sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1f6645f6ef296e960c35ebb01891f743180bf53ebeccf3f999f33310f3b734fb | Python | 2,014 | 52 | #============================================================
#
# Deep Learning Artifact Filtering
# Data Generation
#
# author: Ahmed Shaheen
# email: ahmed.shaheen@oulu.fi
# github id: AhmedAShaheen
#
#===========================================================
import pickle
import argparse
import numpy as np
f... |
4361e7662cf0b8e858db061052f0ffea8e505db5eb53a530a17aa27242f609c6 | Python | 2,014 | 63 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
0e2605144d716a52a786c3ddc7d564514779df60dd1eb4ab43cf83b1bb1df4a4 | Python | 2,015 | 83 | from numpy import (append,
arange,
array,
cumsum,
empty,
isnan,
nan
)
from numpy.testing import assert_array_equal
from pytest import raises
from wonambi.ioeeg.utils import _select_block... |
16079ac2b0fbb4062bca79332cd1e5a31520b254b478bf6d1a4f9907d6e5bb12 | Python | 2,015 | 62 | import boto.sqs
from boto.sqs.message import Message
import json
import os
import sys
def get_connection():
secrets_subpath = ".cloudvolume/secrets/aws-secret.json"
secret_path = os.path.join(os.path.expanduser('~'), secrets_subpath)
with open(secret_path) as f:
aws_secrets = json.load(f)
return boto.sqs.... |
ad715d34b38b38930200911cf6fd4574d1ba56f659c6714c40e749c5bd57bf7b | Python | 2,015 | 63 | """
Quick python script to generate slicetimes.txt for milan
acquired in descending order (“from head to foot”) and using a simultaneous multi-slice factor (multi-band)=2
So, slices were acquired in this order:
1 & 25
2 & 26
3 & 27
.. and so on till
24 & 48
1
25
2
26
3
27
4
28
From: [https://en.wikibooks.org/wiki/... |
25c2582939bcaef6e91bbd92b81bbb7d938fbda24ff4457db554ce7ce9aac420 | Python | 2,016 | 153 | import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import cv2
def plot(samples, X_dim, channel):
fig = plt.figure(figsize=(4, 4))
gs = gridspec.GridSpec(4, 4)
gs.update(wspace=0.05, hspace=0.05)
dim1 = int(np.sqrt(X_dim))
samples = (samples + 1) / 2
for i... |
9f7ab9f5b34e3489f6822eec3c1abdfc24e781f9436f1049a1b677efdf865019 | Python | 2,016 | 41 | release32 = ["-DCMAKE_BUILD_TYPE=Release"]
debug32 = ["-DCMAKE_BUILD_TYPE=Debug"]
release32nolp = ["-DCMAKE_BUILD_TYPE=Release", "-DUSE_LP=NO"]
debug32nolp = ["-DCMAKE_BUILD_TYPE=Debug", "-DUSE_LP=NO"]
release64 = ["-DCMAKE_BUILD_TYPE=Release", "-DALLOW_64_BIT=True", "-DCMAKE_CXX_FLAGS='-m64'"]
debug64 = ["-DCMAKE_BUIL... |
3b9f4167ea380c06d62944e8ee6d5c88ee9ca8f486c86b9080e3f9a3fe15378a | Python | 2,017 | 35 | """Shared defaults for the Laplacian refinement solve.
A leaf module (no imports) so every caller - CLI, GUI, pipeline, both solvers -
reads the same number instead of repeating a magic literal in nine places.
"""
#: Relative *residual* tolerance for the Laplacian CG/LGMRES solve.
#:
#: This is NOT a bound on... |
59ce05d22a37330198fb2ebc34c7fbce21569bbc3eb42d8ba6c825037a11a4da | Python | 2,017 | 70 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, MaiaEnvironment
from lab.reports import Attribute, arithmetic_mean, geometric_mean
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
fr... |
9a2c078bb58ca47dc5a288aedf26e51473cd238b7e757ad00993b585855e1baa | Python | 2,017 | 48 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... |
bab91f96f900ffcb75338be44800b5cdcd00486613cb9a877f850dae9027dab5 | Python | 2,017 | 53 | """No test may leave files in the source tree.
A test that writes into ``regtools/`` pollutes the working copy, shows up as untracked
noise in every ``git status``, and can leak state into a later run. This one was found by
accident - a stray ``regtools/CANCELLED.txt`` after a full-suite run, because a cancel test... |
30a2fc99739e7f2cae1f6fa5ec2d172dbe06e0ccb87a486251507362067fe792 | Python | 2,018 | 56 | #
# Copyright Bradley Lowekamp
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0.txt
#
# Unless required by applicable law or agreed ... |
691d1357a0ae69dfb4181382a897f44e44bcfb568dc6b2842effb020778becc3 | Python | 2,018 | 54 | """Tests for the report's 'View in napari' section
(regtools.registration.report.analysis_figures._render_napari_launch) and its
wiring into build_html_report."""
import json
import os
from regtools.registration.report import build_html_report
from regtools.registration.report.analysis_figures import _render_... |
8f54762fb2ce2a9a6b1f2542fb58db379c0874e01f97adf8a96545a7302d56b2 | Python | 2,018 | 52 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
a5087addd110e17faa6b099ff9c3df594ddfc346735f68a988821dc37ad91e07 | Python | 2,018 | 48 | from .base_converter import BaseConverter
from ..operators import Conv2_5d
from ..utils import _pair_same
class Conv2_5dConverter(BaseConverter):
"""
Decorator class for converting 2d convolution modules
to corresponding 3d version in any networks.
Args:
model (torch.nn.module): model that... |
e1611280d75783259c02d6961694f0937e4b1ced41dc6b5a22b7192c159b94d2 | Python | 2,018 | 60 | import matplotlib
matplotlib.use("Agg")
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import yaml, io
with open("./config/MNIST_config.yaml", 'r') as stream:
config= yaml.safe_load(stream)
config["default_path"]= str(config["default_path"])
config["cuda"]= str(config["cud... |
f4602661854a72a74371134a1e2e1f747128e4a2ea2de7641095ad174fb0576a | Python | 2,018 | 68 | from collections import namedtuple
import numpy as np
from examples.discrete_tamp.viewer import MAX_COLS, MAX_ROWS
from pddlstream.language.generator import outputs_from_boolean
GRASP = np.array([0, 0])
def is_valid(p):
return np.greater_equal(p, [0, 0]) and np.greater([MAX_COLS, MAX_ROWS], p)
def get_length... |
0de4dc7192b476b0c3e3e3dac57e1054a8a4bd073cce3e5d31d7651d3ed041ac | Python | 2,019 | 81 | # Node-group asset "OKLab to Color" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKIN... |
38a89636ac7165d0b9d0519b73d15b7d951384c836af7d9ceca81872db77cdb0 | Python | 2,019 | 72 | """Base functions for universal machine-learned interatomic potentials."""
from __future__ import annotations
import logging
from functools import lru_cache
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from typing import Literal
from ase.calculators.calculator import Calculator
logger = logging.getLo... |
60d00f025e948f9d7793a83e00c9c40689c523dd151bbe44a0adf724bb1cd4a4 | Python | 2,019 | 74 | import numpy as np
from scipy.fftpack import fft, dct
import matplotlib.pyplot as plt
from scipy.integrate import simps
############
fs = 1e-15
A = 1e-10
e = 1.6e-19
kb = 1.38064852E-23 # m^2 kg s^-2 K^-1
c = 299792458. # m s^-1
hbar = 1.05457180013E-34 # kg m^2 s^-1
#############
def load_lmps_dipole(filename):
... |
69b5f35cde406cae2b817451ad0401310951ef915ff81ae73f42c01547946331 | Python | 2,019 | 62 | import argparse
import nibabel as nib
import matplotlib.pyplot as plt
import numpy as np
from skimage.measure import marching_cubes
from scipy.ndimage import zoom
#SEEG0001img_T1_3D_AX_FSPGR_20121118083857_12.nii.gz
# (x, y, z) = (256, 256, 172)
# header.dims = [3 256 256 172 1 1 1 1]
# header.pixdim = [1. 0.9766 0.97... |
d78647f710537785811d037cb04fd3fa7e3583700bceccc9f6c88174ff490243 | Python | 2,019 | 55 | import numpy as np
from scipy.sparse import csr_matrix
class PoissonBinomial():
def __init__(self, probabilities):
"""Initialize the class and calculate the ``pmf``.
:param probabilities: sequence of success probabilities :math:`p_i \\in
[0, 1] \\forall i \\in [0, N]` for :math:`N` inde... |
35d58b79792c7ef846ac62c6162f725bd6d50d2ebe186017c6bb984ccab93351 | Python | 2,020 | 64 | #! /usr/bin/env python2
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
from lab import tools
from downward.reports.compare import ComparativeReport
import common_setup
from common_setup import IssueConfig, IssueExperiment
DIR = os.path.dirname(os.path.abspat... |
f06dee8e0c191d76a947b6169feb67bddc9c455dfef4090bc87e8afefd1fe385 | Python | 2,020 | 54 | import sys, math
sys.path.append("../../../../../bin/")
from pybioshell.core.data.basic import Vec3
from pybioshell.core.data.io import Pdb
from pybioshell.core.data.structural.selectors import SelectResidueByName
from pybioshell.core.protocols import copy_selected_atoms, copy_selected_coordinates
from pybioshell.cor... |
6f4069518e86662bd2db77e767ddf223f13b130151b994c735722948ec0d7313 | Python | 2,021 | 48 | """CLI script to add missing HED IDs to a prerelease schema in the hed-schemas repository.
Maintainers run this once, at release time: ``hed_add_ids <hed-schemas> <standard|library> <version>``.
It reads the prerelease TSV set, assigns the lowest free id in the schema's ``library_data.json`` range
to every element wit... |
f8f1b55717be4f3a9e0521ccabf21245a533e86d0385ecd2f9a84faaf76196cc | Python | 2,021 | 60 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
8a9fac77888f1cf5f0b5896d05ee3ea1cb48bb2aaf94e78b4b1ca8234b82b248 | Python | 2,022 | 52 |
import datetime
def isbetween(time: datetime.datetime, time_range: tuple):
r"""
Function for finding if a certain datetime object exists in a specified datetime range.
The boundaries of the time range are also included.
Args:
time: a datetime object
time_range: a tuple depicting a tim... |
aab846ed15f17416ff9b580da7d109ed5c6d76bb1c857000f76e14638a22ae6e | Python | 2,023 | 57 | ### configs for next hyperparam trial
import optuna
from datetime import datetime
# def train_fn(trial):
# t=trial.suggest_uniform("t", .6, 1.5)
# r=trial.suggest_uniform("r", t, t*2.4)
# params = {
# # 'lr_hyperbolic': trial.suggest_loguniform('lr_hyperbolic', .01, .1),
# 'lr': trial.suggest_log... |
2499a91b1e1259c3e0a3b5bdac2c678a10b7871e88c575cf5b24df37b717d424 | Python | 2,026 | 61 | import hashlib
import tarfile
import urllib.request
from pathlib import Path
from fidnet import config
def download_example_data(force: bool = False):
example_files = [
config.example_file_non_deuterated,
config.example_file_hnca,
config.example_file_nus_reconstruct,
config.exampl... |
af7336a1149a4c37db94c4d4fa63eb90e3a3fa6fd35c790194ad0a4419722b54 | Python | 2,026 | 70 | from random import random
from .utils import irange, argmin, RRT_ITERATIONS
class TreeNode(object):
def __init__(self, config, parent=None):
self.config = config
self.parent = parent
#def retrace(self):
# if self.parent is None:
# return [self]
# return self.parent.... |
6549f5cd561602e5ae55cd8a0862e2fd08cd925bb0921d2258113915d2e8967e | Python | 2,027 | 47 | import random
from PIL import ImageDraw
from typing import Optional
from .shape import Shape, calculate_short_side
class Triangle(Shape):
def __init__(self, x: int, y: int, width: int, height: int,
tip_offset: Optional[float] = None, flip: Optional[bool] = None):
super().__init__(x, y, wi... |
695362c19cb95df7dcf020597971245bd8831e488218af1971f2ed5d497129d6 | Python | 2,028 | 81 | """"""
import logging
from concurrent.futures import Future
logger = logging.getLogger(__name__)
class Packet:
""" A data packet with a timestamp and content. """
def __init__(
self,
stream_name,
device_uid,
timestamp,
source_timestamp=None,
source_timebase="m... |
b1aac20813dda7d7dc96869a5a4ed101f143f73a8c2ebca4b2bf7f1c944218d6 | Python | 2,028 | 54 | """Tests for cellpose_sam.launch_helpers - pure-Python helpers, no Qt required."""
import pytest
from regtools.cellpose_sam.launch_helpers import (
_BROWSE_MODEL_SENTINEL,
_CUSTOM_MODEL_MIN_BYTES,
_CUSTOM_MODEL_EXTS,
discover_custom_cellpose_models,
)
class TestConstants:
def test_sen... |
265a68ddcf957d7c413e5d8212a915f89dabdaa7aa218b9d575feaa2fd0a09f8 | Python | 2,030 | 80 | # Node-group asset "Color to OKLab" (GeometryNodeTree), dumped by nodebpy.assets.dump_library.
# Rebuild the library with nodebpy.assets.build_library (nodebpy build).
# _build_group() is the source of truth: the docstring, __init__ and accessors are regenerated from it on the next dump.
from typing import TYPE_CHECKIN... |
80260d62d65d82613911b014260f28cc3ad859d7b3c4046c2e0382054acecc11 | Python | 2,030 | 63 | import torch
class DecodeStrategy(object):
def __init__(self, pad, bos, eos, batch_size, parallel_paths, min_length, max_length,
return_attention=False, return_hidden=False):
self.pad = pad
self.bos = bos
self.eos = eos
self.batch_size = batch_size
self.pa... |
0d57eafc949a058450cdba96e42d2eb047873bfe5262a575f904b998df4195c0 | Python | 2,031 | 41 | import argparse
import glob
import re
import h5py
import os
from plantsegtools.utils.edit import parse_crop
def main():
parser = argparse.ArgumentParser(description='Copy a dataset from one H5 to another. Files with identical time point in names will be matched.')
parser.add_argument('--source_dir', type=str,... |
136269ef49a25699d90eaea4a706ba29b1485e98697c9ef6037730b134bceadf | Python | 2,031 | 77 | import json
try:
import simplejson as json
except ImportError:
import json
import requests
import os.path
def autodetect_proxy():
proxies = {}
proxy_https = os.getenv('HTTPS_PROXY', os.getenv('https_proxy', None))
proxy_http = os.getenv('HTTP_PROXY', os.getenv('http_proxy', None))
if proxy_h... |
5bbffd714e6df23a8ac96bd42e376ff2fc32f6402a619c349f3a7a7b4c10b39e | Python | 2,031 | 62 | #!/usr/bin/env python
from __future__ import division, print_function
import argparse
import topaz.cuda
from topaz.model.factory import load_model
from topaz.model.utils import segment_images
from topaz.torch import set_num_threads
name = 'segment'
help = 'segment images using a trained region classifier'
def add... |
b1e9a138651bcabd6fbfe3d3c23c6086f5f2f512e5a2d3c5dedbe78f13e24345 | Python | 2,031 | 63 | from typing import cast
from elasticai.creator.file_generation.in_memory_path import InMemoryFile, InMemoryPath
from elasticai.creator.nn.fixed_point.hard_tanh.layer import HardTanh
def test_vhdl_code_matches_expected_hardtanh() -> None:
expected = """-- This is the hard_tanh implementation for fixed point data
... |
56d095ab2e313f7f9790b0751b96938eae2ac876a3e570c8fd198e251f177f51 | Python | 2,032 | 58 | """Python function to smooth surface images
Modified from BrainStat toolbox https://github.com/MICA-MNI/BrainStat/blob/master/brainstat/mesh/data.py
-removed third dimension
-removed printing of messages (messes up the R console)
-remove edges in the medial wall
-converted mesh units to mm
-replace s... |
6eb8e6795548ca74be68e3a64c2b782c20f6fb5483e08eed97a3af6af592cf7b | Python | 2,032 | 68 | # Meep Tutorial: Hz-polarized transmission and reflection through a cavity
# formed by a periodic sequence of holes in a dielectric waveguide,
# with a defect formed by a larger spacing between one pair of holes.
# This structure is based on one analyzed in:
# S. Fan, J. N. Winn, A. Devenyi, J. C. Chen, R. D. Meade... |
b83fb2e561bbbf026bff6002c9489cd1d8232db8d4ccd8fc7ae5d6061f47d47c | Python | 2,032 | 56 | from setuptools import setup, find_packages
setup(
name="scpair",
version="0.1.0",
description="scPair: boosting single cell multimodal analysis by leveraging implicit feature selection and single cell atlases",
long_description=open("README.md").read(),
long_description_content_type="text/markdown... |
d3ac631782f642fd09eb9f4a69a04784ebf5b0498735477ba7a30385f3d5df20 | Python | 2,032 | 80 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
0e71b8e6c2a74c02fc511b8926ba58816cd397f60fafe21ee8300b06a2fdddb8 | Python | 2,033 | 67 | """Final registration with Laplace warping - port of final_registration.m."""
from __future__ import annotations
from pathlib import Path
from typing import Tuple
import numpy as np
from imageio.v2 import imwrite
from .bb_alignment import _preprocess
from .correspondence import final_point_correspondence... |
8c3df58f6c198cada6fad14441cbd7aab0ee953b613f5dd4242a4e7dcb8d71f6 | Python | 2,033 | 71 | from ase.io import read
import numpy as np
import torch
from torch.utils.data import DataLoader
from ..data import get_dataset
from ..data.utils import atoms_collate_fn
from tqdm import tqdm
import os
def eval(model, data_loader, properties, device):
output = {prop: [] for prop in properties}
target = {prop: ... |
2fb61d80f3390403755751cb64ffb3aff1d18dbf2fcd2053344004248ee9148f | Python | 2,034 | 71 | import numpy as np
from nilearn.image import smooth_img
from nilearn.masking import apply_mask, unmask
from os.path import join as pjoin
from argparse import ArgumentParser
from dimensions_encoding.sparseness import hoyer_sparseness
def load_data(betasdir, sub):
ndims = 66
betafiles = [
pjoin(betasdir... |
cbe8757bf1afeb7d79483c5636d80eccd6b303d2890f653e590d46f9d9ed0a85 | Python | 2,035 | 82 | """Define some args."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
def str2bool(s):
"""Convert str to bool."""
return s.lower() not in ['false', 'f', '0', 'none', 'no', 'n']
parser = argparse.ArgumentParser()
parser.add_argum... |
d96436a7f9f1b0a59eb1eb6775af4a989119721549d9c54ce083d86de365bf9c | Python | 2,035 | 76 | import os
import yaml
def get_model_size(model):
param_size = 0
for param in model.parameters():
param_size += param.nelement() * param.element_size()
buffer_size = 0
for buffer in model.buffers():
buffer_size += buffer.nelement() * buffer.element_size()
size_all_mb = (param_size +... |
4281795786b385c9fcde06f1d1ba9572dbaca58d3aa3e901a5af82b498b86b63 | Python | 2,036 | 61 | #!/usr/bin/env python3
"""Inspect SEA-AD AnnData object without running biological analysis."""
from pathlib import Path
import pandas as pd
import scanpy as sc
ROOT = Path(__file__).resolve().parents[1]
RAW = ROOT / "data" / "raw"
TABLES = ROOT / "results" / "tables"
ADATA_PATH = RAW / "SEAAD_Immune_multiregion_f... |
6c0394e79510a67b200a2a2eae55343ddb4fcb4f9a8ca6fe7f5ded9e8891e729 | Python | 2,036 | 59 | from collections.abc import Callable, Iterator, Sequence
from itertools import chain
from elasticai.creator import ir
from elasticai.creator.ir2vhdl import DataGraph, type_handler
from .combinatorial import wrap_in_architecture
from .language import Port, VHDLEntity
from .vhdl_nodes.node_factory import InstanceFactor... |
0a2c3f6e5062c1333aa85f85eb428930204fac14ec704a624f425e94c68ced64 | Python | 2,037 | 56 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
336652496acbff7529c4e4133b922fd2cb21137c9f58318c4ce72c67f03972b6 | Python | 2,038 | 58 | #
# MIT License
#
# Copyright (c) 2022 GT4SD team
#
# 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,... |
3f257d7001064201ef807e57b310e842b78f67ab2456f727960ba0a6710a6379 | Python | 2,038 | 67 | import ctypes
import ctypes.util
import numpy as np
from numpy.ctypeslib import ndpointer
import os
import sys
import sysconfig
def FwdBwdAlg_cpp(initPi, transPi, SoftEv, order='C'):
if not hasEigenLibReady:
raise ValueError("Cannot find library %s. Please recompile."
% (libfilena... |
6b028eb3caafe09ccef8da65a7ec0c4835131f9bea0342b95595328d20647ec9 | Python | 2,038 | 61 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
a160246bf1c62a4f8d7041397fce376743913f9722babf0584d5876b103dec1b | Python | 2,038 | 67 | import torch
import torch.nn as nn
from torch import Generator
from typing import Any
from abc import ABCMeta, abstractmethod
class FinalLayersBlock(nn.Module, metaclass=ABCMeta):
"""
Network final layers performing final prediction and (optionally) loss calculation
"""
@abstractmethod
def ... |
34e45ac3a31816241cfb95efc3157866505b8380febba2e0c4b266b9f46cacad | Python | 2,040 | 68 | import elasticai.creator_plugins.lutron_filter as lf
import pytest
from elasticai.creator_plugins.lutron_filter import remove_redundant_layers
from elasticai.creator.ir import attribute
@pytest.fixture
def ir_repr(request):
layer_type = request.param
return lf.build_sequential_ir(
dict(
m... |
8f4e63bd34f97721e4b678ce5f5b7a17197e2e7a5ce1b734df5079e1e2111cf0 | Python | 2,041 | 72 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
**hybridLFPy**
==============
Provides methods for estimating extracellular potentials of simplified spiking
neuron network models.
How to use the documentation
----------------------------
Documentation is available in two forms:
1. Docstrings provided with th... |
21046b21fdc3b80b55c3b7b9cf74ee8e3be843374581c041418b5dd0d2fb04ff | Python | 2,042 | 56 |
import torch.nn as nn
from monai.losses import DiceLoss, GeneralizedDiceLoss, FocalLoss
# inspired by the NVIDIA nnU-Net GitHub repository available at:
# https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Segmentation/nnUNet
# loss computing makes use of the MONAI toolkit available at:
# https://g... |
53588cfc85b03bf278e389ac6f814d7b34c0a4d9e93671a331258f46062f9241 | Python | 2,042 | 63 | #! /usr/bin/env python
import os
import shutil
import subprocess
import sys
DIR = os.path.dirname(os.path.abspath(__file__))
REPO = os.path.dirname(os.path.dirname(DIR))
DOWNWARD_FILES = os.path.join(REPO, "src", "search", "DownwardFiles.cmake")
TEST_BUILD_CONFIGS = os.path.join(REPO, "test_build_configs.py")
BUILD =... |
6633df3bb8f20cd6436a1f4258c66bfa50dcdcd76ade0b9df79c9726c85b8c2b | Python | 2,042 | 64 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
db209147cdee3c0456df21d8904f4ee118ffdf553a5e5b62021a67f9d8bca876 | Python | 2,042 | 61 | #!/usr/bin/env python
# Purpose: Find remaining volumes (post-censoring) for each session and for each participant overall.
#
# Input: Single-column motion outliers txt file for each bold image. These were generated using the CBIG2016 preproc pipeline.
# Output: Total remaining volumes for each participant's session an... |
ff334a966f968630b21ebb4c7dc48ce362cf1bbdfc8a7d7ec9827848b1cf3405 | Python | 2,042 | 66 | import databpy as db
import numpy as np
from .utils import GeometrySet
def test_get_set(fetch, snapshot):
code = "1BNA"
mol = fetch(code).add_style("ribbon")
geom = GeometrySet(mol.object)
assert snapshot == geom.summary()
def test_spheres_style(fetch, snapshot):
code = "1BNA"
# the point cl... |
66ab461696953634787c62c9d24dcc1361b72e49a709d211bdcf35168a297bc4 | Python | 2,043 | 68 | #!/usr/bin/env python
# Copyright (c) 2019-2020 Intel Corporation
#
# This work is licensed under the terms of the MIT license.
# For a copy, see <https://opensource.org/licenses/MIT>.
"""
Simple freeride scenario. No action, no triggers. Ego vehicle can simply cruise around.
"""
import py_trees
from srunner.scenar... |
7ed2c233394455e7e711faee6a53435286d8ba179a278b00342f8d8fef8d5305 | Python | 2,043 | 72 | import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
def plot_prop(prop):
if "per_" in prop:
x = np.load("target_{}.npy".format(prop[4:]))
y = np.load("output_{}.npy".format(prop[4:]))
n = np.load("n_atoms.npy")
while len(n.shape) < len(x.shape):
... |
a96bddc494efbe91d7cd8be28eb5c0b25bd2bea856d204eb59cab39b17e3280a | Python | 2,043 | 61 | import pandas as pd
import itk
import SimpleITK as sitk
import os
import sys
import json
import argparse
import logging
import numpy as np
def main():
print("ExtractGenes.py has started")
logging.basicConfig(
format='%(asctime)s %(name)s %(levelname)-8s %(message)s',
level=logging.DEBUG,
... |
ee58a4baa4b45da737b4c4aeae82db7c729864e353110f822e8760fc2e795b01 | Python | 2,043 | 56 | from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
from constants import REGRESSION_LABELS, DATA_DIR, TF_ROTATION, TF_PERSPECTIVE, TF_CONTRAST, TF_BRIGHTNESS
from src.dataloaders.semantic_transforms_dataloader import get_dataloader
from src.dataloaders.semantic_transforms_dataset imp... |
f72aea0e551379fd010cd9c62e47fc61ad1d8c06584122a948fb9ee89db136bd | Python | 2,043 | 61 | #!/usr/bin/env python
# Purpose: Find remaining volumes (post-censoring) for each session and for each participant overall.
#
# Input: Single-column motion outliers txt file for each bold image. These were generated using the CBIG2016 preproc pipeline.
# Output: Total remaining volumes for each participant's session an... |
3b1f44fd8a2a5a1a7785edff860af5367e7b66bd5a9a8b34f07359f93f0319f2 | Python | 2,044 | 56 | from typing import Any
from ._attribute import AttributeMapping, attribute
from .datagraph import DataGraph, Edge, Node
from .factories import IrFactory
class IrDeserializer[N: Node, E: Edge, G: DataGraph]:
def __init__(self, factory: IrFactory[N, E, G]) -> None:
self._factory = factory
def _node(se... |
47930f4fb499589207abd6246195baa71d5cbb879e3bbbf96bea9fe8da774929 | Python | 2,044 | 60 | import matplotlib.pyplot as plt
import numpy as np
# Function for visualization of orthogonal views
def show_orthoslices(image,
cross = None,
figsize = (18,6),
cmap = "gray",
vmin = None,
vmax = None,
... |
dec7bfa9d969f395b46ba2fa192f48f729ec2ae7bd7e5645c38078e5bb2b06f3 | Python | 2,044 | 48 | """Testing for the python API of the C++ flagser `.flag` file I/O."""
import pytest
import os
import numpy as np
from numpy.testing import assert_almost_equal
from pyflagser import load_unweighted_flag, load_weighted_flag, \
save_weighted_flag, save_unweighted_flag
from pyflagser._utils import _extract_unweighte... |
fcef5a85a3e58f328e8d89b225639d283fe19f51b5a29bd00bfaa93d0e0eb5d2 | Python | 2,044 | 47 | import os
import sys
import time
datadir = sys.argv[1] # should point to the directory where the fast5 & reds dir exist
sigmap_exe = sys.argv[2]
model_name = 'sigmap'
# Python Script Paths
exedir = "../run_scripts/" # the path to HierarchyCall's test/run_scripts dir
sigmap_script_path = "run_sigmap.py "
extract_re... |
151169d030edc77c97aefb2ebef2c6c79b883c39ff583b4ff53580b14e378b45 | Python | 2,045 | 76 | import sys, os
sys.path.append(os.path.abspath(".."))
sys.path.append(os.path.abspath("."))
import torch
import torch.nn as nn
import torch.optim as optim
from prosper_nn.models.hcnn_compressed import HCNN_compressed
import prosper_nn.utils.generate_time_series_data as gtsd
import prosper_nn.utils.create_input_ecnn_... |
5f61971d1357ead995c8ede2557823ed52d3c647f1b2fd277964cdc11ada3094 | Python | 2,045 | 75 | import os
import pandas as pd
from calvados.cfg import Config, Job, Components
import subprocess
import numpy as np
from argparse import ArgumentParser
parser = ArgumentParser()
parser.add_argument('--name_1',nargs='?',required=True,type=str)
parser.add_argument('--name_2',nargs='?',required=True,type=str)
args = pars... |
aa5bd44310b4136f4106de2cedee0fb4e3f6319658890499678de8c628330506 | Python | 2,045 | 75 | import re
import torch.nn as nn
from pretrainedmodels.models.xception import pretrained_settings
from pretrainedmodels.models.xception import Xception
from ._base import EncoderMixin
class XceptionEncoder(Xception, EncoderMixin):
def __init__(self, out_channels, *args, depth=5, **kwargs):
super().__init... |
2ba08a73e60a769c940c4f0d118dbc25be71ec18f6be16f4b1e5960642e62997 | Python | 2,046 | 40 | # This script uses output from mask_fuser.py (overll produced artifact mask) to create artifact free mask, later used by nonartifact_tiler.py to produce artifact-free regions.
import numpy as np
import os
from PIL import Image
import time
from matplotlib import pyplot as plt
def mask_deducer(listofmasks):
... |
79087d476795946c1fc68349791487955e1170cb983e0e832d191b94ede6b9db | Python | 2,046 | 52 | #!/bin/env python3
"""
Draw coloured grid lines using matplotlib
"""
import numpy as np
import matplotlib.pyplot as plt
import ithildin as ith
from typing import List
# load any simulation and calculate a phase
sd = ith.SimData.from_stem("results/smooka_5")
apd = sd.log.sources[1]['S2 stimulus delivered at t'] - sd.l... |
a16763bdebd271d28a68c807bbc9430b0739886b1b8e85f1e5c5a5dc632c29ae | Python | 2,046 | 60 | import os
import os.path as op
from nimare.meta.ale import ALE
from nimare.dataset import Dataset
from nimare.correct import FWECorrector
import sys
sys.path.append('/home/miriedel/niconn')
import pickle
def macm(prefix=None, mask=None, output_dir=None, ns_data_dir=None):
if mask is None or not op.isfile(mask):
... |
bd79e684658375f285132281cdd059087111771a1599dab095392868d6d8929a | Python | 2,046 | 69 | from typing import Callable, List
from ..base_transform import BaseTransform
class Compose(BaseTransform):
r'''
Compose several transforms together. Consistent with :obj:`torchvision.transforms.Compose`'s behavior.
.. code-block:: python
from torcheeg import transforms
t = transforms.C... |
860b4e4a650ad4686e849497dce48ffa25bdd70be240b59715a7bfdb2633a3f4 | Python | 2,047 | 68 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
8e7140f3c636b09889d99df65b9b7914e68b965773425b79c243ae265a37ab6a | Python | 2,047 | 63 | #! /usr/bin/env python
# -*- coding: utf-8 -*-
import os
from lab.environments import LocalEnvironment, BaselSlurmEnvironment
import common_setup
from common_setup import IssueConfig, IssueExperiment
from relativescatter import RelativeScatterPlotReport
DIR = os.path.dirname(os.path.abspath(__file__))
BENCHMARKS_DI... |
f158e224eeb5d85b5d5e18065997096ea9d3d148ecccc2c28aa5d6858a089ea7 | Python | 2,047 | 72 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Docs here"""
__author__ = "Mitchell R. Vollger"
__credits__ = ["Mitchell R. Vollger"]
__maintainer__ = "Mitchell R. Vollger"
__email__ = "mrvollger@gmail.com"
__status__ = "Development"
import logging
import argparse
import pysam
import sys
from tqdm... |
712dbf669ff854d601e7983024e5ed9d40b01e90497a82ae03996b70ece9db99 | Python | 2,048 | 65 | #!/bin/bash
# Setup of NanoDiP venv with Python 3.7.x
# J. Hench, IfP Basel, 2021-2023
# for use with Ubuntu 20.04 x86_64
# jupyter notebook and minknow_api won't work togehter in python 3.8.8
# Ubuntu 18.04 / jetpack requirements:
# sudo apt install python3.7 python3.7-dev python3.7-venv python3-pip
venvname=nanodipV... |
7c07cbe0e6a9277604071da5131dd5b26b4d4cc61582147e41c61ba9b5ea7b5d | Python | 2,048 | 70 | import json
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from pathlib import Path
from ..prixfixe.predictor import Predictor
from .utils import n2id, revcomp
class AutosomePredictor(Predictor):
def __init__(self,
model: nn.Module,
model... |
8b55ddaf28178c4f7fb7218e00226cb04863ae44927154693aae6b36f56039e3 | Python | 2,048 | 66 | import albumentations as A
import numpy as np
from albumentations.pytorch import ToTensorV2
from wsilearn.utils.cool_utils import is_ndarray, is_dict
imagenet_mean = (0.485, 0.456, 0.406)
imagenet_std = (0.229, 0.224, 0.225)
def create_norm_transform():
return A.Normalize(mean=imagenet_mean, std=imagenet_std)
de... |
0cda9f23bd49e3dd601201ddd4d142d2bf9b6452a59ff7604151d5f01a01dd64 | Python | 2,049 | 76 | import time
import os
import sys
class LIDCEnv():
data = './data' # The lidc data location
video_resnet18_pretrain_path = '.../resnet-18-kinetics.pth'
mednet_resnet18_pretrain_path = '.../MedicalNet_pytorch_files/pretrain/resnet_18_23dataset.pth'
class LIDCSegConfig():
batch_size = 8
n_epochs = ... |
d04d57310b00b3d86cfffb41cfdf6c5c2ccbdc5971e6fc9de2a58535c7c92a2e | Python | 2,049 | 61 | #!/usr/bin/env python
# Purpose: Find remaining volumes (post-censoring) for each session and for each participant overall.
#
# Input: Single-column motion outliers txt file for each bold image. These were generated using the CBIG2016 preproc pipeline.
# Output: Total remaining volumes for each participant's session an... |
1e996b5aa0e20af4740b0a94c52df393385b29f78f4f0c08279248ce794dc2e4 | Python | 2,050 | 50 | import pandas as pd
import numpy as np
import os
celltype_list = ['EX', 'IN', 'ASC', 'ODC', 'OPC', 'MG']
# Get DA-cCRE Set (Human)
for temp_celltype in celltype_list:
Human_df = pd.read_csv('Res/'+temp_celltype+'_Human.txt', sep='\t')
Human_df = Human_df.loc[Human_df['padj']<0.05,]
Human_df = Human_df.loc... |
ccf210bd8740057af374b1f4141b4f941223c78f3efa30d33163a1f6ba9a7c15 | Python | 2,050 | 69 | # set QT_API environment variable
import os
os.environ["QT_API"] = "pyqt5"
import qtpy
# qt libraries
from qtpy.QtCore import *
from qtpy.QtWidgets import *
from qtpy.QtGui import *
import control.utils as utils
from control._def import *
import time
import numpy as np
import cv2
class DisplacementMeasurementContr... |
9abfa704e52b3f4dc2c4a8d6e1525a9fb702150eaf8f5eb6b5d2a2566cf27d2a | Python | 2,051 | 52 | from .functional import acs_conv_f
from .base_acsconv import _ACSConv
class ACSConv(_ACSConv):
"""
Vallina ACS Convolution
Args:
acs_kernel_split: optional, equally spit if not specified.
Other arguments are the same as torch.nn.Conv3d.
Examples:
>>> import ACSConv
... |
cf36f1710afe96b7434bf9b09d4baf0514b462e9c7db783059b6813075c21258 | Python | 2,052 | 85 | import os
import flask
import flask_login
app = flask.Flask(__name__, static_url_path="/", static_folder="_build/html/")
app.secret_key = "super secret string" # Change this!
login_manager = flask_login.LoginManager()
login_manager.init_app(app)
users = {"gt4sd-team": {"password": os.environ["GT4SD_DOCS_PASSWORD"]... |
1510c0cc006c0ae5a213593bc15dd0c40405cb425fab63ed97c1ad3b8b42d225 | Python | 2,053 | 84 | from __future__ import annotations
from importlib.util import find_spec
from pathlib import Path
from typing import TYPE_CHECKING
from monty.dev import requires
from quacc.runners.ase import BaseRunner
has_phonopy = bool(find_spec("phonopy"))
has_seekpath = bool(find_spec("seekpath"))
if TYPE_CHECKING:
from n... |
1a6a98e95ef674fcac3eefd5a301ad751d99a515a1e15db1b59afef78e9e8ca8 | Python | 2,053 | 60 | # regtools/tests/test_ccf_canvas_lock.py
import os
os.environ.setdefault("QT_QPA_PLATFORM", "offscreen")
import numpy as np
import pytest
pytest.importorskip("PySide6")
from PySide6.QtWidgets import QApplication
_app = QApplication.instance() or QApplication([])
def _two_region_volumes(tmp_path, shape... |
2eea27df58b237418bb4d81042dbf49ad47a68a4c6b7f3d8141c32cfc56d828d | Python | 2,053 | 36 | from setuptools import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
import numpy
ext_modules = [Extension("traj_dist.cydist.basic_geographical", ["traj_dist/cydist/basic_geographical.pyx"]),
Extension("traj_dist.cydist.basic_euclidean", ["traj_dist/cydist/basic_... |
749cee22543aeb0af0ffecf72a414a484e8a7d942f6adfa8025e9068569dea65 | Python | 2,053 | 53 | # coding=utf-8
# Copyright 2026 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
62543b3cc524f7361e10e7947fc6681d4d46d8e447ed64a0befd1c3ac023538e | Python | 2,055 | 66 | """Testing CLI command measure."""
import subprocess
import os
def test_measure():
"""Tests for morphometric mesurements."""
os.chdir(os.path.dirname(__file__) + '/data')
proc = subprocess.Popen(['swc', 'measure', 'pass_simple_branch.swc'],
stdout=subprocess.PIPE,
... |
6613a0adfa068d4ed18e53a4ffb4a5c09e541a09ae4e1a76f11bbe31d79074d7 | Python | 2,057 | 79 | """Summarizer for phonopy."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING, cast
from monty.dev import requires
from quacc import __version__
from quacc.schemas.atoms import atoms_to_metadata
from quacc.utils.dicts import clean_dict
from quacc.utils.files ... |
a5f0dda6685c933c2a49cfb47c1aa7f1a4b941a936984b52338b04898602f8ff | Python | 2,057 | 57 | from elasticai.creator.file_generation.savable import Path
from elasticai.creator.file_generation.template import (
InProjectTemplate,
module_to_package,
)
from elasticai.creator.vhdl.auto_wire_protocols.port_definitions import create_port
from elasticai.creator.vhdl.design.design import Design, Port
from elast... |
d264ba84d9596e77d1662e0d9b727b0fb97ce5aeab50af79252f6599cf5243f5 | Python | 2,057 | 79 | #!/usr/bin/env python3
import os
import pandas as pd
import numpy as np
from sklearn.metrics import pairwise_distances
# ------------------------------------------------------------------
# paths
# ------------------------------------------------------------------
projpath = '/project/3011157.03/Simon/proj_2022_CABB_... |
e7940ba2d4c2c649d07c6397511f4a470f1df9d6600820417e2a3212bd0ad8fc | Python | 2,058 | 78 | """Common defect workflows."""
from __future__ import annotations
from importlib.util import find_spec
from typing import TYPE_CHECKING
from monty.dev import requires
from quacc import subflow
from quacc.atoms.defects import make_defects_from_bulk
has_pmg_defects = bool(find_spec("pymatgen.analysis.defects"))
has_... |
0ccee3397a3e91e3fa0984ed8c03bc712d352b4ebbe64c47de24c219caa319de | Python | 2,059 | 59 | import torch
import torch.nn as nn
def get_module_names_dict(model):
"""Recursive walk to get names including path."""
names = {}
def _get_names(module, parent_name=""):
for key, m in module.named_children():
cls_name = str(m.__class__).split(".")[-1].split("'")[0]
num_name... |
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