sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d7d64d2611efa254a2d09187cdd88c3068c6dfeb08d826bdd749bfba710b2092 | Python | 4,428 | 75 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
import common_setup
def main(revisions=None):
SUITE = suites.suite_optimal_with_ipc11()
B_CONFIGS = {
'rl-b50k': ['--search', 'astar(merge_and_shrink(merge_strategy=merge_linear(variable... |
5d3978c971d05e175caa8735fbcf6915c20df9c2266e7b33fd7436515a9b73e4 | Python | 4,429 | 85 | import numpy as np
import pytest
ants = pytest.importorskip("ants")
from regtools.registration.core.registration.two_stage import compose_two_stage, run_ants_intensity_split
def _blob(shift=0):
a = np.zeros((32, 32, 32), dtype="float32")
a[8 + shift : 20 + shift, 8:20, 8:20] = 1.0
return ants.f... |
e642cea2af9e4fb4dabe01359bfe55f3f1a237f5935f2d79d4b8e7fad3d9c4cb | Python | 4,431 | 102 | """Patches that are applied at runtime to the virtual environment."""
from __future__ import annotations
import os
import sys
from contextlib import suppress
VIRTUALENV_PATCH_FILE = os.path.join(__file__)
def patch_dist(dist):
"""
Distutils allows user to configure some arguments via a configu... |
f59f28b4e98cfadcb19f24e876f5e579cb4feb49706a18c68834eb6ebc4f4938 | Python | 4,431 | 149 | import inspect
from functools import partial
from typing import (
Any,
Callable,
Iterable,
List,
Optional,
Tuple,
Type,
TypeVar,
Union,
overload,
)
T = TypeVar("T")
Result = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]]
RichReprResult = Result
class... |
00ecd22b48303e0e126cf94518ee313f830ba4e923eb1db7c6b007796003bb73 | Python | 4,433 | 124 | from timm.models import ByoModelCfg, ByoBlockCfg, ByobNet
from ._base import EncoderMixin
import torch.nn as nn
class GERNetEncoder(ByobNet, EncoderMixin):
def __init__(self, out_channels, depth=5, **kwargs):
super().__init__(**kwargs)
self._depth = depth
self._out_channels = out_channels... |
58267f3b0fe4a9ea0ad4e453d47addb4e566f45953e33817acb1ca9bfb8af124 | Python | 4,433 | 70 | import argparse
from train import main
parser = argparse.ArgumentParser(description='Train prototypical networks')
# data args
default_dataset = 'geometry'
default_split = 'sable-meyer'
parser.add_argument('--data.dataset', type=str, default=default_dataset, metavar='DS',
help="data set name (def... |
722d37717cd6a4d993efd09f9015f7e00fb9976f128d03591698c076b4f65af3 | Python | 4,434 | 115 | from typing import Any, Optional, Dict
import numpy as np
from models.model import Model
from models.kan_layers import KAN
import torch
import torch.nn as nn
from torch.nn import init
import torch.optim as optim
class MLP_KAN_Net(nn.Module):
"""
Baseline network
"""
@staticmethod
def weight_ini... |
8ec4567057d1cf9f5e029d0db7cfc2ec080328fa400fc4e5e368ed36c4e8dbfa | Python | 4,434 | 98 | from typing import Tuple
import torch
import torch.nn as nn
class FBCCNN(nn.Module):
r'''
Frequency Band Correlation Convolutional Neural Network (FBCCNN). For more details, please refer to the following information.
- Paper: Pan B, Zheng W. Emotion Recognition Based on EEG Using Generative Adversarial ... |
ddbfe14f8ad077624f7f1e1632a1df525ab85d7b5b4c6e1f59f188272bad9e8a | Python | 4,434 | 131 | # Node-group asset "Is Side Chain" (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.
from typing import TYPE_CHECKING... |
6add1ea4f6a2219f33f652fba628342cdc6a3eef05a1c5ee3a444728cd79c884 | Python | 4,435 | 120 | #!/usr/bin/env python
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Basic CARLA Autonomous Driving training scenario
"""
import py_trees
from srunner.scenarioconfigs.route_scenario_configuration import RouteConfiguration
from srunner.scenar... |
9943bd0051848f86d8ab28e67f0b7037ea2c890b085f82219bf81361c8c4e4f8 | Python | 4,435 | 158 | # 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... |
a2e5e9b9dfa3792f313f24cfb1727e9b7e0d3ef2b9a2ce39a2d03375257f2091 | Python | 4,435 | 146 | import sys
import textwrap
from typing import List, Optional, Sequence
# Shim to wrap setup.py invocation with setuptools
# Note that __file__ is handled via two {!r} *and* %r, to ensure that paths on
# Windows are correctly handled (it should be "C:\\Users" not "C:\Users").
_SETUPTOOLS_SHIM = textwrap.dedent(
"""... |
4758494ebee95f6a099f296f2f8810a4e981334836237367b9928b921506c9b6 | Python | 4,436 | 121 | """Tests for validate_hed_sidecar script."""
import io
import json
import os
import tempfile
import unittest
from unittest.mock import patch
from hed.scripts.validate_hed_sidecar import main
class TestValidateHedSidecar(unittest.TestCase):
"""Test validate_hed_sidecar script functionality."""
def setUp(sel... |
d25837c1881b182a807e6cd46e1d9611f4aa25658028912c0b5a5886506f415f | Python | 4,437 | 106 | import os
import numpy as np
import pandas as pd
from datetime import datetime
import filenames
import read_files
import write_files
import scaling
import vmd
import AnDePeD.ReRe_main_functions as andeped_func
import anomaly_detection_metrics
import nab_custom_windows as nabwindows
import main_config as conf
import o... |
f1387e759105a72179b38036ce1fcae31dd98bdc170e06f4247a29f2659fa94e | Python | 4,438 | 102 | #!/usr/bin/env python
import os
import sys
com=sys.argv
if '-n' not in com or '-r' not in com or '-1' not in com or "-2" not in com:
print ("USAGE: python TAURUS-MH -n <sample name> -r <genome_folder> -1 <G to A converted mate> -2 <C to T converted mate> -Trim1 <Trim off first Xbp of reads> -Trim2 <Trim off last Xbp ... |
12f29aa6af1f4f29c01a9f022614b6891a63715abcd8b5a0380c9fb43c0ca705 | Python | 4,439 | 124 | #!/usr/bin/env/python
import argparse
from datetime import datetime
import random
import numpy as np
# from task import *
import os
import time
from utils import *
from params import *
import sys
# from manifold import *
# from gnn import RiemannianGNN
import pickle
import train_inductive
def set_up_fold(args):
# ... |
27f601d10c87fde6048fd1247d2545c509f89484ad6726390b83b7b6fd8a9c80 | Python | 4,439 | 112 | __author__ = 'martinez'
import matplotlib
matplotlib.use('Agg')
import pyseg as ps
import numpy as np
from unittest import TestCase
from pyseg.spatial.sparse import UniStat, PlotUni, gen_sin_points, gen_rand_in_mask
# ### Test variables
# Global
RES = 0.684 # nm/pixel
# Mask
MASK_R = 2000 # nm
SIZE = (200, 100, 100... |
6a479c423b28ec365f0781617dab3bcf8acf5fb836ca4d6789a65cfcbaa83d8c | Python | 4,439 | 134 | #!/usr/bin/python
# GK September 2023
# Scripts for running methyl decoupling DNNs
#
# note in methyl_decoup_funcs model paths need to be set
# requires nmrpipe to be installed to work
import os
from fidnet.methyl import methyl_decoup_funcs
def write_initial(input, outfile, com_file, min_1H, max_1H, p0):
with ... |
996096744ce4a036c3802366fcdaf5597ddc68fc35333bec9104e94d0f7dbf91 | Python | 4,439 | 108 | """Figure taxonomy / grouping in the registration report."""
import base64
import os
import struct
import zlib
from regtools.registration.report.figures import (
_classify_figure,
_collect_figures,
_render_figures,
)
def _tiny_png_bytes():
def chunk(tag, data):
return struct.... |
9c60df85b6730212a8d66c479118dc84b5dcdd399435f1a6a6df4863f4ddf879 | Python | 4,439 | 123 | # imports
from pathlib import Path
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from simulation import Simulation
from tqdm import tqdm
# set plotting style
plt.style.use(['default', 'biophysics.mplstyle'])
# define the kymograph function
def kymo_plot(sim: Simulation, save_file: Path):... |
a2493e2b15fdefff2d20cc19c0e1d6244ade5d14ebf5694d17454a509a7f4881 | Python | 4,440 | 133 | import pandas as pd
import numpy as np
from sklearn.base import BaseEstimator
class RandomResampler(BaseEstimator):
def __init__(self, up_target=None, up_ratio_thres=None, down_target=None, down_ratio_thres=None, seed=0):
self.up_target = up_target
self.up_ratio_thres = up_ratio_thres
sel... |
22d2ed69ce8411a56ef737a6b79652252b3e105b7a5172a2573d3cf88cadd90e | Python | 4,441 | 133 | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def p... |
27ac09e7a2282b6f74b67d71898a615d57a268f403951a4985b62769bdef1851 | Python | 4,441 | 118 | """"""
"""
Prosper_nn provides implementations for specialized time series forecasting
neural networks and related utility functions.
Copyright (C) 2022 Nico Beck, Julia Schemm, Henning Frechen, Jacob Fidorra,
Denni Schmidt, Sai Kiran Srivatsav Gollapalli
This file is part of Propser_nn.
Propser_nn is free softw... |
3515676510334e914731e4a271b757a58c1eb3cd294f609726dd6c0411a87979 | Python | 4,442 | 105 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_port
from elasticai.creator.vhdl.design.design import Design
from elasticai.c... |
65b3d6992367f68c95df40f8505c5e9caa24278d5c6f095ec39a1456faf80456 | Python | 4,442 | 133 | # A simple torch style logger
# (C) Wei YANG 2017
from __future__ import absolute_import
import matplotlib.pyplot as plt
import os
import sys
import numpy as np
__all__ = ['Logger', 'LoggerMonitor', 'savefig']
def savefig(fname, dpi=None):
dpi = 150 if dpi == None else dpi
plt.savefig(fname, dpi=dpi)
def p... |
4ee122c2a4a637c4f1271ec0e73184771c9a0ed16a8fb93c4315d8c8c919fb4c | Python | 4,443 | 142 | # 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... |
be09e4706d387536db378a2b837077f9a0283f748ea5a46d1afa4766117d9348 | Python | 4,443 | 135 | """Save embeddings script.
Copyright (c) 2024 University of Michigan. All rights reserved.
Licensed under the MIT License. See LICENSE for license information.
"""
import os
import logging
from shutil import copy2
from functools import partial
from typing import List, Union, Dict, Any
import gzip
import yaml
import ... |
29385c3c7ec8263d6469f7b3101c8aacb45c7b5c2ad4dc3c291f4f1235c56971 | Python | 4,444 | 120 | # 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... |
24e3d3d9e940abe9cc144d36891f8aa51d96ddea7c2459f618070469813498eb | Python | 4,445 | 101 | import os
import pandas as pd
import numpy as np
from ckg.graphdb_builder import builder_utils
def parser(projectId):
data = {}
config = builder_utils.get_config(config_name="wes.yml", data_type='experiments')
directory = '../../../data/experiments/PROJECTID/wes/'
if 'directory' in config:
dire... |
7489395744dbc6dfc21bd1c25a6efd1834a70bd7df4b6794ab2f0483adc6d005 | Python | 4,445 | 128 | # Copyright (c) 2022, Ko Sugawara
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice,
# this list of conditions and t... |
1450c91e5a8e94d2149bbe4494d4a8cd7fd818092e8204a9c0a72419497e7ed8 | Python | 4,446 | 126 | import torch
import random
import time
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from tqdm import tqdm
from utils import gram_schmidt,compute_bpc
from data import get_dataloader
from model_Customadc_noise import PTB_LSTM
EPOCHS=20
DATA_PATH='dataset'
BATCH_SIZE=8
SLICE_LENGTH=128
NUM_W... |
7b496245eb9014da503a96739d01273fd232683d5ce384f8fe048b171b39f21d | Python | 4,446 | 126 | import numpy as np
from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.nn.integer.ram.design import Ram
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_p... |
afaa2604c55b5f5b6af92b106986d0e7789b8d18ec3fff2a0bcc2cf39620e355 | Python | 4,448 | 115 | import numpy as np
import pandas as pd
from tests.mock_data import MockTrainingData
from cuppa.components.prob_overriders import SexProbFilter, FusionProbOverrider
from cuppa.classifier.cuppa_classifier import CuppaClassifier
class TestFusionProbOverrider:
def test_sample_with_two_target_fusions_has_increased_t... |
19606f4ce902fb8f653ec8383d74f87ee231d3ebd002984218376c827d984d68 | Python | 4,449 | 108 | from ..region_1020 import (FRONTAL_REGION_LIST, GENERAL_REGION_LIST,
HEMISPHERE_REGION_LIST)
from ..standard_1005 import STANDARD_1005_CHANNEL_LOCATION_DICT
from ..utils import (format_adj_matrix_from_adj_list,
format_adj_matrix_from_standard,
format_... |
2570fc7957d4cb1e47f268a5ac65f34e92da3cc5c939f3177188dd94dde3cf0e | Python | 4,449 | 144 | """
Bilateral Filter for GraphMCF
Input: - GraphMCF (pickle file)
Output: - Bilateral filtering stored in a vertex property
"""
__author__ = 'Antonio Martinez-Sanchez'
# ################ Package import
import sys
import time
import getopt
from globals import *
from factory import unpickle_obj
from g... |
90a56d191cbbee66cde7ca5fc1cc70fecf60f59069250fdae07ab975a85f9daf | Python | 4,449 | 133 | import os
import random
from PIL import Image
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import Dataset
import shutil
from tqdm import tqdm
adj_maxval, adj_minval = 0.388, -0.239
lab_maxval, lab_minval = 11.31, -11.31
def get_data_from_folder_path(fp, path_label_dict, data_num, data_... |
a91397d007b130f5ed7f30b62238ab7b7925e58129089d8586a3c74f14b38f0b | Python | 4,449 | 120 | """Performance regression tripwires for the CCF Boundary Editor.
These tests fail when a hot path gets dramatically slower - i.e.
they're loose ceilings, not benchmarks. Tight benchmarks belong in
pytest-benchmark; what we want here is "if the next person to
edit ``_apply_alignment_to_ccf`` makes it 10× slower, C... |
bffd4362153712869af43ecc302c82b09e933f35dfdcb4fc86c3a75f7de574e3 | Python | 4,449 | 152 | # Node-group asset "Set Nucleic 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.
from typing import TYPE_C... |
d1c330ada279a9c01258824bd858bec569b60e7b88ac2701b6bdadb8f7cf2459 | Python | 4,449 | 123 | """Per-region metrics are bincount reductions; pin them to the naive definition.
`per_region_ncc` / `per_region_log_jacobian` used one boolean mask per region:
``for r in region_ids: mask = ann == r``. That is O(R * N) - with ~700 CCF
structures over a 77M-voxel 25um atlas it is ~700 full passes over the volume,
... |
00d1685e99c2ddabf843e3749d13ee9613feba26b134bd33dd622895f24f4e40 | Python | 4,450 | 111 | # -*- coding: utf-8 -*-
"""Helper functions to save/load datasets into tfrecords"""
def write_tfrecord(fname, dataset, log_every=100, pre_fn=None):
"""Helper function to convert dataset object into tfrecord file.
fname must end with .yml or .yaml.
The data will be written in a .tfr file with the same suf... |
6cff6fc8c3f8dcdbc20abbf3897f500b87fd3b09e4f28e26b1a84a82764ba1f7 | Python | 4,450 | 78 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
from downward import suites
from lab.reports import Attribute, gm
import common_setup
def main(revisions=None, combinations=None):
LIMITS = {"search_time": 1800}
SUITE = suites.suite_optimal_with_ipc11()
B_CONFIGS = {
'rl-b50k': ['--search', 'astar(mer... |
f03bd3ff14aee4a82289ba13ad33d6bf42ff087bc7a6000e592b56aaa783cd5a | Python | 4,450 | 172 | # Node-group asset "Menu Residue Name" (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.
from typing import TYPE_CHEC... |
dbe121c16e51d3d618a83231b2e22e330503fb8d18ea1f7bd317be3c795af90b | Python | 4,451 | 141 | # Node-group asset "Field Remap" (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.
from typing import TYPE_CHECKING
f... |
66c2ac8a742a8743224c60852fceb759497812b4e3415ef852cbff596ddd0860 | Python | 4,452 | 110 | """
This script converts DICOM files to NIFTI and organizes the files in pseudo-BIDS format.
Requirements:
- DICOM files organized and labeled as #_sequence (e.g. 5_EPI). No subfolders!
See step0_organize_mrifiles.sh for inspiration
- dcm2niix installed in a folder that can be acc... |
fc9d78a87e49a284388cd7bb9f879c2ce61eb1563a6dc322bd56997ac80bf09b | Python | 4,452 | 111 | import numpy as np
from pddlstream.utils import find_unique, flatten
from pddlstream.algorithms.constraints import PlanConstraints, WILD as X
from perception_tools.common import create_name
from learn_tools.collectors.common import INFEASIBLE, get_contained_beads, stabilize, fill_with_beads, \
InitialRanges, crea... |
5d994c59287651ab23a91e545525b2d9f2066dfda62d75c97601574577a140ef | Python | 4,453 | 112 | import numpy as np
from skfeature.utility.construct_W import construct_W
def trace_ratio(X, y, n_selected_features, **kwargs):
"""
This function implements the trace ratio criterion for feature selection
Input
-----
X: {numpy array}, shape (n_samples, n_features)
input data
y: {numpy ... |
beaa07e7e5da3376876995c3d16b381c79746d68a8ed6b8f71321c38d6f5ed19 | Python | 4,453 | 125 | from collections import deque
from typing import Generator, Optional
import cv2
import torch
from torch import Tensor
from torch.nn import Module
from torchvision import transforms
import random
def resize(vid, size, interpolation='bilinear'):
# NOTE: using bilinear interpolation because we don't wor... |
fcc5fbbba6153b32ebe6ea9fe6917857de87155234a584f49b794af91bd95411 | Python | 4,453 | 154 | import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
import os
import random
import pickle
fr... |
651ea9d08b70a78ce5ec7ae223ed0c6372be9f60085bf13ed2ac4b82d456a1e3 | Python | 4,454 | 164 | """
Adapted from 2020 Ross Wightman
https://github.com/rwightman/pytorch-image-models
"""
import torch
import torch.nn as nn
from einops import rearrange
from pathlib import Path
import torch.nn.functional as F
from timm.models.layers import DropPath
class FeedForward(nn.Module):
def __init__(self, dim, hidden... |
75203d5dccd94a53d48261c67371ec01d54bd4e499d7aad00fcd66e25452933d | Python | 4,454 | 125 | """"""
import logging
from pupil_recording_interface.decorators import process
from pupil_recording_interface.process.calibration import Calibration
import numpy as np
logger = logging.getLogger(__name__)
@process("validation", optional=("resolution",))
class Validation(Calibration):
""" Validation during runti... |
c56e2d2868816c8e19d91160a8637a87d27cec1b2196e13eecb6376be8566f20 | Python | 4,454 | 117 | """Space-flash pristine-overlay tests for the CCF Boundary Editor.
Holding Space swaps the canvas overlay between modified (default)
and pristine annotation. Releasing restores the modified view.
Faster than dragging the existing compare-mode divider for
quick boundary inspection.
The flag is ``_peek_pristine`... |
988a485dc9e2b59f7f7aa63664beb25d148a8dc029581520a63b2a50f254eb81 | Python | 4,455 | 150 | # 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... |
cbddadf9449ac72611d1869989adae3319cd0db36c43c5166a35551d2a516428 | Python | 4,455 | 114 | """Per-region categorical-fill color override tests.
The viewer exposes a right-click menu action "Override colour..." that
persists a per-region hex color in
``ViewerSettings.region_color_overrides`` (keyed by stringified
region ID). The LUT-build path consults the overrides dict before
falling back to the atla... |
99793d885ec01c98267b0728add93c5d8c6c575bb335137a8dd1692c03e68571 | Python | 4,456 | 153 | import numpy as np
import numba
from ...utils import model_utils as mu
def timeIntegration(params):
"""
setting up parameters for time integration
:param params: Parameter dictionary of the model
:type params: dict
:return: Integrated activity of the model
:rtype: (numpy.ndarray, )
... |
1a3188cddd6c6474ca9f53433eb4c06c4060e84856599f523605dc11b0cc8a50 | Python | 4,457 | 119 | import argparse
import logging
logging.basicConfig(level=logging.DEBUG, format="%(asctime)s [%(levelname)-5s] %(message)s", datefmt="%H:%M:%S")
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', 100)
pd.set_option('display.width', 300)
class HlaAlleleFreqNormaliser:
SAMPLE_SIZE_CAP = 10... |
48c3e8b96c1afda184e4cbc446c24a438b6db915a95e9f151a26aa709bbf1f39 | Python | 4,458 | 130 | # -*- coding: utf-8 -*-
from typing import Iterable, Union
import torch
from supar.config import Config
from supar.models.dep.biaffine.parser import BiaffineDependencyParser
from supar.models.dep.crf.model import CRFDependencyModel
from supar.structs import DependencyCRF, MatrixTree
from supar.utils.fn import ispunct... |
54d0369e11b6252841c7567ea3cd9b6f0410471957185c1e093d9a121949de7f | Python | 4,458 | 125 | """Compare data length of the LEMON and CamCAN datasets
"""
# Set up dependencies
import os
import pickle
import numpy as np
from sys import argv
from osl_dynamics import data
# Define custom functions
def measure_data_length(dataset, sampling_frequency=None):
"""Get the length of each data in a given dataset.
... |
d2a7fe18411b5fc7c1a3e7c541faedec191a2964fd8689c793ca708e3b07a5a0 | Python | 4,458 | 125 | """
Script for comparing two classifications
Input: - The STAR files with the classification to compare
Output: - Table with the similitude (number of shared particle / number of particles per class) among classes
"""
__author__ = 'Antonio Martinez-Sanchez'
###### Global variables
import pyseg as p... |
c08f1e704679815855f3ddb4974fe951cb65997ba1ef37fe50e86c3c662347ca | Python | 4,459 | 128 | # Manuel A. Morales (mmoraleq@bidmc.harvard.edu)
# Beth Israel Deaconess Medical Center
# Harvard Medical School
import torch.nn as nn
import torch.nn.functional as F
import torch
from torchvision.models import vgg19
import math
import numpy as np
image_In = 64 #"Image size of high resolution image."
scale_factor =... |
d8b5ecaf641f3b7eb8977ca248c34cbacb7b91c4d6e5d85a13ec082f205572a3 | Python | 4,459 | 118 | import os
import numpy as np
import torch
from rdkit import Chem
from rdkit.Chem import Draw
from rdkit.Chem.Draw import SimilarityMaps
import matplotlib.pyplot as plt
from .nn_utils import index_select_ND
def shorten_filename(filename, limit = 254):
if len(filename) <= limit:
return filename
else:
... |
f37ff77e8e1602c18a94496cf40d989434f82b3480b72a5d305ac927ff7d1005 | Python | 4,459 | 143 | # 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... |
ef2c5b7acfbe5174bc8adcc8d8f493fb6cfa1a8e0662aacdad2e7801676f30e4 | Python | 4,460 | 119 | """Processing worker for RxData objects using a persistent process pool."""
import logging
import multiprocessing
import signal
from concurrent.futures import Future, ProcessPoolExecutor, wait
from console.interfaces.rx_data import RxData
log = logging.getLogger("RxProc")
# 'spawn' is the only safe start method on ... |
f53204ab5c2562469c53fe28091f0c3b39c83da90149fe4a221343ae5944078a | Python | 4,460 | 119 | from classification_models import get_submodules_from_kwargs
def slice_tensor(x, start, stop, axis):
if axis == 3:
return x[:, :, :, start:stop]
elif axis == 1:
return x[:, start:stop, :, :]
else:
raise ValueError("Slice axis should be in (1, 3), got {}.".format(axis))
def GroupC... |
1e8e26cb2e7b412d6c63c301bcea65d36737fb4a29c1cb301d67703be3f991ba | Python | 4,461 | 136 | """
Contains class Affine3D for preforming affine transformation (general linear
transformation followed by translation) on points (vectors) in 3D.
# Author: Vladan Lucic (Max Planck Institute for Biochemistry)
# $Id$
"""
from __future__ import unicode_literals
from __future__ import absolute_import
from past.builtins... |
ab347615a50f474fc0e42da696080eb7e5ccafa965ffc500a4fb538d4897b3ce | Python | 4,462 | 140 | import warnings
warnings.filterwarnings("ignore",category=DeprecationWarning)
import numpy as np
import tensorflow as tf
import os
import random
from glob import glob
import shutil
if(len(glob("save_model"))):
shutil.rmtree("save_model")
os.mkdir("save_model")
if(len(glob("training_loss"))):
shutil... |
4f7448463c63aeceffc84d63c1095f840c91d537e18353180ee2f2686411e7fc | Python | 4,463 | 129 | # 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... |
56cd8d6fc163d7e20703a53c386fb3be5076b81fb4e0fcb18f10aa6845db690e | Python | 4,463 | 162 | # 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... |
a16ecb40f477688c2e3d370a8bd65ba1fd7742139861dd0bfffb65d0fb80c4d4 | Python | 4,463 | 153 | #!/usr/bin/env python
# This scripts computes the distance between neurons from a raster.
# Uses multiprocessing for parallel processing
# Make sure current directory is added in the PATH variable
import h5py
import numpy as np
import os
import time
import sys
from functools import partial
from itertools import combin... |
f4eb6ff71abfcdcf5bd009147ef7fe697d4f52cb43c7e799d5203bbb31ffd324 | Python | 4,463 | 139 | """Auxiliary plotting routines for the radiation pattern of a dipole."""
import math
from typing import Tuple
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np
NUM_POLAR = 30
NUM_AZIMUTH = 50
def plot_radiation_pattern_3D(dipole_pol: str, radial_flux: np.nd... |
5518651f6435ec0088169ab5da94cb9420281ca94914b3b630b3409ebb2a3567 | Python | 4,464 | 147 | from __future__ import division, print_function
import os
import sys
from typing import Union
import numpy as np
import topaz.mrc as mrc
import torch
from PIL import Image
from topaz.utils.data.loader import load_image
def crop_image(arr:Union[np.ndarray,torch.Tensor], xmin:int, xmax:int, ymin:int, ymax:int,
... |
ba62bd13b1cb8da065149ea8b68d852f97418aa2b91a114e717f9e2ab556fcfe | Python | 4,464 | 108 | #!/usr/bin/env python
# coding: utf-8
import argparse
import pandas as pd
import random
import numpy as np
from keras import models, layers, regularizers
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
# Set up argument parser
par... |
0f6ef6d75486f07707cf51fea64a2776226beee43493d9161e911403668fc885 | Python | 4,465 | 113 | """
Introduction to the model_selection Module
=============================================
Welcome to the guide on TorchEEG's ``model_selection`` Module! This module provides you with a rich array of dataset-splitting strategies.
In EEG-based emotion recognition, the division of training and test sets
may differ ba... |
ad051b93cb333f7d3b7b2ee7407186eca3ce6c56270fc45201d4d437e21791ad | Python | 4,465 | 158 | #!/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 access to the CARLA game time and contains a py_trees
timeout behavior using the CARLA game time
"""
import... |
0fb59900cfe0e3696caa2760de3f8516b45765545793541a6554c7bc01675acc | Python | 4,466 | 112 | """Per-dataset configuration.
The .mat files in this repo carry no timestamps, no stimulus-onset markers,
and no sampling-rate metadata. All of that has to be supplied externally.
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Literal
SigmaMethod = Literal["engbert2003",... |
3f117d65c17e68be145f3672953e641ab80078cf8ecfd2e7f8b1ac1f3be34add | Python | 4,466 | 108 | from ..region_1020 import (FRONTAL_REGION_LIST, GENERAL_REGION_LIST,
HEMISPHERE_REGION_LIST)
from ..standard_1005 import STANDARD_1005_CHANNEL_LOCATION_DICT
from ..utils import (format_adj_matrix_from_adj_list,
format_adj_matrix_from_standard,
format_... |
39dcdbb2490b1e702588354daf9550c35569ff9e99b756c193e4f8ef4c915fc6 | Python | 4,467 | 112 | #!/usr/bin/env python
import yaml
import argparse
import sys
import subprocess
import os
from pprint import pprint
from time import gmtime, strftime
import numpy as np
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='')
parser.add_argument('--config', type=str, default=None)
parser... |
981a998646391745c783b3cf36637a5f67783f45bfe0c477d8fc8ce2188f39a3 | Python | 4,467 | 121 | # 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... |
e07c81ebece9f3e6f73999e538dd8fe00bc3b4ee343d1f985fb31d4fcf9826d9 | Python | 4,467 | 130 | from __future__ import print_function
from random import random
from time import time
from .utils import INF, argmin, elapsed_time
class OptimalNode(object):
def __init__(self, config, parent=None, d=0, path=[], iteration=None):
self.config = config
self.parent = parent
self.children = ... |
77b1d0c927e8e25016422e7938ff77611a75660fb8dbf40154872b20ac219b27 | Python | 4,468 | 126 | """
Iterative removal of line noise and harmonics.
Authors: James Bonaiuto <james.bonaiuto@isc.cnrs.fr>
Maciej Szul <maciej.szul@isc.cnrs.fr>
"""
import numpy as np
import matplotlib.pylab as plt
import copy
from mne.time_frequency import psd_array_multitaper
from meegkit.dss import dss_line
def nan_basi... |
95fe9b09cc3379ddf0402ce67e9470613d71cc21a2eb93b474f7628994a4bb7b | Python | 4,468 | 105 | """
This script is used to resquiggle the reads that have different tandem repeat number than the reference.
"""
import os
import toml
import argparse
import numpy as np
from functools import partial
from tqdm import tqdm
from multiprocessing import Pool
from multiprocessing import Queue
from xron.nrhmm.kmer2seq import... |
b5e62157941b0aa91e19f2d4c06e9301be13794c5b545c685ce08e5c4e1ceaef | Python | 4,468 | 114 | import numpy as np
import mdtraj as md
import pandas as pd
from mdtraj import element
from argparse import ArgumentParser
import logging
import time
import string
import gc
parser = ArgumentParser()
parser.add_argument('--proteins',nargs='+',required=True)
parser.add_argument('--numbers',nargs='+',required=True)
parse... |
5273e915b03650c94100f18a47c3606979d58e1088c47fdda437d9493061eba8 | Python | 4,470 | 137 | # Node-group asset "Select Res ID Range" (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.
from typing import TYPE_CH... |
2565f1a3efa857fb9f244b24cb30eb45006232a0470aa108d01115160317a317 | Python | 4,471 | 115 | import argparse
import numpy as np
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--vocab_file', default='vocab.txt', type=str)
parser.add_argument('--vectors_file', default='vectors.csv', type=str)
args = parser.parse_args()
with open(args.vocab_file, 'r') as f:
words... |
ba44040071e0f81c8e6569660dec9df0aaedd0c4d41fc50ade82b70687983376 | Python | 4,472 | 137 | import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Merge
from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D
from keras import backend as K
from random import shuffle, choice
from keras.preprocessing import sequence
from matplotlib import py... |
cf910493e645bf22e19a756a0924c15d30c749e5903cc11e82f976fc4f0bb7a3 | Python | 4,473 | 134 | # Node-group asset "Boolean Run Trim" (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.
from typing import TYPE_CHECK... |
fe5281d625706efec91cfdc756ee953d2b4ea34bba478d9da5ad8d82ff65a2f8 | Python | 4,473 | 118 | import torch
import torch.nn.functional as F
from sklearn.metrics import confusion_matrix
@torch.no_grad()
def evaluate(model, dataset, split_idx, eval_func, criterion, args, result=None):
device = torch.device("cuda:" + str(args.device)) if torch.cuda.is_available() else torch.device("cpu")
if result is not N... |
b0264b7ce1b2fd7db76e21d3e08ae15edfb75bd8b13f8ace581a730b67752ff6 | Python | 4,475 | 101 | import os
import pandas as pd
import pytorch_lightning as pl
from statistics import mean
from typing import Any, Callable, List
import torch
from torchmetrics import Accuracy
# local imports
from shapelearningtheory.networks import make_resnet50, make_vit_b_16, \
make_vgg_19, \
make_mlp_small, make_convnet_smal... |
d38a3eba4e8e265e48c3cb11386370e3d9ebe80eafec01533792a6a01063c631 | Python | 4,477 | 109 | #!/usr/bin/env python
# coding: utf-8
import argparse
import pandas as pd
import random
import numpy as np
from keras import models, layers, regularizers
from sklearn.model_selection import KFold
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as p... |
71887d301d4c908c433e68a794fbc919de3f8b81fea54e2a0b0285588ec3957b | Python | 4,478 | 134 | from typing import List, Union
import numpy as np
from rdkit import Chem
from rdkit.Chem import AllChem
class MolMatrices:
"""
A :class:`MolMatrices` represents the atomic matrices of a single molecule.
A MolMatrices computes the following attributes:
* :code:`n_atoms`: The number of atoms in the mo... |
19f1ded471b1c94660c233805c346fcd311d0b2d0b1fddb7a750c8ead079bec1 | Python | 4,479 | 133 | import logging
from concurrent.futures import Future
from pupil_recording_interface.base import BaseConfigurable
from pupil_recording_interface.decorators import process
from pupil_recording_interface.utils import DroppingThreadPoolExecutor
logger = logging.getLogger(__name__)
class BaseProcess(BaseConfigurable):
... |
9c238b77544265f01ce1fa55d791d27ff8cba2543b0581453cf0112b1562665b | Python | 4,479 | 105 | """Optional torch/CUDA backend for the compute-heavy numeric metrics.
FSC and NGF are FFT / gradient kernels over the whole volume - exactly what a
GPU does well. ``metrics.py`` routes to these when CUDA is available and falls
back to the identical NumPy path on ANY failure (no CUDA, out-of-memory, a torch
quirk)... |
9de5cc5902cdee9db467065bdf0153313f11f9dccdd7d48c49578c32bbbbe7db | Python | 4,479 | 99 | import torch.nn as nn
from types import SimpleNamespace
from coral_pytorch.layers import CoralLayer
from src.models.components.resnetblock import ResNetBlock
from src.models.components.preactresnetblock import PreActResNetBlock
resnet_blocks_by_name = {
"ResNetBlock": ResNetBlock,
"PreActResNetBlock": PreActRe... |
1c7d43c80dece1bd523e62480173929c66e6f13337967e7e29d9d55bc56457ee | Python | 4,480 | 117 | """
Script for ploting alignment confidence over scales
Input: - A list of workpsaces with the column alignement computation
- A paired list with the alignment distance used
Output: - Plottings
"""
import time
import numpy as np
from matplotlib import pyplot as plt, rcParams
try:
impor... |
4e010863422c55642e03294de2c9b2f73a7df288be9808df6a5394fce6894a18 | Python | 4,480 | 146 | """Plot performance Medication OFF vs ON."""
from __future__ import annotations
import csv
from enum import Enum
from pathlib import Path
import numpy as np
import pandas as pd
import pte_decode
import scipy.stats
from matplotlib import pyplot as plt
import motor_intention.plotting_settings
import motor_intention.pr... |
f6982fae9022d9780fe7dd8b144ffa0e7f470c8e162852a91f55d0d923c1e28c | Python | 4,481 | 146 | import sys
import os
def extract_stats(gd_file_name, test_file_name, usecase):
print("ref: " + gd_file_name)
print("test: " + test_file_name)
gd_file = open(gd_file_name)
test_file = open(test_file_name)
seq_dict = {}
result_dict = {}
for line in gd_file:
if not line.startswith("... |
1eadf2f17e5d3db4edc3df24d5d463f597f5d4652b2ca19609004c67f7217223 | Python | 4,482 | 115 | #
# 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,... |
907b19b453216d974f7a3b1f41d3ca6566e1cd4eda91479c0cd85c001d1dc570 | Python | 4,482 | 131 | ###############################################################################
# This code generates the Supplementary Figure 2 of the paper "Longitudinal #
# single-subject neuroimaging study reveals effects of daily environmental, #
# physiological and lifestyle factors on functional brain connectivity" #
... |
d8a4714f3563b3d1c91ab73d7ef766982b0732c707a5107f2730e119d785d936 | Python | 4,482 | 125 | from typing import Optional
import torch
from torch import Tensor
import torch.nn as nn
from cl_3d.models.components.distributed import sync_ddp_if_available
class GatherLayer(torch.autograd.Function):
"""
Gather tensors from all process, supporting backward propagation.
From https://github.com/Spijker... |
dd8d7dea6bd40bf91c671938e31379f4e160862e32fa28462ca33b137f60803a | Python | 4,482 | 96 | """
MOABB
=====
This script shows how to use rCCA from PyntBCI for decoding c-VEP trials from real (not synthetic) EEG data, obtained
from the Thielen et al. (2015) c-VEP dataset [1]_ via MOABB (Mother of All BCI Benchmarks) [2]_. Cross-validation is performed
with a few lines of standard scikit-learn code, since `rCCA... |
6a33e1b3be2df5a4fa7d96f780d99fba1f3d6941f802eccdacd7fd410b7ba741 | Python | 4,483 | 105 | from hmp.models import EventModel, EliminativeMethod, CumulativeMethod
from hmp import basedata, projectors, patterndata, visu
from hmp.crossvalidation import LOOCV
import copy
import pytest
import numpy as np
from test_io import init_data
def example_simple_func(data, n_events, channel_pars=None, time_pars=None, ver... |
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