
Streams the base level and builds the pyramid disk-to-disk so peak memory stays
bounded regardless of channel size. See the design spec for the verified facts.
"""
import os
import numpy as np
import zarr
def add_ngff04_top_level_m... |
ab9d945743352d634578f6b361e8e7070d8f51b2e18320ba5868bb0b6b850cb6 | Python | 7,360 | 209 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Jun 13 2019
@author: jmagraner
"""
# %%
from autograd import grad
from autograd.misc.optimizers import adam
import autograd.numpy as np
def fitmodel(params,target,specs):
### Define LDS as a recurrent neural network ###
def LDSmo... |
a8cb23534942481b2049aeea14c2a5ff548b8e64ba2af022e475c7bea420fcca | Python | 7,362 | 201 | import bct
import networkx as nx
import numpy as np
from tqdm import tqdm
from sklearn.utils import check_random_state
def in_strength_preserving_rand_sa_dir(A, rewiring_iter = 10,
nstage = 100, niter = 10000,
temp = 1000, frac = 0.5,
... |
afb10066c37be83f4a993302051d5178602b06a08518cb88187aa0b0f90153cd | Python | 7,368 | 201 | # --------------------------------------------------------
# Based on BEiT, timm, DINO and DeiT code bases
# https://github.com/microsoft/unilm/tree/master/beit
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# https://github.com/facebookresearch/deit
# https://github.com/facebookresearch/dino
# --... |
876067aa7519127fabc3450521eda5b35d06b9f013ca6f9074857332274a4e68 | Python | 7,369 | 195 | # Node-group asset "Symmetry Dihedral" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
import math
from typing impo... |
d0ae6a8aee3e75a817bef274e593acdf9ab81712158a67a375ebe4dda9fbe3ef | Python | 7,369 | 185 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import numpy as np
import warnings
from sklearn.cluster import MiniBatchKMeans as KMeans
from scipy.stats import zscore
def _scaled_kmeans_init(X, n_clusters=100, n_local_trials=None):
"""Init n_clusters see... |
9184a7bfd6b1e0347e413e67281f911d5a354c43866915893f51b1510c9f77fa | Python | 7,370 | 175 |
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
import numpy as np
import torch
import networkx as nx
import GNN_core
import argparse
import random
import torch.optim as optim
import copy
from sklearn.metrics import confusion_matrix, roc_curve, auc
parser = argparse.ArgumentParser(description="Simulate a GNN with... |
9a4eae6a94eac323ae4a0493543bd83c5b646d0b63001fdbc97031d27be83835 | Python | 7,370 | 207 | """Engbert & Kliegl 2003 microsaccade detector.
The pipeline for a single eye trace:
velocity ──┐
├─► sigma ──► elliptic threshold ──► run-length grouping
velocity ──┘ │
▼
... |
c7fe3c38ebf76d83807061c7b7a8afe8a64419f8c580da9429372f18ce7d335f | Python | 7,370 | 222 | #!/usr/bin/env python
"""
Batch execution of scripts.
Modifies scripts is a specified way, executes them, and modifies files that
hold information about the results (catalogs).
As an example of a possible usage existing scripts for segmentation and
analysis of individual tomograms are first modified (to change para... |
aa2b154323ff5acc78f56a57b7e50f1f05c4a8a153d4187bf62136fb237b871c | Python | 7,372 | 138 | # coding=utf-8
# Copyright 2020, Microsoft and the HuggingFace Inc. team.
#
# 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 require... |
56101a8c17c9940d0fcf9b0aa3bded891fa2d81e163c85e2d57839bf281a819d | Python | 7,373 | 193 | from __future__ import division, print_function
import os
from typing import List
import numpy as np
import torch
from PIL import Image
from topaz.utils.data.loader import load_image
def load_state_dict_from_pkg(pkg: str, path: str, map_location="cpu"):
"""
Load a torch state_dict from a packaged resource.
... |
b1c12d3dbe734c6d27f2faf449ce69e72a82734d3f01557aee7b384a7a5e0da5 | Python | 7,373 | 218 | """
Defines class Experiment that holds data form one experiment.
This class is not meant to really hold data (Observations is the main
data-holding class). It is only used for some special purposes.
# Author: Vladan Lucic (Max Planck Institute for Biochemistry)
# $Id$
"""
from __future__ import unicode_literals
fro... |
1fbe4f9636f082096e81f3905874d21d21937cd0118dc090a34d08b30c5ebd29 | Python | 7,378 | 172 | import torch
from torch import nn
from torch.nn import functional as F
from torchdrug import core, layers, tasks, metrics
from torchdrug.core import Registry as R
from torchdrug.layers import functional
@R.register("tasks.ContactPrediction")
class ContactPrediction(tasks.Task, core.Configurable):
"""
Predict... |
76233e1d0fec33d41e176dc909a4c5948855f609f556b0090068703b3063555d | Python | 7,381 | 214 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
a9a4fd6fbda4147fc81197ae57dd926edb495bf0aa483852a87702b3697406d5 | Python | 7,381 | 217 | """Image processing functions designed to work with OpenSRH datasets.
Copyright (c) 2024 University of Michigan. All rights reserved.
Licensed under the MIT License. See LICENSE for license information.
"""
from typing import Optional, List, Tuple, Dict, Callable
from functools import partial
import random
import ti... |
7c37df6a68abd476943130b6ce14d61352372a3772779b4ada801b79fbf29762 | Python | 7,383 | 155 |
import pandas as pd
import numpy as np
import rpy2.robjects as robjects
import os
fitdistPath=os.path.join(os.path.dirname(__file__), "fitdistrplus/fitdist.R")
quantilePath=os.path.join(os.path.dirname(__file__), "fitdistrplus/quantile.R")
# def iterateFitting(DBLmetricsDF):
# '''
# Iterating over ranger of Seco... |
d40a29bbe34642c9d89fc839e5d4e03d09f0bd3a5b6e26dab8a29840cc2c6a19 | Python | 7,384 | 235 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
2fed9c30e9ac3eaa3de6e682b60dffbfb7bd599450fc2499d8ed0d0dda5986d2 | Python | 7,387 | 189 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import numpy as np
import os, sys
from qtpy import QtGui, QtCore
from qtpy.QtWidgets import QMainWindow, QApplication, QSizePolicy, QDialog, QWidget, QScrollBar, QSlider, QComboBox, QGridLayout, QPushButton, QFra... |
c26a4bf2c50519e566be61b112e6665117ece1466d11a718ccf0f9033890260d | Python | 7,388 | 244 | import math
import numpy as np
rad = math.pi / 180.0
R = 6378137.0
def spherical2Cart(lon, lat):
clat=(90-lat)*rad
lon=lon*rad
x=math.cos(lon)*math.sin(clat)
y=math.sin(lon)*math.sin(clat)
z=math.cos(clat)
return [x,y,z]
def cart2Spherical(x,y,z):
r=math.sqrt(x**2+y**2+z**2)
clat=ma... |
5ed4b50bc5a193706f7d2bc65139b9e2b5693784e125062c5f2134d45297c794 | Python | 7,389 | 246 | import argparse, importlib, logging, os, subprocess
from .. import __version__, __picture__
from ..logger import set_logger
def parse_args():
parser = argparse.ArgumentParser(
description="MiaoNet",
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
)
parser.add_argument(
"-v"... |
81e74a0973f52d12a7b6ef465e52339c67a60916e322fcdc09c71e2fcdc8fc0a | Python | 7,391 | 308 | from __future__ import annotations
from dataclasses import dataclass
DEFAULT_MONTAGE_PADDING = 20
DEFAULT_FILL_ALPHA = 1.0
@dataclass
class MappingConfig:
"""
Configuration for mapping the preferred morphology-state solution
back to observed cells and source images.
Canonical Mapping outputs
-... |
b4369d3453412b5425c4d8abe33ea67041c8cc049c3e7568a771824c3cdc25e8 | Python | 7,391 | 173 | """Select eyes-closed segments from the EEG LEMON data
"""
# Install dependencies
import os
import glob
import mne
import numpy as np
from sys import argv
from osl_dynamics.data import Data
# Set hyperparameters
if len(argv) != 2:
print("Need to pass one argument: use of structurals (e.g., python script.py subje... |
ec997c4e9d3dfe731a5cea652c2843c73dea19a07e956cca454d3c0a2a4b6960 | Python | 7,392 | 247 | #!/usr/bin/python
# encoding: utf-8
import random
import os
import torch
from PIL import Image
import numpy as np
from core.utils import *
import cv2
def scale_image_channel(im, c, v):
cs = list(im.split())
cs[c] = cs[c].point(lambda i: i * v)
out = Image.merge(im.mode, tuple(cs))
return out
def dis... |
d24b77a1ce126dccb6aed2433a909d0a40b40cd9dfba87ff060e81678a37a657 | Python | 7,394 | 215 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
6acd4381ee1b56ea01ad1ac0bb3bc42677d2e17780815dd8298b43f4d355fe1b | Python | 7,398 | 201 | from __future__ import division, print_function
import os
import sys
from typing import List, Tuple
import numpy as np
import pandas as pd
import topaz.mrc as mrc
import topaz.utils.star as star
import torch
from torch.nn.functional import conv2d, conv3d
from topaz.utils.image import downsample
def as_mask(shape:Tu... |
51b314938758a1e0facb190f1d389c80414c2f7ef1e986605d075dd4b51ea3a4 | Python | 7,399 | 212 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
8717e4b5323cff950bc7f3776b1e801f564c062371cc41a343d4802d0186f0c7 | Python | 7,399 | 221 | from __future__ import annotations
from typing import Any
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import is_color_like
from SurvivalEVAL.evaluations.custom_types import NumericArrayLike
def _validate_plot_indices(
indices: int | list[int],
size: int,
input_name: str = ... |
6506aeb05ef10443a01f9e33118aa12bb0da8055031ca02495e97bb81c793fa8 | Python | 7,403 | 158 | """
AUTOMATIC TUNING OF WS AND AP PARAMETERS
This file contains the algorithm for automatically tuning the size of the sliding window and the age power parameter.
These parameters are tuned based on the output of Detector 1.
"""
import AnDePeD.ReRe.auto_ws_ap_criteria as criteria
import numpy as np
def init_auto_ws... |
a5fd2cfe2d9e32c2213dd3da360844422551823842962f34b5f40db38915ba8d | Python | 7,403 | 158 | """
AUTOMATIC TUNING OF WS AND AP PARAMETERS
This file contains the algorithm for automatically tuning the size of the sliding window and the age power parameter.
These parameters are tuned based on the output of Detector 1.
"""
import AnDePeD.ReRe.auto_ws_ap_criteria as criteria
import numpy as np
def init_auto_ws... |
d11472efb28615652b3030de3023068e2cd482157cb50766563bdbe489a13356 | Python | 7,404 | 216 | import atexit
import logging
import os
import random
import shutil
import ssl
import string
import sys
import tempfile
import time
import urllib
from functools import partial
from typing import Optional
import hashlib
import json
from importlib import import_module as _import_module
from pathlib import Path
_log = lo... |
30f67a87058db4f524f4ce55a103cf0dd3474d54a06a5b510cb95259547d77fb | Python | 7,405 | 289 | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn.decomposition import PCA
import seaborn as sns
import colorcet as cc
from correlation_analysis.regression import linreg
CLUSTER_METHOD = 'agglomerative'
FONTSZ = 25
def get_correlation(
annotations: np.ndarray,
coordinates... |
331534e1f03459240204598c1fd81877f4b1a730750fa7f152650a254bb6ea0f | Python | 7,405 | 194 | # -*- coding: utf-8 -*-
#
# Syncopy data slicing methods
#
# Builtin/3rd party package imports
import numpy as np
# Local imports
from syncopy.shared.errors import SPYInfo, SPYTypeError, SPYValueError
__all__ = ["show"]
def show(data, squeeze=True, **kwargs):
"""
Show (partial) contents of Syncopy object
... |
1b01145fd0ab5b6449d65e374ff77e75fef206ca0dad90759caaf2e14009e923 | Python | 7,410 | 253 | from typing import Callable, Tuple
from functools import partial
import jax.numpy as jnp
import jax
from netket.utils.types import PyTree
import netket.jax as nkjax
from netket.stats import statistics as mpi_statistics, Stats, total_size
import numpy as np
from tre_tvmc.utils import mpi_logmeanexp_jax
def expect_2di... |
bfd02738a547394095542a3b0f776ad055d28513d9509f0f9d0bbbea39638c09 | Python | 7,410 | 201 | import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
from typing import Any, Dict, Optional
from models.model import Model
class ParallelConvBlock(nn.Module):
def __init__(self,
inp,
out):
super().__init__()
self.conv1 = nn.Conv3d(in... |
ca1d118d20a2db28f82fd5dba815be1446a151af69d67f8455565b762c4aba7a | Python | 7,412 | 172 | import argparse
import copy
import numpy as np
import os
import pandas as pd
from tabulate import tabulate
import json
import sys
import random
import hyperparameters_multilabel
import torch
from constants import *
from config_train import config
from src.dataloaders.rocf_dataloader import get_dataloader
from src.mode... |
2d907acdb1798df7a13ce4c9e278f630a40fc42c94f569ed484a7e9821078f4b | Python | 7,413 | 221 | """
version 1.1
author: Felix Plasser, Hans Georg Gallmetzer
usage: Package for reading a molden input vibration file.
This input is used for normal mode analysis by nma.py.
Also a multiple xyz vibration file that can be read by jmol can be printed out.
changes: xrange() changed to range() for compatabili... |
c49c22854a5c9143bb973926aeeddc55e14140d94eb5bdf24bdf707da3026914 | Python | 7,416 | 237 | import hydra
from omegaconf import DictConfig
import numpy as np
import pandas as pd
import os
import logging
import json
import matplotlib.pyplot as plt
from dataset import ProteinDataset
from steps.clustering import apply_clustering, compute_score
from steps.hypothesis_testing import test_hypotheses
from steps.dim_r... |
d2685b81220105324e6bf38a79676f5efb2ffcb2c76170c3f4e5f4b0035748a5 | Python | 7,416 | 231 | #!/usr/bin/env python
# Copyright (c) 2018-2020 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
This module provides the ScenarioManager implementation.
It must not be modified and is for reference only!
"""
from __future__ ... |
342f3348257485300a5ea1c37419c52cf33f3e2f6eb852c46e6fcf702fbbd82c | Python | 7,417 | 190 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
5d7c087e67a87db623b07620678e664bb734b80ea6ae35130d3a19f9f7da7466 | Python | 7,417 | 208 | from ._base import EncoderMixin
from timm.models.resnet import ResNet
from timm.models.resnest import ResNestBottleneck
import torch.nn as nn
class ResNestEncoder(ResNet, EncoderMixin):
def __init__(self, out_channels, depth=5, **kwargs):
super().__init__(**kwargs)
self._depth = depth
self... |
25da8c82d5d34221f95155cb2909e79abc4a624e1b29c0ebc0dbb34827b8e1df | Python | 7,418 | 203 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge,... |
839759c97623543dd2a29d9cbe070c9109ac4060dc7bc4eb0b1f3d55ffbe0be3 | Python | 7,419 | 169 | import h5py
import numpy as np
import click
import logging
from lib.sequence import SeqClassificationDataLoader1D
@click.command()
@click.option('--ism',
'ism_file',
required=True,
type=click.Path(exists=True, readable=True),
help='Test sequences')
@click.option... |
55ca0db380e19d8bd7ed03569af94da2385b5d7905d66a480b43e3d072b9d12f | Python | 7,421 | 177 | import torch
import torch.nn as nn
import torch.nn.functional as F
class AdaptiveGraphStructureLearningModule(nn.Module):
def __init__(self, num_nodes: int, sequence: int, embed_dim: int, gain: float, k_top: int,
need_activation: bool, need_scale: bool, need_adj_return: bool, dropout: floa... |
6007dfcaefdad780d057abaebed98d3d64aa2890eb2ef421d84b552bd354e048 | Python | 7,423 | 193 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
a648f421273b77a3363510a44b6be7391d17dfe60fdda8b07fa1542426d117cf | Python | 7,425 | 261 | import numpy as np
from sklearn.metrics import pairwise_distances
import tslearn.metrics
from tslearn.barycenters import (
euclidean_barycenter,
dtw_barycenter_averaging,
dtw_barycenter_averaging_subgradient,
softdtw_barycenter,
)
from hyppo.independence import (
MGC,
Dcorr,
HHG,
Hsic,
)... |
a231b50a2bc6c9241f02517e3c3904d66d81b680e78f4dafaf06ab862d09158d | Python | 7,428 | 171 | import numpy as np
import pandas as pd
def find_nan(BPall_channel):
# select one channel
BP_all_df = pd.Series(BPall_channel)
na_groups = BP_all_df.isnull().astype(int).groupby(BP_all_df.notnull().astype(int).cumsum()).sum()
nan_cons_values = na_groups[na_groups>0]
# na_groups = BP_all_df.notna(... |
fdb980bcae38745ba845f2cacddaa42c2f658fb1e552564102aae1557b456513 | Python | 7,429 | 150 | import unittest
from unittest import mock
import os
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import sys
sys.path.append(os.path.dirname(os.path.realpath(__file__)) + "/../../src")
from hornet.uml import create_pca_kmeans_clustering
class TestCreatePcaKmeansClustering(unittest.TestCase)... |
b163d32574603d681508c199c02049d7056eb3a54869e92506c775fa87b2f104 | Python | 7,433 | 233 | import numpy as np
import pandas as pd
from abc import ABC, abstractmethod
from ..utils import printv
from ..distance import calculate_distance
class DataSet(ABC):
def __init__(self, label):
self.label = label
self.neurons = np.zeros((0,), dtype=np.int64)
def __len__(self):
return ... |
bf00a03d078352b5d9046cd5594a9e877a75dd5e932a8b3186256fc632f93af9 | Python | 7,434 | 172 | #! /usr/bin/env python3
import os
import subprocess
import logging
import pickle
import APT_interface
import time
import mmpose.utils
from argparse import Namespace
class cd :
"""Context manager for changing the current working directory, and automagically changing back when done"""
def __init__(self, path)... |
2350c733202065bf234fc0989dd5a5d9ac3674debbe69f8128f0ae261bdd86b6 | Python | 7,435 | 188 | import functools
import importlib.metadata
import logging
import os
import pathlib
import sys
import zipfile
import zipimport
from typing import Iterator, List, Optional, Sequence, Set, Tuple
from pip._vendor.packaging.utils import NormalizedName, canonicalize_name
from pip._internal.metadata.base import BaseDistribu... |
120c60dcfa0f604223481576744e0bc8f293d4861b9046ae7648938921f29926 | Python | 7,436 | 167 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 16 08:00:51 2020
@author: daltonm
"""
#%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
import dill
import os
import glob
import math
import seaborn as sns
from sklearn.decomposition import ... |
5180336a035fd26a825671320187ac11dbdf62d5e63c0727aee55d6a840d6cc4 | Python | 7,436 | 172 | #! /usr/bin/env python3
import os
import subprocess
import logging
import pickle
import APT_interface
import time
import mmpose.utils
from argparse import Namespace
class cd :
"""Context manager for changing the current working directory, and automagically changing back when done"""
def __init__(self, path)... |
cc2cf82150440bddafc77f2872167b6a720d38f388a75eea23f6b303a23669fc | Python | 7,437 | 193 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
b4541b745d5a4f235ed17b6f6a091a7dd6bc955dfbdf9d184c585c6dfc873b49 | Python | 7,438 | 203 | import os
import sys
from PyQt5 import QtCore, QtGui, QtWidgets
import about
import apps
import globals.global_style as gStyle
import prog_panel
FILE_PATH = os.path.split(os.path.realpath(__file__))[0]
class EmittingStream(QtCore.QObject):
textWritten = QtCore.pyqtSignal(str)
def write(sel... |
b31750a5e2d48472804b47fbec3c27480fadeb21810f051737b7943e6a5266de | Python | 7,439 | 251 | import abc
import os
from collections import deque
import numpy as np
from pupil_recording_interface.decorators import process
from pupil_recording_interface.encoder import (
VideoEncoderFFMPEG,
VideoEncoderAV,
)
from pupil_recording_interface.externals.file_methods import PLData_Writer
from pupil_recording_i... |
bee35f405161d5c6293e34504fb8b202791971c98ed158b7f7cf41b3b1540c57 | Python | 7,439 | 238 | import string
from math import floor, log10
from codex.utils.parsing import tokenize, tokenize_and_fold_for_highlight
# Replaces all newline/return/tab chars with plain whitespace, and string delimiters with back quotes.
# This enables passing strings safely back and forth to jinja templates without urlencoding them... |
8b67a8f628847ab5c30aa843aa912a1df35cb2719b8f95f6cdce125116c279dc | Python | 7,441 | 173 | # -*- coding: utf-8 -*-
"""This file implements the potential model
Atomic predictions from the network are interpreted as atomic energies. This
model is also capable of fitting and predicting forces and stresses.
"""
import numpy as np
import tensorflow as tf
from pinn import get_network
from pinn.models.base import... |
9135ae6cceb697d110d011cb7a3d1bf0677e812a94b8b0afb6f18666d28b7a78 | Python | 7,442 | 143 | """
ARMC_JOIN_DEDUP_BIAS.py - written 2026-09-16. Reconciliation for D-04 / Step 8.
WHY THIS FILE EXISTS.
Two committed scripts build "arm C" / "branch C" - the probabilistic flip model that
D-05 chose - and they disagree about how much of the brain it moves:
03-CODE/EFFECTOME_MATCHED_NULL.py ARM C ... |
f12ebb147d4caab25fcc6fa4713fb4d8be627abb70c508a08eef15369a54f762 | Python | 7,442 | 244 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
50e2c7649b90ce4ecf87f6a023abc7e87a9aba4fd447aca0b58e28223d60f756 | Python | 7,444 | 156 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Jun 28 15:56:56 2024
@author: caroline
"""
import argparse
from itertools import combinations
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import seaborn as sns
import scipy.stats
from scipy.stats import spearmanr
... |
f991339b939edd883961911d8db96e0f06b307248ada6a72beb64987d04ec5d4 | Python | 7,444 | 236 | import importlib.util
import logging
import os
import sys
from contextvars import Token
from types import ModuleType
from typing import Mapping
from opentelemetry import trace
from opentelemetry.context import (
Context,
)
from opentelemetry.context import (
attach as attach_context,
)
from opentelemetry.conte... |
7379cd1730e11fcae98a5f06a2f60a8b62991af78e1a50e40720f6b11efa8bb0 | Python | 7,445 | 162 |
#%%%%
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 17 16:05:43 2022
@author: caroline
This script draw x lists of n subjects with replacement and compute the difference
between the correlation of prior weights in T1 and T2
and the correlation of likelihood weights in T1 and T2.
Th... |
5c37ce962828685bd20e62cc5cb432062fe469299e1a39ad5d86c9b67e93ac37 | Python | 7,446 | 193 | import collections
from itertools import repeat
import warnings
from typing import Any, Callable, List, Optional, Sequence, Tuple, Union
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from .block_utils import conv1x1, conv3x3, Block
def _make_ntuple(x: Any, n: int) -> Tupl... |
648b7b1af5a1c86da8632fbd7a085f6aac0ea62f35c13eb0c1e53788ff2e92c8 | Python | 7,447 | 196 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
efa7ce07bfa45aefead359a28eba48a6bce2d1450d0620d58a363cb91123d263 | Python | 7,449 | 231 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# 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 applicab... |
40220014ba0ce7f9cf3b6fa244b800d9330ff34afeb0ba34ae08e09c772944e4 | Python | 7,450 | 163 | import os.path
import random
from collections import defaultdict, Counter
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from sklearn.metrics import confusion_matrix, classification_report
from ord.utils import json_load
sns.set_theme()
from ord.evaluation.evaluate_functions import get_pai... |
4f450eb4fd7d4fc007ff536f5241af9fcc6abf286abc9e5e1c6ee42eae12bed7 | Python | 7,450 | 181 | from __future__ import annotations
import pytest
pytest.importorskip("tblite.ase")
from copy import deepcopy
import numpy as np
from ase.build import bulk, molecule
from numpy.testing import assert_array_equal
from quacc import change_settings
from quacc.recipes.tblite.core import freq_job, relax_job, static_job
... |
4f0f5bb7c2165732f0974ee83d545121fc9576967d22ebcc7ddf8e81c75e4c28 | Python | 7,451 | 233 | import nibabel as nb
import numpy as np
import pandas as pd
import pytest
from atlasreader import atlasreader
EXAMPLE_COORDS = dict(
affine=np.array([[1, 0, 0, -90], [0, 1, 0, -150], [0, 0, 1, -80], [0, 0, 0, 1]]),
coords=[
dict(ijk=[0, 0, 0], xyz=np.array([[-90, -150, -80]])),
dict(
... |
54522a6011e2c8f6307ede4f4c34cd892272f40a9522b0e57a057750494f616d | Python | 7,451 | 356 | from __future__ import annotations
import argparse
from pathlib import Path
from .config import PipelineConfig
from .pipeline import run_pipeline
from .config import PIPELINE_STAGES
# ======================================================================
# PARSER
# ==================================================... |
e0acacdc8888a8a09da7ecdcad6b069d18e493d77b97a67ececa6e4982a074bd | Python | 7,451 | 223 | import h5py
import click
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from basenji import plots
from basenji import dna_io
import pandas as pd
@click.command()
@click.option('--score',
'score_file',
required=True,
type=click.Path(exists=True, reada... |
97560dd10715e1e8310a6bfed38754cf4fcb1728f271d263c7305b2c834fb718 | Python | 7,453 | 337 | import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl
import dgl.function as fn
from dgl.backend import pytorch as F_dgl
from dgl.nn.functional import edge_softmax
class MLP(nn.Module):
def __init__(
self,
input_dim,
hidden_dim,
output_dim,
bias=True,
act=F.relu,
):... |
4ec78cdbf2f74739a57bbc0d132155835d46bd15332585db20055bcca0cdf6d5 | Python | 7,454 | 194 | import pddl
import timers
import options
NEGATIVE_SUFFIX = '-negative'
def handle_axioms(operators, axioms, goals):
axioms_by_atom = get_axioms_by_atom(axioms)
axiom_literals = compute_necessary_axiom_literals(axioms_by_atom, operators, goals)
if options.negative_axioms:
# caelan: pretends all li... |
c27e73b93cb365160805968400b01e7bbd9636571d3d310dc41c8f1367e27fe5 | Python | 7,454 | 172 | import os
import warnings
import torch
from torch import nn
import esm
from torchdrug import core, layers, utils, data
from torchdrug.layers import functional
from torchdrug.core import Registry as R
@R.register("models.ESM")
class EvolutionaryScaleModeling(nn.Module, core.Configurable):
"""
The protein lan... |
95de28904f6170f7f07851445d5acfaa498defca009bbee646190e9be029f5ec | Python | 7,458 | 189 | from typing import Tuple
import torch
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
from torch import nn
class FeedForward(nn.Module):
def __init__(self, in_channels: int, hid_channels: int):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(in... |
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