sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2abf9787637e563d2b3f8070e8693d3a257684d3783a5b158826c0d09a7b873d | Python | 4,200 | 114 | import math
from collections import defaultdict, deque
from bidict import bidict
def szudzik_pair(*numbers):
if len(numbers) < 2:
raise ValueError("Szudzik pairing function needs at least 2 numbers as input")
elif any((n < 0) or (not isinstance(n, int)) for n in numbers):
raise ValueError("Szu... |
827cada04a126ec3e273d6a3bdd8d52ad692bc28bf40ed920e62e1f1de66945f | Python | 4,200 | 114 | import math
from collections import defaultdict, deque
from bidict import bidict
def szudzik_pair(*numbers):
if len(numbers) < 2:
raise ValueError("Szudzik pairing function needs at least 2 numbers as input")
elif any((n < 0) or (not isinstance(n, int)) for n in numbers):
raise ValueError("Szu... |
c684d3b9a301f9f9656412110a6f50d4a3549eb0c66f1ea46413446e93f6081e | Python | 4,200 | 137 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/panoptic_hand2d.py'
]
evaluation = dict(interval=10, metric=['PCKh', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
... |
4643718de0b74f796adacbbdf204428deb36f706616ae57ab135e1b68db9d55a | Python | 4,202 | 122 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Feb 23 12:29:15 2022
@author: baris
"""
import pickle
import numpy as np
import seaborn as sns
import pandas as pd
from phase_to_rate import grid_model
from phase_to_rate.figure_functions import (_make_cmap, _precession_spikes,
... |
8d25e51204645ef36d5f0c4455eabd6b0cf3ed170c7d94806a6ae4f78954577a | Python | 4,206 | 111 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import bbox2result
from mmdet.models.builder import DETECTORS
from ...core.utils import flip_tensor
from .single_stage import SingleStageDetector
@DETECTORS.register_module()
class CenterNet(SingleStageDetector):
"""Implementation of Ce... |
05627317bb7cd6c08e69bb1432fe462b4d0330f05cfb3562350692294995e7e0 | Python | 4,213 | 129 | """Maximum likelihood and Bayesian phylogenetic analysis.
P4 is a phylogenetic toolkit that does Bayesian and maximum likelihood
phylogenetic analyses of molecular sequences. It's specialty is that
you can use heterogeneous models, where the model parameters can
differ in different parts of the tree, or over differen... |
c9ba9bc8af52fad2bd4a6ad939838e5359afcec0b5e615132f70d8a1ad6ad56f | Python | 4,213 | 143 | """
===============================================================================
:mod:`foolbox.criteria`
===============================================================================
Criteria are used to define which inputs are adversarial.
We provide common criteria for untargeted and targeted adversarial attack... |
e298d63e5a0ed2971e3c47401e3d4a4e9f2b3fc2dd86fd0b7a708d1b943466aa | Python | 4,216 | 122 | import json
import os
import numpy as np
import torch
def read_data(train_data_dir, test_data_dir):
'''parses data in given train and test data directories
assumes:
- the data in the input directories are .json files with
keys 'users' and 'user_data'
- the set of train set users is the same... |
26b0fec38d5606658a123506feeeec87101f366ba23c553210404cf756fc7c35 | Python | 4,218 | 144 | #!/usr/bin/env python
# Copyright [1999-2017] EMBL-European Bioinformatics Institute
#
# 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
#
# Unl... |
496004d9d517bc3cd9cdcf8de3cea75a18018edd16217be594d268e110c48470 | Python | 4,220 | 126 | # Copyright (c) OpenMMLab. All rights reserved.
from queue import Full, Queue
from threading import Thread
from typing import List, Union
import cv2
from ..node import Node
from ..registry import NODES
@NODES.register_module()
class RecorderNode(Node):
"""Record the video frames into a local file.
:class:`... |
c09841420a25ccf24842fb6e824c44f2ec717e32cb6eb1667052e08130367aef | Python | 4,220 | 137 | # Copyright (C) 2024-2025 ETH Zurich, Niels Maeder and other DASH contributors.
from pathlib import Path
from typing import Any, Union
import pytest
from serenityff.charge.data import (
additional_data_dir,
default_dash_tree_path,
)
from serenityff.charge.tree.retrieve_data import (
DataPath,
data_is... |
84f35156436b3f1051c409db3e9d3c8284ba7e1d9ff09fdbadf25c63aa6bb174 | Python | 4,221 | 105 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import os
import pyabf
import seaborn as sns
import shelve
import sys
from scipy.stats import zscore
df_summary = pd.read_csv('/network/lustre/iss01/wyart/analyses/2pehaviour/MLR_analyses/data_summary_BH.csv')
fishlabel, plane = ... |
9196d22fef4bda889021aadb6d9b5c82d03bc78ad6c55d0af4e5743ac495c4c5 | Python | 4,221 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
cc57c508739ea43fdcfce2d38a15ed4b79ac449e75e6c8cd01d860ccc801a553 | Python | 4,221 | 111 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from ..builder import BBOX_SAMPLERS
from ..transforms import bbox2roi
from .base_sampler import BaseSampler
@BBOX_SAMPLERS.register_module()
class OHEMSampler(BaseSampler):
r"""Online Hard Example Mining Sampler described in `Training Region-based
... |
597ca0740000f4136859bc70c05c5717017e599582fc8e7b79094a1994f448d7 | Python | 4,223 | 116 | from django.db import models
from django.contrib.postgres.fields import ArrayField
import pathlib
def get_figures_path():
current_dir = pathlib.Path(__file__).resolve(strict=True).parent
figures = current_dir.parent.parent
return str(figures)
class Article(models.Model):
doi = models.CharField(pri... |
17eef0d8664afbf35229c01c4aefddebcf0b4b9417664cf2f4e37ab07fed9408 | Python | 4,225 | 89 | #######################################################################
# Software License Agreement (AGPL-3 License)
#
# OpenViBE SDK Test Software
# Based on OpenViBE V1.1.0, Copyright (C) Inria, 2006-2015
# Copyright (C) Inria, 2015-2017,V1.0
#
# This program is free software: you can redistribute it and/or modify
#... |
7ce69ee8be3ceac3b2f31cbf002362c13d3feccc9ddf2fd9d54c053d7ecf7b0d | Python | 4,226 | 108 | from collections import defaultdict
import taxopy
from genomad import utils
def get_conservative_taxon(taxon, taxdb):
for rank, taxid in taxon.ranked_taxid_lineage:
if rank not in {"subfamily", "genus", "subgenus", "species"}:
return taxopy.Taxon(taxid, taxdb)
return taxon
def write_ta... |
561fe9e741fa291bb982f705f83b2d866d4150357b4a24a9035c6d95e3e18872 | Python | 4,229 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
7045959d4899d0699a16adbd5c8b8288924a2743fc9aaafb79f7ffddb7cea4a7 | Python | 4,229 | 137 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/panoptic_hand2d.py'
]
evaluation = dict(interval=10, metric=['PCKh', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
... |
d717ad13486dc573463d142696db1ca5d1bb12cafc5a012c2dee472d97f36691 | Python | 4,229 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
0c43a4160fa038293ac5841f22f37c5f9e67c9f9c49651580ae33726fbc01d02 | Python | 4,230 | 114 | import jax
import jax.numpy as jnp
from collections import namedtuple
from functools import partial
import data_utils
from einshape import jax_einshape as einshape
'''
the semantics of jax.lax.scan() are given roughly by:
def scan(f, init, xs, length=None):
if xs is None:
xs = [None] * length
carry = init
... |
6c8b603b5f680ee03e4478367aac788a69d3f3147027d580e8e181b5122fb0c0 | Python | 4,239 | 138 | import numpy as np
import tensorflow as tf
import random as python_random
import os
import tensorflow_datasets as tfds
from tensorflow.keras.models import load_model
from gradient_accumulator import GradientAccumulateOptimizer
tf_version = int(tf.version.VERSION.split(".")[1])
def normalize_img(image, label):
"... |
ca734d1653111131e493028b0a03f75a9f9af963afd13e71ce100c259e6e0fd0 | Python | 4,239 | 113 | from copy import copy, deepcopy
from transformers import TrainingArguments
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_Over_BERT
from config_path import LOG_PAT... |
29ee14c5d4c987c12ce1b8ebecfa3d8024209cd1ce5a1165ca3c9683236c4737 | Python | 4,240 | 103 | import os
import sys
import logging
import time
import warnings
import shap
import pickle
import numpy as np
from metstab_shap.data import load_data
from metstab_shap.config import parse_shap_config, utils_section
from metstab_shap.utils import get_configs_and_model, find_and_load
from metstab_shap.savingutils import... |
2d4992e3b35e6c865cacac6c2332f783a141876caea3d354e3df4b035958b0c4 | Python | 4,241 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP', rle_score=True)
optimizer = dict(
type='Adam',
lr=1e-3,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
a85e539c115d920cde135bfa15cdf7d3bb82062dde3f01c9a0604e411507413d | Python | 4,241 | 90 | """ Plot reconstruction errors after network training.
Example (from terminal):
$ python train.py --data mnist_extended_fast.npy --labels labels_mnist_extended.npy --trafos 0 0 0 --resultfolder fig7b_translation --epochs 20 --n_runs 4
$ python train.py --data mnist_extended_fast.npy --labels labels_mnist_exte... |
011848467f136348646fc613c2ce1009bafd5032e324efce7cf151a8289cf9be | Python | 4,243 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP', rle_score=True)
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
27f69ed09c15108bcae8e7271ad7d797375ac36bcced3481caf7bf58cbe9e1e4 | Python | 4,243 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP', rle_score=True)
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
eaf9397ffa8034756fd8bdd5ac6659734b561ded578be42434140ea246189efb | Python | 4,243 | 140 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP', rle_score=True)
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step... |
c06cdce5fdd843df2a23c8e5c038fb50c36f4f11e7e7ce685c55cab570fa9d39 | Python | 4,248 | 133 | import numpy as np
import random
import os
from matplotlib import pyplot as plt
import prettytable as pt
from sklearn.metrics import roc_curve, auc, f1_score
def load_amp(dir):
"""
Load peptide sequences and labels from AMPXXX.txt
:param dir:
directory of AMPXXX.txt
:return:
... |
e8e973702dcfbda91e26f50bbef97b96b89d41b7ce444e7a413f85ff6aedf2df | Python | 4,249 | 118 | '''
* Copyright (c) 2022, salesforce.com, inc.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
* For full license text, see LICENSE.txt file in the repo root or https://opensource.org/licenses/BSD-3-Clause
* By Junnan Li
'''
import argparse
import os
import ruamel_yaml as yaml
import numpy as np
imp... |
0b2580b5ada3d7be7c7369f3eee8168ca65e8e7ce01d8a21c6ba516c59cd6c40 | Python | 4,250 | 149 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 10 09:32:34 2020
@author: ravinderjit
Reject trials with large p2p deflections
Calculate system functions from EEG data for Dynamic Binaural Msequence project
Also compute noise floors
"""
import matplotlib.pyplot as plt
import matplotlib.colors a... |
c9c9e0256657c98aa1e7719888c15c7b3d127e0e78d347a49e8fc060dd11205b | Python | 4,251 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
ea5102cacdcb0734107ec1f3cbb7256cf1d3e8fb101284d34fc7a8c1c74ff0dc | Python | 4,252 | 124 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import numpy as np
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weighted_loss
@mmcv.jit(derivate=True, coderize=True)
@weighted_loss
def balanced_l1_loss(pred,
target,
beta=1.0,... |
038c85a89fc214e2291a273b4e889550fed0e219debc795c7a14754639c9b19d | Python | 4,253 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
75b13389410f93afbfcf7dfc6d9e94c994b0c8eb7825c5160236b8677ef9691a | Python | 4,253 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
8990c081671017cfe21358b4e302f10e7f878d5cf0ebfffcb4e193def70ebab8 | Python | 4,253 | 142 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/posetrack18.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/resnet/res50_coco_256x192-ec54d7f3_20200709.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric='mAP', save_best... |
97854f0f642ede38f6c6869e9767c387014ba9e26dbc0caa86fa9d71a7010e0a | Python | 4,253 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
a2510bc6710fb6fd466c2ba8d58dfc6f76defc63322ba22cdd19681421b42e07 | Python | 4,253 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/aic.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='li... |
63d92d5958f03e7ca54d387cbea27c6a09dbb7d8d6ec48f5e921b819e3b92e66 | Python | 4,256 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/cpm/cpm_mpii_368x368-116e62b8_20200822.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_best=... |
aed42ab3415ecec0901b204d06f0f43c7f7bfc97938ba58dc5f33009626453f8 | Python | 4,256 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/cpm/cpm_mpii_368x368-116e62b8_20200822.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_best=... |
e6d20fc153c83793cdb85fd2cd134796b1747930a02976dd76bd16d5db0a20c2 | Python | 4,256 | 144 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/jhmdb.py'
]
load_from = 'https://download.openmmlab.com/mmpose/top_down/cpm/cpm_mpii_368x368-116e62b8_20200822.pth' # noqa: E501
checkpoint_config = dict(interval=1)
evaluation = dict(interval=1, metric=['PCK', 'tPCK'], save_best=... |
a718eb45fb1447499304914afbf67a6d6895fcc44128164ed7744cffda015eef | Python | 4,261 | 138 | # Copyright (c) OpenMMLab. All rights reserved.
import os
import warnings
from argparse import ArgumentParser
import cv2
import mmcv
import numpy as np
from mmpose.apis import (inference_top_down_pose_model, init_pose_model,
vis_pose_result)
from mmpose.datasets import DatasetInfo
def main(... |
bb853548a11bde5eb4375df68898276120f84377895837cc3636a2c275bca5bf | Python | 4,263 | 143 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
6cb5bcc14b517c9c177b099b5d1f25098110d65f1d6e87c10d0a45a0dc226c2c | Python | 4,268 | 109 | # -*- coding: utf-8 -*-
"""
Created on Wed Jul 7 16:23:37 2021
@author: fhu14
Testing file for playing around with the new repulsive model
predicted_train_elec_only.p and predicted_validation_elec_only.p are
pickle files that contain the Electronic energy targets predicted from
trained models, 150 epochs with lj d... |
5053faf3fcbeae850b1b02d347e2008b7cf7a2f20530e6bb7b71ce3fba988e87 | Python | 4,273 | 138 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/panoptic_hand2d.py'
]
evaluation = dict(interval=10, metric=['PCKh', 'AUC', 'EPE'], save_best='AUC')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
... |
b15591972f2c122d9f0d7a2d9e085984425a535fec04575b5456ea1a135dc615 | Python | 4,273 | 118 | import os
from os.path import join, basename
import pandas as pd
from config_path import TEST_RESULTS_PATH
from os import listdir
import numpy as np
from utils.evaluate import evalualte
def read_predictions(dirs_df,max_f1, _class= 0):
model_dict={}
for i, row in dirs_df.iterrows():
dir_ = row.file
... |
bb6ccee45b4163188291d784e8d7c42ca09f06c3c62d0ae2b223fc334b213476 | Python | 4,275 | 156 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpi_inf_3dhp.py'
]
evaluation = dict(
interval=10,
metric=['mpjpe', 'p-mpjpe', '3dpck', '3dauc'],
save_best='MPJPE')
# optimizer settings
optimizer = dict(
type='Adam',
lr=1e-4,
)
optimizer_config = dict(grad_c... |
8cd4ed73c46173aee4afc75dada7eced64d72f2a74faeb1fa49e3d95e810a88a | Python | 4,277 | 84 | # ----------------------------------------------------------------------- #
# Copyright (c) 2023, UChicago Argonne, LLC. All rights reserved. #
# #
# Software Name: Fast Autonomous Scanning Toolkit (FAST) #
# By: Argonne Na... |
8984597e43e51fee8b57b2e3d7cd35d876cbcccc65baf611c4fb3811c31bcd1c | Python | 4,279 | 124 | import numpy as np
import scipy.io as sio
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from preprocess import preprocess_data
def load_all_data(data_path):
X_train, y_train = [], []
for s... |
c59582e88046f19f6dc4af18b62b3e704c79645657251efe7429418ac1e32426 | Python | 4,280 | 131 | import gzip
import argparse
import math
import sys
from argparse import ArgumentParser
import tables
import util
def parse_options():
parser = ArgumentParser(description="""This script adjusts
heterozygote probabilities in CHT files to account for
possible genotyping errors. Total ... |
47df16e259d3ed59918e650417a20c0752d8a6bb095e5247716254fc64d8f873 | Python | 4,281 | 118 | # This file contains modules common to various models
import math
import torch
import torch.nn as nn
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
def DWConv(c1, c2, k=1, s=1, act=True):
... |
ab0e68c422e55321e240d5560dfafd7549960de929382c30acff10f1ae1fd2a5 | Python | 4,281 | 125 | import numpy as np
import scipy.io as sio
from tensorflow.keras.utils import to_categorical
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from preprocess import preprocess_data
def load_all_data(data_path):
X_train, y_train = [], []
for s... |
d410370c0b24a8a0829e04c6e29d6311335e098a2b3a3f4a8dc9f3ca6dbb5742 | Python | 4,283 | 124 |
from chromosome import Chromosome
class CoordError(Exception):
"""An exception indicating that something is wrong with a coordinate"""
def __init__(self, value):
self.value = value
def __str__(self):
return str(self.value)
class Coord(object):
def __init__(self, chrom, start, end, s... |
d3433dcff44866596fbbda8ee41f04a8d974137c3e9b4ae07a27f0682668e5bf | Python | 4,287 | 115 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
d75919ebbd50c8d8ff43191b5c418eab7af9fbec5452dbbd242ff3fdc2d224b3 | Python | 4,288 | 137 | import sys
import subprocess
import os
DNA_COMP = None
def comp(seq_str):
"""complements the provided DNA sequence and returns it"""
global DNA_COMP
if DNA_COMP is None:
DNA_COMP = str.maketrans("ATCGMRWSYKNatcgmrwsykn",
"TAGCKYWSRMNtagckywsrmn")
return seq_s... |
5b892b7bbb5a063769fb2e7c08255b6982bde6d416a36e2b0c24b2c677aa6d65 | Python | 4,292 | 108 | # Copyright (c) OpenMMLab. All rights reserved.
executor_cfg = dict(
name='Gesture Recognition',
camera_id=0,
camera_max_fps=15,
synchronous=False,
buffer_sizes=dict(_input_=20, det_result=10),
# Define nodes.
# The configuration of a node usually includes:
# 1. 'type': Node class name... |
eae061a09b480436684dc473845d706853a36c59b54f34f766e313f8470cd745 | Python | 4,292 | 143 | import torch
import torch.nn as nn
def compute_scale_and_shift(prediction, target, mask):
# system matrix: A = [[a_00, a_01], [a_10, a_11]]
a_00 = torch.sum(mask * prediction * prediction, (1, 2))
a_01 = torch.sum(mask * prediction, (1, 2))
a_11 = torch.sum(mask, (1, 2))
# right hand side: b = [b_... |
cb7566eaf25f3ea6a706d47ec0ad5c1a8072aa6c610c5f63f410d68a34214e02 | Python | 4,295 | 105 | from pydantic import BaseModel, ConfigDict, Field, PositiveInt, model_validator
from typing_extensions import Self
from oligo_designer_toolsuite.config._types import (
GCContentMaxT,
GCContentMinT,
GCContentOptT,
TmMaxT,
TmMinT,
TmOptT,
)
class IndependentSetSelection(BaseModel):
model_co... |
2845bea837d0434d1d5a4f6bba9efacd8de2611a3d06b27040955d39b83f2171 | Python | 4,297 | 105 | # Copyright (c) OpenMMLab. All rights reserved.
from collections import OrderedDict
from mmcv.utils import print_log
from mmdet.core import eval_map, eval_recalls
from .builder import DATASETS
from .xml_style import XMLDataset
@DATASETS.register_module()
class VOCDataset(XMLDataset):
CLASSES = ('aeroplane', 'b... |
60b853ab062d65e2ed2c55faba6a31f222910eaf4914c05dc81028ebaf436115 | Python | 4,298 | 115 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
9bfa8f7cbecb0875e90a9212a9e80d0b34901941bcb32fe1252c89f71063213d | Python | 4,298 | 101 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 16 15:32:47 2021
@author: fhu14
"""
#%% Imports, definitions
import numpy as np
import matplotlib.pyplot as plt
import os
from MasterConstants import Model
from InputLayer import Input_layer_pairwise_linear_joined, Input_layer_pairwise_linear
import pickle
#%% Code behin... |
33ce64bf614b110cdd1f874b9f828dcb281335983ffb7a81716a58cfd21ad8b4 | Python | 4,301 | 147 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
977cc398162387dbfe2bc9569ff64929ead90799cc0c0829cc6e9923e88f30bd | Python | 4,301 | 113 | import os, sys, math, pickle
import pandas as pd
root = os.path.dirname(os.getcwd())
db_file = root + "/data/chembl/chembl_v31.csv"
cpds = root + "/data/chembl/chembl_tautomers.csv"
kinases = root + "/data/kinases.csv"
db_header = ["kinase_name","hgnc_mgi","accession","kinase_group","chembl_id","standard_inchi_key","... |
f7f09b15a24eae3eaaaf9ef3ee22c8ab44160a83c64336a57f6a501f2504d2e8 | Python | 4,301 | 147 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/mpii.py'
]
evaluation = dict(interval=10, metric='PCKh', save_best='PCKh')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup... |
c4c257bafe1a08f7011fb0539550da3116681df99ab24f98fca5aac0cee04910 | Python | 4,302 | 133 | import numpy as np
import h5py
import importlib
import stringcase
from skimage.transform import resize
from fedml_api.data_preprocessing.my_transforms.compose import Compose
def init_transform(transforms, transforms_args):
transform_instances = []
for module_name in transforms:
transform = importlib.i... |
e8577885177480e1072637b7ed476da856097e67dd41f68b704cdd41bc1113db | Python | 4,302 | 101 | # -*- coding: utf-8 -*-
"""
Created on Wed Jun 9 17:18:18 2021
@author: fhu14
TODO: Add more functionalities that you might want for manipulating datasets
"""
#%% Imports, definitions
import os, re, pickle, random, math
from functools import reduce
#%% Code behind
def randomize_existing_set(src_dir: str, dest_dir:... |
55c1d9156695a91f10706cb86fbd5d5cd134cb58c971757a6bed7f8085cb5312 | Python | 4,303 | 96 | import os
import cv2
import numpy as np
import scipy.io as scio
from csutil import util
from csutil.imgfeature import ImageFeature
from csutil.coarsealign import AlignViaPro
if __name__ == '__main__':
#
scale_factorreg = 12
root_dir = 'E:/Zhu/ETreg/newdata/HUA3/'
TEMs_coarse_b_dir = root_dir + '/TEM... |
9ef9fced3df5aa6003b62d0f34270291169fb75af7f4d99fe80fb37c5b88d128 | Python | 4,305 | 120 | """test_checks.py
This file is part of the test suite for keras2c
Implements tests for the checks run on the model before conversion
"""
#!/usr/bin/env python3
import unittest
from tensorflow import keras
from keras2c import keras2c_main
import numpy as np
__author__ = "Rory Conlin"
__copyright__ = "Copyright 2020, ... |
356ad4765ac1128d8f7a56b3eba3593adcefb3a98f03b2c6881e5c78b8f6dcc8 | Python | 4,306 | 120 | """test_layer_wrappers.py
This file is part of the test suite for keras2c
Implements tests for layer wrappers
"""
#!/usr/bin/env python3
import unittest
import time
from tensorflow.keras.models import Sequential, Model
from tensorflow.keras.layers import (
Input, Dense, LSTM, Bidirectional, TimeDistributed, Conv1... |
99877f4d8d835e051be0b11451f1ba7f7270b4e8b8a3762979f5d138a4759fa6 | Python | 4,314 | 131 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import mmcv
import torch
from mmcv.parallel import collate
from mmcv.utils import build_from_cfg
from mmdet.datasets.builder import PIPELINES
from mmdet.models import build_detector
def model_aug_test_template(cfg_file):
# get config
cfg ... |
748f1f85249747f94cfa28d9a375e8f8759b2cb747e2d42a073970509811ebe1 | Python | 4,315 | 104 | # -*- coding: utf-8 -*-
"""
Created on Sun May 29 12:58:27 2022
@author: fhu14
A master dataset is generated as a list of molecule dictionaries containing
the energy targets for both coupled-cluster (cc) and DFT (wt). The max_config
parameter is set to int(10E6) to ensure that we get all possible configurations
from... |
8185d3b6e34b2d2c7d5342999b6fdd1c7425d7fdfa7e6ef392aac56d3e55f7a8 | Python | 4,320 | 119 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
import warnings
import pytest
import torch
from torch import eye, zeros
from torch.distributions import MultivariateNormal
from sbi.inference import BNR... |
9787b65b368e5ce33c79c232d983632669024cb0b3cf661f886357e298dbb5ad | Python | 4,324 | 73 | """ Functions to load data and labels as well as to add noise to them.
"""
import torch
import numpy as np
def load_data(dataset, n_instances_per_class_train, trafos, labels_filename, device):
"""Get (visual) input data and labels, split into training and validation.
Only mnist_extended and mnist_extended_fa... |
77dfda696092c20bc13779ea5cf54c6a4693ee362c2fbd6c8019a429c0043067 | Python | 4,325 | 73 | import argparse
from itertools import chain, repeat
# import itertools
import multiprocessing as mp
import os
import torch
from typing import Any, List
from selbstaufsicht import datasets
from selbstaufsicht.models.self_supervised.msa.transforms import MSATokenize
from selbstaufsicht.models.self_supervised.msa.diversi... |
e574284223c437f7210900eb6237113397f2ea83d6348648d6f415ebca50ec16 | Python | 4,326 | 129 | # 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... |
f40c522dbe6dbf4796deb8ec7121acf0185b8c0f3ce8a0356eac12868fecdabe | Python | 4,332 | 155 | from __future__ import annotations
from abc import ABC, abstractmethod
from dataclasses import dataclass
from enum import IntEnum
from typing import Dict
import numpy as np
import numpy.typing as npt
from pydapsys.toc.display import DisplayProperties
from pydapsys.toc.exceptions import ToCNoSuchChildError, ToCPathEr... |
0bf8584f9da6ca484a427dc47ba137a0471851bb16d89ad52a772a26e8f2c774 | Python | 4,334 | 133 | # coding=utf-8
from typing import Callable, Union
from torch_geometric.typing import OptPairTensor, Adj, OptTensor, Size
import torch
from torch import Tensor
import torch.nn as nn
import torch.nn.functional as F
from torch_sparse import SparseTensor, matmul
from torch_geometric.nn.conv import MessagePassing
from .co... |
24bd067ffebf2eb637f0fac01a191ca4ef95b46a633454223f10dddcfb7fcec4 | Python | 4,339 | 120 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
import flyrpc.multicall
from time import sleep
"""
Demonstrates:
1) subscreens with different screen... |
f0e17e479cfe7e3c79f9ed203d4643080b2d2ab9179d4763cc5183cb91537263 | Python | 4,339 | 116 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 22:12
@project: MalariaDetection
@description:
"""
import tensorflow as tf
import os
from tensorflow.python.keras.layers import Layer
from tensorflow.python.keras import backend as K
cla... |
f74fe53ebbd4544ee56f80bb31a572d9dc2b688f65d87c20b527ceac2aaa7995 | Python | 4,340 | 123 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from ..builder import LOSSES
from .utils import weight_reduce_loss
def dice_loss(pred,
target,
weight=None,
eps=1e-3,
reduction='mean',
avg_factor=None):
"""Cal... |
c2da238b857cb899b0345a2d70d44dce9bf1155c13706a697a09c146e233efa1 | Python | 4,342 | 147 | # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org
# Distributed under the MIT License.
# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
from pytest import mark
from glob import glob
from os.path import dirname, join
import lightning as pl
from torchmdnet ... |
b50cb2df6db32e6d449ca9c8ec419318cea3927fb8030b2dc62c48b1f158cee6 | Python | 4,350 | 111 | """
Functions for RDM computation.
Example:
compute_rdm(y2_reps_train, train_data_original_format.cpu().numpy().shape, results_path)
"""
import torch
import matplotlib.pyplot as plt
import numpy as np
def distance(vec_a, vec_b):
"""Computes the cosine distance between two vectors.
Args:
vec_a (... |
14d2b9493a8569e6ced5fb8855811c8b731fe927cd9d569c8eeb8bef5775557b | Python | 4,352 | 146 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
44d0f6804819165dc289139687c788421e18333ae3029aa9d0e9e87d82b614e3 | Python | 4,352 | 146 | _base_ = [
'../../../../_base_/default_runtime.py',
'../../../../_base_/datasets/coco.py'
]
evaluation = dict(interval=10, metric='mAP', save_best='AP')
optimizer = dict(
type='Adam',
lr=5e-4,
)
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='l... |
f18e9e82844ed8c4dc26e00c3c2f0b7c278ac8ea8427c4fab7331939bf69829d | Python | 4,352 | 96 | import torch
import torch.nn as nn
from layers.Embed import DataEmbedding_wo_pos
from layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
from layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
class Model(nn.Module):
"""
Autoformer is the... |
3b54b72113cf34c985a3ae0fea2546b4620224a3315a8f870b68c8cdb57bbc81 | Python | 4,354 | 108 | import unittest
import warnings
import yaml
from .._yaml_constructors import MetadataInfo
class YamlConstructorTests(unittest.TestCase):
"""
YAML Constructors are used to handle the custom YAML tags defined by the
framework.
"""
def test_unknown_tag(self):
"""
Makes explicit the ... |
ba82f7cb7338957f68afd811d927c1df7327e9f678df4fb41cc7ce673df247a4 | Python | 4,359 | 109 | import utils as imutil
import pyodbc
import MySQLdb
import logging
#SQL db setting
dsn = 'sqlserverdatasource'
user = ''
password = ''
database = ''
def get_db_connection():
con_string = 'DSN=%s;UID=%s;PWD=%s;DATABASE=%s;' % (dsn, user, password, database)
cnxn = pyodbc.connect(con_string)
cursor = cnxn... |
2a8ce5a976561dc96b5f9fb208d114675325be55658ce25cb67e2aa2d9ed9eac | Python | 4,360 | 120 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN_... |
eb526a08c7e314e5e0ddc2c4c2292a04eb5049743111dcad4ee155058797ccd6 | Python | 4,362 | 146 | import os
import sys
import numpy as np
import h5py
import scipy.io as spio
import nibabel as nib
import torch
import torchvision
import torchvision.models as tvmodels
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
import torchvision.transforms as T
from PIL import Image
i... |
2dada2c30d7ee264a3757627ae8c10463e4d8f88c0f3d9c9962a09dd42eeba21 | Python | 4,365 | 179 | # -*- coding: utf-8 -*-
#File Name : openvibe.py
#Created By : Aurelien Van Langhenhove
from StimulationsCodes import *
import sys, traceback, collections
from io import StringIO
class NewStd(StringIO):
def __init__(self):
StringIO.__init__(self)
def flush(self):
self.truncate(0)
self.seek(0)
def isempty(sel... |
fdcf8dd52f872d0929929c00805b28dd50e65b08456dc068b315cab36b6ffd24 | Python | 4,365 | 124 | import glob
import os
import pathlib
import random
import re
import subprocess as sp
from copy import deepcopy
import numpy as np
import torch
from Bio import AlignIO, SeqIO
from Bio.PDB.Atom import Atom
from Bio.PDB.PDBParser import PDBParser
from Bio.PDB.Residue import Residue
from torch.utils.data import Dataset
f... |
0b83eafb0bfec04357b7850137c8b08b047ba41c7c01932c26147b3855481810 | Python | 4,368 | 116 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
6cfe145821f326249e1dee7602b81a9858d6427d545a0cc776b6933be2790928 | Python | 4,368 | 161 | """io_parsing.py
This file is part of keras2c
Copyright 2020 Rory Conlin
Licensed under MIT License
https://github.com/f0uriest/keras2c
Helper functions to get input and output names for each layer etc.
"""
__author__ = "Rory Conlin"
__copyright__ = "Copyright 2020, Rory Conlin"
__license__ = "MIT"
__maintainer__ = "... |
2089b645cae19ddb6dd2c1875073fb8487f2769af1fe5233ead26b66ab8a54cb | Python | 4,369 | 116 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
4a2df593d9bf0394547e6aaa99f0f127ea8efd179c83cd2c10af2949a19976d5 | Python | 4,369 | 116 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
a8200265660640d79113dd7aac06c7b98da3407b688d138499622a6ac8acfd2c | Python | 4,369 | 116 | from copy import copy, deepcopy
from transformers import TrainingArguments, EarlyStoppingCallback, IntervalStrategy
import os
from os.path import join, splitext
from model.torch_models.bert_cnn import CNN_Over_BERT
from model.torch_models.bert_linear import Linear_Over_BERT
from model.torch_models.bert_rnn import RNN... |
fb734d4e0d27307f965e6eb8724324fa3719cbbbb621516c583a47b344dd2808 | Python | 4,369 | 137 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Nov 4 10:15:39 2021
@author: baris
"""
from neural_coding import load_spikes, rate_n_phase
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
trajectories = [75, 74.5, 74, 73.5, 73, 72.5, 72,
... |
bb64e86d6e0c8716c095294bd09bfa1145029b5a0d3947fbe2b0cbf5eed894db | Python | 4,371 | 96 | #For generate labkit subregions
#first open a python.exe, call imaris with a self-defined ID
#import subprocess
#subprocess.call(['C:\\Program Files\\Bitplane\\Imaris 9.9.0\\Imaris.exe', 'id101'])
import ImarisLib
import numpy as np
import math as m
import random
import nrrd
filename = r"R:\22.gaj.49\200826-... |
d2c879cbb28002429eae036431b93adac701c90676468f61ec9f43a192e2a429 | Python | 4,375 | 69 | """Rebuild the release tree from interface2 and fireflies at the commits in pins.tsv.
python3 provenance/build_tree.py <interface2 clone> <fireflies clone>
Each stage folder is self-contained: files come from that stage's pinned commit, found by
following .m names from its entry points (closure.py) plus the stage's... |
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