sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
19ec156b13d8017314510d206311a992d1fd2278f448791675548358597acb04 | Python | 1,040 | 30 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import textwrap
from drf_spectacular.authentication import TokenScheme
class AccessTokenAuthenticationScheme(TokenScheme):
"""
Adds the token auth method to schema. The description includes extra info
comparing to what is generated by ... |
77c1957d75e8874aaabe6d35434b88050d78ae4516ac3040af10757e94b59dc3 | Python | 1,041 | 38 | # Copyright 2016 Numenta Inc.
#
# Copyright may exist in Contributors' modifications
# and/or contributions to the work.
#
# Use of this source code is governed by the MIT
# license that can be found in the LICENSE file or at
# https://opensource.org/licenses/MIT.
from nab.detectors.base import AnomalyDetector
from na... |
7c4734065c001aa8d3491a5c0e82f445cf29efaf96385d4593d9c7f5e2434d83 | Python | 1,041 | 30 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import os
from datetime import timedelta
ACCESS_TOKEN_STALE_PERIOD = timedelta(
seconds=int(os.getenv("CVAT_ACCESS_TOKEN_STALE_PERIOD", 6 * 30 * 24 * 60 * 60))
)
"""The time period after which unused API access tokens automatically become invali... |
d95e8f6466695da0888ae5f58d955a7be849cd5f8ff45045dab6e8332750e13c | Python | 1,042 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['bst.zip'])
tmp_dir = tempfile.Temporar... |
1cf45bd05c05ecfb75e89e0cf9f175ebb3bd81759fb2969c27354ddeb82c5ce1 | Python | 1,043 | 41 | import json
import os
import random
from dataclasses import dataclass
from typing import Any, Dict
import numpy as np
import torch
def set_seed(seed: int) -> None:
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
def save_json(obj: Dict[str, Any], path: ... |
1ef720a6112bda3c1d4c3648fb69c730251f507e8c7e4c4ad7545eeca7618403 | Python | 1,043 | 41 | #!/usr/bin/env python
"""
Create a site profile vector showing the average signal accumulated from a
bigwig file around the center of each interval from a BED file.
Output is the average signal value at that relative position across the
intervals.
usage: %prog bigwig_file.bw padding < bed_file.bed
"""
import sys
f... |
259bacfcaf3d7b950a6861021426674567159a89d097a2e3852475b28280c7f7 | Python | 1,043 | 31 | from torch import nn
from torch.autograd import Variable
class TripletLoss(object):
"""Modified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid).
Related Triplet Loss theory can be found in paper 'In Defense of the Triplet
Loss for Person Re-Identification'."""
def __init__(self, margin=None):... |
6b7bc7732508e2a8b754cdab557d2f223c61d4b0135a648839e1b198e8c1f7aa | Python | 1,043 | 40 | import torch
import torch.nn as nn
import torch.nn.functional as F
class DNN_Class(nn.Module):
def __init__(self, input_dim=420, output_dim=300):
super(DNN_Class, self).__init__()
self.fc1 = nn.Linear(input_dim, 4096)
self.dropout1 = nn.Dropout(0.5)
self.fc2 = nn.Linear(4096, 2048)... |
f63e04dd4465b7bcecfd9940de38b18fc7c45f122c0977333bc77906fbf34d15 | Python | 1,043 | 40 | import torch
import torch.nn as nn
import torch.nn.functional as F
class DNN_Class(nn.Module):
def __init__(self, input_dim=420, output_dim=300):
super(DNN_Class, self).__init__()
self.fc1 = nn.Linear(input_dim, 4096)
self.dropout1 = nn.Dropout(0.5)
self.fc2 = nn.Linear(4096, 2048)... |
3204b7069a708af7ee0fd898eb51b611d98449a1fe5b8c74e7ef4dafade030f4 | Python | 1,044 | 31 | import argparse
import os
import os.path as osp
import time
import numpy as np
import torch
import torch.nn.functional as F
from sklearn.metrics import f1_score
import pandas
import json
import torch_geometric.transforms as T
from torch_geometric.nn import ChebConv, GCNConv # noqa
from tqdm import tqdm
from sklearn.me... |
ae38e70f3dfd04e297bb3eba69ac5b6b0681444e2247dccde1f805c140cc3e16 | Python | 1,044 | 32 | """Classical Evolutionary Algorithm."""
import random
from analogainas.search_spaces.sample import random_sample
class EAOptimizer:
def __init__(self, max_iter, population_size, mutation_prob):
self.max_iter = max_iter
self.population_size = population_size
self.mutation_prob = mutation_pro... |
b9d559e60d8f7d2c8a76a853a0d7b41d9ba1b8dce4cabb2f0f5cdcde355d8a30 | Python | 1,044 | 35 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 11 19:03:57 2025
@author: axthi
"""
import sys
# --------------------------------------------------
# NOTE: Update this path to fit to your installation
path_to_utils = '/mrhome/axelt/memoslap_utils/memoslap'
sys.path.append(path_to_utils)
from simnibs_memoslap_utils.uti... |
052eaa0a027b3ff9373f6bfbd636a217879d3dac59f60a482bd0449f61c45222 | Python | 1,045 | 33 | """Nottingham MEGUK: Sign flipping.
Aligns the sign of each parcel time course across sessions using the
covariance-based search in :code:`Data.align_channel_signs`, then writes
the sign-flipped data back to a fif file alongside the original.
"""
from glob import glob
import mne
from osl_dynamics.data import Data
f... |
1bdc9c9f9abb0225dd4eb797feac19ced3085e1cae656f1382ccf94321c2adb3 | Python | 1,045 | 26 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
41b66125d12a3ff3d43b813bd711cc430688c329e1db1bc7badda2920db2e551 | Python | 1,046 | 33 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import numpy as np
def save_comparison(original, predicted, ground_truth, epoch, batch_idx):
fig, axarr = plt.subplots(1, 3, figsize=(15, 5))
original = original[0].squeeze().cpu().numpy()
predicted = predicted[0].... |
50855c2cc3d842563b78f0c4dc11490d11a94712fc2db592da1f5475f3e5836c | Python | 1,046 | 46 | """
Utility functions
"""
import os
def get_data_root(verbose=True):
"""
Get root to data as an environment variable
"""
# Get the value of DATAROOT
data_root = os.getenv('DATAROOT')
assert data_root is not None, \
('DATAROOT is not set!'
'please run export DATAROOT=/path/to/... |
50fd94ca420bec39403c17120866bb4f0a60c82bfdd030fdb73f2ce6dd8cb8fb | Python | 1,046 | 50 | #!/usr/bin/env python
"""
Read a set of ranges and a nib file, print portions of nib overlapping
those ranges to stdout
TODO: General sequence handling would be nice, as well as merging with
'nib_intervals_to_fasta.py'.
usage: %prog nib_dir < range_file
"""
import sys
import bx.seq.nib
from bx.cookbook impor... |
517694c82e638c217faf26d72de11d8c68fbfd53da4940947ef5ee934b813d23 | Python | 1,046 | 47 | from setuptools import setup, find_packages
import re
import os
import io
def _read(*parts, **kwargs):
filepath = os.path.join(os.path.dirname(__file__), *parts)
encoding = kwargs.pop("encoding", "utf-8")
with io.open(filepath, encoding=encoding) as fh:
text = fh.read()
return text
def get_v... |
913c2ca55da05015b070de69a03cde4918e58e23d23a6e75e4b6d5ea6932efeb | Python | 1,046 | 33 | """Wakeman-Henson: Dipole Sign Flip.
Aligns the sign of each parcel time course across sessions using the
covariance-based search in :code:`Data.align_channel_signs`, then writes
the sign-flipped data back to a fif file alongside the original.
"""
from glob import glob
import mne
from osl_dynamics.data import Data
... |
488e86971496abad091928519de63329e26fb0f52a9db576fccff81beaa8be6d | Python | 1,048 | 51 | import tqdm, sys
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
import numpy as np
from scipy.spatial import ConvexHull
import os
import ngauge
ngauge.__version__
########################
# Read SWC neuron
########################
path='/mnt/DD/Sc RNA-Seq/LR/morpho/swc_file'
#Change directory
os.c... |
4b5052da94e4d5132e276fb3c67040fe3cf44c8506411001112f24cc934212e7 | Python | 1,048 | 33 | from typing import Optional
from torch import nn, Tensor
import torch.nn.functional as F
from .functional import label_smoothed_nll_loss
__all__ = ["SoftCrossEntropyLoss"]
class SoftCrossEntropyLoss(nn.Module):
"""
Drop-in replacement for nn.CrossEntropyLoss with few additions:
- Support of label smoothi... |
4c3ad216c5ba71500f23c7f8b0ab452b6451edaadeec6b5a8bb9bc46202a3850 | Python | 1,048 | 28 | # /------------------------------------------------------------------------------+
# | 06-JUL-2022 |
# | Copyright (c) Bone Imaging Laboratory |
# | All rights reserved ... |
b303aed6eb73ec2515c992ae580b88a7a172215047a25382d584fbd724c6500b | Python | 1,048 | 30 | """
Support for chopping a list of alignment blocks to only the portion that
intersects a particular interval.
"""
def chop_list(blocks, src, start, end):
"""
For each alignment block in the sequence `blocks`, chop out the portion
of the block that overlaps the interval [`start`,`end`) in the
componen... |
cb3c298d3d4efc6c5103d95f5a466b9f737630f4cef22e2c4a2107ffab877d6e | Python | 1,048 | 45 | """
Base classes for site maskers.
"""
from bx.filter import (
Filter,
Pipeline,
)
class Masker(Filter):
def __init__(self, mask="?"):
self.mask = mask
self.masked = 0
self.total = 0
class MaskPipeline(Pipeline):
"""
MaskPipeline implements a Pipeline through which align... |
da9c17a776a349a6ceba546e5333bfddaa6da6681e133d9ade10bd954e9cb541 | Python | 1,048 | 39 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
from mmcv import Config, DictAction
from depth.apis import init_depther
def parse_args():
parser = argparse.ArgumentParser(description='Print the whole config')
parser.add_argument('config', help='config file path')
parser.add_argument(
... |
454fd1cdac21f1d8b69e77497a56328d61ca6657b9c19746c29572db3252f09f | Python | 1,049 | 31 | from curvature_calculation import new_workflow, extract_curvatures_after_new_workflow
import time
t_begin = time.time()
fold = r"/Users/benjaminbarad/Dropbox (Scripps Research)/Grotjahn Lab/Data/Surfaces/Lam2_TS1/" # output will be also written there
base_filename = "all"
# seg_file = "TS1_surf.labels.mrc"
seg_file = ... |
9d5d4b0e2a89b6b225b2aff8c92c3b9407fc29f463047dffb71198f7f56487d8 | Python | 1,049 | 35 | import networkx as nx
import numpy as np
import os
from graphwave import *
from tqdm import tqdm
# edge_path=r'/data2/whr/lyh/baseline2/Twibot-22/edges.npy'
# edge_index=list(np.load(edge_path))
# edges=[]
dataset_name='Twibot-22'
edges=np.load('/data2/whr/lyh/baseline2/Twibot-22/edges.npy')
G = nx.Graph()
G.add_edg... |
f99dc5b368f5d02229cd9e142dc54cedefacfbfb951891600fd8d8a04f762153 | Python | 1,049 | 27 | import torch
from utils import loss_and_miner_utils as lmu
from distances.base_distance import BaseDistance
class LpDistance(BaseDistance):
def __init__(self, **kwargs):
super().__init__(**kwargs)
assert not self.is_inverted
def compute_mat(self, query_emb, ref_emb):
dtype, device = ... |
3a6501176e9270ebabc59096d8c3b1a02466a839f804a2c7bcbc0aa61d5b660d | Python | 1,050 | 37 | import csv
import numpy as np
from tqdm import tqdm
import json
dataset_name='Twibot-22'
edges=[]
id_list=np.load('/data2/whr/lyh/twibot22_baseline/'+dataset_name+'/id.npy')
id_list=list(id_list)
f_write=open('/data2/whr/lyh/baseline2/edge_cleaned.txt','w')
count=0
with open('/data2/whr/czl/TwiBot22-baselines/dataset... |
5261bb57d4194e9c4eb9fca16935435c76c459850d2e178fd1ea99bc64cfd4d3 | Python | 1,050 | 38 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import django_rq
from django.conf import settings
from rq import Retry
from rq.types import JobDependencyType
from .models import Webhook
def add_to_queue(
webhook: Webhook,
payload: dict,
redelivery: bool = False,
depends_on: Job... |
6f0b07a75016c86fc19fa52f41f012f4105d78250c6b38d859e692e773bec1f3 | Python | 1,050 | 38 | import base64
from io import BytesIO
import requests
from PIL import Image
def encode_image_to_base64(file_path: str) -> str:
with Image.open(file_path) as image:
buffered = BytesIO()
image.save(buffered, format="JPEG")
return base64.b64encode(buffered.getvalue()).decode("utf-8")
def se... |
a268a78a7f5aff52df0e96661ac7fc1098ac53cbcde8e6fd656f8997f7e4fe35 | Python | 1,050 | 24 | class Defaults(object):
window_size = 7
hidden_sizes = [300]
hidden_activation = 'relu'
max_vocab_size = 1000000
optimizer = 'sgd' # 'adam'
learning_rate = 0.1 # 1e-4
epochs = 20
iobes = True # Map tags to IOBES on input
max_tokens = None # Max dataset size in tokens
encodi... |
88db5abdd35212b8a42898f2b1aac15077a78a1f662a115b35670e06b11082f4 | Python | 1,051 | 44 | # Train a Linear Regressor
# Imports
import numpy.typing as npt
from sklearn.kernel_ridge import KernelRidge
from modules.tools.types_abbrv import KERNEL_RIDGE_REG_TYPE
def train(
X: npt.NDArray[float], y: npt.NDArray[float], cfg: dict
) -> tuple[KERNEL_RIDGE_REG_TYPE, dict]:
"""Train a kernel ridge regres... |
5f39601c7f6286128004e1c6c3786929061d283c842c5b03ae00bb754a66764d | Python | 1,052 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/botwiki-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'human':
... |
6c585a441da0c6243254937ef0c0004a8c50f2d96b84dbc1d5db91d6a2d94df5 | Python | 1,052 | 44 | import torch
import os
# global variable: cache_root
cache_root = os.path.expanduser(os.path.join("~", ".flair"))
# global variable: device
device = None
if torch.cuda.is_available():
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
from . import data
from . import models
from . import visu... |
e212760ecd155792c9388567ce26f88513aa13207a87c89e85dfd435c2cb3603 | Python | 1,052 | 33 | import os
import numpy as np
from impedancefitter.fra import bode_csv_to_impedance, bode_to_impedance
freq = np.logspace(1, 6)
attenuation = np.linspace(1, 10)
phase = np.linspace(-90, 0)
def test_bode_to_impedance():
"""Test correct frequency conversion."""
omega, _impedance = bode_to_impedance(freq, atte... |
09ad33fbf86818b74e36c54de8d91be3c2f08383a38abf4813d5585bebcf4bf8 | Python | 1,053 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/pronbots-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'human':
... |
63774ca8e7bf4d70b71061c18644e2681eb67fddb65f2452af637fecca258fb1 | Python | 1,053 | 36 | import pathlib
from setuptools import setup
# The directory containing this file
HERE = pathlib.Path(__file__).parent
# The text of the README file
README = (HERE / "README.md").read_text()
# This call to setup() does all the work
setup(
name="density-peak-for-hic",
version="0.0.1",
description="Read the... |
f552ea203f238ddc3f527dbbd05deb40bbcdcfdbd032137d9adb3b924fcc6bf6 | Python | 1,053 | 43 | """ Layer/Module Helpers
Hacked together by / Copyright 2020 Ross Wightman
"""
from itertools import repeat
import collections.abc
# From PyTorch internals
def _ntuple(n):
def parse(x):
if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
return tuple(x)
return tuple... |
fc1deb833e4d00631e7ea23e32aa13675c684c87f7b6cc5ef00ebfe947e1467d | Python | 1,053 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/verified-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'human':
... |
236acbefe2efdf02b2ba65c7895b99c205316027c0222c82848c00a17375732c | Python | 1,054 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['lesions.zip'])
tmp_dir = tempfile.Temp... |
33bdf44f8f65a5a10189cf33a8b89e605db21e5bf0ccdc05c73b32824bb83ec5 | Python | 1,054 | 36 | #!/usr/bin/env python
"""
Read BED file and extend each record to the specified minimum length. If chromosome
size information is provided trim extended intervals.
usage: %prog amount [ chrom_file ] < bed_file
"""
import sys
from bx.intervals.io import GenomicIntervalReader
length = int(sys.argv[1])
chrom_len = No... |
941e7a2a539adfe61be64e3a233c7fa516070dfd5f5cc67a49e780726207764d | Python | 1,054 | 34 | import subprocess
import sys
import yaml
script_plot = 'SOM/clusterSOM/clusterplot_SOM.py'
script_labels_spont = 'SOM/clusterSOM/plot_clusterlabels.py'
script_labels_resp = 'SOM/clusterSOM/plot_clusterlabels_response.py'
pathpath = 'PATHS/filepaths_carlen.yml'
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_... |
d6e9949efb27710f0128c7f4239edc50406f2101c47a32e82e8ebf085a20e752 | Python | 1,054 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/celebrity-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'human':
... |
63faec9fd7fe2cf946cd994bc34cfe4634edc554ce4bdab392e7a1c1a9dcb2e1 | Python | 1,055 | 44 |
import scanpy as sc
import pandas as pd
from anndata import AnnData
import warnings
from builtins import int
warnings.filterwarnings('ignore')
import losses, miners, distances, reducers, testers
from utils.transformations import *
from utils.accuracy_calculator import AccuracyCalculator
import torch
import torch.optim... |
961df1ef27b7546d527defcab228a3ae65175c235d02fe1fe0fcb9e5a4fac7e9 | Python | 1,055 | 40 | """Example script for plotting power maps from static spectra.
"""
print("Setting up")
import os
import numpy as np
from osl_dynamics.analysis import power
# Make directory to hold plots
os.makedirs("figures", exist_ok=True)
# Load spectra (calculated with static/calc_spectra.py)
f = np.load("spectra/f.npy")
psd = ... |
996f7732c533f25c8da074aa5f827495537b9cf797be0f9fb4b0fcecab9ac105 | Python | 1,055 | 59 | # -*- coding: utf-8 -*-
# external inputs
import numpy as np
def get_white_2d(nx, ny, mu, sigma):
"""Get white 2D.
Creates a 2D array with Gaussian noise.
Parameters
----------
nx : int
Array size in x-direction.
ny : int
Array size in x-direction.
mu : float
... |
26f10403f8815c6f1067d5af13c679c7647a2c5c5aada1e286891a0abbbc6dab | Python | 1,056 | 29 | import json, subprocess, os
os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = '1'
with open("hyperopt/best_param.json", "r") as f:
params = json.load(f)
for fold in [0, 1, 2, 4]: # range(5): fold_3 is not used as it is undertrained
predict_command = f"""chemprop predict \
--test-path data... |
3b356c4fcedcf8f9bfcd34c3436fb71c96d48788fee2d04867cf66ef08a71b6f | Python | 1,056 | 40 | """Base simulation class."""
from typing import Optional
import numpy as np
class Simulation:
"""Simulation base class.
Parameters
----------
n_samples : int
Number of time points to generate.
"""
def __init__(self, n_samples: int) -> None:
self.n_samples = n_samples
... |
5dbc54f74f752d5c74b98b1462ae59c3c299fb9f7b019ec84382ba83cec8c73f | Python | 1,056 | 31 | import os
from nipype.interfaces.fsl import Reorient2Std
from PUMI.engine import BidsPipeline
from PUMI.engine import NestedNode as Node
from PUMI.pipelines.func.deconfound import motion_correction_mcflirt
ROOT_DIR = os.getcwd()
input_dir = os.path.join(ROOT_DIR, 'data_in/pumi-unittest') # path where the bids data is... |
1dab79f4d72d199d80a7d079c293249c5db4004b0daf1e81072ddddb98e8d8c4 | Python | 1,057 | 26 | import torch
# Pytorch loss functions
LOSS_FUNCTIONS = {
"l1loss": torch.nn.L1Loss,
"mseloss": torch.nn.MSELoss,
"crossentropyloss": torch.nn.CrossEntropyLoss,
"ctcloss": torch.nn.CTCLoss,
"nllloss": torch.nn.NLLLoss,
"poissonnllloss": torch.nn.PoissonNLLLoss,
"gaussiannllloss": torch.nn.Ga... |
82bc38a6a6f1e215656e3df72486a6ab006f369a9c1592394be62b5650e31178 | Python | 1,057 | 36 | import numpy as np
import torch as th
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score
def write_list(li):
formattor = "{:.4f}"
return ",".join(map(lambda x: formattor.format(x), li)) + "\n"
def training_one_data(data, label):
data = np.array(data)
... |
dd6ff430caf3e30307138f95ce19c1ec73e9b0202665810af348a9a86b7cd367 | Python | 1,057 | 33 | import fire
import pickle
import numpy as np
from rsatoolbox.data.dataset import Dataset
from rsatoolbox.rdm.calc import calc_rdm
from nilearn.glm.contrasts import expression_to_contrast_vector, compute_contrast
import nilearn
import nilearn.plotting
import nilearn.image
data_path = "../bids_dataset/derivatives"
mapp... |
ea49133fc88bec89fd851a07a255c8a6e2594f26d02cf8c2659a598772656cca | Python | 1,057 | 38 | import base64
import io
import json
import yaml
from model_handler import ModelHandler
from PIL import Image
def init_context(context):
context.logger.info("Init context... 0%")
# Read labels
with open("/opt/nuclio/function.yaml", "rb") as function_file:
functionconfig = yaml.safe_load(function... |
44da0e5a39e4126fa9fa45001c618a8a04d2669cc56f783496a36f79e0391d49 | Python | 1,058 | 35 | def chunk_indices(indices, num_batches):
batch_size = (len(indices) + num_batches - 1) // num_batches
for i in range(0, len(indices), batch_size):
yield indices[i:i + batch_size]
def contrast_index(x1, x2, double=True):
"""
Calculate the contrast index between two arrays.
The contrast inde... |
50d0cb6c001d847fc087ce14aa0d1d6f4fca28077b4a027a828fa97be1728e57 | Python | 1,058 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['connectivity.zip'])
tmp_dir = tempfile... |
de99060583d75df11d1e1837a1e97dd48d08c8bf0fda2b0c5330bbb8c4bcc912 | Python | 1,058 | 30 | from __future__ import annotations
import numpy as np
from brainways.pipeline.brainways_params import AffineTransform2DParams
from brainways.transforms.affine_transform_2d import BrainwaysAffineTransform2D
from brainways.utils.atlas.brainways_atlas import AtlasSlice
from brainways.utils.image import ImageSizeHW, brai... |
f116d4c35da76f966f22c8575e89c2fd6bcd8335ab4a03278ae4dee8223d37a7 | Python | 1,059 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/political-bots-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'huma... |
2a200490c0fc6386bf95b2b5f9453a7a089af2067fdf60b8c1a197d51e581e4a | Python | 1,061 | 43 | #!/usr/bin/env python
"""
Focus lock find sum tests.
"""
from storm_control.test.hal.standardHalTest import halTest
def test_hal_tcp_fs_1():
halTest(config_xml = "none_tcp_config.xml",
class_name = "FindSum1",
show_gui = True,
test_module = "storm_control.test.hal.tcp_tests")
... |
6073dba77826786d9efa81fac4d0d4be1ce7f08ced3eb0c7d2476d5c5bb5045e | Python | 1,061 | 46 | #!/usr/bin/env python
"""
Read an alignment from stdin and for each block print the result of
evaluating `template_string` (in cheetah template format). The alignment
block will be placed in the template context as `a` and the list of components
as `c`.
usage: %prog template [options]
-f, --format = maf: Input fo... |
8255751f01061c0828f5fd8a005dec9ed624cd7c0baf9ddba790fc6718f7f181 | Python | 1,061 | 35 | import pandas as pd
label = pd.read_csv('/data2/whr/czl/TwiBot22-baselines/datasets/vendor-purchased-2019/label.csv')
main_user = pd.read_csv('./user_feature.csv')
user = main_user.loc[0:len(main_user)-1]
user_list = list(user['user_id'])
del_list = []
bool_label = []
for i,r in label.iterrows():
if r[1] == 'hu... |
8c001d0c6728a3541b0a00b229161ef9713fd9e595d5ac921d84d31d05053059 | Python | 1,061 | 37 | from graphrole import RecursiveFeatureExtractor, RoleExtractor
import networkx as nx
from karateclub import Role2Vec
import numpy as np
import os
from tqdm import tqdm
edge_path=r'Twibot-22/edges.npy'
edge_index=list(np.load(edge_path))
edges=[]
dataset_name='Twibot-22'
id_include=(np.load(dataset_name+'/id_include.npy... |
47e9ebb144389496f1543a893b84feffc0c5cf2cda69c299987f4d3bcb6c3f8d | Python | 1,062 | 27 | # Copyright (c) 2015 Tobias Neumann, Philipp Rescheneder.
#
# This file is part of Slamdunk.
#
# Slamdunk is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your ... |
4de78739d28226865a506c61d4bc8ee1218007a1c54611bf9a3231a88d20ea0c | Python | 1,062 | 29 | import os
from fairseq.tasks import register_task
from fairseq.tasks.translation import TranslationTask
from dasa_fairseq.data.dep_bias_dataset import DepBiasDataset
@register_task("translation_dasa")
class TranslationTaskDASA(TranslationTask):
"""
Same as TranslationTask, but wraps split datasets with DepBi... |
705e774c89a42636e5fd22554b39e6c73751d084316c8cbed10735e0243e8366 | Python | 1,062 | 32 | '''
Python implementation of "native thalamus". Currently does as little as possible, just allows the task controller to run.
'''
import asyncio
import typing
import logging
import grpc
from .. import thalamus_pb2
from .. import thalamus_pb2_grpc
LOGGER = logging.getLogger(__name__)
class PipelineServicer(thalamus_... |
4a702e6e99ffe34260bd01496624d0a9185d790b366a64121cb2dd6e44767557 | Python | 1,063 | 29 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['bundles.zip'])
tmp_dir = tempfile.Temp... |
d3d2c0b23b054265e8803b73441d0ecfca3d9b40d3f92704b9ac5a79b613d5ab | Python | 1,063 | 44 | import numpy as np
from scipy.constants import epsilon_0 as e0
from impedancefitter.single_shell import eps_cell_single_shell
from impedancefitter.suspensionmodels import bh_eps_model, bhcubic_eps_model
em = 10.0
Rc = 0.5e-6
dm = 7e-9
dn = 7e-9
km = 1e-6
kcp = 1.0
ecp = 80
kmed = 1.0
emed = 80.0
c0 = 1e-12
p = 0.3
kn... |
e652072aa04166e1106f4fcdd0b21e86fb5982412132d4e1d64da1822c9ac317 | Python | 1,063 | 32 | from .eqtransformer import EQTransformer
class OBSTransformer(EQTransformer):
"""
Initialize an instance of OBSTransformer model.
OBSTransformer is built based on the original (non-conservative) EqTransformer model.
.. warning ::
Creating an OBSTransformer instance does not automatically loa... |
77f69a2583e3ccab4522229a4c55a625bdab43ca8cb6f870fd52ebb1053817fc | Python | 1,064 | 30 | import numpy as np
import os
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
def get_callbacks(early_stop, output_prefix):
early_stop_callback = EarlyStopping(monitor='val_loss',
patience=early_stop,
verbose=1,
... |
e31f89a072cd5f11d1394b977c8d23394b003e3df4c3c632b76d1c1cfc2efc52 | Python | 1,064 | 35 | from __future__ import annotations
from PySide6 import QtCore
from PySide6 import QtGui
from PySide6 import QtWidgets
from .._utils._qt import new_icon
class InfoButton(QtWidgets.QToolButton):
def __init__(
self, *, tooltip: str, parent: QtWidgets.QWidget | None = None
) -> None:
super().__i... |
a306cc13902cc9b3cc47308a8f73c58916ff894b648a4c2f7e0620b193857c96 | Python | 1,065 | 27 | from brainscore_vision import model_registry
from brainscore_vision.model_helpers.brain_transformation import ModelCommitment
from .model import get_model, LAYERS
### AlexNet_SIN with different fields of view than 8 visual degrees (default) ###
# exploring 4, 12, 16 visual degrees
# code is adapted from the original s... |
0515bd9e8c30659d522eaaa1216d8fa021d30969531fe9466fb500379e13e096 | Python | 1,066 | 48 | # -*- coding: utf-8 -*-
# external inputs
import numpy as np
def get_meshlines(vtx_pial, vtx_white):
"""Get meshlines.
This function returns a vertex and a corresponding face array to visualize
point-to-point connections between two congruent surface meshs.
Parameters
----------
vtx_pial : ... |
6804acd6a0a9fdc7c4fafcd25dd2a757771aad13a832b4d8e287062246b17428 | Python | 1,066 | 33 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import os
import tempfile
from scilpy import SCILPY_HOME
from scilpy.io.fetcher import fetch_data, get_testing_files_dict
# If they already exist, this only takes 5 seconds (check md5sum)
fetch_data(get_testing_files_dict(), keys=['processing.zip'])
tmp_dir = tempfile.T... |
089a370f7f9b09b11e9d5fb54925eff82f37cdcfddf251333c5ff2be7e97b6af | Python | 1,067 | 25 | from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv3D, ReLU, BatchNormalization, MaxPool3D, Lambda, Flatten, Dense, Activation
from tensorflow import pad as tf_pad
def Model(image_shape=(160,192,160,1)):
def conv_block(n_filters, padding1='same'):
return Sequential([
... |
232bb9ec6f069f613205489f36aa1405367e6674172708fecb147104977ccaf0 | Python | 1,067 | 28 | from causallib.metrics.scorers import PropensityScorerBase
class SKLearnScorerWrapper(PropensityScorerBase):
def __init__(self, score_func, sign=None, **kwargs):
super().__init__(
score_func=score_func,
sign=1, # This keeps original scorer sign
**kwargs
)
... |
d265ba332302ba70f0bea4d05ddb4adb89a677da797314c5aefa4165a91046b2 | Python | 1,067 | 29 | import pytest
from pytest import approx
from brainscore_vision import benchmark_registry, load_benchmark
@pytest.mark.parametrize('benchmark', [
'BMD2024.texture_1Behavioral-accuracy_distance',
'BMD2024.texture_2Behavioral-accuracy_distance',
'BMD2024.dotted_1Behavioral-accuracy_distance',
'BMD2024.d... |
0704eef6d11a9ac9aa17e0c77641e0581bfc3da7f7fc213cbfe2066622d4d477 | Python | 1,068 | 32 | import torch
import torch.nn as nn
class SpaceToDepth(nn.Module):
bs: torch.jit.Final[int]
def __init__(self, block_size=4):
super().__init__()
assert block_size == 4
self.bs = block_size
def forward(self, x):
N, C, H, W = x.size()
x = x.view(N, C, H // self.bs, s... |
2ff6da08f0cd62662de0dcb4f5e1c352f838b12e19915ad8994fd0062b1a498c | Python | 1,069 | 36 | import os
import numpy as np
def short_summary(model):
NUM_PARAMETERS = {"Not Trainable": 0, "Trainable": 0, "Overall": 0}
for params in model.parameters():
num_params = params.numel()
trainable = params.requires_grad
key = list(NUM_PARAMETERS.keys())[trainable]
NUM_PARAMETERS[... |
c2700fdad69b780dfdff3e21b4f3fc7835c00e0fb3a331c5a1253cf3c591d1f7 | Python | 1,069 | 32 | import torch
from torch.utils.data import Dataset
class BotDataset(Dataset):
def __init__(self, name, path):
self.name = name
# different features dataset
# self.cat_features = torch.load(path + "cat_properties_tensor.pt")
# self.prop_features = torch.load(path + "num_prope... |
01b66cfa58cb3236b0f036cd0de876b461ef3840d9a18d42dd14f6a4e6a47263 | Python | 1,071 | 33 | import numpy as np
import torch
import xdas
from unittest.mock import patch
import seisbench.models as sbm
def test_deepsubdas():
# Simple test to check that the code does not crash
data = np.random.rand(4000, 2000)
nt, nd = data.shape
dt = np.timedelta64(10, "ms")
dx = 10
t0 = np.datetime64... |
61efa7e078171ab545ced5a9ed6b2251be59e0b9d9b2afef99484cd24a2352c9 | Python | 1,071 | 37 | import json
import base64
from PIL import Image
import io
from model_handler import ModelHandler
import yaml
def init_context(context):
context.logger.info("Init context... 0%")
# Read labels
with open("/opt/nuclio/function.yaml", "rb") as function_file:
functionconfig = yaml.safe_load(function_... |
fb0ee8e2864d9683f755f9b12dde06355f56b9cc68e84948cec724ff74307060 | Python | 1,071 | 41 | from setuptools import setup, find_packages
packages = find_packages()
print(packages)
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
version = "0.0.1"
setup(
name="deep_events",
version=version,
description="Deep-Events",
long_description=long_description,
... |
fee8bfb54477c8f6757ce74cbb5a48293ca47776a522e2c78a4bd3ca6e767b99 | Python | 1,072 | 32 | """
Hyperparameter Optimization Package
A comprehensive suite of tools for machine learning hyperparameter optimization
using Optuna and MLflow with advanced features like stratified data splitting,
feature importance analysis, and experiment tracking.
Key Components:
- hyperparameter_optimizer.py: Main optimization ... |
8ebea6cb45c8a4b5ac6bfe1ffe9b60f3059f01c50205d16e512448ca0fa39cdd | Python | 1,073 | 33 | import numpy as np
from eir.models.input.omics.omics_models import (
al_omics_model_configs,
al_omics_model_types,
al_omics_models,
get_omics_model_class,
get_omics_model_init_kwargs,
)
from eir.setup.input_setup_modules.common import DataDimensions
def get_omics_model_from_model_config(
mode... |
fcf70f3ebbfad450881cea33653ccfaba0b9a565e37971a4328400606d26f5b4 | Python | 1,073 | 35 | import os
import pytest
import torch
from {{cookiecutter.project_slug}}.datamodules.mnist_datamodule import MNISTDataModule
@pytest.mark.parametrize("batch_size", [32, 128])
def test_mnist_datamodule(batch_size):
datamodule = MNISTDataModule(batch_size=batch_size)
datamodule.prepare_data()
assert not da... |
389abff7abfa7bd532cbc749dc717f76e1c5b88eb860bd92cde8cdb43fcb3dbf | Python | 1,074 | 42 | # myelinfccoupling/config.py
from dataclasses import dataclass, field
from typing import List
@dataclass
class Config:
"""
Configuration for Myelin–FC coupling modeling (Python).
Mirrors the logic in the MATLAB pipeline.
"""
# Column names in edges.csv
col_i: str = "i"
col_j: str = "j"
... |
dc909f7a10a4a91e1822b78003534d7dd34febf1d0da349547014a7195aceab7 | Python | 1,074 | 22 | # A script to visualize the ERF.
# Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (https://arxiv.org/abs/2203.06717)
# Github source: https://github.com/DingXiaoH/RepLKNet-pytorch
# Licensed under The MIT License [see LICENSE for details]
# -----------------------------------------------------... |
e7f09a74a3287272d428e67c2310df4f421d4fbe225c8226322df63d3e92c3e8 | Python | 1,074 | 50 | from impedancefitter.cpe import cpe_model, cpe_ct_model, cpe_ct_w_model
import numpy as np
omega = 2. * np.pi * np.logspace(1, 8, num=20)
k = 1e-7
alpha = 0.9
Rct = 100
Aw = 10
def test_cpe_ct_w_type():
Z = cpe_ct_w_model(omega, k, alpha, Rct, Aw)
assert isinstance(Z, np.ndarray)
def test_cpe_ct_w_shape():... |
103860fbfdde787baf9e01841f261f39b6dcd0fd11399cc1b07104f86ae03318 | Python | 1,075 | 42 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
from .ascend import onnx2ascend
from .axelera import torch2axelera
from .coreai import torch2coreai
from .coreml import torch2coreml
from .deepx import onnx2deepx
from .engine import onnx2engine, torch2onnx
from .executorch import torch2executorch
fro... |
25d6e9558c07c6b5c231fd2a73f85bc12149c5b210519e942ce8ca04936d16aa | Python | 1,075 | 46 | # -*- coding: utf-8 -*-
"""
Created on Tue Jan 28 08:54:50 2014
@author: dan
"""
#!/usr/bin/env python
# a bar plot with errorbars
import numpy as np
import matplotlib.pyplot as plt
srint = sorted('ADVRGT')
N = 6
menMeans = (20, 35, 30, 35, 27,9)
menStd = (2, 3, 4, 1, 2,4)
ind = np.arange(N) # the x locations fo... |
db5af751e5e77a9c3ad1ab80b078963403d156162cce862fde925ec640093c7c | Python | 1,075 | 42 | """Example script for plotting functional connectivity (correlation) maps.
"""
print("Setting up")
import os
import numpy as np
from osl_dynamics.analysis import static, connectivity
from osl_dynamics.data import Data
# Make a directory to save plots to
os.makedirs("figures", exist_ok=True)
# Load data
data = Data(... |
f29a5f0b3832258d676e0d8948d5373deebf0b4f6507a13cf7b49782eb757191 | Python | 1,075 | 42 | #!/usr/bin/env python
"""
Test correlation focus lock fitting.
"""
import numpy
import random
import storm_analysis.simulator.draw_gaussians_c as dg
import storm_control.sc_hardware.utility.corr_lock_c2dg as clf
def test_cl_1():
sigma = 2.0
cl_fit = clf.CorrLockFitter(roi_size = 8,
... |
9bf5ea5261c3ec7767c9fdd2d5743b474e707656eee6fd500a5251fd927ffb6d | Python | 1,076 | 31 | import os
from MyClient import MyClient
from utilities import bearer_token
from utilities import get_user_timeline_by_id
from utilities import get_tweet_dict
import random
import tqdm
import json
from tweepy.errors import (
BadRequest, Forbidden, HTTPException, TooManyRequests, TwitterServerError,
Unauthorized... |
a8ce0fa0727b4ebe2472d4105c89ba95ec80c74b0de9c618039bbd871f0cbe98 | Python | 1,076 | 29 | import os
import sys
import click as ck
import pandas as pd
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
@ck.command()
@ck.option('--data-root', '-dr', default='data', required=True, help='data root')
@ck.option('--file-name', '-fn', default='proteins', required=True, help='pickle file name')
def mai... |
11b296f3447106e75dbe3a29fa2e3afe12e53b0f27fc868d1e3e69541e961b62 | Python | 1,077 | 46 | import pandas as pd
import re
import os
save_dir = r""
root_path = r""
def count_occurrence(anchor_result_path, save_dir):
for method in []:
if method in anchor_result_path:
label = method
anchor_result = pd.read_excel(anchor_result_path)
anchors = anchor_result['An... |
24258eb76a87e8e5ce962dc07a19456d0670ff0c8b522e20c8fe1bb13dac6c11 | Python | 1,077 | 30 | import os
import numpy as np
import pandas as pd
from brainscore_core.supported_data_standards.brainio.stimuli import StimulusSet
from brainscore_core.supported_data_standards.brainio.packaging import package_stimulus_set
def collect_stimuli(data_path):
assert os.path.isdir(data_path)
stimulus_df = pd.read_pi... |
34319df3882dd3ea48e9bc66d04ca82d856b10ab0d9363a38b5229a1c20e9235 | Python | 1,078 | 35 | # Required libraries
import matplotlib
matplotlib.use('Agg') # Set globally for all matplotlib usage
import matplotlib.pyplot as plt
import os
import numpy as np
def save_comparison(original, predicted, ground_truth, epoch, batch_idx):
fig, axarr = plt.subplots(1, 3, figsize=(15, 5))
original = original[0].... |
45063925ccfe178e061ab370d9fb1c5dcdf9f4942f7d1eb25d69cbf110863319 | Python | 1,078 | 33 | """Script to clean FASTA sequences (e.g., remove sequences with stop codons) to use with ESM (https://github.com/facebookresearch/esm)."""
from pathlib import Path
from Bio import SeqIO
from Bio.SeqRecord import SeqRecord
from tap import Tap
from tqdm import tqdm
class Args(Tap):
data_path: Path # Path to FASTA... |
d25a6cb6a2635f527bdfa07bc05ecae1ceda4b216ed1961db30f1cb222106c7d | Python | 1,078 | 27 | import numpy as np
import pandas as pd
import scanpy as sc
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('-l', '--lambda-file', type=str, required=True,
help='Path to HDF5-formatted AnnData object with lambdas.')
parser.add_argument('-x', '--layer', type=str,
help='Layer in AnnDa... |
d4ba626ac9e8f7b79160310a8e0522b6c5283ce49e5416db57f423dcb9f69c63 | Python | 1,078 | 29 | from Cython.Build import cythonize
import numpy
from setuptools import setup, Extension
define_macros = [('NPY_NO_DEPRECATED_API', 'NPY_1_7_API_VERSION')]
# Modules to be compiled and include_dirs when necessary
extensions = [
Extension('scilpy.tractograms.uncompress',
['src/scilpy/tractograms/uncom... |
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