sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
8e09d0d9bd81ab63cacf6985745a24cef8c403c7be290e2f42f68658cc54f47c | Python | 1,006 | 40 | import pandas as pd
import cooler
import numpy as np
ctrl = snakemake.input.ctrl
bedpe = snakemake.input.bedpe
of = snakemake.output.bedpe
clr = cooler.Cooler(f"{ctrl}::resolutions/15000")
pixels = clr.pixels()[:]
bins = clr.bins()[:]
a = pd.read_table(bedpe, header=None)
a.columns = ['chrom1', 'start1', 'end1', 'ch... |
dee79a8778a5e398a2b1931d78866cf5572b595143fe2f56d7fa5737ac4a25e2 | Python | 1,006 | 30 | #!/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... |
621e3d991e44df0042dd3b69f7a590c4d93aec7bcbf31ad931995fdcae391f89 | Python | 1,007 | 25 | # 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 o... |
85dcc71e8041f8998193f0afb02f29ef153d0eacd593b44c9c16b7b2539ef8de | Python | 1,007 | 23 | from brainscore_vision import metric_registry
from .metric import OSTCorrelation
metric_registry['ost'] = OSTCorrelation
BIBTEX = """@article{kubiliusschrimpf2019brain,
title={Brain-like object recognition with high-performing shallow recurrent ANNs},
author={Kubilius, Jonas and Schrimpf, Martin and Kar, Kohitij ... |
8da7ff67d927fc638348d7ad811c386177beef5977547eb8343fffc965367c7d | Python | 1,007 | 43 | #!/usr/bin/env python3
import sys
from optparse import OptionParser
USAGE = """
python trf-to-bed.py --trf <trf file name> --bed <bed file name>
combine the split trf files into a single file
"""
###############################################################################
parser = OptionParser(USAGE)
parser... |
b5cad24cfb74622a5fe7092c3298bdeaebd99f43c1cfb04c107f375237d04a90 | Python | 1,007 | 26 | import torch
from peft import LoraConfig, get_peft_model, PeftModel
def init_lora(target_modules, lora_r=16, lora_alpha=64, lora_dropout=0.2):
lora_config = LoraConfig(
r=lora_r,
lora_alpha=lora_alpha,
target_modules=target_modules if not target_modules is None else 'all-linear', # Specific... |
d20affe431131f6b1c59086b5c6142b8f942f0d7174fa40bec1b725315e3ee2a | Python | 1,007 | 41 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""
ASGI config for CVAT project.
It exposes the ASGI callable as a module-level variable named ``application``.
For more information on this file, see
https://docs.djangoproject.com/en/3.2/howto/deployment/asgi/
"""
import os
from django.core.as... |
d2f32af0086c029bc8406d4c5e3c84048efcb299dda2a89e07895247ff259be3 | Python | 1,007 | 44 | """
nelpy.analysis
=====
This is the nelpy analysis sub-package.
nelpy.analysis provides several commonly used analyses.
"""
# from .hmm_sparsity import HMMSurrogate
from .ergodic import fmpt, steady_state
from .replay import (
column_cycle_array,
get_significant_events,
linregress_array,
linregress_... |
311b59ad75051dfdb5cea9b3d09d7711471aee5873b3c5d7334802d6eb6822ad | Python | 1,008 | 38 | import numpy as np
import pandas as pd
import os
import time
import seaborn as sns
import matplotlib.pyplot as plt
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import fcluster, to_tree, dendrogram, linkage
from matplotlib.pyplot import cm
import copy
def get_entropy(vector):
'''
Ge... |
4dba5919a010a7e25f45a731577ec4f1c2f5012f9e9a5cc2e1c9bcefae71e9de | Python | 1,008 | 45 | import numpy as np
import torch
import pickle
import os, sys
import glob
input_folder = sys.argv[1]
subsets = ["test_id", "test_ood_ads", "test_ood_both", "test_ood_cat"]
def flatten(d, index):
res = []
for x in d:
res.extend(x[index])
return np.array(res)
def one_ckp(folder, subset):
s = ... |
ad037d95e269ec31f220964670864ef00f3c5872301d4b2f30fa0889d3abdca1 | Python | 1,008 | 41 | #!/usr/bin/env python3
import arbor as A
from arbor import units as U
from util import plot_spikes, plot_network
from unconnected import unconnected
# global parameters
# cell count
N = 5
# total runtime [ms]
T = 1000
# numerical time step [ms]
dt = 0.1
class ring(unconnected):
def __init__(self, N) -> None:
... |
54a583cfb2452e0459c8acb7bb3d135f21f5164b78a7b913f4af9c6b00ad2cce | Python | 1,009 | 43 | #!/usr/bin/env python
"""
Find continuous regions that are covered by the first bed file (`bed_file_1`)
but not by the second bed file (`bed_file_2`)
usage: %prog bed_file_1 bed_file_2
"""
from bx.bitset_builders import binned_bitsets_from_file
from bx.cookbook import doc_optparse
def print_bits_as_bed(bits):
... |
5d4d8a9b14ba6ee059a28ca9f3eb962824c2a1ad2001c7eaee91cea2d9204c68 | Python | 1,009 | 40 | #!/usr/bin/env python3
# Author: Jeffrey M Kidd
# bed-length.py
# Returns total length of intervals in in a UCSC (0-based style) bed file
import sys
from optparse import OptionParser
USAGE = """USAGE:python bed-length.py -f <bed file>
Reads a UCSC style bed file and reports total interval size. Assumes that inter... |
002de3f615f887e496e5137c4411881cf6c33fa8006cdef4b0372e5e63aa4065 | Python | 1,010 | 43 | import pytest
from pyment.models.sfcn import (
BinarySFCN,
CategoricalSFCN,
MultiTaskSFCN,
RegressionSFCN,
sfcn_factory,
)
@pytest.mark.parametrize(
'name, expected',
[
('sfcn-reg', RegressionSFCN),
('regression', RegressionSFCN),
('sfcn-bin', BinarySFCN),
... |
0339bfadf563269264ab16c27e09c0113fa5de3516036e91958243489ada3b93 | Python | 1,010 | 45 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
import subprocess
# local inputs
from ..io.get_filename import get_filename
def smooth_surface(file_in, file_out, n_iter):
"""Smooth surface.
This function smoothes a surface mesh using freesurfer.
Parameters
... |
6a09e66af3e081a601866d8f359634c80c64f041fcd2dca3170ffa634d1e156b | Python | 1,010 | 33 | # anonymize_bam.py - de-identify sequencing data
import setuptools
# Set __version__ for the project.
exec(open("./BAMboozle/version.py").read())
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="BAMboozle",
version=__version__,
author="Christo... |
a7e98ba0b211c6a340f0a75f1b7205fde0f455da2116c9184e1f781a41abd9fa | Python | 1,010 | 38 | from pathlib import Path
data_dir = Path("data")
file = "database.csv"
# plots and visuals
ColorMap = ["#ADE1DC", # soft aqua
"#C5C2E4", # lavender
"#F28C8C", # warm coral
"#9FCBE1", # sky blue
"#F8BC63", # amber
"#CBEA7B", # light lime
"#F5C6DA", # blush pink
"#BFD8B8", # mint
... |
f26eab69fefb1e95f0ea2c984b43298524265e8d2328c909f3d3cb4033ade6a9 | Python | 1,010 | 42 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
# external inputs
import numpy as np
import nibabel as nb
def get_series(file_in, path_out, name_output):
"""Get series.
This function creates a 4D nifti time series from a set of 3D nifti files.
Parameters
----------
file_... |
7a390dfc2d759d4674e9a2f26bf8d200c214b14e5d824245b83e89ef2ff3f268 | Python | 1,011 | 35 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import pytest
pytestmark = pytest.mark.xdist_group("serial")
def test_generate_option(script_runner):
ret = script_runner.run(['scil_search_keywords', 'test',
'--regenerate_help_files',
'--processes', '4'])
... |
0047f9df57fa71a6a07efe039f8de06d61f08d1b6bd1aefc2888dcc3da15954a | Python | 1,012 | 28 | # -*- coding: utf-8 -*-
# @Author : LG
from PyQt5 import QtWidgets
from ISAT.ui.info_dock import Ui_Form
class InfoDockWidget(QtWidgets.QWidget, Ui_Form):
def __init__(self, mainwindow):
super(InfoDockWidget, self).__init__()
self.setupUi(self)
self.mainwindow = mainwindow
self... |
d9dd4a6a0aaac5623b4de266934de643817faea6c497c5c82aa1884049d88486 | Python | 1,013 | 26 | # fslcpgeom
# Usage: fslcpgeom <source> <destination> [-d]
# -d : don't copy image dimensions
import os
from nipype.interfaces.base import (CommandLineInputSpec, CommandLine, traits, TraitedSpec, BaseInterface, BaseInterfaceInputSpec, File)
from traits.api import Directory, Str, List, Bool, Enum, Int, Float, TraitErr... |
c120a795660fc11c6e8cd7546c5cd2f71e763770923fb483195708b2c3a8641b | Python | 1,014 | 43 | import sys
import os
import signal
import traceback
def error_handler(exctype, value, traceback):
print()
print("Error:", value, file=sys.stderr)
sys.exit(1)
def exit_handler(signal, frame):
print()
print("User abort (CTRL-C) received.")
sys.exit(0)
def show_error_dialog_win(title, message):
"""Display... |
dd012be52adaf83e465065398ae71f5f1d1f6421c58e4a2d58a8956d3868ab22 | Python | 1,014 | 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=['atlas.zip'])
tmp_dir = tempfile.Tempor... |
8872aa69e960cce12b30fe9dde1a646d42e714418e58be9727d8debfe43d7eec | Python | 1,015 | 36 | """
Tests for `bx.seq.qdna`.
"""
import unittest
from bx.seq import qdna
test_qdna = "test_data/seq_tests/test.qdna"
# Same sequence data as stored in test.qdna
valid_seq = (
"C7wMwHQrMKqEtSREuUv5nsLinpTS8l7jXpbI7IipvCbHnhOdgx"
"5tzRgzYl4j85d:xSlvKPEKEIvZkfiX1YPkBi1Ibhfn9fTZd8gG"
"Wy284hJnwf93W4eHOjeRk... |
d64f40f71c3bdf3ab00edd66ea928365ff0feffabff01462c82a9e5d1f0a1a0f | Python | 1,015 | 36 | """
Tests for `bx.seq.fasta`.
"""
import unittest
from bx.seq import fasta
test_fa = "test_data/seq_tests/test.fa"
# Same sequence data as stored in test.fa
valid_seq = (
"TGGAGGCATTTGTGATTCAATAGATGCAGAAAGAAACCTTCCTAGAGCTG"
"GCGTTCTCTAACTAAAAGTGGAAAGTTCTGAGGAATGAGGACTGTTATAA"
"ATCCCACCCCACACCGCACCTTCTC... |
6b579494c3bd54d5d307e47f765c6ae250dc9162a4bcde83b18c80788008b030 | Python | 1,016 | 25 | from brainscore_core.supported_data_standards.brainio.assemblies import NeuronRecordingAssembly
from brainscore_vision import data_registry, load_stimulus_set
from brainscore_core.supported_data_standards.brainio.s3 import load_assembly_from_s3
BIBTEX = """@article{lee2019sensitivity,
title={Sensitivity of inferote... |
6fe007a6956ece79b3cdcc1ae07f4b68410eab3495656ff5aaefb195037f71cf | Python | 1,016 | 35 | from dataclasses import dataclass
from typing import List
import numpy as np
@dataclass
class Obj:
acc: float # maximize
analog_macs: float # maximize
dig_w_mb: float # minimize
def dominates(a: Obj, b: Obj) -> bool:
return (
a.acc >= b.acc and a.analog_macs >= b.analog_macs and ... |
b43df6abc867bbf9156b955d54a3bb92973ab232357c1350576af14cbbd2f5ff | Python | 1,016 | 36 | from setuptools import setup
import os.path as op
import glob
from setuptools.command.build_py import build_py
class build_py_with_references(build_py):
def run(self):
references_src = op.join("docs", "source", "references.bib")
references_dst = op.join("AFQ", "_references.bib")
with open... |
14a218bb3493d8c1c9546ecf6757b2526f624b6cfebd1d8430a84b8cf771de58 | Python | 1,017 | 27 | # Package marker for ui.widgets
from .navigation_controls import NavigationControls
from .double_zoom_widget import DoubleZoomWidget
from .image_mouse_move_handler import ImageMouseMoveHandler, ImageMouseMoveState
from .display_options_panel import DisplayOptionsPanel
from .collapsible_right_panel import CollapsibleRig... |
281e883ef41c65790def33cc6d78799f1e8077c680115ad9d2bf8f0e9cc4b08d | Python | 1,017 | 24 | from typing import Union, Tuple, List
import torch
from batchgeneratorsv2.transforms.base.basic_transform import SegOnlyTransform
class RemoveLabelTansform(SegOnlyTransform):
def __init__(self, label_value: int, set_to: int, segmentation_channels: Union[int, Tuple[int, ...], List[int]] = None):
if not i... |
39a71d4211c3e26a51729616008db51c80857da4382bd3067ae157779d3c0d67 | Python | 1,017 | 48 | # %%
# imports
import os
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
import ffmpeg
# %%
# Argle parser
parser = ArgumentParser(
prog='resample_video_fps',
description='Resample a directory of videos to 10 fps.',
formatter_class=ArgumentDefaultsHelpFormatter
)
parser.add_argument(
... |
3cf599a18a62bca2de3df8f2cf3566d7b16657e00182f1b0cd955615075dca36 | Python | 1,017 | 25 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import torchvision.models
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
from .helpers.cornet_helpers import v... |
c5f83019ad74a558371aa37fe19fa87fa3aa14d1154659dbf0f1f50200d4efce | Python | 1,017 | 31 | #! /usr/bin/env python3
import anatomy
import argparse
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="mp2rage_recon-all (requires freesurfer v7.1, CAT12 and optionally gradient_unwarp.py)"
)
parser.add_argument(
"inv2", type=str, help="path to nifti file containin... |
f3c866c9cf9d5a5b4feba31b8eb5be54705c5603ce234574064ad5df407e2f27 | Python | 1,017 | 31 | """ Assign weights to distances in a way such that farther values are weighed less """
import numpy as np
def shepards(distances, power=2):
""" Basic inverse distance weighting function
Parameters
----------
distances: list/numpy.ndarray
1D list of numbers (ex. distance results from call to KD... |
63e7be9373a34e17e06fa4bebb602bdc661f7ecd9f45c3cabc1380407550c111 | Python | 1,018 | 31 | #!/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=['tracking.zip'])
tmp_dir = tempfile.Tem... |
8a02c5959e543a2588c40a9c91f9cc057008b2e5c3d3b74b31658426ef8e3394 | Python | 1,018 | 26 | import torch
import torch.nn as nn
import torchvision.models as torch_models
class TorchVisionOverwrite(nn.Module):
def __init__(self, arch='vgg16', pretrained=False, num_classes=1000, in_chans=3):
super().__init__()
model = torch_models.__dict__[arch](pretrained=pretrained)
# Layers are... |
d434d81706b0f6c699a1cab5b99346cca8c257177be4ab20c31cdd0e51f88479 | Python | 1,018 | 30 | """Get training data.
This script will download source reconstructed resting-state (eyes open)
MEG data.
This data is part of the MEGUK dataset. It is CTF data from the
Nottingham site. 65 subjects are part of this dataset.
"""
import os
def get_data(name, output_dir):
if os.path.exists(output_dir):
pri... |
da9ce6fabf12a79d78b484f65e8dc9f73577af39c4b3922e2477df3574786567 | Python | 1,018 | 36 | from ..util import NodeSelector
from ..qt import *
class AlgebraWidget(QWidget):
def __init__(self, config, stub):
super().__init__()
self.config = config
node_selector = NodeSelector(config, 'Source', False)
equation_edit = QLineEdit()
grid = QGridLayout()
grid.addWidget(QLabel('Equation:'... |
20ed009d4145801ff4e7b48415adf49377df82b31d5dca092872cb46066c8f85 | Python | 1,019 | 23 | from step1_dataimport.step1_2_filter import FilterDataset
from step1_dataimport.step1_2_import import DataImport
from step1_dataimport.xnatconnectionprovider import XNATConnectionProvider
from step1_dataimport.step1_2_duplicates import IdentifyAndMoveDuplicates
from step1_dataimport.get_env_vars import get_environment_... |
438fd33b62c91ee0dba30b4b5bba5a0cbe29ab99f719f3cd546ac0b5cae4b29c | Python | 1,020 | 28 | """Collection of performance metrics and related utilities.
Score model outputs by prediction correlation and other metrics.
Measure equivalence, sparsity, and other properties of model predictions and/or
recorded neural responses.
# TODO: These contents don't all exist yet, sketching this out based on
# the st... |
a347c47abffc073def7624c6bd585676b5241914f17158fdc4f7a4d12802294e | Python | 1,020 | 31 | import ants
import antspynet
import sys
import tensorflow as tf
t1_file = sys.argv[1]
output_prefix = sys.argv[2]
threads = int(sys.argv[3])
tf.keras.backend.clear_session()
config = tf.compat.v1.ConfigProto(intra_op_parallelism_threads=threads,
inter_op_parallelism_threads=threads)... |
d3ff167902bc6053b7562a33d97fc52d23a8fd338eabcda09c2cc6701e6b278a | Python | 1,020 | 49 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
import distutils.cmd
import platform
import os
import setuptools.command.build_py
import subprocess
import sys
from setuptools import setup, find_packages
version = "2.0"
description = "STORM microscope control code."
long_description = ""
setup(
name='storm_cont... |
d50b501a3224ebfe679c3f9e60cead0914861eebcd59a79dea062b1248469727 | Python | 1,020 | 29 | import os
import scanpy as sc
import pandas as pd
import anndata as ad
os.chdir("/data/wuqinhua/phase/covid19/datasets")
adata = ad.read_h5ad('./pre_data/10_Schuurman_2021/pbmc.h5ad')
adata.obs.rename(columns={'id_new': 'sample_id'}, inplace=True)
adata.obs = adata.obs.rename(index=lambda x: x.replace('.', '-'))
csv... |
f11faa403f384a7e68c7abe24cdd2460727c9c89eceb00d2dfe107c312fe9005 | Python | 1,020 | 40 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
def gem(x, p=torch.ones(1)*3, eps: float = 1e-6):
return F.avg_pool2d(x.clamp(min=eps).pow(p), (x.size(-2), x.size(-1))).pow(1./p)
class GeM(nn.Module):
def __init__(self, p=3, eps=1e-6):
sup... |
3b67bd33a8b6fa0ab66407ecf80ddbcc8988d9c30e1267faa4a7cf3d61f85425 | Python | 1,021 | 34 | #!/usr/bin/env python3
"""Interactive installer for metaprivBIDS.
This script prompts the user whether the optional ``pygraphviz``
dependency should be installed. ``pygraphviz`` is used for
advanced graph visualisation but can be troublesome to install
on Windows systems. If the user declines, installation proceeds
... |
87a576e4a7c89dafe0217f8766fb4edfe439a08dfbae3f25633c777f6933cd0c | Python | 1,021 | 22 | from tests.utils import pytest_importorskip
def test_qt3danimation():
"""Test the qtpy.Qt3DAnimation namespace"""
Qt3DAnimation = pytest_importorskip("qtpy.Qt3DAnimation")
assert Qt3DAnimation.QAnimationController is not None
assert Qt3DAnimation.QAdditiveClipBlend is not None
assert Qt3DAnimatio... |
e38d1d5d4468c285ece2b1f3fa705fe373ffd6b28a17130033cc5c55ab2242b2 | Python | 1,021 | 32 | #! /usr/bin/env python3
import sys
import os
from nipype.interfaces import spm,matlab
spm_path = '/data/pt_02389/Software/spm12'
def spm_bias_correction():
# bias correct INV2
seg = spm.NewSegment()
seg.inputs.channel_info = (0.001, 20, (False, True))
seg.inputs.affine_regularization = 'mni'
seg.i... |
008f4d397a79f5056992a1d9f1ef93fff57dcdee8978c912f186b0c251f88d42 | Python | 1,022 | 35 | from magicgui.widgets import protocols
class _OrientationMixin:
"""Properties for classes wrapping widgets that support orientation."""
_widget: protocols.SupportsOrientation
@property
def orientation(self) -> str:
"""Orientation of the widget."""
return self._widget._mgui_get_orient... |
0e762d040a40278eb3030a17ad99ca81fd44eb7fa21dbdb0d6b1f2e6f87f87af | Python | 1,022 | 35 | from typing import Protocol
from eir.models.input.sequence.transformer_models import (
MaskedTransformerFeatureExtractor,
TransformerFeatureExtractor,
TransformerWrapperModel,
)
class SequenceModelClassGetterFunction(Protocol):
def __call__(
self,
model_type: str,
force_masked... |
9c9e502bac839aee8a5fa2a8b404c8c98b107ecfbf0ab910adbbaaa47fac216f | Python | 1,022 | 36 | 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... |
bfd28d9dd5108c843c3bcc459629f743787c8d46b89e9d6644e24da89a8227be | Python | 1,022 | 26 | import dask_image.imread
import numpy as np
from skimage import draw
import os
def loading_by_dask(image_path, crop_coords=None):
'''
Load images by dask_image.imread.imread
Args:
image_folder: path to image folder
crop_z_range: crop z range (only for fixed image)
moving_crop_coords... |
f01e1998a9fd292e908682f401a52fdb4520c2fc38d3afaf6db3c9c70c3ec512 | Python | 1,022 | 44 | import os
from collections.abc import Callable
from functools import wraps
from typing import TYPE_CHECKING, Any
from torch.nn.parallel import DistributedDataParallel
from eir.utils.logging import get_logger
if TYPE_CHECKING:
pass
logger = get_logger(name=__name__)
def in_distributed_env() -> bool:
return... |
3d9278637cccfa528b3fdbf6a301a84c97f01d2f04024ad5d03f026bce796d82 | Python | 1,023 | 27 | from brainvoice.data.fmri import load_fMRI_data
from brainvoice.visualization import plot_fMRI_maps
import numpy as np
if __name__ == "__main__":
root_path = '../Data/charly/BrainVoice_11-02-23'
sub_name = 'sub-01'
contrast_name = 'averaged_single_trial_vs_sil'
#rois = ['LA1', 'RA1', 'LmTVA', 'RmTVA'... |
8c63570fac8f602d83ed99fb0ab24bd24ce838c272cf6e1bed0aaf3c614c7e03 | Python | 1,023 | 33 | from typing import Any, Dict, Type
from pymongo import MongoClient
import pprint
from dotenv import load_dotenv
import os
import logging
load_dotenv()
class Container:
FACTORIES: Dict[str, Type[Any]] = dict()
ENTITIES: Dict[str, Any] = dict()
PLUGINS: Dict[str, Type[Any]] = dict()
DAOS: Dict[str, Any... |
c8e48ce7b1f085c00d2a87637e13bb58a1ee01bad2069860aa1554a3f4b07d9b | Python | 1,023 | 35 | #!/usr/bin/env python
"""
Test parameter file IO.
"""
from storm_control.test.hal.standardHalTest import halTest
def test_hal_params_1():
halTest(config_xml = "none_classic_config.xml",
class_name = "ParamTest1",
test_module = "storm_control.test.hal.param_tests")
def test_hal_params_2()... |
1b318ed46effa701b7bc4caab13158eb8463bb6756a072b598607da4c54fe560 | Python | 1,024 | 40 | from __future__ import annotations
from pathlib import Path
import pytest
from pytestqt.qtbot import QtBot
from ..conftest import close_or_pause
from .conftest import MainWinFactory
@pytest.mark.gui
def test_MainWindow_open(
*, main_win: MainWinFactory, qtbot: QtBot, pause: bool
) -> None:
win = main_win()... |
41ee3644c4dd48d1b3c7f3f6feab0e2ca41d2bb24148c565b3debab13848b532 | Python | 1,024 | 35 | #!/usr/bin/python
import sys
import re
import os
from src.hierarchy import Hierarchy
try:
os.mkdir('res')
except OSError:
pass
PYTHON_PATH = './'
GENERAL_HIERARCHY_FNAME = 'data/KO_gene_hierarchy/KO_gene_hierarchy_general.tms'
CHANGES_FNAME = 'data/KO_gene_hierarchy/KO_gene_hierarchy_changes.csv'
MAPPINGS_PA... |
4519b09d7e775a1404b96528bd64cc9fbaf018e1234d8a42a8db081d5588b0f4 | Python | 1,024 | 23 | from brainscore_vision.model_helpers.check_submission import check_models
import functools
import timm
from brainscore_vision.model_helpers.activations.pytorch import PytorchWrapper
from brainscore_vision.model_helpers.activations.pytorch import load_preprocess_images
def get_model(name):
assert name == 'deit3_bas... |
8054121c4989573b990f412f6bcacda386ab41a883c7c50c59771a8fc7f92a03 | Python | 1,024 | 45 | """
Binary classificatios sytem
DB van de Pavert and MB Vessies
"""
import torch
import torch.nn as nn
from ecgxai.systems.base_system import BaseSystem
class ClassificationSystem(BaseSystem):
def __init__(
self,
model: nn.Module,
mode: str,
**kwargs
):
super().__ini... |
5202316fd2ff962c76b789349dd50a4980249fbad4c34d681d873ab3e228c126 | Python | 1,025 | 34 | """Example script for plotting the activity maps from the state/mode means.
In this example we plot the state means from an HMM fit, but this can be
easily substituted with mode means from DyNeMo.
"""
import os
import numpy as np
from osl_dynamics.analysis import power
def get_data(name, output_dir):
if os.path... |
9599fcb969eae7809cedaee0079d3749461588158361f80acc0a134f4c2e7074 | Python | 1,026 | 28 | #!/usr/bin/env python3
"""Run CNN vs rule-based comparison for all artifact types."""
import logging
from artifact_identification.evaluation.cnn_vs_rules import evaluate_model
def main():
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.... |
e1180af356a7c874e501e4c9dcd54fba24eceb61cf642de6b50c35f45ad69429 | Python | 1,026 | 23 | from brainscore_core.supported_data_standards.brainio.assemblies import NeuronRecordingAssembly
from brainscore_vision import load_stimulus_set
from brainscore_vision import stimulus_set_registry, data_registry
from brainscore_core.supported_data_standards.brainio.s3 import load_assembly_from_s3, load_stimulus_set_from... |
10d6e59d8b5657de852a5ecee56438e2c026c6ecf8dc2f490cfe0b8dcebb3af0 | Python | 1,027 | 38 | # 2023-03-01
import pandas as pd
import numpy as np
from scipy.stats import entropy
import sys
from util import get_max_of_dict, get_min_of_dict, read_list, read_expr
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from filter_gene import *
wt_expr = pd.read_csv(sys.argv[1])
pcgs = read_... |
e8d823ff6d644e37e9145a3c074da26c46aa73e66fba784daa2a611ecf2c7914 | Python | 1,027 | 34 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 13 15:58:31 2022
@author: Yoshi
"""
def modulation_color_key_generator():
# Dictionary to store colors
color_key = {}
# Calculates HSV values for negative values
negative_list = [-1.0,-0.8,-0.6,-0.4,-0.2]
for index in negative_list:
... |
063ca7280d2d8959098ac214a1385aca40d8452ce1dabd41503042c7e062a366 | Python | 1,028 | 44 | from cProfile import label
import numpy as np
import pandas as pd
import json
import torch
from pathlib import Path
def str_to_int(s):
if s == 'human':
return 1
else:
return 0
path1 = Path('datasets/Twibot-22')
data = pd.read_csv(path1 / 'label.csv')
label = data['label']
label = torch.tensor(... |
392f9e00eb5286025e2d29f39fe6fa3defe6936ce042489c61186e4a6c646afe | Python | 1,028 | 21 | """"
save key code and log
"""
import shutil
import os
def save_log(save_path):
shutil.copytree("./dataloader/", save_path+"/dataloader/")
shutil.copytree("./models_for_cifar/", save_path+"/models_for_cifar/")
shutil.copytree("./models/", save_path+"/models/")
shutil.copytree("./utils/", save_path+'/uti... |
c5006e3dcaf59f18c9067f1496da27734772485bf532c204e05f7c10050bef10 | Python | 1,028 | 44 | import gzip
import os, sys
import pickle
from tqdm import tqdm
from multiprocessing import Pool
import lmdb
from rdkit import Chem
import torch
split = torch.load("split_dict.pt")
train_index = split["train"]
os.system("rm -f label_3D.lmdb")
env_new = lmdb.open(
"label_3D.lmdb",
subdir=False,
readonly=F... |
d530a8aeec9534b6eb0f7c6e66fe15f7caf018fbe9fc2d71293a82167412e91d | Python | 1,028 | 30 | from typing import List, Union
from torch_geometric.data import Data, HeteroData
from torch_geometric.data.datapipes import functional_transform
from torch_geometric.transforms import BaseTransform
@functional_transform('normalize_features')
class NormalizeFeatures(BaseTransform):
r"""Row-normalizes the attribut... |
8d99b5943c5c29cfff4ece3c44493040a2bcecb141ef1e20cd56a2117390532b | Python | 1,030 | 33 | # This updates usage/methods.rst to the latest API methods
# Developers can run this after modifying any arguments the user can see
from textwrap import dedent
from AFQ.api.utils import methods_descriptors
prologue = """
.. _methods_docs:
The pyAFQ API methods
---------------------
After defining your pyAFQ API obj... |
911c983beea2b48c8a4e61844e7b035383f50eb79e1dd22d1c589d6044173078 | Python | 1,030 | 21 | import pandas as pd
import matplotlib.pyplot as plt
## probably figure one here. I don'y know yet if I want to seperate each state or overlay them. I think seperate.
vermont_raw = pd.read_csv('C:/Users/Alfred/OneDrive - Noorda College of Osteopathic Medicine/Flu-modeling/Vermont/Vermont_Data_Combined.csv')
... |
a0973d3af2dc0dd788dd0fee3762287c5558250e5fc3fb499e176f711b46ca42 | Python | 1,030 | 34 | #!/usr/bin/env python3
import os
from glob import glob
import numpy as np
import pandas as pd
from photonai.base import Hyperpipe
from photonai_neuro import BrainMask
from photonai_neuro.nifti_transformations import ResampleImages
def main():
df = pd.read_csv('/input/input.csv', index_col='subject')
files =... |
2be91dbb2aa4d2bf850971ecfa85dc8509bd7ebd74494594388943b2c37c73f5 | Python | 1,031 | 41 | # -*- coding: utf-8 -*-
import torch
class Embedding(object):
def __init__(self, tokens, vectors, unk=None):
super(Embedding, self).__init__()
self.tokens = tokens
self.vectors = torch.tensor(vectors)
self.pretrained = {w: v for w, v in zip(tokens, vectors)}
self.unk = u... |
621d04e6e5fa2aab153449df0b4f14b6fbc05f9ba13d16c66ca60bb6f171cb07 | Python | 1,031 | 31 | import torch
import torch.nn.functional as F
def la_loss(anchors, positives, negatives, tau: float = 1.5):
"""
La loss as described in Section 3.1.3.
anchors: visual features f_a, [B, D]
positives: positive text features f_p, [B, D]
negatives: negative text features f_n, [B, D]
All assu... |
7d644b41ee20554dc002143e22bb7bef195ed8eaba0def43f7fa842ee575b825 | Python | 1,031 | 37 | import subprocess
import sys
import yaml
script_compare = 'enrichments/compare_nw_ww.py'
pathpath = 'PATHS/filepaths_carlen.yml'
with open(pathpath, 'r') as myfile: pathdict = yaml.safe_load(myfile)
sys.path.append(pathdict['code']['workspace'])
from pfcmap.python import settings as S
setkeys = [key for key in S.run... |
afc0192e66ea938808028af462f46072a08e44a834fa13aaa7b1c23caf8556a1 | Python | 1,031 | 47 | """
FLYNC Assets Module
This module provides access to packaged model files and configurations.
"""
from pathlib import Path
import importlib.resources as pkg_resources
__all__ = ["get_asset_path", "list_assets"]
def get_asset_path(asset_name: str) -> Path:
"""
Get the path to a packaged asset file.
P... |
6c211ce2a87363f7a0df7608fb92d93fd00116d331fa8e2651ec296b51f2bc03 | Python | 1,032 | 45 | # -*- coding: utf-8 -*-
# python standard library inputs
import os
import sys
import subprocess
# local inputs
from ..io.get_filename import get_filename
def inflate_surf_mesh(file_in, file_out, n_iter):
"""Inflate surf mesh.
The scripts takes a generated FreeSurfer surfaces and inflates it.
Param... |
7daca94cd0e2e112216a8b9f6186f3bdfb286489c623ba7d6a134da7adc0e6d8 | Python | 1,032 | 37 | import base64
import io
import json
import numpy as np
from model_handler import ModelHandler
from PIL import Image
def init_context(context):
context.logger.info("Init context... 0%")
model = ModelHandler()
context.user_data.model = model
context.logger.info("Init context...100%")
def handler(con... |
03492e9efa958cc3adfbedf55d3b8e1a42d1eb3d1e00158ee5f4a406d2c27815 | Python | 1,033 | 17 | raw={}
raw["words"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI_TimestampedWords.csd'
raw["phones"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI_TimestampedPhones.csd'
highlevel={}
highlevel["glove_vectors"]='http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/language/CMU_MOSEI... |
7e33528c93bf48bf2d9a5e66154202458e09f4ccb9c06a2831f307a2e04c1755 | Python | 1,033 | 42 | """Example script for plotting Amplitude Envelope Correlation (AEC) functional connectivity 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)
#... |
eb4cc1c83d11b823a61c5df06ec63d5a912454d057934f929341c60a96feff28 | Python | 1,033 | 44 | """
Find which runs used which patterns
"""
import pandas as pd
import glob
import os
ROOT = '/Volumes/sDresBackUp/s1Anfunco/Nifti'
subs = sorted(glob.glob(os.path.join(ROOT, 'sub*')))
for sub in subs:
sub = sub.split('/')[-1]
print(sub)
funcDir = f'{sub}/derivatives/{sub}/func'
runs = sorted(glob... |
ee05dda5283399038d5960f895c7bd6c1f9dabc682d26630d7d9ffe0d400195b | Python | 1,033 | 40 | import torch
class Config:
# 数据参数
M = 10 # 阵元数量
f = 1500 # 信号频率 (Hz)
v = 1500 # 声速 (m/s)
d = v / (2 * f) # 阵元间距
T_values = [50, 80, 100, 200, 500] # 快拍数
snr_range = [-20, 20] # SNR范围 (dB)
# 噪声类型
noise_types = ['white', 'pink', 'red', 'blue', 'violet', 'impulse... |
18e10779ef68b2321c1b1a5edc670c761ed4685f790446ebb9d3122f2d45e04f | Python | 1,034 | 31 | # data_loaders/inference_dataset_loader.py
import torch
from torch.utils.data import Dataset
import pandas as pd
import numpy as np
class class_ls(Dataset):
def __init__(self, x, transform=None):
df = pd.read_pickle(x)
self.X = df['coo_format_data'].tolist()
self.N = df['Precursor'].tolist... |
3155f8f7107e8f31993a9627debcb9c4a69e5a7b50e631f29c83e3321d6f4cd6 | Python | 1,034 | 34 | import pandas as pd
import sys
import numpy as np
def my_groupby(gdf):
real = gdf[gdf.case == "Real"]
null = gdf[gdf.case == "Null"]
return (real.length * real["cov"]).sum() - (null.length * null["cov"]).sum()
bed = sys.argv[1]
df = pd.read_csv(bed, sep="\t", header=None)
df.columns = ["ct", "st", "en",... |
3859e7adb282a1ccf3ef554f0b0c022e3a6aa31849a9f378ab2cc4a0ee0091f2 | Python | 1,034 | 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... |
7ebce6f21eea2b52de5a276803ab20b09542bdeaced319481343bbe1f577bd2c | Python | 1,034 | 36 | import pandas
import numpy as np
from tqdm import tqdm
def read_single_csv(input_path):
import pandas as pd
df_chunk=pd.read_csv(input_path,chunksize=1000)
res_chunk=[]
for chunk in df_chunk:
res_chunk.append(chunk)
res_df=pd.concat(res_chunk)
return res_df
if __name__ == '__main__':
... |
4d963da8b64ae7c342b42b081ac5c649d2e890ac9497b187fd1d1dc765ee5bf7 | Python | 1,035 | 35 | #!/usr/bin/python3
import os
import numpy as np
from nibabel.testing import data_path
import nibabel as nib
import sys
lobar_map_filename = os.path.join(data_path, sys.argv[1])
concentric_map_filename = os.path.join(data_path, sys.argv[2])
lobar_map = nib.load(lobar_map_filename)
concentric_map = nib.load(concentri... |
6ac9d0d93754ca3c92d5b8dc63b71b7eb6ff18f7d4b6144f8def39ea74008174 | Python | 1,035 | 28 | import numpy as np
from nose.tools import eq_
from fancyimpute.knn import KNN
from low_rank_data import XY, XY_incomplete, missing_mask
def test_knn():
# get a baseline error from just zero-filling the missing entries
sad_zero_fill = np.sum(np.abs(XY[missing_mask]))
mad_zero_fill = sad_zero_fill / missi... |
89583aa17fa100d3e14003ea0d57c3c156f011282d5c7601215a992317a6619a | Python | 1,035 | 52 | _dti_ = {
"md": "dti_md",
"rd": "dti_rd",
"ad": "dti_ad",
"fa": "dti_fa",
"fe": "dti_fe",
"trace": "dti_trace",
"odf": "dti_odf",
}
_dki_ = {
"mk": "dki_mk",
"rk": "dki_rk",
"ak": "dki_ak",
"kfa": "dki_kfa",
"mkt": "dki_mkt",
"trace": "dki_trace",
"odf": "dki_odf... |
a9afb8327a4ada4fa6b33e29d82bd3b330eeed2944a4c516a349027410cc660b | Python | 1,035 | 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=['processing.zip'])
tmp_dir = tempfile.T... |
daeb56fd486fc84e3eea6ab9ed2e8b4676f764c13aa48a50972b0ad22de72f7d | Python | 1,035 | 22 | # Copyright (c) OpenMMLab. All rights reserved.
from .ckpt_convert import swin_convert, vit_convert
from .embed import PatchEmbed, PatchEmbedSwin
from .inverted_residual import InvertedResidual, InvertedResidualV3
from .make_divisible import make_divisible
from .res_layer import ResLayer
from .se_layer import SELayer
f... |
f8ca5b29be0ae95a760db4202cb851ae8c893bbd534f89ef3c91f0333ef19c76 | Python | 1,036 | 30 | #!/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'])
fetch_data(get_testing_file... |
154631a9e72f5aba960b79eef930c88f3213459e3b18caf0421811975e860977 | Python | 1,037 | 44 | #!/usr/bin/env python
"""
Given two bed files print the number of bases covered 1) by both, 2) only by
the first, and 3) only by the second.
usage: %prog bed_file_1 bed_file_2
"""
from bx.bitset_builders import binned_bitsets_from_file
from bx.cookbook import doc_optparse
def coverage(bitsets):
total = 0
f... |
6a245800f74fd2f13a4d40584f214c7654aef806eada1b529d42e0e9d0b0fa1c | Python | 1,037 | 33 | import mne
import pandas as pd
import numpy as np
from scipy.stats import t
import matplotlib.pyplot as plt
import pickle
from joblib import Parallel, delayed
subs = [f"sub-{(i+1):02}" for i in range(20)]
idx_ref_vs_out = 3
main_delta = []
for sub in [x for x in subs]:
res = mne.read_evokeds(f"../bids_data/d... |
d8d3a21452944ea870d1e88c15db04411552324a92828cc55e319be880da33f2 | Python | 1,037 | 22 | import torch
from torch import nn
class LocalSpatialAttention(nn.Module):
def __init__(self, in_channels, num_reduced_channels):
super().__init__()
self.conv1x1_1 = nn.Conv2d(in_channels, num_reduced_channels, 1, 1)
self.conv1x1_2 = nn.Conv2d(int(num_reduced_channels*4), 1, 1, 1)
... |
f566b37c9782f8ecb33bcb931df2ad052f78b66d1aadb481f6369dfbc5c7b248 | Python | 1,037 | 31 | #!/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=['tractometry.zip'])
tmp_dir = tempfile.... |
731636cc3e3a7ef0b9dc43adce60f97c719f1b14427b4acf49a3e91f18da8297 | Python | 1,038 | 27 | # This file is part of connectome-manipulator.
#
# SPDX-License-Identifier: Apache-2.0
# Copyright (c) 2024 Blue Brain Project/EPFL
"""Null manipulation module - does not do anything."""
from connectome_manipulator import log
from connectome_manipulator.connectome_manipulation.manipulation import Manipulation
class... |
53f7e565f1df86ec7ad60883464177b97fb46895694442c6d93bbeca95ce2e2f | Python | 1,039 | 39 | from __future__ import annotations
import pytest
from pytestqt.qtbot import QtBot
from labelme._widgets._zoom_widget import ZoomWidget
@pytest.fixture
def widget(*, qtbot: QtBot) -> ZoomWidget:
zoom_widget = ZoomWidget()
qtbot.addWidget(zoom_widget)
return zoom_widget
def test_zoom_widget_defaults_to_... |
866201ae6303ae1b9a2340ee1211dc2e76eb9630647d568936730614dee7b2c9 | Python | 1,039 | 30 | #!/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=['filtering.zip'])
tmp_dir = tempfile.Te... |
b12fa2f345a02c24786c268857b7a027497fef667032098a11dd9fbbb726bff7 | Python | 1,039 | 25 | """genetate scheduler
"""
from torch.optim import lr_scheduler
def get_scheduler(optimizer, cfg):
if cfg['learning_schedule'][
'main_scheduler'] == 'lr_scheduler.CosineAnnealingLR':
main_scheduler = eval(cfg['learning_schedule']['main_scheduler'])(
optimizer,
T_max=cfg[... |
04e5a7c9b4e85b160616378bd4a6d1975e3efa758c0a55405468426bb43de9d6 | Python | 1,040 | 29 | from typing import List, Optional
import torch
from torch import Tensor
import numpy as np
from sklearn.utils.extmath import randomized_svd
def dual_pca(x, y, dim=50, singular=False, backend='sklearn', use_gpu=True):
assert x.shape[1] == y.shape[1]
device = 'cuda' if torch.cuda.is_available() and use_gpu els... |
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