sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
5157d25f2fc30f59d4fb20cdd8e10dd12b662d6be45addca9a790308663836e2 | Python | 1,616 | 58 | """
This is ported from Matlab code from the original TDR codebase.
"""
import numpy as np
def tdrVectorOrthogonalize(coef_fix, ortpars):
"""
tdrVectorOrthogonalize orthogonalize regression vectors
Inputs:
coef_fix: regression vectors
.name: vector names {nvc 1}
.response: vector coe... |
71726e35742a5df17e159e96e1bfcf3fc3b545f4f3d6b8a084f845256571cc65 | Python | 1,616 | 39 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
from django.core.management.base import BaseCommand, CommandError
from cvat.apps.engine.models import Asset, Data
from cvat.apps.engine.utils import get_path_size, take_by
class Command(BaseCommand):
help = "The command initializes 'content_si... |
c92d1ff3be5d449c3a9a02fb6932fbee0f18310afad901c2f665dbe7942dcf87 | Python | 1,616 | 59 | import argparse
from datetime import datetime
from deepreg.predict import predict
name = "unpaired_us_prostate_cv"
# parser is used to simplify testing, by default it is not used
# please run the script with --full flag to ensure non-testing mode
# for instance:
# python script.py --full
parser = argparse.ArgumentP... |
f34ac6eb45a59887a2b8f0e0912b603c4c0fe78b9648170a88755f7de25d706e | Python | 1,616 | 77 | from typing import TypeVar
import torch
from torch import Tensor
import my_setup
from custom_layers.custom_module import BatchEvalModule
T = TypeVar('T', bound='Module')
class Dropout(BatchEvalModule):
# A dropout layer following the idea of Tensorflow
def __init__(self, p=0):
super(Dropout, self).... |
33fee08829514a9939df9cdc2a455939eb5cc4ff1e4342b8b4c3abda017cf4d9 | Python | 1,617 | 34 | from .bedtools import *
from .feature_generator import *
from .interactions import *
from .samtools import *
chroms = ['chr{}'.format(_) for _ in list(range(1, 22 + 1)) + ['X', 'Y']]
# http://genome.ucsc.edu/FAQ/FAQformat.html#format1
generic_bed_columns = ['chrom', 'start', 'end', 'name']
bed6_columns = generic_bed_... |
57f663b028ddab9e9a8216e8ecfb95fbe6a1876ff509acf16bc3e57d9d2b705c | Python | 1,617 | 46 | #!/usr/bin/env python3
from pathlib import Path
import shutil
import subprocess
import sys
import os
import glob
_, new_data, old_data, test_case_path = sys.argv
data_name = Path(old_data).name
test_data_path = Path(old_data).parent
subprocess.run(["git checkout master"], shell=True, cwd=test_data_path)
subprocess.r... |
52269b77be5e6f148664f2e66dd218dce13f0e5db5ea925b88cba957871c22d3 | Python | 1,618 | 45 | import os
from setuptools import setup
def read(fname):
return open(os.path.join(os.path.dirname(__file__), fname)).read()
setup(
name="mygene",
version="3.2.2",
author="Chunlei Wu, Cyrus Afrasiabi, Sebastien Lelong",
author_email="cwu@scripps.edu",
description="Python Client fo... |
82dbe3ab6b5d2134d0125dcf517b2dd98574a501676ee06b5cc27652010eb3f8 | Python | 1,618 | 42 | import logging
import os
import sys
import boto3
from botocore import UNSIGNED
from botocore.config import Config
from tqdm import tqdm
_logger = logging.getLogger(__name__)
_DEFAULT_BUCKET = 'brain-score-models'
_DEFAULT_REGION = 'us-east-1'
_NO_SIGNATURE = Config(signature_version=UNSIGNED)
def download_folder(f... |
cc956d72a3a0a8b8df675479c6fce255a6fd35c5a279abd762c7f183880b81ae | Python | 1,618 | 39 | import torch
from torch import Tensor
def dice_coeff(input: Tensor, target: Tensor, reduce_batch_first: bool = False, epsilon=1e-6):
# Average of Dice coefficient for all batches, or for a single mask
assert input.size() == target.size()
if input.dim() == 2 and reduce_batch_first:
raise ValueError... |
23c38a53b157301d309b39ad12d4b01b85f92bb0fb18f1fb8e632c9d78df678e | Python | 1,619 | 71 | """Train DyNeMo on time-delay embedded/PCA data.
"""
from sys import argv
if len(argv) != 3:
print("Please pass the number of modes and run id, e.g. python 1_train_dynemo.py 6 1")
exit()
n_modes = int(argv[1])
run = int(argv[2])
#%% Import packages
print("Importing packages")
import pickle
from osl_dynamic... |
247b3692f88c3bbdba6c6a2133ae9db1416594bbd34a97c453fb2e4c282bfbf0 | Python | 1,619 | 62 | # user feature generation: Alhosseini
import json
import datetime
import pandas as pd
import numpy as np
import csv
#path="/data2/whr/zqy/327/"
#chooses = ["gilani-2017","cresci-stock-2018","cresci-rtbust-2019","cresci-2015","botometer-feedback-2019","Twibot-20"]
chooses = ["cresci-2015"]
for choose in chooses:
... |
ee32d75be810847f051fb3d2c4a714a3b06eb6096aa8c23f4942a1453df50edc | Python | 1,619 | 43 | import numpy as np
import torch
from utils import common_functions as c_f
from miners.base_miner import BaseSubsetBatchMiner
class MaximumLossMiner(BaseSubsetBatchMiner):
def __init__(self, loss, miner=None, num_trials=5, **kwargs):
super().__init__(**kwargs)
self.loss = loss
self.miner =... |
4745f731d7cec979277d0be7f2cf5f65895275f60e1dbebcf7936d5ae4b6bb65 | Python | 1,620 | 47 | # encoding: utf-8
import numpy as np
class BaseDataset(object):
"""
Base class of reid dataset
"""
def get_imagedata_info(self, data):
pids, cams = [], []
for _, pid, camid in data:
pids += [pid]
cams += [camid]
pids = set(pids)
cams = set(cams)... |
7253b911c26487c8388a6e5da635b836635ed2f7dde3336e9c51420ffa560d0f | Python | 1,620 | 56 | #!/usr/bin/env python
"""
Build windows of length `window_size` over the sequences defined by
`len_file` excluding regions in `gap_file`.
After removing the gaps, windows of exactly `window_size` units will be
placed in the remaining regions, with the extra space evenly placed
between the windows.
`len_file` is LEN ... |
d61d0372e0cc5e8a2fde571555953f7d7d398291d7323260deec2a80e5390502 | Python | 1,620 | 68 | import re
import os
import spacy
import zipfile
def count_urls(tweets):
total = len(tweets)
unique =[]
for text in tweets:
urls = re.findall(r'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\(\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+', text)
unique += urls
count = len(unique)
unique_count = len... |
db0d23f3e525810ddf863e01a1371fa4ce2e36c32952cccc5803d3067e43cd86 | Python | 1,620 | 68 | #!/usr/bin/env python3
import sys
from optparse import OptionParser
USAGE = """
python convert-RM-to-bed.py --rmfile <rm file name> --bed <bed file name>
combine the split trf files into a single file
"""
###############################################################################
parser = OptionParser(USAGE... |
fbc1ab94bf757fac1303be5d620f91eef96f839786bce308e1eca3bdd5f3bceb | Python | 1,620 | 41 | import numpy
from numpy import median
def lowess(x, y, f=2./3., iter=3):
"""lowess(x, y, f=2./3., iter=3) -> yest
Lowess smoother: Robust locally weighted regression.
The lowess function fits a nonparametric regression curve to a scatterplot.
The arrays x and y contain an equal number of elements; each pair
(x[i]... |
0c36fd934d56a2cf041a827a444343af9de95307273fc8621054d1a7d1589e9a | Python | 1,621 | 42 | from __future__ import annotations
from PySide6 import QtGui
from PySide6 import QtWidgets
from PySide6.QtCore import Qt
from ._label_list_widget import LABEL_COLOR_ROLE
from ._label_list_widget import TrailingColorDotDelegate
class _EscapableQListWidget(QtWidgets.QListWidget):
def keyPressEvent(self, keyEvent:... |
2b01e1c56481b325484aaa594302f262b551890ff2f7fceae3a638dbcaf5e94c | Python | 1,621 | 67 | #!/usr/bin/env python
"""
Read a MAF from standard input and print counts of alignments, bases, or
columns.
usage: %prog [options]
-c, --cols: count alignment columns rather than number of alignments
-b, --bases: count bases in first species rather than number of alignments
-s, --skip=N: when counting bases,... |
31faac9d703bedbd430f9ad06c96bad88ca9ec57e03ba894cd2ff9358145e53b | Python | 1,621 | 51 | import nibabel as nib
import numpy as np
from pyment.preprocessing.crop import (
calculate_bounds,
crop_nifti_image_if_necessary,
)
def test_calculate_bounds_centers_on_brain_with_left_adjustment():
image = np.zeros((10, 10, 10), dtype=np.float32)
image[1:3, 1:3, 1:3] = 1.0
target_shape = (6, 6, ... |
cb459aef90606f09a40089c1a221b696285043be1837aea443647d82d455f985 | Python | 1,621 | 33 | import unittest
import base
class Test1(base.BaseScriptTest, unittest.TestCase):
command_line = "./scripts/maf_build_index.py ${maf} ${maf_index}"
input_maf = base.TestFile(filename="./test_data/maf_tests/mm8_chr7_tiny.maf")
output_maf_index = base.TestFile(filename="./test_data/maf_tests/mm8_chr7_tiny.m... |
2d5c8a987ca67dc813f8b12729f23388820435364a018eff32dab77a6ab9d4ae | Python | 1,622 | 34 | """
Copyright 1999 Illinois Institute of Technology
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publis... |
556a71f0654fc79315867dbe9195eee87011fb2813277772bfbc5bd86a9253d0 | Python | 1,622 | 79 | import numpy as np
def test_simple_functions():
from cec2017.simple import all_functions
assert len(all_functions) == 10
M = 2
dims = [2, 10, 20, 30, 50, 100]
for i, f in enumerate(all_functions):
assert f.__name__ == f"f{1 + i}"
for d in dims:
x = np.zeros((M, d))
... |
5f749bc4c0ad628e0cac67877f6df1fd7786d273cbe6f409f465c8934590daf6 | Python | 1,622 | 56 | #!/usr/bin/python
#
## @file
#
# Defines a series of abstract exceptions that can be sub-classed and which Hal
# can recognize
#
# Jeffrey Moffitt 3/16
#
class HalException(Exception):
pass
class HardwareException(HalException):
"""
A generic hardware exception.
"""
pass
class ModuleException(Ha... |
64e883ba3a4e273134354190af977bfed600bade2ba517b0afcbcd14610f2a94 | Python | 1,622 | 59 | # Copyright (C) 2020-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import json
import base64
from PIL import Image
import io
import numpy as np
from model_handler import ModelHandler
def init_context(context):
context.logger.info("Init context... 0%")
model = M... |
f04f411f004e28836b48d54767c2e7f642aba991ba699a99687dd36d7bf422af | Python | 1,622 | 62 | """Misc. tools for supporting Layer implementations."""
def pop_shape(keyword_options):
"""Parse first keyword option that begins with a digit as a shape.
Parameters
----------
keyword_options : list of str.
Result of `keyword.split('.').
Returns
-------
shape : tuple or None
... |
5932f3c50ce74c718c7b3599d5a5f8d976b3b5c5679a187275f43883296c6569 | Python | 1,623 | 64 | # -----------------------------------------------------------------------------
# Copyright © 2009- The Spyder Development Team
#
# Licensed under the terms of the MIT License
# (see LICENSE.txt for details)
# -----------------------------------------------------------------------------
"""Provides QtStateMachine clas... |
2224eb23af303b1b8e175740f62d1d8fdee991b9ba32e74d4f805daf3f40fb92 | Python | 1,624 | 37 | import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
cudnn.deterministic = True
cudnn.benchmark = True
import torch.nn.functional as F
from .gat_conv import GATConv
class STAGATE(torch.nn.Module):
def __init__(self, hidden_dims):
super(STAGATE, self).__init__()
... |
94c33c6ccde0831ba7bd016a40207de3749901f63b370d38c3e94568c064c6df | Python | 1,624 | 49 | from typing import Tuple
import torch
def ndgrid(*tensors) -> Tuple[torch.Tensor, ...]:
"""generate N-D grid in dimension order.
The ndgrid function is like meshgrid except that the order of the first two input arguments are switched.
That is, the statement
[X1,X2,X3] = ndgrid(x1,x2,x3)
produc... |
e241c264d23c0bd40a9fd6e2c3793f64eb3953b1bf71dd9852fe5de5b6c14349 | Python | 1,624 | 32 | from dtiplayground.dmri.common.tools.base import ExternalToolWrapper
class DTIProcess(ExternalToolWrapper):
def __init__(self, binary_path=None, **kwargs):
super().__init__(binary_path, **kwargs)
self.binary_path = None
if binary_path is not None:
self.binary_path = binary_path... |
ebfd4e06dcd14ab48a46bb423e122f7a530a180088e81e3741774c0d5d73ed78 | Python | 1,624 | 48 | # Copyright (C) 2021-2022 Intel Corporation
# Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
import zipfile
from datumaro.components.dataset import Dataset
from cvat.apps.dataset_manager.bindings import (
GetCVATDataExtractor,
detect_dataset,
import_dm_annotations,
)
from cvat.apps.da... |
63656a028e206749b6a97fa6284a485000b692f1a19880a2918effae5da3e70b | Python | 1,625 | 65 | import json
import os.path
import subprocess
import textwrap
from pathlib import Path
def load_src_dst(name: str = 'phys',
src_name: str = 'bkrunch2',
dst_name: str = 'nerf2024') -> tuple[str, str]:
p = Path('../../../res/sync') / 'backup.json'
with p.open('rb') as f:
... |
cd39704b65355f8a1835f82ffc8b327d1e3562b44c08c0181022da9a1c19671f | Python | 1,626 | 40 | #!/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... |
1137006544d1682948feb5332436fae0084ce1158c34af3990ac75b696c94c18 | Python | 1,627 | 49 | # -*- coding: utf-8 -*-
import torch
import torch.nn as nn
from transformers import BertModel
from .scalar_mix import ScalarMix
class BertEmbedding(nn.Module):
def __init__(self, model, n_layers, n_out, requires_grad=False):
super(BertEmbedding, self).__init__()
self.bert = BertModel.from_pret... |
ad36d8ae6907056e020daf2a4c5a3612d04bf19ead81f5810c83cb882b0066ac | Python | 1,627 | 63 | import warnings
from functools import wraps
from time import time
import numpy as np
from scipy.optimize import OptimizeWarning, curve_fit, direct
def time_this_function(f):
@wraps(f)
def wrap(*args, **kw):
ts = time()
result = f(*args, **kw)
te = time()
print("func:%r args:[%... |
f9d074a266b9d3ae0ea91d218e2bd810efe78aecea9d5e16c25f3f3df8162091 | Python | 1,627 | 44 | # -*- coding: utf-8 -*-
import numpy as np
from scilpy.gradients.bvec_bval_tools import is_normalized_bvecs
from scilpy.gradients.utils import random_uniform_on_sphere, \
get_new_gtab_order
def test_random_uniform_on_sphere():
bvecs = random_uniform_on_sphere(10)
# Confirm that they are unit vectors.
... |
4f17796b66718e7148f705bf2fda89a5badc34a80abde3001f750f24d0979d47 | Python | 1,628 | 59 | import argparse
from datetime import datetime
from deepreg.predict import predict
name = "grouped_mask_prostate_longitudinal"
# parser is used to simplify testing, by default it is not used
# please run the script with --full flag to ensure non-testing mode
# for instance:
# python script.py --full
parser = argparse... |
8465b81b23bc01d0b53616b1b2b511e52e15b11b4c757e807d947e789ccb3350 | Python | 1,628 | 52 | import matplotlib.pyplot as plt
import torch
def visualize_input_and_segmentation(test_loader, model, device, epoch):
model.eval()
# Fetch one batch from the test loader
inputs, labels = next(iter(test_loader))
inputs = inputs.float().to(device)
# Predict using the model
with torch.no_grad():... |
d613234e93379d7528c8bdd9ec6f7838df31af4080c757e99d94dd877629325f | Python | 1,628 | 59 | # pylint: disable=line-too-long
import argparse
from datetime import datetime
from deepreg.predict import predict
name = "paired_ct_lung"
# parser is used to simplify testing, by default it is not used
# please run the script with --full flag to ensure non-testing mode
# for instance:
# python script.py --full
parse... |
9c2fb0ea58a8c037dfb64275546ca508936fb3dfe689bbf1dc2a310ad0b6f5c0 | Python | 1,629 | 44 | import sys
from pathlib import Path
p_dir = str(Path(__file__).absolute().parents[2])
if p_dir not in sys.path: sys.path.insert(0, p_dir)
import os
from os.path import join
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import numpy as np
class enet_hydra(nn.Module):
... |
fb8aab39811da8b0f2fe333a7cad41f3e81e452933d8ca4479835ac5a3578982 | Python | 1,629 | 55 | #!/usr/bin/env python3
import argparse
import numpy as np
from ase import Atoms
from ase.io import read, write
from ase.optimize import *
from KerNNCalculator.KerNNCalculator import *
'''
Script to calculate the potential energy of a structure.
Usage: python predic_mol.py -i structure.xyz
'''
#parse command line argu... |
4b0bd534c60ade59404d1dabd5b695080d21cbf7f02489060f5d7476ef56a28b | Python | 1,631 | 52 | import numpy as np
from impyute.ops import matrix
from impyute.ops import wrapper
@wrapper.wrappers
@wrapper.checks
def em(data, eps=0.1):
""" Imputes given data using expectation maximization.
E-step: Calculates the expected complete data log likelihood ratio.
M-step: Finds the parameters that maximize t... |
4d6e38e10c00cc018739c8cdb8c0172aaefcee528520fa6ae82d7bba0ba6041c | Python | 1,632 | 50 | # Copyright (c) DP Technology.
# 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.
from argparse import Namespace
from unicore.optim import UnicoreOptimizer
class UnicoreLRScheduler(object):
... |
b877acd1b32435de4a0c29036160d340db32e386df6f1a6714b5f70306f1693c | Python | 1,632 | 45 | import functools
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.check_submission import check_models
import torchvision.models
import ssl
ssl._create_default_https... |
0db0dd9c1352885d1b3be1174788092c128fc8efdbc1889bd5e4f4c8d411c929 | Python | 1,633 | 25 | from testsetclass import TestsetClass
class ACONF(TestsetClass):
def __init__(self):
self.weak = 1.0
self.molecules = ['H_t+g+x-', 'B_T', 'P_GX', 'H_x+g-x+', 'P_GG', 'P_TG', 'H_tgt', 'P_TT', 'H_ttt', 'H_tgg', 'H_gtt', 'H_g+x-g-', 'H_x+g-g-', 'B_G', 'H_ggg', 'H_gtg', 'H_g+t+g-', 'H_g+x-t+']
... |
289827a214b5983cc27d9b2a44e948abce02cbc78e1b10aaffa89812d0a7011a | Python | 1,633 | 42 | import gzip
import os
import glob
from pathlib import Path
gene = snakemake.params.simplegene
smkout = snakemake.output.done
countfiles = glob.glob(f"cats/*{gene}*/count/*tsv.gz")
print(f"Countfiles globbed as {' '.join(countfiles)}")
fragments = {}
viewpoint = None
for countfile in countfiles:
with gzip.open(co... |
3f9d435673e1bc505dd6cf4f1d43299ead0558d7a5c65a3de34c55aef9f9e44e | Python | 1,633 | 52 | from collections import OrderedDict
import numpy as np
from brainscore_vision.model_helpers.activations.core import ActivationsExtractorHelper
from .descriptor import FoveatedGaborFilterEntropyExtractor, GaborEntropyConfig
MODEL_IDENTIFIER = 'gabor_filter_entropy_4x4_v2'
LAYER_NAME = 'gabor_entropy_foveate... |
8d6e519cd45135d39163da95d14e038c38d02625bb125456535eec60d93e02b2 | Python | 1,633 | 44 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import os
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from pathlib import Path
fr... |
2407bb7115761c403c8a1de1e73b336cd159fde7d89fb15023e66c3a766dd6fa | Python | 1,634 | 55 | import sys
from qtpy.QtCore import QCoreApplication, Qt, QTimer
from qtpy.QtWidgets import QApplication
try:
from qtpy import QT6
except ImportError:
QT6 = False
from magicgui.application import APPLICATION_NAME
from magicgui.widgets.protocols import BaseApplicationBackend
class ApplicationBackend(BaseAppl... |
af1839826bf1e4219d3814cffe4c96b03c38ba0a8653ab4e0dc04a0111f6d244 | Python | 1,634 | 63 | import os
import sys
import torch
from model.Ouroboros import Ouroboros
import pandas as pd
if __name__ == '__main__':
# check GPU
print('CUDA available:', torch.cuda.is_available()) # Should be True
print('CUDA capability:', torch.cuda.get_arch_list())
print('GPU number:', torch.cuda.device_count())... |
c68c0a1b6c9d2bcbed30320fa3b204380efb8fdf08af3235d66bd85b32c9ccb7 | Python | 1,634 | 55 | from scipy.signal import butter, lfilter, freqz
class LowPassFilter:
def __init__(self, cutoff, fs, order=5):
self.cutoff = cutoff
self.fs = fs
self.order = order
self.b, self.a = butter(self.order, self.cutoff / (0.5 * self.fs), btype='low', analog=False)
def apply(self, data)... |
0d0676f637d411a7e6e83bf922454069df73c7b5e59f2ed98165084c1c814236 | Python | 1,635 | 44 | import os
import scanpy as sc
import pandas as pd
import anndata as ad
os.chdir("/data/wuqinhua/phase/covid19/datasets")
all_adatas = []
adata1 = ad.read_h5ad('./pre_data/9_Schulte-Schrepping_2020/sce1.h5ad')
adata2 = ad.read_h5ad('./pre_data/9_Schulte-Schrepping_2020/sce2.h5ad')
adata1 = ad.AnnData(X=adata1.raw.X, ... |
982b07dae7277b92a1bb53221c3bdc39e8d47c08abc89bfb241062eea3fd50db | Python | 1,635 | 59 | #!/usr/bin/python
#
## @file
#
# Generate HTML for Dave web update & upload it to
# a webserver using the the DAV protocol and the
# tinydav library, available here:
#
# http://code.google.com/p/tinydav/
#
# This is not used.
#
# Hazen 08/11
#
import time
import tinydav
class HTMLUpdate(object):
def __init__(self... |
39c5bd14ba38908f75930c7eac281188ab9b5597d2b320e7da92ac3e63c8c309 | Python | 1,636 | 54 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
from mmcv.cnn import ConvModule, xavier_init
from depth.ops import resize
from depth.models.builder import NECKS
@NECKS.register_module()
class SkipNeck(nn.Module):
"""SkipNeck.
Reshape the skip features. Simple hack of reassemble lay... |
51ed7262cb81d3be94275a1d2f5ec2450b4fe18e7213374df16aef157e3aa182 | Python | 1,636 | 60 | import argparse
import os.path
import sys
import warnings
from bism.config.config import get_cfg_defaults
import torch
import torch.nn as nn
import torch.multiprocessing as mp
from bism.utils.distributed import setup_process, cleanup, find_free_port
from bism.train.default_engine import train
from bism.models.constr... |
611f328a12b9963524dfbba675c4186d0060248fca7192709af2e6066ba31eb8 | Python | 1,636 | 27 | from .resnet import *
from .vgg import *
from .HMAX import *
from .alexnet import *
from .ALEXMAX import *
from .RESMAX import *
from .ALEXMAX3 import *
from .ALEXMAX3_optimized import *
from .HMAX_old import *
from .hmax3 import *
from ._builder import build_model_with_cfg, load_pretrained, load_custom_pretrained, r... |
75197373c470afa34faf7eb7e525576afb15ae61979003d5d5633c284b698175 | Python | 1,636 | 50 | from setuptools import setup, find_packages
from os import path
import sys
from io import open
here = path.abspath(path.dirname(__file__))
sys.path.insert(0, path.join(here, 'ogb'))
from version import __version__
print('version')
print(__version__)
# Get the long description from the README file
with open(path.joi... |
406d87e199b456024260036b1de82cd3ac4e82ad2e063fa80f9fb8c77e959117 | Python | 1,637 | 48 | #!/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=['mrds.zip'])
tmp_dir = tempfile.Tempora... |
5747f9d23ac91c0562f1bcff1f33600ad3226f2cdbd5c9c54c9c723106e895bd | Python | 1,637 | 48 | import os
import random
import torch
import numpy as np
import pandas as pd
from utils import process_data, dataloader_generation_pred, model_performance_test, metrics, parse_arguments, load_model, process_and_predict, save_results
from model import Cross_Attention_DDG, apply_masked_pooling, SinusoidalPositionalEncodi... |
7d1a126bc6aaefd54aec15097ee4c91ce99df70f8b35fa3f26a25866c6b37081 | Python | 1,637 | 72 | import numpy as np
from scipy.constants import epsilon_0 as e0
from impedancefitter.double_shell import eps_cell_double_shell
from impedancefitter.double_shell_wall import eps_cell_double_shell_wall
omega = 2.0 * np.pi * np.logspace(0, 12, num=30)
# for double_shell_model
c0 = 1.0
em = 10.0
km = 1e-6
kcp = 0.5
ecp... |
a37a0607288a82420d277029ea16b6498cd4e23aeca505b4b2349f68a2fd86d4 | Python | 1,637 | 57 | # -*- coding: utf-8 -*-
"""
Created on Thu May 20 16:02:00 2021
@author: Yoshi
"""
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 pyp... |
7c022a7ac0095b12abcb5198bc50a6d9f6cb860c3f35df449796a0820ca758e9 | Python | 1,639 | 52 | from torch import nn
import torch
class EmbLayer(nn.Module):
def __init__(self,
hidden_dim=None,
aggregate='sum',
bias=True,
activation=None,
ntypes=None,
dropout=0.0,
conv_dict=None,
... |
e6b6094359befdd6ce76894cce70e5f2b3d925a4c804df5cacaf16e337d9824f | Python | 1,640 | 51 | from os.path import join, dirname, basename, isdir
from os import makedirs
from imageio import imread
from glob import glob
import numpy as np
from ..cpn import masks2labels
import torchvision
__all__ = ['download_bbbc038', 'BBBC038Train']
def download_bbbc038(directory):
"""Download BBBC038.
Download and e... |
67849f74cc5da327217b577e7a9508aac697a2c86826ea7c8ba3954f82476604 | Python | 1,641 | 60 | # pylint: disable=line-too-long
import argparse
from datetime import datetime
from deepreg.predict import predict
name = "unpaired_ct_lung"
# parser is used to simplify testing, by default it is not used
# please run the script with --full flag to ensure non-testing mode
# for instance:
# python script.py --full
pa... |
cbdd1a4fce74e0c7955535c791d80b6a09c5e3013c59838c7b0ef772d5187612 | Python | 1,641 | 59 | from rscvp.model.bayes_decoding.main_decode_analysis import DecodeAnalysisOptions
from rscvp.selection.main_neuropil_error import NeuropilErrOptions
from rscvp.selection.main_trial_reliability import TrialReliabilityOptions
from rscvp.util.util_demo import run_demo
from rscvp.visual.main_reliability import VisualReliab... |
d9d9c2513fc420fbec9b47a01cf0c307bda6a6686fce3e574c3a160cf452e82c | Python | 1,641 | 57 | # Generated by Django 4.2.1 on 2023-07-18 07:09
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
("engine", "0073_alter_attributespec_default_value_and_more"),
]
operations = [
migrations.AlterField(
model_name="labeledimage",
... |
3b429bcad7e6a73577fd660c129a510f0df0afec0148b2d1bf2c26fd0db5c249 | Python | 1,642 | 51 | from __future__ import annotations
from dataclasses import replace
from typing import Optional
import numpy as np
from brainways.elastix.elastix import elastix_registration
from brainways.pipeline.brainways_params import BrainwaysParams, TPSTransformParams
from brainways.transforms.tps_transform import TPSTransform
... |
d2626227a6c5391b6c08d480c21960f3e74cfaf3167a74153b58c07726e327a0 | Python | 1,642 | 51 | import logging
import os
from contextlib import contextmanager
from pathlib import Path
import pandas as pd
logger = logging.getLogger("benchmark")
@contextmanager
def set_cwd(path: Path):
"""
Sets the cwd within the context and resets it afterwards
Adapted from https://dev.to/teckert/changing-director... |
e9b3e11725465c60aa3618c97ec63666db9f0bf1bc9b57898918b891c2dc3daa | Python | 1,642 | 64 | #!/usr/bin/env python
"""
For every column that occurs in a multiple alignment print the column
and the number of times it occurs (one column/count per line, tab
separated), sorted by count descending.
This version allows special handling of the 'wildcard' symbol in alignments.
Note: all blocks must have exactly the... |
4fa325b6be9814fcaaa83ef9da026fa9fb7d923c08e0c08521a9e771a5884400 | Python | 1,643 | 50 | import numpy as np
import scipy.stats as stats
def CPT(cpts, truncate=False):
# truncate: set elements below zero to zero
if truncate:
cpts[cpts < 0] = -0.1
return np.nanmean(cpts, axis=1)
def HPT(hpts):
return hpts.mean(axis=1)
def MPT(mpts):
# logarithm of geometric mean
return np... |
8e2c0ffd892c6b4aab7c4332f289cf23c2088063bbb249735d80530d0166b4c1 | Python | 1,643 | 49 | import os
import numpy as np
import pandas as pd
import scanpy as sc
import anndata as ad
import hdf5plugin
from anndata import AnnData
os.chdir("/data/wuqinhua/phase/covid19/datasets/pro_data")
adata1 = ad.read_h5ad('./1_Arunachalam_2020.h5ad')
adata3 = ad.read_h5ad('./3_COMBAT_2022.h5ad')
adata4 = ad.read_h5ad('./4... |
a58966cfb87ea1b516618a5e9cdfa71337fc02ebfabcc26ac3d2f44882287c3c | Python | 1,643 | 54 | """
test_demultiplex.py
-------------------------------
Tests for the `demultiplex.py` module
"""
import unittest
import os
import sys
import shutil
import utils
# Include the parent directory in the PYTHONPATH for relative imports
sys.path.append('..')
import demultiplex
TEST_SAMPLE_BARCODES = {'AGGCATGAGATCGC':... |
f7fca686ebe95155b1db077dad5ae0f2526134f5a3612266700a0bca674481e6 | Python | 1,643 | 43 | #!/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='surface_vtk_fib.zip')
tmp_dir = tempfil... |
036a99c26302a1e34709e77143749d8c69148e2cc14092b45c81c71b71205e35 | Python | 1,644 | 53 | from django.db import migrations, models
def fix_default_task_dimension(apps, schema_editor):
Task = apps.get_model("engine", "Task")
Task.objects.filter(models.Q(data_id__isnull=True) | models.Q(data__size=0)).update(
dimension=""
)
def restore_default_task_dimension(apps, schema_editor):
... |
0a9809ee89d827d00e7140fed509bf68781fdb57abc69926cccc431b387f345a | Python | 1,644 | 52 | import scipy as s
import scipy.stats as stats
from .basic_distributions import Distribution
from mofapy2.core.utils import *
class Poisson(Distribution):
"""
Class to define Poisson distributions.
Equations:
p(x|theta) = theta**x * exp(-theta) * 1/theta!
log p(x|a,b) = x*theta - theta - log(x!)
... |
96ec5b9a6173d0498a381a60b09e66a3afb67eea3f5f1ea53604a86870114540 | Python | 1,644 | 53 | """Test the compat module."""
import sys
import pytest
from packaging import version
from qtpy import PYQT5, PYQT_VERSION, QtWidgets, compat
from qtpy.QtCore import QSize
from qtpy.QtGui import QImage
from tests.utils import not_using_conda
@pytest.mark.skipif(
(
(sys.version_info.major == 3 and sys.ve... |
af163234170d68181d0e4be2fa7355aa980788968a72f5a6c9e01a82e71815d4 | Python | 1,644 | 51 | import json
import os
import os.path as osp
from argparse import ArgumentParser
import pandas
from sklearn.metrics import accuracy_score, roc_auc_score, f1_score, precision_score, recall_score
def metrics(y: list, pred: list):
plog = "ACC: {}".format(accuracy_score(y, pred)) + '\n' + \
"ROC: {}".form... |
64d09cdd82f308c75e447519de7177394f33bef4b925e2df42bd71183b7822fc | Python | 1,645 | 23 | import argparse
from .test import test
import torch
def main():
parser = argparse.ArgumentParser(prog="rapidparc-test", description="Program to benchmark RapidParc on the test split of the TractCloud dataset")
parser.add_argument("--model_name_or_path", type=str, required=True, help="Name of the Model, i.e. `r... |
fe7c1f3c497aca04f46175e73419de86ffd63544009efba489425d383fb21ad1 | Python | 1,646 | 69 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
def get_surface_voxel(file_in, path_output):
"""Get surface voxel.
Compute surface voxels for to illustrate curvature dependence in surface
representations as introduced in ... |
1d149bbf1c7796a0f76c866a231f108561102d8afa5f29444148ca2780141963 | Python | 1,647 | 53 | # -*- coding: utf-8 -*-
"""
Tools for the analysis of the Graph
"""
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import seaborn as sb
import scipy as sc
def laplacian(a):
n_nodes, _ = a.shape
posinv = np.vectorize(lambda x: float(1.0)/np.sqrt(x) if x>1e-10 else 0.0)
... |
93cd9c1245820c689002edae219e1a9174fa5a9bf99f3b3392f9564cead91833 | Python | 1,648 | 36 | import pandas as pd
goif = snakemake.input.goi
dots_npc = snakemake.input.dots_npc
dots_aux = snakemake.input.dots_aux
dots_ctrl = snakemake.input.dots_ctrl
bedpe_out = snakemake.output.bedpe
goi = pd.read_table(goif, header=None)
goi.columns = ['chrom', 'start', 'end', 'gene', 'db', 'strand']
npcdf = pd.read_table(... |
0cc539d461465b19395a856a0db2bc30f430f4ba851168c9667e5f2b70ed652e | Python | 1,649 | 69 | """
nelpy full API
==============
``nelpy`` is a neuroelectrophysiology object model and data analysis suite
based on the python-vdmlab project (https://github.com/mvdm/vandermeerlab),
and inspired by the neuralensemble.org NEO project
(see http://neo.readthedocs.io/en/0.4.0/core.html).
"""
from . import decoding, fi... |
410bb681ff10427c04d6e4e27e4fe5ae9ab23eb37449474c1742651f7dd15b6a | Python | 1,649 | 53 | import os
import os.path as osp
import torch
import numpy as np
import scipy
import pandas as pd
import argparse
import os
import torch_geometric.transforms as T
parser = argparse.ArgumentParser(description='display graph features and summary.')
#parser.add_argument('-d','--devices', help='Specify CUDA_VISIBLE_DEVICES... |
b96668877a55cc7cf9bd158a673beab29110203941ec2b92881958b0d8a12e4c | Python | 1,649 | 56 | # 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... |
d7787139f9264130f2bc3182f2c7079254d2161448d06a996e4bb6192330f220 | Python | 1,649 | 47 | import json
from copy import deepcopy
from pathlib import Path
from eir.experiment_io.input_object_io_modules.input_io_utils import (
load_input_config_from_yaml,
)
from eir.experiment_io.io_utils import load_dataclass
from eir.setup import schemas
from eir.setup.input_setup_modules.setup_image import (
Comput... |
84dce2d3766b1a2cde8011040011ebea43bf4d6bed7c1b86adbdb9cf169383b4 | Python | 1,650 | 46 | # Example of how to read a PET DICOM file from a Siemens mMR scanner
# The code reads all DICOM files in a folder, sorts them by InstanceNumber and creates a 3D array with the pixel data.
# The code also scales the pixel data by the RescaleSlope and RescaleIntercept values to convert the data into the correct units.
i... |
c96958928297d0de062147bc4b5fb9036bb78e14c9ea0055568333feb3059d32 | Python | 1,650 | 54 | import numpy as np
import torch
from eir.data_load.data_preparation_modules.input_preparation_wrappers import (
_should_skip_modality,
)
from eir.train_utils.evaluation_modules.evaluation_handlers_utils import (
_streamline_tabular_data_for_transformers,
)
class MockLabelTransformer:
def __init__(self, n... |
695f078280cf458e9c99c01f253f97e9e261410ea7ac0b0162efdbfa148c3be4 | Python | 1,651 | 65 | import tempfile
import pytest
gtf_file = 'tests/data/test.gtf'
fasta_file = 'tests/data/hg19.nochr.chr17.fa'
vcf_file = 'tests/data/test.vcf.gz'
exon_file = 'tests/data/test_exons.csv'
junction_file = 'tests/data/test_junction.csv'
junction_psi5_file = 'tests/data/test_junction_psi5_file.csv'
junction_psi3_file = 'tes... |
92c522fafd5d23ec695837881c721af89a1ed85825e1394999108aa8f9a641e2 | Python | 1,652 | 72 | import matplotlib.pyplot as plt
import numpy as np
def plot_overlap(
title,
eval_frame,
plot_keys=['Motorcortex', 'Hippocampus', 'Cortex'],
metric=None,
file=None,
):
mean = np.zeros(len(eval_frame))
for k in plot_keys:
if metric is None:
m = eval_... |
f73cf0e666bc78436aed624a1b380237246c525ace889e323216b368104035b5 | Python | 1,652 | 25 | """System prompts for the RAG agent workflow."""
rag_agent_prompt = """You are an expert research assistant specializing in computational chemistry and scientific literature analysis. You have access to tools for:
1. **Document retrieval** -- loading documents (.txt or .pdf) and querying them for relevant information... |
662a7c1fcbc78d543326596dea9a96983882abc48726fb809da79da58902405a | Python | 1,654 | 53 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Shuffle the ordering of streamlines.
"""
import argparse
import logging
from dipy.io.streamline import save_tractogram
from scilpy.io.streamlines import load_tractogram_with_reference
from scilpy.io.utils import (add_overwrite_arg, add_reference_arg,
... |
72aea951ad0c9d2f6b3adec39889e0512681d32481efb923b75415ffd4a8c680 | Python | 1,654 | 37 | import numpy as np
def create_mask(cont,arr,x0):
#contour, array to inscribe the mask on, position of arr[0,0]
for i in range(min(np.array(cont)[:,0])-x0[0],max(np.array(cont)[:,0])-x0[0]+1):
calcar=[y for x,y in cont if x==i+x0[0]]
if len(calcar)!=0:
mini=min(calcar)
ma... |
d531114fff7cc24ad6197828390ee240344c9ed1d36af9138caac2e4280c8a52 | Python | 1,654 | 56 | # Generated by Django 3.2.12 on 2022-06-10 18:29
import os
import shutil
from django.conf import settings
from django.db import migrations
from cvat.apps.engine.log import get_logger
MIGRATION_NAME = os.path.splitext(os.path.basename(__file__))[0]
MIGRATION_LOG = os.path.join(settings.MIGRATIONS_LOGS_ROOT, f"{MIGRA... |
0b31556fa41f8aa37429e074843da4efb3501ed1f330fee14d2772a6fdcaa610 | Python | 1,655 | 48 | import sys
import subprocess
import yaml
import pandas as pd
import numpy as np
script = 'preprocessing/quiet_extraction/extract_from_off_and_trans.py'
pathpath = 'PATHS/merge_quiet_paths.yml'
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_load(myfile)
sys.path.append(pathdict['code']['workspace'])
t... |
2c2d331c54f7197d1b01b6ed8bed06f7a7c2b29fee9de6a78e35582b476d337b | Python | 1,655 | 51 | """Helper functions to help with reproducibility of models. """
import logging
import os
import random
from typing import Optional
import numpy as np
import torch
log = logging.getLogger(__name__)
def seed_everything(seed: Optional[int] = None) -> int:
"""
Function that sets seed for pseudo-random number g... |
ad521cf4ef3f0a142ed2feb1770b2c5344997484ee35e1ba7bfc3ad26c7d8741 | Python | 1,655 | 45 | import fasttext
import fasttext.util
import os
from pathlib import Path
from Deps.CustomFuctions.classes import IMAGENET2012_CLASSES
from collections import OrderedDict
import torch
from tqdm import tqdm
script_dir = Path(__file__).parent
project_root = Path(__file__).resolve().parent.parent
def extract_before_comma(... |
cb8aaf842b4c15c0f469ad5e87e07c858f6ac702793b6dedbb132e50a7d231ab | Python | 1,656 | 49 |
_colors = [
'#d60000', '#e2afaf', '#018700', '#a17569', '#e6a500', '#004b00',
'#6b004f', '#573b00', '#005659', '#5e7b87', '#0000dd', '#00acc6',
'#bcb6ff', '#bf03b8', '#645472', '#790000', '#0774d8', '#729a7c',
'#8287ff', '#ff7ed1', '#8e7b01', '#9e4b00', '#8eba00', '#a57bb8',
'#5... |
f8e9256e5027282d4822330d9be565ad5e581eda20c4655f6a3f6a931ee6f35b | Python | 1,656 | 58 | """Download and unzip the FlyWire data if not already cached."""
import os
import urllib.request
import zipfile
DATA_URL = (
"https://github.com/BenJourdan/ntac/releases/download/v0.1.0/dynamic_data.zip"
)
def download_flywire_data(
data_url=DATA_URL,
cache_dir=os.path.join(os.path.expanduser("~"), ".ntac... |
17d4793dcc8338c74e7e5ee9c131562fc083427a30e16eebadfe47efa284147f | Python | 1,657 | 38 | # ==============================================================================================================================
# Author: Lucas Berg
# ==============================================================================================================================
import os
import sys
import numpy as np
i... |
1d4836966f7b93aaebad5c3dbd57f473fde510e108509c8dad5e4e3265963fb5 | Python | 1,657 | 55 | #!/usr/bin/env python
from __future__ import print_function
import operator, os, sys, subprocess, time
############################################################
# exec_par
#
# Execute the commands in the list 'cmds' in parallel, but
# only running 'max_proc' at a time.
#############################################... |
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