sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
14d5fe66b58d48df42d71b55f379f318e0804eb46eabf016fad6b4a79bd96081 | Python | 2,868 | 81 | import json
from tqdm import tqdm
import os
import argparse
label2desc={'true':'是','false':'不是'}
def load_data(file_path,is_training=False):
with open(file_path, 'r', encoding='utf8') as f:
lines = f.readlines()
result=[]
for line in tqdm(lines):
data = json.loads(line)
... |
2b3bdcbc68a8d44c473a12083118685e96f2ce4b40a887cbbf728caf2b64f230 | Python | 2,868 | 93 | _base_ = [
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py',
]
# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa
# Licensed under the MIT License
class_weight = [
0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.87... |
b77017c60d72c42f09c72c319c8005fa9d857750d1f28d58a152a7e0151b9e2a | Python | 2,868 | 93 | _base_ = [
'../_base_/datasets/cityscapes_1024x1024.py',
'../_base_/default_runtime.py',
]
# The class_weight is borrowed from https://github.com/openseg-group/OCNet.pytorch/issues/14 # noqa
# Licensed under the MIT License
class_weight = [
0.8373, 0.918, 0.866, 1.0345, 1.0166, 0.9969, 0.9754, 1.0489, 0.87... |
2a0bd817e9f73e0223501b8081621b47d53c02c503bfc072108fd6cdc1d11971 | Python | 2,875 | 97 | import os
import yaml
import argparse
import numpy as np
from utils import get_file_basename
class Statistics:
def __init__(self, data_path, params_file, out_path):
"""A class wrapper for calculating the statistics of the prepared dataset.
The following statistics are calculated:
'
... |
0030219bb770a183bf1427675df7d7b39a4dc9e9b4254589356251ba4d43460b | Python | 2,878 | 94 | import os
from medperf.exceptions import InvalidArgumentError, InvalidEntityError
from medperf.tests.utils import calculate_fake_file_hash
import pytest
from medperf.comms.entity_resources import sources, utils
import synapseclient
# prefixes
DIRECT = sources.DirectLinkSource.prefix
SYNAPSE = sources.SynapseSource.pre... |
2705889bbd31024ef042f7abf0b2862bef81e126444b9d4b5e0c5b534fcf9069 | Python | 2,878 | 90 | _base_ = [
'../_base_/models/setr_mla.py', '../_base_/datasets/ade20k.py',
'../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py'
]
crop_size = (512, 512)
data_preprocessor = dict(size=crop_size)
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
data_preprocessor=data_preproce... |
7d2d351c0e3cc0253f98470254be2f665da0851f433c7d3c6285afa1ed3b1ce1 | Python | 2,878 | 96 | #!/usr/bin/env python
#
# Compute flow from velocity encoding (VENC) sequence, based on the MRI phase image. More details in: https://mriquestions.com/what-is-venc.html
#
# Copyright (c) 2023 Polytechnique Montreal <www.neuro.polymtl.ca>
# License: see the file LICENSE
import sys
from typing import Sequence
from textw... |
6c76b7ac5edf5731502adeebb80ce69972675c5d9bbd019ed536a163adfcabb3 | Python | 2,880 | 85 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import shutil
from unittest import TestCase
import torch
from mmengine.structures import PixelData
from mmseg.evaluation import DepthMetric
from mmseg.structures import SegDataSample
class TestDepthMetric(TestCase):
def _demo_mm_inputs(self,... |
faf6970e00a671102263015e2edd7cd437081e194042296ff7cbee91ec06f9be | Python | 2,882 | 68 | """Unit tests for the settings GuPPy remembers between launches."""
import json
import pytest
from guppy.settings import load_settings, remember_root_folders, remembered_root_folders
@pytest.fixture
def roots(tmp_path):
"""An input root folder and an output root folder that both exist on disk."""
input_roo... |
74747d22376b2e89d74e44b01c57afb47b970974e4c44874c27e5c8bd57d3fc5 | Python | 2,884 | 98 | """Room-update feed delivery is scoped to authorized viewers.
These tests drive `broadcast_room_update` directly (no socket client), which
keeps the assertions isolated to the routing logic — the MockSioServer
captures emit calls and the tests check which rooms received the event.
"""
from uuid import uuid4
import p... |
a9e92836d2359efa8f722fa127b144ff8e8c820bd4d77fa73880042a6b7dc837 | Python | 2,884 | 78 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
from dash import Input, Output, callback
import base64
import os
from PIL import Image
# cropping images before plotting splitstree graphs.
def crop_image(image_path):
im = Image.open(image_path)
right, lower = im.size
left, upper = 0,0
h1, h2 = 150,... |
6f91d0fec73556be412c7efa0086e002ea74bbb9d8f1d8ecd8c53c8cb9833dbd | Python | 2,885 | 85 | import torch.nn as nn
import torch
import torch.nn.functional as F
import numpy as np
import pandas as pd
import os
import random
import mlflow
import sys
from torch.backends import cudnn
def get_device(cfg):
device = torch.device(
"cuda:{}".format(cfg.train.device_ids[0]) if torch.cuda.is_available()
... |
851d4b95432a84c23c9b3c8924f2863c992b9ab3bc2db6b528150455545c4556 | Python | 2,885 | 102 | import pytest
import base64
import uuid
import medperf.config as config
from medperf.commands.certificate.submit import SubmitCertificate
from medperf.exceptions import CleanExit, MedperfException
from medperf.tests.mocks.certificate import TestCertificate
from medperf.enums import CryptoKeyType
PATCH_SUBMIT = "medpe... |
7a5fc06d0801c09d0954e9e949b7c24384cbdb4e7e7a1bd71c12775d37c206f0 | Python | 2,886 | 61 | # parse config files
import json
def parse_json_config(json_file):
with open(json_file) as f:
decoded_dict = json.loads(f.read())
# check if we have all the arguments:
assert("transcript_counting" in decoded_dict)
assert("isoform_parameters" in decoded_dict)
assert("alignment_parameters" i... |
76c806406ad3e8d737403cb0716db8b7a0e6e068b8c6d73ce23939c9c178c927 | Python | 2,888 | 99 | import random
import numpy as np
import pandas as pd
import os
import torch
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
os.environ['CUBLAS_WORKSPACE_CONFIG'] = ':16:8'
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
... |
085b653b41c0ae22dc625fd12d4d6bcabf1ee1ad78091e4267ea7508352fa164 | Python | 2,889 | 72 | import numpy as np
def ellipse_params(c, width=1.177):
a = np.diag(c).sum()
b = np.sqrt(np.square(c[0, 0] - c[1, 1]) + 4 * np.square(c[0, 1]))
u2 = (a + b) / 2
v2 = (a - b) / 2
cu = width * np.sqrt(u2)
cv = width * np.sqrt(v2)
theta = np.arctan((u2 - c[0, 0]) / c[0, 1]) * 180 / np.pi
re... |
d918257d52b9a306777a7e8fd898bdbb9e1f3abcb73600770a102ec9eb80a0ff | Python | 2,889 | 77 | from copy import deepcopy
from CybORG.Shared import Observation
from CybORG.Simulator.Actions import Action
from CybORG.Shared.Session import VelociraptorServer
from CybORG.Simulator.State import State
class Monitor(Action):
""" Collects events on hosts and informs Blue Agent.
This action runs automatically... |
3badc5a49da5f9c09332b435cf606b4da122831b67343cae8a37b24a27b64fd7 | Python | 2,890 | 92 | from django.utils import timezone
from rest_framework import serializers
from benchmarkdataset.models import BenchmarkDataset
from benchmarkmodel.models import BenchmarkModel
from .models import ModelResult
class ModelResultSerializer(serializers.ModelSerializer):
class Meta:
model = ModelResult
... |
82ac77d266cf8de5f2a2d1be3aedd23c4ebc90657b2c8e159a9c634b3c8b6a57 | Python | 2,891 | 88 | import torch
import torchvision
import numpy as np
import tqdm
import Adversarial_Observation as AO
def trainModel(model, train_loader, optimizer, loss, epoch, filename):
"""
Trains the model using the provided data loader, optimizer, and loss function for one epoch.
Saves the training loss to the specifie... |
91c3e5995e37ba79b1a3ad803463d419b653e42f74ed3b5fe68c05e4bf1df870 | Python | 2,892 | 83 | import subprocess
import numpy as np
import os
import argparse
# Setup argument parser
parser = argparse.ArgumentParser(description="Run script with custom iterations")
parser.add_argument('--N', type=int, default=5, help='Number of iterations')
# Parse arguments
args = parser.parse_args()
# Use the argument
num_ite... |
17b7c0f82199582b6b1eb6b965aab3cd015b45a0a24161bbe80598436342e266 | Python | 2,893 | 92 | import logging
import subprocess
import sys
from collections.abc import MutableMapping
from io import StringIO
from pathlib import Path
from typing import cast
from cwl_utils.types import CWLObjectType
from cwltool.context import RuntimeContext
from cwltool.factory import Factory
from cwltool.workflow_job import Work... |
72d0e9b644be47e68585c8bf2bf8ffdda6e5689716673de5d989cca8f36cd3e0 | Python | 2,893 | 65 | # pytest unit tests for sct_apply_transfo
import pytest
import logging
import numpy as np
from spinalcordtoolbox.image import Image
from spinalcordtoolbox.scripts import sct_apply_transfo
from spinalcordtoolbox.utils.sys import sct_test_path
logger = logging.getLogger(__name__)
@pytest.mark.sct_testing
@pytest.ma... |
45e8c5f529d4b61343bc6b86a9acfe98ef5e5a40339eb5d34d2ff8b39cd6dada | Python | 2,894 | 80 | import numpy as np
import gc
import pytest
import xgboost as xgb
from hypothesis import given, strategies, assume, settings, note
import sys
import os
# sys.path.append("tests/python")
# import testing as tm
from xgboost import testing as tm
parameter_strategy = strategies.fixed_dictionaries(
{
"max_dept... |
87587f170616d469e2c5cf6f37846e2ed220204719d5e05efd4b2fad60a3acc8 | Python | 2,894 | 77 |
baseFileName = './shellScripts/sparse100_10class_noise_'
baseSimTag = 'sparse100_10class_noise_'
def generateCommand(trial_num=0,
num_trials=10,
learning_rate=0.001,
sim_tag='default',
exc_hid_rat=0.5,
epochs=30,
log_file='example.log',
w1_std=-1,
w1_s... |
950469561fde12d9cb940b80e544f55fc82df8812cfef453d72118458b0ed689 | Python | 2,894 | 67 | import holoviews as hv
import numpy as np
import pytest
from holoviews.streams import RangeXY
from guppy.visualization.shading import shade_trace
from guppy_test_data import resolve_plot
# A flat trace long enough to need shading, carrying a single one-sample artifact spike.
# 200,000 samples at 0.01 s spacing span t... |
dffa9d837239f95990fc2306d3d6bb66ff9e4c56126f9d6474c33b9833e08cfe | Python | 2,894 | 77 |
baseFileName = './shellScripts/sparse095_10class_noise_'
baseSimTag = 'sparse095_10class_noise_'
def generateCommand(trial_num=0,
num_trials=10,
learning_rate=0.001,
sim_tag='default',
exc_hid_rat=0.5,
epochs=30,
log_file='example.log',
w1_std=-1,
w1_s... |
ebfbfee224aed869662bc563895c00e856afa1393cfff4c48e4c4c05e0a0b001 | Python | 2,896 | 85 | from cryptography.hazmat.primitives import serialization
import yaml
from distutils.dir_util import copy_tree
import os
import tomllib
import tomli_w
import base64
from cryptography.hazmat.primitives.serialization import (
load_pem_private_key,
Encoding,
PublicFormat,
)
from cryptography import x509
def g... |
b57a32537414c5690b73beb2a29edb9dfa44f853f741a592a0a82cc9d0007c0c | Python | 2,898 | 91 | import typer
import medperf.config as config
from medperf.decorators import clean_except
from medperf.commands.certificate.client_certificate import GetUserCertificate
from medperf.commands.certificate.server_certificate import GetServerCertificate
from medperf.commands.certificate.submit import SubmitCertificate
from... |
9d30d7e59db8a0879dea68de6585f5c1418f5b59bf24d663f3db39029232cf3d | Python | 2,899 | 89 | import itertools
import os
from src.babel_utils import ThrottledRequester
def chunked(it, size):
"""Wraps an iterable, returning it in chunks of size: size"""
it = iter(it)
while True:
p = tuple(itertools.islice(it, size))
if not p:
break
yield p
def lookup(meshes):
... |
ca6f9affdb8de5a1a549478502d5438dbe1a4de565f65d1da7bb286ff81b8710 | Python | 2,902 | 100 | import torch
import torch.nn as nn
import torch.nn.functional as F
from .resnet import conv3x3x3, conv1x1x1, get_inplanes, ResNet
class PreActivationBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super().__init__()
self.bn1 = nn.BatchNo... |
e21d173aabed65e8c6568238720fd0555c9975cfe0c77663dbec02945c2f4681 | Python | 2,902 | 101 | from __future__ import annotations
from rest_framework.permissions import BasePermission
from benchmarkmodel.models import BenchmarkModel
from benchmark.models import Benchmark
from .models import Certificate
from django.db.models import OuterRef, Subquery
class IsAdmin(BasePermission):
def has_permission(self, r... |
82f853d6ea416606e7a1871143cda4dc72cf47640eebeca2e96118748d290b59 | Python | 2,903 | 95 | import functools
from torch.nn import functional as F
def reduce_loss(loss, reduction):
"""Reduce loss as specified.
Args:
loss (Tensor): Elementwise loss tensor.
reduction (str): Options are 'none', 'mean' and 'sum'.
Returns:
Tensor: Reduced loss tensor.
"""
reduction_en... |
5eab90232444426bcfd36d142b4a32e83e9c940be1c019b2233bc590af073c45 | Python | 2,905 | 75 | """Query list of all contributors and reviewers in a release"""
import json
import re
import sys
import requests
from sh.contrib import git
if len(sys.argv) != 5:
print(f'Usage: {sys.argv[0]} [starting commit/tag] [ending commit/tag] [GitHub username] ' +
'[GitHub password]')
sys.exit(1)
from_com... |
76db6992b93d2cb3a5d68f146b424eec1d35a97e88a5bf4df86cf0e6c7169e76 | Python | 2,905 | 65 | #!/usr/bin/env python
"""
extract dvars and fd info from fmriprep_confounds_timeseries
calculate mean and append it with BIDS data
"""
__author__ = "Heejung Jung, Owen Collins"
__copyright__ = "Spatial Topology Project"
__credits__ = [""] # people who reported bug fixes, made suggestions, etc. but did not actually wri... |
36c401c4bcd1b4f61dccfa2c5dc672cffc29fda6f4ddad363dff6714238bf3b5 | Python | 2,906 | 82 | import shutil
import pytest
from medperf.commands.certificate.delete_client_certificate import DeleteCertificate
from medperf.tests.mocks.certificate import TestCertificate
from medperf.exceptions import CleanExit
from medperf import config
from medperf.enums import CryptoKeyType
PATCH_DELETE = "medperf.commands.certi... |
8eacf4cdbb29c6fe13225e7f189dea99d70b91ab2dae199907e28e6be5b587d7 | Python | 2,908 | 74 | # -*- coding: utf-8 -*-
"""
This is a custom version of the postgresql_psycopg2 adaptor that
overrides last_insert_id so that it works with inherited tables.
Instead of using pg_get_serial_sequence, this finds the default value
of the primary key for the table and parse out the sequence name from
that.
As of May 2015... |
154231d41948d91bee6efff463057146e7754082d9cb45b4b8ab4e08c9f1049b | Python | 2,910 | 93 | """
Validate model predictions output for BraTS-MEN-RT
and BraTS-Path 2024.
"""
import os
from glob import glob
import numpy as np
import pandas as pd
import SimpleITK as sitk
import yaml
from cnb_tools import validation_toolkit as vtk
def check_image_dims(path):
base_size = np.array([182, 218, 182])
image ... |
687b4052f682f646712bfc8e68c06ced93b6b6be3ac9a9c7848306872aee072f | Python | 2,913 | 131 | # -*- coding: utf-8 -*-
""" Mapping the CATMAID datamodel to a graph database
Each project has its own annotation graph.
- Can I point from one graph to the vertex of another graph?
- Classes could be derived from Ontologies
http://bioportal.bioontology.org/
"""
import time
from bulbs.model import Node
from bulbs.p... |
6a9e2aad38bd2e9f16eb2c72c1b813722191341f2226b3cfe2839aaba065b497 | Python | 2,913 | 111 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import math
from typing import Optional
import torch
from ..initializers import he_orthogonal_init
class Dense(torch.nn.Module):
"""
... |
4382c316e9269ef4052051612b25787ba6455935f6c29423c2acb02f6837af1a | Python | 2,915 | 56 | # scans_list = sorted(glob.glob('sub-*/**/*scans*.tsv', recursive=True))
# for scan_fname in scans_list:
# # NOTE: Step 1: Get the scans.tsv using datalad
# run_command(f"datalad get {scan_fname}")
# print(f"datalad get {scan_fname} ")
# # Check if scans_file is not empty and unlock it using git annex
#... |
4f0037bad70dabb4dd95e9f80d386174b164213df3440f02b3c0327e2b06832e | Python | 2,915 | 55 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, 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 of the License at
#
# http://w... |
e39f58ca617be3852d0adb16c94407c07afe300a44b0d4193b65643472b53eb6 | Python | 2,916 | 97 | """
Contains functions for initialising models and loading their default configurations.
Depends on: ./model_configs.json.
Licence: BSD-3-Clause
Author: Hanna M. Tolle
"""
import os
from typing import Dict
import json
import models
def init_model(model_type: str, params: Dict = {}):
"""Initialises model."""
... |
076e2a3f5b3c59a6c63fcbbd7cbf2d35abfe2e61e3365f01678fb606f05e239d | Python | 2,919 | 83 | # Copyright 2013 Google Inc. All Rights Reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the foll... |
1ab144f471460d5b02d96531e9283721486fd0c483b46c18c64b722050467609 | Python | 2,922 | 79 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import io
import random
import pytest
import requests
import torch
from ase.build import add_adsorbate, fcc111
from ase.optimize import BFGS... |
21360bf1a9da23d573b84a7581c4082b9bc06363ec0ee56c80cb287723d14fed | Python | 2,923 | 68 | # SPDX-License-Identifier: Apache-2.0
from cpuinfo import get_cpu_info
import ort_test_dir_utils
import onnxruntime
import onnx
import os
from shutil import rmtree
import tarfile
import test_utils
def has_vnni_support():
return "avx512vnni" in set(get_cpu_info()["flags"])
def run_onnx_checker(model_path):
... |
51da08ccb0d444e99cc2748b10f16137587371634f15b5b39ebbbaa0d5e61c9c | Python | 2,923 | 83 | #!/usr/bin/python
import logging
import os
import shutil
import sys
from isorefiner.common import get_version, run_command
logger = logging.getLogger(__name__)
def main(args):
try:
# Set variables
raw_bam_files = [os.path.abspath(_) for _ in args.bam]
raw_genome_file = os.path.ab... |
f09491c104f05c4cf3fcd9aafb8fd7973fb200861e3b6ee0d3a278ac4209a226 | Python | 2,923 | 65 | from ipaddress import IPv4Address
from CybORG.Shared import Observation
from CybORG.Simulator.Actions.Action import RemoteAction
from CybORG.Simulator.HostEvents import NetworkConnection
from CybORG.Simulator.State import State
class Portscan(RemoteAction):
def __init__(self, session: int, agent: str, ip_address... |
c40a8d13632c2fac486d0716938b50752cee54f7f28502f6daddc1fcf4409d56 | Python | 2,925 | 98 | import os
from kgx.source.sssom_source import SssomSource
from kgx.transformer import Transformer
from tests import RESOURCE_DIR
from tests.unit import load_graph_dictionary
def test_load1():
"""
Read a SSSOM formatted file.
"""
t = Transformer()
source = SssomSource(t)
g = source.parse(
... |
2c5c56d9f9cf00bce8ccf701fd3be41af4ceda7290755254aa8fe47687fed857 | Python | 2,926 | 87 | # -*- coding: utf-8 -*-
from django.db import migrations, models
forward_history = """
SELECT disable_history_tracking_for_table('nblast_similarity'::regclass,
get_history_table_name('nblast_similarity'::regclass));
SELECT drop_history_view_for_table('nblast_similarity'::regclass);
ALTER TABLE... |
7c8650b5e1f4f01f2c22c982cf98da9866187f6864cf6aae288aef1be4bad047 | Python | 2,926 | 75 | """Context-Aware Implicit Neural Representations to Compress Earth Systems Model Data
DEVELOPED AT:
Department of Chemical Engineering
University of Utah, Salt Lake City,
Utah 84112, USA
DIRECTOR: Prof. Salah A Faroughi
DEVELOPED BY:
... |
f1c15576a4d59b79d4288c46ff01485f0573e01fd1c56bbe896d6904d280faf6 | Python | 2,926 | 104 | from __future__ import annotations
from typing import Optional
import pydicom as dcm
from heudiconv.utils import SeqInfo
scaninfo_suffix = ".json"
def create_key(
template: Optional[str],
outtype: tuple[str, ...] = ("nii.gz",),
annotation_classes: None = None,
) -> tuple[str, tuple[str, ...], None]:
... |
199e67b574cef9c6078d5217d62fa226b64fc20b9ee01fc1bca24942a0065390 | Python | 2,929 | 71 | import sys
from pathlib import Path
import numpy as np
import types
sys.path.append(str(Path(__file__).resolve().parents[1]))
from eigenstrapping.datasets import get_surface_examples
def test_get_surface_examples_download(tmp_path, monkeypatch):
sample_data = "1 2 3\n4 5 6\n"
calls = []
def fake_downloa... |
18e40fdf1ded6eb2202199d1e21f3bc98774fe4620f964df55f34c6ebe12dc4a | Python | 2,931 | 102 | #!/usr/bin/env python
import argparse
import boto3
from jinja2 import Environment
KEY_NAME = 'single_cell_opt.pem'
HEAD_INSTANCE_NAME = 'single_cell_opt_head'
WORKER_INSTANCE_NAME = 'single_cell_opt_worker'
hosts_template = '''
[neuron-optimizer-head]
{{ head_public_ip }}
[neuron-optimizer-worker]
{% for ip in wo... |
674eb477e8213cb19640deabc4ead50e496eb67b772882eefeb735b84ec6bf83 | Python | 2,931 | 79 | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import math
import scienceplots
import matplotlib.gridspec as gridspec
from functools import reduce
import torch
import math
from scipy import stats
centaur_70b = torch.load('../generalization/results/generalization_full_log_l... |
ab9552cd43d24879d8fb31172e2beef449bff2657a74d18fa58ca1f2d2cad28e | Python | 2,931 | 87 | import numpy as np
from scipy.spatial import KDTree
import nearl
import nearl.featurizer
import nearl.io
from nearl.features import Feature
# Result is a 4*9 (36 dimensions) contact map
# Rows (protein) : C, N, O, S
# Columns (ligand) : C, N, O, F, P, S, Cl, Br, I
PROT_MAP = {6: 0, 7: 1, 8: 2, 16: 3}
LIG_MAP = {6: ... |
b6a3574bfface6a7f463166e927a75a0052f483228144deb0ab53a56f96b9fb9 | Python | 2,931 | 113 | # Author: Huaxu Yu
"""
mzpkl.py - pickle utilities for MassCube
This module defines the pkl format the raw files in MassCube:
1. Structure of the pickle file format.
2. Convert the raw data to pickle file.
"""
# imports
import pickle
def convert_MSData_to_mzpkl(d, output_dir: str = None):
"""
Convert the... |
6214fdc676a3b971f9b7b5c12cff7e30336334a24027dd42b36b2a986605733a | Python | 2,940 | 117 | # This file was automatically generated by SWIG (http://www.swig.org).
# Version 3.0.8
#
# Do not make changes to this file unless you know what you are doing--modify
# the SWIG interface file instead.
from sys import version_info
if version_info >= (2, 6, 0):
def swig_import_helper():
from os.path impo... |
c2b04b7596c44392dd9039356799affa074bbd1dbf9f36a9ca15b9853f288719 | Python | 2,941 | 132 | # src/zndraw_joblib/schemas.py
from __future__ import annotations
from datetime import datetime
from typing import TYPE_CHECKING, Any, Generic, TypeVar
from uuid import UUID
from pydantic import BaseModel, Field
from zndraw_joblib.models import TaskStatus
if TYPE_CHECKING:
from zndraw_joblib.models import Provi... |
b1af7a04f75585a3b5976b631053c1d4eba58c621fe40b920617f4840b7bfa4a | Python | 2,944 | 83 | #!/usr/bin/env python
"""Pipeline to import a series of RAW files from mesoSPIM and export to TIFs.
Designed to import RAW files output by the mesoSPIM microscope and export
to a TIF format that the BigStitcher ImageJ/FIJI plugin can stitch together.
Assumes that files are in the format, `<chl>_<tile-coords>.raw` in a... |
f6e78534c94e8d533e1a055e1ec2e97f66ab8310ba5ea9cca85b7a035c2e2506 | Python | 2,944 | 85 | ## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881
import numpy as np
import os
import argparse
from tqdm import tqdm
import torch.nn as nn
import torch
im... |
22bc26d64e596711e43bff550d6e91d76afd0c2105debeabbd22479ad1f71a0a | Python | 2,946 | 82 | import torch
import numpy as np
from utils import run_forward
from ablate import ablate
def evaluate(model, loader, args, ab_type=None, ab_level=None):
model.eval()
acc = {} # dictionary to store accuracies
# Forward pass to get predictions
with torch.no_grad():
for batch_i, batch in enu... |
92e9c408d816bda7973463a57923efea467b4d346c368bdc85df961af887fdf2 | Python | 2,946 | 91 | norm_cfg = dict(type='BN', requires_grad=True)
data_preprocessor = dict(
type='SegDataPreProcessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_val=0,
seg_pad_val=255)
model = dict(
type='EncoderDecoder',
data_preprocessor=data_preprocessor,
pre... |
4984ece027c3ad7be6e7861dc547bd71ecd8b6ba86c80024caa726b885aa4fa1 | Python | 2,947 | 94 | #!/usr/bin/env python3
import os
import sys
import argparse
from glob import glob
import csbdeep.io
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("--baseDir", help="directory in which all your network will live", default='models')
parser.add_argument("--n... |
5c96fed1aa4ecd1da268d9d9499af537f9d35ebd5b268b935f78a34200ca8d70 | Python | 2,947 | 94 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import logging
from collections import deque
from pathlib import Path
from typing import Optional
import torch
from torch_geometric.data imp... |
c20b1f8d67879882f7fa37e2474818c7d76ab76704692e57742fa1ffdac5c745 | Python | 2,948 | 80 | import matplotlib.pyplot as plt
from datasets import load_dataset
import numpy as np
from transformers import pipeline
import torch
from sklearn.manifold import MDS
import seaborn as sns
import scienceplots
embeddings = []
colors = []
feature_extractor = pipeline("feature-extraction", framework="pt", model="answerdo... |
d4e7bfdd602a2aae5d252ce11e6312086f92054bc442ae957089731bf5775c7d | Python | 2,948 | 85 | from rest_framework import serializers
from .models import Dataset
from user.serializers import UserSerializer
class DatasetFullSerializer(serializers.ModelSerializer):
"""A private serializer. used for users who are permitted to see
"sensitive" information. This serializer is also used for POST"""
class... |
186b8f8c79fa7f739d14879e5e124895a0fb88fc2ade3b36b68db885510b5805 | Python | 2,949 | 94 | from transformers import TrainingArguments
from trl import SFTTrainer, DataCollatorForCompletionOnlyLM
from unsloth import FastLanguageModel
from datasets import load_dataset
import pandas as pd
import argparse
import torch
def full_log_likelihoods(logits, labels):
with torch.no_grad():
logits = logits.float().cpu(... |
6f3ddd3ae30a0912795d753d90f32a6a2259f93c3dcc7f1573e33e86fd520700 | Python | 2,949 | 58 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, 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 of the License at
#
# http://w... |
1dbfdcee3d565f02bb995f9783d2a5bddce315ecc3948862b9d41c42d44948b8 | Python | 2,951 | 82 | #!/usr/bin/python
# -*- coding: utf-8 -*-
# Create a project and stack associated HDF5 file with additional
# data such as labels, meshes etc.
import os.path as op
import h5py
from contextlib import closing
import numpy as np
from django.conf import settings
project_id = 3
stack_id = 4
filepath = settings.HDF5_ST... |
7c79fa86a09343e531dd7971c3d614e24e82783768b9619c94847575df2f65c5 | Python | 2,951 | 81 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, build_norm_layer
from mmseg.registry import MODELS
from ..utils import Upsample
from .decode_head import BaseDecodeHead
@MODELS.register_module()
class SETRUPHead(BaseDecodeHead):
"""Naive upsampling head and P... |
d8e1a705a8bcc165a19a8b30ce1c5a88ae4d1ec09a2b779c532edc1a0562c490 | Python | 2,951 | 87 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
from functools import partial
import numpy as np
from detail import Detail
from mmengine.utils import mkdir_or_exist, track_progress
from PIL import Image
_mapping = np.sort(
np.array([
0, 2, 259, 260, 415, 324, 9, 258, ... |
ad6e6bb97a1c5596cac348768781eaa1b6d6bff9dbf4ab22300bf5bf5030e0e3 | Python | 2,954 | 87 | ## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881
import numpy as np
import os
import argparse
from tqdm import tqdm
import torch.nn as nn
import torch
i... |
7234655c05764a554c9c5a592cfa069e4469d1a0db5d0b2361b5d4f1eb372b76 | Python | 2,955 | 97 | import argparse
import pathlib
import json
import glob
from load_aokvqa import load_aokvqa
def eval_aokvqa(dataset, preds, multiple_choice=False, strict=True):
if isinstance(dataset, list):
dataset = { dataset[i]['question_id'] : dataset[i] for i in range(len(dataset)) }
if multiple_choice is False... |
9b0bd4b1eec9a0ced5abef8f19efa1323b06d2951a5bf3ac8723481694ab5f1a | Python | 2,955 | 95 | import pytest
import tsgm
import numpy as np
import keras
def test_vae():
seq_len = 32
feat_dim = 1
latent_dim = 4
model_type = tsgm.models.architectures.zoo["vae_conv5"]
architecture = model_type(seq_len=seq_len, feat_dim=feat_dim, latent_dim=latent_dim)
encoder, decoder = architecture.enc... |
038be6bbcd63c0105adaf39ac49bd6babc26e0d24df99b790c6e84081895cd66 | Python | 2,957 | 86 | import socket
import time
import panel as pn
import pytest
from playwright.sync_api import expect
from guppy.frontend.frontend_utils import scanPortsAndFind
from guppy.orchestration.store_labeling import build_store_labeling_template
@pytest.fixture(scope="session")
def store_labeling_server_url(panel_extension, ui... |
f2a868e2711e7aaf3d4de4392b109b10cbb580238866535c56cd693c855477ed | Python | 2,958 | 97 | """Plane geometry for ZnDraw."""
from pydantic import Field
from .base import (
BaseGeometry,
ColorProp,
InteractionSettings,
PositionProp,
RotationProp,
ScaleProp,
Size2DProp,
)
class Plane(BaseGeometry):
"""Flat rectangular plane geometry with customizable dimensions.
Planes a... |
22bac12be351e20cc21929b2a6f0fbb14a1ae0f3948a064c80a42d28828b8578 | Python | 2,963 | 78 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmseg.models.decode_heads import FCNHead
from mmseg.structures import OHEMPixelSampler
def _context_for_ohem():
return FCNHead(in_channels=32, channels=16, num_classes=19)
def _context_for_ohem_multiple_loss():
return FCNHead(
... |
5bbfca1c3c13b8152d032c42d4b6e1b58aef4332a86b44e154268a99a1bea607 | Python | 2,963 | 80 | # pytest unit tests for sct_compute_mtr
import pytest
import logging
import numpy
import nibabel as nib
from spinalcordtoolbox.image import Image
from spinalcordtoolbox.scripts import sct_compute_mtr
from spinalcordtoolbox.utils.sys import sct_test_path
logger = logging.getLogger(__name__)
@pytest.mark.sct_testing... |
855851680181bc2231da026e6236d1949722e2c9238160374c22e309c91addc0 | Python | 2,963 | 92 | import pytest
from medperf.commands import utils as utils_module
PATCH_UTILS = "medperf.commands.utils.{}"
class DummyConfig:
def __init__(self):
self.active_profile = {
"certificate_authority_id": 1,
"certificate_authority_fingerprint": "abc",
}
@pytest.fixture
def prof... |
47ab037f123e031d2d6f3a5e7afa9dfab727adef2cd87f498b492781c6f96dd6 | Python | 2,965 | 85 | from django.db import migrations
forward = """
-- Re-create all triggers associated with history tracking on a particular
-- live table (if it has a history table). This function has to be called
-- after a live table was changed (e.g. a column was added or remove).
CREATE OR REPLACE FUNCTION reload_h... |
abd99322e6b99fe9de9375bd0d87f519a7d83fa864ac145eccc69111e837d243 | Python | 2,968 | 75 | import numpy as np
import pandas as pd
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse
# df = pd.read_csv('DMPNN_Random_Split.csv')
# print(len(df))
# r2_list = []
# for seed in range(1, 10):
# sub_df = df[df[f'split{seed}'] == 'train']
# print(len(su... |
de5a8126d54ff64e26cd84963e0fabb2836243ea0201e0e2f5b510826ee6779f | Python | 2,968 | 75 | # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
# pyre-strict
import torch
def smooth_l1_loss(
input: torch.Tensor, target: torch.Tensor, beta: float, reduction: str = "none"
) -> torch.Tensor:
"""
Smooth L1 loss defined in the Fast R-CNN paper as:
::
| ... |
f833d6197a3f31424b2ed3e01b57de49f886341ffcc026584ce03d229cb77dff | Python | 2,968 | 76 | """Unit tests for `src.babel_utils.norm()`, the CURIE prefix renamer.
`norm()` is what turns a source's spelling of another vocabulary's prefix into Babel's
(`config.yaml: disease_xref_prefixes`). A prefix it fails to rename is not an error -- the
un-renamed CURIE still reaches `glom()`, joins nothing, and fuses every... |
6545dd0ba0765c8b71df9226995a839f9f301993fec4f07b6c96eb5682ab2492 | Python | 2,970 | 97 | """Main Graupner-Brunel STDP example script"""
import numpy
import bluepyopt as bpop
import stdputil
def gbParam(params):
"""Create the parameter set for Graupner-Brunel model from an *individual*.
:param individual: iterable
:rtype : dict
"""
gbparam = dict(
theta_d=1.0,
theta_... |
22a5d68b0f0c02a7f55eed2480267900af3c02bb96057ee55c7212c2014d3df7 | Python | 2,973 | 94 | import torch
from torch import nn
class Fire_Block(nn.Module):
def __init__(self, in_channels, sequeeze_channels):
super().__init__()
self.squeeze = nn.Sequential(
nn.Conv3d(in_channels, sequeeze_channels, kernel_size=1),
nn.ReLU(inplace=True),
)
self.expand... |
c033984298957318cffe77faf0fa777213b13a289871596523df2eb8e6cd12f3 | Python | 2,973 | 77 | import torch
import torch.nn as nn
from nnunet_mednext.network_architecture.custom_modules.custom_networks.UTNet.utnet import UTNet as UTNet_Orig
from nnunet_mednext.training.network_training.nnUNet_variants.architectural_variants.nnUNetTrainerV2_noDeepSupervision import \
nnUNetTrainerV2_noDeepSupervision
from nnu... |
521585de747ed2cb3630bed210c5447833dec741027bd2d9c1d498b9c5bd4fb9 | Python | 2,974 | 84 | # -*- coding: utf-8 -*-
"""
Getting Started
---------------
Quick introduction to brain maps and eigenmodes
===============================================
Patterns on the cortical surface or in the subcortex ("brain maps") more often
than not express with smoothness - spatial autocorrelation. Accounting
for this in ... |
dec4ec73257a04ba9515399e71872d579658e79fb151c2be4d9a26d339d0ff40 | Python | 2,977 | 88 | import pandas as pd
def adding_label(df):
"""
Adds normalized and binary labels based on the 'PAMPA' column.
Parameters:
- df (pd.DataFrame): Input DataFrame with a 'PAMPA' column.
Returns:
- pd.DataFrame: DataFrame with added label columns.
"""
if 'PAMPA' not in df:
raise Va... |
25411314e3bb36824c35ffbd7070679229d544cd66e8b755cdbcb7f2de865cb8 | Python | 2,980 | 92 | import os
import zipfile
import typing as T
import hashlib
import logging
import tarfile
import urllib
import urllib.request
logger = logging.getLogger('utils')
logging.basicConfig()
logger.setLevel(logging.DEBUG)
def _archive_type(file: str) -> str:
_, ext = os.path.splitext(file)
if ext in EXTRACTORS.key... |
4b5700c17872309065ddcb933039db88b6296f1b4fcfe3a7c377887925f1e969 | Python | 2,980 | 75 | import torch
import math
import matplotlib.pyplot as plt
import seaborn as sns
import scienceplots
import matplotlib.gridspec as gridspec
import numpy as np
hermes = torch.load('../results/custom_metrics_full_log_likelihoods_unsloth-Hermes-3-Llama-3.1-70B-bnb-4bit.pth')
nemotron = torch.load('../results/custom_metrics... |
6bd9e1ffa3d2b3d13e499c863d997011d012412f977b983a7c9ca5fb56747edb | Python | 2,980 | 87 | import unittest
from provdbconnector.exceptions.database import InvalidOptionsException, AuthException
from provdbconnector import Neo4jAdapter, NEO4J_USER, NEO4J_PASS, NEO4J_HOST, NEO4J_BOLT_PORT
from provdbconnector.prov_db import ProvDb
from provdbconnector.tests import AdapterTestTemplate
from provdbconnector.test... |
9f3cc6de19da26ef887b26c4cb739d906448afe401848947eb56daf320b04f4d | Python | 2,980 | 84 | from django.db import models
from django.contrib.auth import get_user_model
User = get_user_model()
class Benchmark(models.Model):
BENCHMARK_STATUS = (
("PENDING", "PENDING"),
("APPROVED", "APPROVED"),
("REJECTED", "REJECTED"),
)
BENCHMARK_STATE = (
("DEVELOPMENT", "DEVEL... |
9d1c13ecaa2427631225b0a176d181689b8fd54b6dc020fbf75bf79620957ac9 | Python | 2,981 | 88 | """Tests for interaction constraints."""
from typing import Optional, Sequence, Union
import numpy as np
from .._typing import FeatureNames
from ..core import DMatrix
from ..training import train
from .utils import Device
def run_interaction_constraints( # pylint: disable=too-many-locals
tree_method: str,
... |
670886ae96a03c96c7ce74adc6c5b3bb03777366c9530a6eb0666d6966b28e90 | Python | 2,982 | 72 | """
这是基于hugging face社区开源的框架accelerate制定的基础量化推理方案
该框架主要实现了int8、int4量化，以及cpu或者disk offload
实现了用低存储，小设备运行大模型
具体可以见wiki：http://wiki.team.idea.edu.cn/pages/viewpage.action?pageId=31464125
"""
import time
from transformers import AutoModelForCausalLM, AutoTokenizer
import bitsandbytes as bnb
from bitsandbytes.nn import Linea... |
c241e7d5f6e1dbf104cd36f634870b445793f59d4686ee926112992adb77648e | Python | 2,986 | 66 | import torch
import torch.nn.functional as F
from fengshen.models.transfo_xl_denoise.tokenization_transfo_xl_denoise import TransfoXLDenoiseTokenizer
from fengshen.models.transfo_xl_denoise.modeling_transfo_xl_denoise import TransfoXLDenoiseModel
from fengshen.utils import top_k_logits, get_masks_and_position_ids
def... |
fe66585fc18c576883509a8b8567911c594ad4feb6badf83b762052c1f894fc8 | Python | 2,986 | 92 | """
Copyright (c) Facebook, Inc. and its affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
import argparse
import copy
import logging
from typing import List
import submitit
from ocpmodels.common.flags import flags
from ocpmodels.... |
6378dae90e285fa94e4a90e92c7d233f3ec22e1225aeae6168b3ba91673a7a9a | Python | 2,988 | 90 | ## Restormer: Efficient Transformer for High-Resolution Image Restoration
## Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang
## https://arxiv.org/abs/2111.09881
import numpy as np
import os
import cv2
import math
def calculate_psnr(img1, img2, border=0):
# img1 ... |
142f294b8110d536d72a7f0d91934db5e0856b2cf46308c6fc97c05b57766690 | Python | 2,990 | 78 | #!/usr/bin/env python
# Noise2Void - 2D Example for SEM data
from n2v.models import N2VConfig, N2V
import numpy as np
from csbdeep.utils import plot_history
from n2v.utils.n2v_utils import manipulate_val_data
from n2v.internals.N2V_DataGenerator import N2V_DataGenerator
from matplotlib import pyplot as plt
import urlli... |
f37a50512a0af57920766724c65a9333baa5733d3297257a7b6068f410ce5915 | Python | 2,993 | 69 | from __future__ import division
from sciunit import Score
import numpy
import math
from sciunit.utils import assert_dimensionless
from quantities import nA
class ZScore_depolblock(Score):
"""
Z scores. Float indicating standardized difference
from reference means for current threshold (Ith), and equilibriu... |
ffc3a85b2be2455fe4c5f4c669aed6b2a2d21b876747629c37973aaffe2253bc | Python | 2,995 | 71 | import ssm
from ssm.util import find_permutation
def fit_glmhmm_em(prior_config: dict, n_session, inpts, true_choices, true_latent=[]):
"""
Fit a Generalized Linear Model-Hidden Markov Model (GLM-HMM) to the given choices and inputs.
Args:
prior_config (dict): Dictionary containing prior hyperpar... |
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