sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c456980d7a44104a86fa1dddf6953295af6711753d05a8c52ba2ee3bbe9a7541 | Python | 6,642 | 147 | # assumes run from parent directory
from plumbum import local, FG
import os
import yaml
import numpy as np
def main():
# Harnet ablations
model_types = ['CRNN', 'harnet', 'harnet_unfrozen', 'harnet_random', 'RNN', 'rf']
low_data = [False, True]
dataset_names = ['baglione_crows',
... |
dd3b7a6bccdb3f4254bd82f2a043946474f94194ec69418e91e008bee952fed8 | Python | 6,642 | 145 | #!/usr/bin/env python
import sys
import argparse
import os
import csv
import re
from datetime import datetime
from IlluminaBeadArrayFiles import GenotypeCalls, BeadPoolManifest, code2genotype, NormalizationTransform
delim = "\t"
#get commandline parameters
parser = argparse.ArgumentParser("Generate a final report fr... |
067db26c70e624dfadf67868208af3204e4a1ac45330c56e7162f3166a0a3bf9 | Python | 6,643 | 188 | import json
from dreamcoder.program import Primitive
import os
import numpy as np
import re
import arckit
def load_task(task_id):
return arckit.load_single(task_id)#.dreamcoder_format()
# def load_task(task_id, task_path='data/ARC/data/training/'):
# filename = task_path + task_id + '.json'
# with open(... |
b05f4cc4e22417dbb68e8a6cec92f984cf1bd0403be5647f028c9a34cabe1f51 | Python | 6,643 | 147 | #!/usr/bin/env python
import sys
import argparse
import os
import csv
import re
from datetime import datetime
from IlluminaBeadArrayFiles import GenotypeCalls, BeadPoolManifest, code2genotype, NormalizationTransform
delim = "\t"
#get commandline parameters
parser = argparse.ArgumentParser("Generate a final report fr... |
24a604d936f9fbe7b749f6c31d3de7e8743411f4a055999ebd329c74bb62ebea | Python | 6,644 | 211 | import numpy as np
import pytest
from openmm import openmm, unit
from calvados import interactions
def _particle_parameter_names(force: openmm.CustomNonbondedForce) -> list[str]:
return [
force.getPerParticleParameterName(i)
for i in range(force.getNumPerParticleParameters())
]
def _bond_pa... |
446372254f6bd008e859321b0fec48de6b32732863c3fd8b537992c56a70b0a3 | Python | 6,645 | 155 | import os
import re
from copy import copy
from typing import Tuple, Dict
import pandas as pd
from torcheeg.datasets.module.base_dataset import BaseDataset
class Subcategory:
r'''
A tool class for separating out subsets of specified categories, often used to extract data for a certain type of paradigm, or for... |
d0fb5c2b5504c4474e95cc98edc2e71b0ec065af851ac102beb8bcdb894d28ee | Python | 6,645 | 231 | from __future__ import annotations
from dataclasses import dataclass
import numpy as np
import pandas as pd
from .config import FeaturePreparationConfig
@dataclass
class FeaturePreparationResult:
"""
Output of the unsupervised MorphoGlia Feature Selection stage.
"""
data: pd.DataFrame
analytic... |
18b10e72ffb55040c6ea6b3fe24c393249db04697cb3e7fa1eb1c13b50ec024a | Python | 6,647 | 189 | # 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... |
7a55c01155ee5c455fe782b8233b367228c4084b6d4bf5d4c8494d3e3225f9de | Python | 6,649 | 190 | import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
import torch
import torch.optim as optim
import sys
sys.path.append("..")
from PIDAO_SI import PIDAccOptimizer_SI
from PIDAO_Sym_Tz import PIDAccOptimizer_Sym_Tz
from PIDAO_SI_AAdRMS import PIDAccOptimizer_SI_AAdRM... |
43884ac2e6437392588ebad9efd68698fde5b054ace1521a90558e6b08a2c47f | Python | 6,650 | 192 | import sys
import numpy as np
import gzip
import random
# params
vcf_path = sys.argv[1]
sample_path = sys.argv[2] # list of samples to keep
outname = sys.argv[3] # with .vcf
min_read_depth = int(sys.argv[4]) # min read depth, DP field
min_sample_prop = float(sys.argv[5]) # min proportion of samples with data
seed ... |
cd1e63a5ef6048b097500615ab7bd0ad5874210de200665e97317d8b10e758a6 | Python | 6,653 | 186 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
07bd9751e0e6cd8874a889fecd1101bd1ec4b1d2ba970bd6a804568f7320b539 | Python | 6,654 | 170 |
import argparse
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
from dataloaders.data_loader_general_equ import NetDataset_new
from torch.utils.data import DataLoader
from model.resinf import ResInf
from engine import *
import wandb
parse... |
3bb94f62d9e9042fc8593873303aa1882899424d0cc712139b713c335b0ac408 | Python | 6,654 | 172 | # Plots metrics used to identify ideal time-step to balance accuracy and speed
# for Fig 8
from os.path import join
from get_paths import *
import matplotlib.pyplot as plt
import numpy as np
import pickle
from kneed import KneeLocator
from colors_paultol import cset_high_contrast
if __name__ == "__main__... |
518b87832ea3f22b634d661ca3bd102e3cbd4edf80330f9bd319199d04106d9b | Python | 6,656 | 147 | # 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... |
3b770deaab6e1f297c5a6d3b125e677522d6321aee530cb1c9b4d88d1a7788dd | Python | 6,657 | 257 | import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from burst_space import BurstSpace
from help_funcs import load_exp_variables
# ----- #
# Hyperparameters.
metric = "rocauc"
if metric == "rocauc":
metric_str = "score"
elif metric == "accuracy":
metric_str = metric
band = ... |
566277948875e4e00fa71d51c7a2014e36de92d88e57ac1c6bfa978776c11c35 | Python | 6,657 | 160 | # 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... |
f485188e9df32c36f60a299e4f453a418e241e2cfdb975244a85c8559c1c4902 | Python | 6,657 | 181 | # Node-group asset "Point Edge Angle" (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_CHECK... |
44b0ebdec7235699c8b9b3ebd7c51b6fc2a4a34f30f6c7700c2cb212408d0e60 | Python | 6,658 | 181 | from collections.abc import Iterable, Iterator, Sequence
from dataclasses import dataclass
from itertools import chain
from typing import Any, TypeVar
@dataclass(eq=True, frozen=True)
class DataFlowNode:
dsts: tuple[str, ...]
sources: tuple[str, ...]
name: str
@classmethod
def top(cls, name: str)... |
d71fd09544b0615db18620d2587630fc93ecdb6ec01dd7fcab9bbbf699962281 | Python | 6,659 | 184 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
1191275b012b85b3a9ad35bd016848c627c2428d4f8f9e87f27459fa3e5b85e4 | Python | 6,661 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
b31e8dae3da3f29c379316d39f5b7bb7a7673bf4770125760f17d884cec4b34f | Python | 6,661 | 186 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
302ae004faa96a18f2ae8cd723688f41c1d89d9bdb70e6252b51c268297a63ee | Python | 6,662 | 144 | import os
import numpy as np
import pandas as pd
import anndata as ad
import scanpy as sc
import scvi
#from util.utils_B import set_seed
from time import time
#from memory_profiler import profile
#os.environ['PYTHONHASHSEED'] = '0'
#os.environ["HDF5_USE_FILE_LOCKING"] = "FALSE"
from memory_profiler impor... |
708b4226a27e125a61f5578c647810d2e5c9693bce272b57d7f79ad603b90888 | Python | 6,662 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
e7a81e44fb8f210497b4cbc3c50baffd2b35d66581346c406f360b0accbdfb2c | Python | 6,662 | 166 | import numpy as np
import unittest
from sklearn.base import clone
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
import pyntbci
FS = 120
PR = 60
SHIFT = 2
v = pyntbci.stimulus.make_m_sequence()
V = pyntbci.stimulus.shift(v, SHIFT)
V = np.repeat(V, FS // PR, axis=1)
LAGS = np.arange(0, v.shape... |
160ba9f99268bd646dfca7faaa82218ae0d1f2e55957e622bb5fd400599dc807 | Python | 6,663 | 176 | # 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... |
8448ac1c376c8256ba54be790cc6d63a83dd90aef40ff90f28160745f6ed7bbd | Python | 6,663 | 152 | import os
from scipy.io import loadmat
import numpy as np
import pandas as pd
import torch
from sklearn.model_selection import train_test_split
from datasets.SequenceDatasets import dataset
from datasets.sequence_aug import *
from tqdm import tqdm
from itertools import islice
#Digital data was collected... |
6eee07686de4555188a86034a95863fac11654c2b6b4d53406186d7a1ea4e76f | Python | 6,665 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
8b5fba2b48bc708f4f8863983e29700c4f0ff53093c95ad7c6b92abcc56ec9b3 | Python | 6,665 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
a5d6ec822d089c37b76e88b7c241f352edddeb67775442866973d6ecba1f65c0 | Python | 6,665 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
b6270aaf5e6649eae59ef563e9989d1ccc1a230b0fcdbd25ce87c772fc7966c1 | Python | 6,665 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
381ca2fb71e4faa2f9d5a5750c7912838e5ddb56f4de21c9157cef30e720f9a9 | Python | 6,667 | 137 | # ------------------------------------------------------------------------
#
# 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
#
# Unle... |
a0891ad20b8e4f082775814eb9eea64a1c4846e9ddc8e6815b59959dec26671f | Python | 6,667 | 185 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Hybrid LFP scheme example script, applying the methodology with the model of:
Potjans, T. and Diesmann, M. "The Cell-Type Specific Cortical Microcircuit:
Relating Structure and Activity in a Full-Scale Spiking Network Model".
Cereb. Cortex (2014) 24 (3): 785-806.
doi: ... |
ffca6e10a5cd1fd1e52fc9ccbd43380292ebc24a3d11649723be7f53e8e9975e | Python | 6,667 | 174 | """napari reader plugin for XuLab-RegTools.
Bridges napari's file-open / drag-and-drop machinery to the unified
``load_volume()`` I/O layer so the standalone napari viewer
(``12_run_napari.bat``) and every regtools :class:`NapariViewer` can open
formats napari has no built-in reader for: **NIfTI, NRRD, DICOM, VSI... |
c73df2a0dbd70071435be7b85d4888deeb3d3057e853a0716bc467326a0e79e0 | Python | 6,670 | 164 | # 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... |
c1baa51b8a7a4eb3702d27bbdc88f693579365469df0e212732fd62c9b85870c | Python | 6,671 | 196 | import json
from pathlib import Path
import numpy as np
import torch
import tqdm
from scipy.stats import pearsonr, spearmanr
from .prix_fixe_net import PrixFixeNet
from .dataprocessor import DataProcessor
from typing import Any
from abc import ABCMeta, abstractmethod
# default metrics to compute
DEFAULT_METRICS = ... |
c55593a1bcdad2bc420e36f256dd0ec7b1f6ce68c7046acecb2f267ad610a7b3 | Python | 6,672 | 227 | # %%
import sys
import os
sys.path.append(os.path.abspath("../.."))
sys.path.append(os.path.abspath(".."))
sys.path.append(os.path.abspath("."))
# %%
import torch
import torch.nn as nn
import pandas as pd
from tqdm import tqdm
from pathlib import Path
from prosper_nn.models.ecnn import ECNN
from prosper_nn.models.e... |
51027d77e445c146785e230df984ac558c423073ce8f78c11b1a2d3bb1c7ff18 | Python | 6,674 | 184 | # -*- coding: utf-8 -*-
#
# General, CR agnostic, JackKnife implementation for trial statistics
#
import h5py
import numpy as np
# Syncopy imports
import syncopy as spy
from syncopy.shared.computational_routine import propagate_properties
from syncopy.shared.errors import SPYValueError, SPYError
def trial_avg_replic... |
5699d40331b159aad6432dd4767fb89c2f0dabda0ec6833fd75bef98e4556289 | Python | 6,674 | 216 | from itertools import cycle
import torch
import torch.nn as nn
from torch.optim.lr_scheduler import StepLR
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def train(Epoch, train_labeled_loader, test_loader, model, eval, loss, optimizer, weight): # passing optional arguments as dictionary step_si... |
bdc0cd02e9053dd8206f71f8d23092168902c25eb87d56ba142c94995d338978 | Python | 6,675 | 175 | # By Shuran Liu, 2023
import argparse
import yaml
import math
import matplotlib.pyplot as plt
from tqdm import tqdm
from PIL import Image
import os
from skimage.metrics import structural_similarity as ssim
parser = argparse.ArgumentParser(description='Input a config file.')
parser.add_argument('--config', help='Config ... |
9883ee1d99571509878d4c73fa410767ad4dd701dfe8ee863883903a7765ce10 | Python | 6,676 | 87 | =
# Batch parameters
sleep_time = 0.2
logs_folder = "./logs/"
output_folder = ["../output"]
args_file = "./arguments.dat"
args_lock = "./arguments.lock"
lock_timeout = 300
jobs_count_tars = 12 # 132+12
jobs_count_t_bayes = 132
jobs_count_onsager = 1
jobs_count_chloe = 2
manager_script = "job_manager.py"
DETACHED_PRO... |
aa55aa99d5b7a26605947f77ecfbca03ce7c247c47d5d60fc7712687900a65d6 | Python | 6,676 | 205 | # -*- coding: utf-8 -*-
"""
Created on Fri Jan 12 11:27:20 2018
@author: Laure Spieser and Boris Burle
Laboratoire de Neurosciences Cognitives
UMR 7291, CNRS, Aix-Marseille Université
3, Place Victor Hugo
13331 Marseille cedex 3
This file is part of Myonset.
Myonset is free software: you can redistribute it and/or m... |
c18d893d96361238b5be147b6d5a3ec8204f27d2c2cba3fcd223808590f5562f | Python | 6,676 | 171 | """Logic that powers autocompletion installed by ``pip completion``.
"""
import optparse
import os
import sys
from itertools import chain
from typing import Any, Iterable, List, Optional
from pip._internal.cli.main_parser import create_main_parser
from pip._internal.commands import commands_dict, create_command
from ... |
cdc4969230d3dd4b990fa1765aa34bb0727e3e72d693b8719a7df8a42026deab | Python | 6,676 | 207 | import itertools
from typing import Callable, Generator, Iterable, List, Tuple, TypeVar, Union
import editdistance
import numpy as np
import scipy.stats as st
from numpy.typing import ArrayLike
Message = str
Messages = List[Message]
Meaning = Tuple[int, float]
Meanings = List[Meaning]
def form2meaning_ratio(meanin... |
13a0ebcffb76985f6d2c3175aa43e201d3790b68193ba065512ab079829f8d78 | Python | 6,677 | 152 | #!/usr/bin/env python
import argparse
import random
import time
from pybullet_tools.pr2_utils import set_arm_conf, get_other_arm, arm_conf, REST_LEFT_ARM, \
get_carry_conf, get_gripper_link, GET_GRASPS, IR_FILENAME, get_database_file, DRAKE_PR2_URDF, \
set_group_conf, get_group_conf, get_base_pose
from pybull... |
d499f7a1b44e5d86636fe835fc61ed88170ce7f3aef4ff3a666197990d01b185 | Python | 6,677 | 114 | """TIPPING_POINT.py 2026-09-29. Pre-registration: 04-NOTES/PREREG-2026-09-29m-TIPPING-POINT.md
Random type-level LN sign sets at 10-70% excitatory LN output: smell code (whole brain, DL5 + DA1) and fit to the real fly
(Seki, AL cut-out). Where does the model tip over, and where do the published / best-guess / probabili... |
fca34eddfc521c7b3a37089d8f81308aa5a98e393ec65ebff71037653d6ad816 | Python | 6,678 | 169 | # trace generated using paraview version 5.11.0-RC1
#import paraview
#paraview.compatibility.major = 5
#paraview.compatibility.minor = 11
import os,glob
from paraview.simple import *
#### disable automatic camera reset on 'Show'
paraview.simple._DisableFirstRenderCameraReset()
# get the material library
... |
85f27d9ef7431c54643b4afacc12542503557b36829396bc6cbade0f0d905c65 | Python | 6,679 | 277 | # Node-group asset "Switch Residue Name" (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_CH... |
eea2accddb52366a740b30e694e80ca54cc66666c1e68dd8ae8f0a267ee12fd6 | Python | 6,679 | 197 | import argparse
from datetime import datetime
import random
import numpy as np
import os
import time
from utils import *
from utils.dataloader_utils import load_data_graphalg,load_dataset, analyze_dataset_hyperbolicity,relations_to_degree_analysis
from params import *
import sys
from datetime import date
import pickle
... |
43d3cd4f88c5aac23538a5baf2974a44029b02dcbd7d1ab909cafa1fc9409063 | Python | 6,680 | 164 | """
Behavioural tests for the itk-elastix library backend.
Elastix runs entirely in-process; the command-line ``elastix``/``transformix``
binaries were retired. These cover the three operations the pipeline needs -
registration, image transformation, and POINT-SET transformation - all against
the project's own p... |
9068763071a7ed7192f2d2ab801e8765f3c473887a9449b11a0491c15682dd8b | Python | 6,680 | 268 | from scconfluence import dataset_utils, unimodal
import numpy as np
from torch.utils.data import DataLoader
from context import (
create_synthetic_adata,
create_synthetic_mdata,
run_unimodal_ae,
run_scconfluence,
)
def test_umodal_dataset():
adata = create_synthetic_adata(n_obs=100, n_features=200... |
216314d449594aa07a5b8e0c15903103147198e2fb58675fc0ca9aac7efe2f0a | Python | 6,684 | 160 | #!/usr/bin/env python
from __future__ import print_function
import cProfile
import pstats
from examples.pybullet.utils.pybullet_tools.pr2_primitives import Pose, Conf, get_ik_ir_gen, get_motion_gen, \
get_stable_gen, get_grasp_gen, Attach, Detach, Clean, Cook, control_commands, State, apply_commands
from example... |
702c162eff3330bfbea12117ee90012f253d095dcdc085dfa271993bf9aa7593 | Python | 6,685 | 148 | """Read the shipped midsagittal plane; fit one for the maintainer's tool.
``plane_for_atlas`` is what everything on the registration hot path calls. It
reads the parameters committed in ``regtools/hemisphere/planes.json`` and
validates them against the user's annotation HEADER - it never fits, and never
reads vox... |
3f9af5ea56dbe4a63e16f48e6a1222a041773f834711f0e0c7d646df63095e06 | Python | 6,688 | 148 | # flake8: noqa
# This file is used for deploying replicate models
# running: cog predict -i img=@inputs/00017_gray.png -i version='General - v3' -i scale=2 -i face_enhance=True -i tile=0
# push: cog push r8.im/xinntao/realesrgan
import os
os.system('pip install gfpgan')
os.system('python setup.py develop')
import cv... |
651c234604e64a336b242b9570107ff5a5f18c7f6b1ff94a15773a0c027dae67 | Python | 6,692 | 178 | # part of this script was taken from https://github.com/jocicmarko/ultrasound-nerve-segmentation
import argparse
from glob import glob
import numpy as np
from PIL import Image
from keras import backend as K
from keras import losses
from keras.callbacks import ModelCheckpoint, EarlyStopping, ReduceLROnPlateau
from kera... |
609927d2a7229173f6020092b13b197e9c6a86971ceeeb769b259f85715e7491 | Python | 6,696 | 187 | # --------------------------------------------------------
# DaSiamRPN
# Licensed under The MIT License
# Written by Qiang Wang (wangqiang2015 at ia.ac.cn)
# --------------------------------------------------------
import numpy as np
from torch.autograd import Variable
import torch.nn.functional as F
from control.DaS... |
d29e857a841413bcb04c7c4f0bd38c9db0f63a1cc420774b6ffcd68198fce70a | Python | 6,696 | 111 | #!/usr/bin/env python
import itertools
from SCanSNP.VCFUtils import *
from sklearn.linear_model import LogisticRegression
from SCanSNP.DBLsutils import *
from SCanSNP.GenUtils import *
def LocusSpecNoisCalc(DropToDBLDict,Doublet,SingularLociScoreDF,GenotypesDF,vcf,HomozlociList,RefHetLoci,AltHetLoci):
comp1 = Double... |
0ddd8fa2020dab4a61542c9bb598bd15befe04ebe8ec68d7e38beb826a36aab5 | Python | 6,697 | 189 | '''
A colections of points Point() are correlated in a manner that creates
a perimeter Perimeter(). Every perimeter should be nice, i.e.:
1) Not self-intersecting;
2) No overlaping points;
3) Have a prefered orientation.
If we can garantee this properties, than we proceed to stitch a... |
f159f3e80a060df5229a61157b1f8cd72a04561559b3577b93080365a8ade7c5 | Python | 6,697 | 117 | from iblutil.util import Bunch
import numpy as np
import pandas as pd
from one.api import ONE
import os
one=ONE(base_url='https://openalyx.internationalbrainlab.org/', password='international')
def bleach_correct(nacc, avg_window=60, fr=25):
"""
Correct for bleaching of gcamp across the session. Cal... |
653d8c108c6b908cc49676fc3dc7adddfa5f55df078197b862475e72b91ae135 | Python | 6,700 | 191 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Example plot for LFPy: Single-synapse contribution to the EEG
Execution:
python example_EEG.py
Copyright (C) 2017 Computational Neuroscience Group, NMBU.
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General P... |
10c05e8060153b4c6b829514021b7883a4b69a562ca9c8286490ac0ec10b81db | Python | 6,702 | 167 | # Copyright 2022 HYBRID Team INCIA, UMR5287, CNRS, Université de Bordeaux
# 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 b... |
2b89fb0188180472e27e23bb0664c4187d86136f9883f50200a063455febfe25 | Python | 6,702 | 177 | # basic
import os, random
import numpy as np
from tqdm.auto import tqdm
from joblib import Parallel, delayed
try:
from .ModelData import *
except:
from ModelData import *
def get_mixed_loader(bag_data, label_data, size_data = None, site_data = None, split = True, signal_only = False):
### datalo... |
9df5c2bbb38759d5df239e5c57531b839900166e768e4235a3b6d9280af403c4 | Python | 6,703 | 125 | #!/usr/bin/env python
# relion_run_topaz
# This is to run Topaz picker (https://github.com/tbepler/topaz) from Relion as an External job type
# Rafael Fernandez-Leiro 2020 - CNIO - rfleiro@cnio.es
# Alex J. Noble 2020 - NYSBC - anoble@nysbc.org
# Run with Relion external job
# Provide executable in the gui: ... |
60f167cd0d846578964fa850fee789062f61e0f824417312832ca7acabd808ee | Python | 6,704 | 185 | # -*- coding: utf-8 -*-
"""
Created on 15 october 2023
@author: divyoj
"""
## importing libraries:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation
import os
# # note that this must be executed before 'import numba'
# os.environ[... |
bb52d31717061269eeecdd282faefaa88bfa330fba2f3f2e232e095fa0aea197 | Python | 6,704 | 158 | from __future__ import print_function
try:
from Tkinter import Tk, Canvas, Toplevel
except ImportError:
from tkinter import Tk, Canvas, Toplevel
# NOTE - this will overwrite (but remember) existing drawings
# TODO - try PyGame, PyCairo, or Pyglet
SUCTION_WIDTH = 1.5 # 1.01 | 1.5
STEM_WIDTH = 1.
STEM_HEIGHT =... |
71cadc75e5b417cbe3a600878af84779aa9de4d250bfa63bad3e7b32afc4811d | Python | 6,705 | 182 | # 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... |
7919690c1410281cc8101b795b8bd783b07b4e598efab1890b094a4947220d39 | Python | 6,708 | 168 | import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
class AudioResNet152(nn.Module):
def __init__(self, num_classes):
super(AudioResNet152, self).__init__()
# Load pre-trained ResNet-152 model
self.resnet = models.resnet152(mod... |
97847138bc23377220ea1e2f2cc347aeea0693db2f535715f513570acba100c8 | Python | 6,708 | 191 | import warnings
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import numpy as np
import pytest
from SurvivalEVAL import IntervalCenEvaluator, SingleTimeEvaluator, SurvivalEvaluator
from SurvivalEVAL.evaluations.calibration.single_time import (
integrated_calibration_index,
one_cal_... |
1cb367da92fbe563eae48b373976e47a5f8f62ab2ddaccaa8b86c9a1df321a68 | Python | 6,709 | 201 | # import libraries
import torch
import numpy as np
def Cosine4AIF_ExtKety_deep(t, aif, ke, dt, ve, vp, device='cpu'):
# offset time array
t = t - aif[0] - dt
ct = torch.zeros(np.shape(t)).to(device)
shapes = t.size()
ke = ke.repeat(1, shapes[1])
ve = ve.repeat(1, shapes[1])
vp = vp.repeat(... |
8f6a67a5b6f30293a1e5c9647ecab7475242a63203c5d544e4a29c93d5982320 | Python | 6,710 | 222 | #
# 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,... |
aaecb82f3dc471473b07da1e07d53571e962d5d89cebd04338619ea9c13b71da | Python | 6,710 | 165 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 15 00:04:58 2021
@author: haotian teng
"""
import os
import sys
import umap
import torch
import argparse
import numpy as np
import torch.utils.data as data
from itertools import islice
from typing import List,Union
from torchvision import transforms... |
98db6e25d978a69386ac9a0a448d06dc24dda7b6b31aa58bef34fd54801c9d93 | Python | 6,711 | 139 | import unittest
import numpy as np
from torcheeg.datasets import constants
from torcheeg.datasets.constants.utils import format_channel_location_dict
from torcheeg.transforms import Compose, ToGrid
class TestGrid(unittest.TestCase):
def test_location_helper_omits_channels_absent_from_grid(self):
channel... |
e4386f2b12e15a2d2709c3a60de71dd3e991f129e41372102ed62a28ec5080f3 | Python | 6,711 | 162 | import os
import torch
import torch.nn as nn
import torch.autograd as autograd
import torch.nn.functional as F
class PaddingAutoencoder(nn.Module):
def __init__(self, input_len, input_dim, encoding_dim):
super(PaddingAutoencoder, self).__init__()
self.input_dim = input_dim
self.input_len =... |
5d1805430227bfeb4d7a3c9346b043245705f999037d7bbb12e58152d236b328 | Python | 6,712 | 170 | import numpy as np
import math
from collections import namedtuple
from itertools import islice
from control_tools.common import get_arm_prefix
from plan_tools.common import get_reference_pose, is_obj_type, get_type, CUP_WITH_LIP
from plan_tools.samplers.generators import TOOL_FRAMES, TOOL_POSE, set_gripper_position
f... |
223617f3ec59263b9c716ac0940b7c2c0577d3596ccf99e49dc8eca7d8c788fd | Python | 6,716 | 177 | """
Bonito Basecaller
"""
import os
import sys
import numpy as np
from tqdm import tqdm
from time import perf_counter
from functools import partial
from datetime import timedelta
from itertools import islice as take
from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter
from bonito.aligner import align_ma... |
4d069543ef427fbb9ff3e1a1c95ef6e6d91f035f73bca4f8b94b2c5a8aaede6b | Python | 6,717 | 161 | """
FREEZE_CANDIDATES.py - written 2026-09-17.
WHY A FREEZE.
The method now produces a list of cell types it says carry chemistry the six-class vocabulary
does not contain. Some of those will turn out to be in the literature already. If the list is
compared with the literature BEFORE it is fixed in place, then ... |
c402576c3667f9bc735535a8e98480fe181fea99916860ef27898767f8f854a9 | Python | 6,717 | 217 | import os
from collections import defaultdict
import torch
import torch.nn.functional as F
import numpy as np
from scipy import sparse as sp
from sklearn.metrics import roc_auc_score, f1_score, cohen_kappa_score, precision_score, \
recall_score, balanced_accuracy_score, adjusted_rand_score, normalized_mutual_info_... |
c61e301502581b7d575184c883dcf93b74dd2558bfe2b0233f22655fead08368 | Python | 6,717 | 260 | from matplotlib.colors import LinearSegmentedColormap
_CM_DATA = [[0.2422, 0.1504, 0.6603],
[0.2444, 0.1534, 0.6728],
[0.2464, 0.1569, 0.6847],
[0.2484, 0.1607, 0.6961],
[0.2503, 0.1648, 0.7071],
[0.2522, 0.1689, 0.7179],
[0.254, 0.1732, 0.7286],
[0.2558, 0.1773, 0.7393],
[0.2576, 0.1814, 0.7501],
[0.2594, 0.1854, 0.7... |
e4560cb032eea6cd570046fc1f5bafcedfc4c3bc4c590e30d1ee02310b65e159 | Python | 6,717 | 147 | from __future__ import annotations
import os
from pathlib import Path
from ase.build import bulk, molecule
from quacc import change_settings
from quacc.recipes.gulp.core import relax_job, static_job
def test_static_job(tmp_path, monkeypatch):
monkeypatch.chdir(tmp_path)
atoms = molecule("H2O")
output... |
e64d9988ca0ebf8d3e16dd5d1ed5218c934fe9437097c9ce26e4b0db877365e0 | Python | 6,717 | 235 | """Recipes for slabs."""
from __future__ import annotations
from typing import TYPE_CHECKING
from quacc import flow, job
from quacc.recipes.common.slabs import bulk_to_slabs_subflow, slab_to_ads_subflow
from quacc.recipes.vasp._base import run_and_summarize
from quacc.wflow_tools.customizers import customize_funcs
... |
5ca351968a41ce5890595c798a3812082cdef7360752360b8bcea0ce249ffd16 | Python | 6,719 | 226 | import numpy as np
from os.path import join
from burst_space import BurstSpace
from help_funcs import load_exp_variables
# ----- #
# Dataset selection.
datas = ["zhou2016", "2014004", "2014001", "munichmi", "cho2017", "weibo2014", "dreyer2023"]
# Number of repetitions.
reps = 30
# Components to use.
cta = [2, 3, ... |
740448e08342dabf312b05ef0e1efcaf448a9a845a6ac2ea74c83d660b5bc652 | Python | 6,719 | 211 | """Test various helper functions."""
import numpy as np
import pytest
from numpy.random import RandomState
from pyprep.utils import (
_correlate_arrays,
_eeglab_create_highpass,
_get_random_subset,
_mad,
_mat_iqr,
_mat_quantile,
_mat_round,
_print_progress,
)
def test_mat_round():
... |
86f6a23ed4bff902d947e8a1f6c8cfe801a42d953b4fd76efc301870d409989e | Python | 6,722 | 188 | from distutils.util import convert_path
from distutils import log
from distutils.errors import DistutilsOptionError
import os
import glob
import io
from setuptools.command.easy_install import easy_install
from setuptools import _path
from setuptools import namespaces
import setuptools
class develop(namespaces.Develo... |
51c74b810e9aac9eb1f191b7fcf1d0cd3a354b85fd900323401ccd255ec30578 | Python | 6,723 | 177 | '''
4SM/model.py Copyright (C) 2021 Sharif Amit Kamran, Hussein Moghnieh
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation; either version 2 of the License, or
(at your option) any later version.
This ... |
70d5fc609ffe7c8bbac09dd08a5a0b7668cdf4c0289fc05733037aa633b63008 | Python | 6,724 | 156 | """
Created on Tue Aug 3 23:24:39 2021
@author: Haotian Teng
"""
import numpy as np
import os
import sys
import pandas as pd
import argparse
import itertools
from typing import Tuple,Iterable
def load_data(prefix:str,renorm = False,read_meta = False)->Tuple[np.array,Iterable,np.array,np.array,np.array]:
"""
L... |
74c8b039ba204d486e2b1d5c468f4a0480ae8e9ea796baf2970763624a90299d | Python | 6,724 | 206 | # Node-group asset "Ensemble Instance" (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_CHEC... |
bc2453481b0d6b79361289b373086b99d88623a68886563d654ed138aa9c2e44 | Python | 6,724 | 182 | # 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... |
253e39edec0877707c8aaf10a7355b3894c9be75585456886b65a8411d73b83c | Python | 6,725 | 170 | from typing import Tuple
import torch
import torch.nn as nn
class BGenerator(nn.Module):
r'''
TorchEEG provides an EEG feature generator based on CNN architecture and GAN for generating EEG grid representations of different frequency bands based on a given class label.
.. code-block:: python
g_... |
eab3f9d362f18d8363e7ce125159a65676c430e243a48aaebd2908028bac6fe4 | Python | 6,725 | 163 | import os
import numpy as np
import PIL
import cv2
import tifffile
from scipy.signal import convolve2d
import merlin
from merlin.core import dataset
from merlin.data import codebook as cb
class MERFISHDataFactory(object):
"""
A class for simulating MERFISH data sets.
"""
def __init__(self):
... |
7bb3f96cb1198edfe28b7ef12753655c40f7f8398e5348be0e5778bb828d00e0 | Python | 6,726 | 221 | ## for debug..
import os
os.environ["CUDA_VISIBLE_DEVICES"] = '0'
import PoseUNet
from poseConfig import aliceConfig as conf
import h5py
import multiResData
import tensorflow as tf
import numpy as np
import PoseTools
import pickle
from scipy import io as sio
import math
self = PoseUNet.PoseUNet(conf, name='pose_unet_... |
2acbbcf1bbe088a75f40eb03d1fd43f3584989e47355090a806aa0efac5ba79e | Python | 6,727 | 171 | import numpy as np
import networkx as nx
from eval_metrics import roc_auc_score
def minkowski_dist(x, y, c=1):
"""Minkowski separation between x and y, c = speed of light parameter"""
d0 = -c**2 * (x[0] - y[0])**2
d1 = np.sum((x[1:] - y[1:])**2)
return d0 + d1
def LorentzMDS(M, d):
"""Performs Lor... |
5e578050f3176a18bee00e40b37885736f19e02145bf6b861f907a0bb38915e4 | Python | 6,727 | 123 | """REAL_ODOURS.py 2026-09-25. Pre-registration: 04-NOTES/PREREG-2026-09-25c-REAL-ODOURS.md
Real ORN responses (Hallem & Carlson 2006, via DoOR) drive Shiu's LIF model; can the PN layer tell 110 odours apart
better than the ORN layer (the known answer in real flies, Bhandawat et al. 2007)?
usage: python REAL_ODOURS.py [... |
e320aac3b7b52c503a84a25dcac0744318e12f3325126761582510fcc3d66225 | Python | 6,727 | 90 | """ONE_INEQUALITY.py 2026-10-02. Pre-registration: 04-NOTES/PREREG-2026-10-02o-ONE-INEQUALITY.md
One inequality for one wrong sign and for many: a set of sign changes breaks the lobe iff it moves s_U, the excitatory share
of the local neurons' synapses onto uniglomerular PNs, past one value s_break. L1: s_break from th... |
3ce1970c155ad6216764f18f002b20dcebf211e638131bfc39517bb2e964e830 | Python | 6,729 | 164 | # 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... |
70a0d05784d5ed9cc2c53f3ef47c475582e73d6c8e9d5070165002ad20ac3dda | Python | 6,731 | 122 | """Helper functions to parse and create a hyperparameter object."""
import os
import json
import tensorflow as tf
DEFAULT_DATA_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), 'data'))
def add_arguments(parser):
"""Helper function to fill the parser object.
Args:
parser: Parse... |
c21df1ce51ed105e3597916513ed258ba9bb12159b2c9ac34ff1dc44449e5384 | Python | 6,733 | 214 | """
Script that applies the CONN pipeline to a set of subjects
Uses the same structure as run_pipeline_prime.py
COPY script/Base_batch.m to the CONN base directory for this to work!
For each subject:
- Create new output directories
- Moves the necessary data to the new directories
- Converts the label generated b... |
1b357ea9de4ce0413eafdb41d44bbf8bc35155d5f35d86c860fabdf9ed980622 | Python | 6,734 | 143 | import torch
import numpy as np
import argparse
import utils
import time
from tqdm import tqdm
from engine_train import Trainer
def arg_option():
parser = argparse.ArgumentParser("The required parameters of the model")
parser.add_argument('--device', default=None, type=str)
parser.add_argument... |
69d32672c3cff854ffa496c3f54003296f9997144d16d731dbfb3d3f532c5827 | Python | 6,734 | 176 | import numpy as np
import pytest
from lifelines import AalenJohansenFitter, KaplanMeierFitter
from SurvivalEVAL.nonparametric_estimator.competing_risks import (
AalenJohansenCompetingRisks,
)
@pytest.fixture
def no_cross_ties():
"""Disjoint cause timelines allow a deterministic comparison with lifelines."""
... |
94395e51254bf926a8c02423e35b3e04e31b10e0a1668e43d76caf0379df6311 | Python | 6,734 | 168 | from typing import Tuple, Final
from schnetpack.data import AtomsDataModule
import torch
from spainn.multidatamodule import calculate_multistats
__all__ = ["SPAINN"]
class SPAINN(AtomsDataModule):
"""
Adapted AtomsDataModule class of SchNetPack 2.0 for calculating
statistics (mean and standard deviation... |
9e8e570364be94be8e613c5a9753c004aeca087e7eee2d18030d0b12e436fa42 | Python | 6,734 | 177 | """
Classes for parsing command line options for ASL tools
Copyright (c) 2008 University of Nottingham
"""
import sys
from optparse import OptionGroup, OptionParser, Option, OptionValueError
from collections import defaultdict
from copy import copy
import tempfile
import numpy as np
from fsl.data.image import Image
... |
08cd74f859286bf68008cd737d5c85304c46c6958a09522910dc60dad8e234c7 | Python | 6,736 | 164 | import os
import cv2
import numpy as np
from merlin.core import analysistask
from merlin.util import deconvolve
from merlin.util import aberration
from merlin.util import imagefilters
from merlin.data import codebook
class Preprocess(analysistask.ParallelAnalysisTask):
"""
An abstract class for preparing da... |
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