sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ba11eb61690bc44feb1793a41ca2279b41d4b2b8e02871d542fb6ddd472fa2d0 | Python | 27,055 | 238 | ######################## BEGIN LICENSE BLOCK ########################
# The Original Code is Mozilla Communicator client code.
#
# The Initial Developer of the Original Code is
# Netscape Communications Corporation.
# Portions created by the Initial Developer are Copyright (C) 1998
# the Initial Developer. All Rights R... |
968dd6e439bf9808db872c2876f7534cd8a147d1b59e7cd2e16bfa321230292e | Python | 27,065 | 771 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 26 13:05:23 2021
@author: joaquin
"""
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import scipy.io
import pandas as pd
import os
import mat73
from scipy import signal
from scipy import stats
def eegfilt(data,srate,flo... |
10b6309006c0f4f912b51dbf497006e5f3fecca271e8b1ba7bedbe0c7e1852ab | Python | 27,069 | 749 | """
Internal ADT (Abstract Data Types) for predictor introspection.
This module defines the type system used internally for introspecting
predictor inputs and outputs, and validating input values.
"""
import dataclasses
import os
import typing
from dataclasses import dataclass
from enum import Enum, auto
from typing ... |
de18a8707ff837cbf0466dfef32156ccceed4b08e312f7a7ebd5ea59ab124303 | Python | 27,073 | 796 | import sys
from functools import lru_cache
from marshal import dumps, loads
from random import randint
from typing import Any, Dict, Iterable, List, Optional, Type, Union, cast
from . import errors
from .color import Color, ColorParseError, ColorSystem, blend_rgb
from .repr import Result, rich_repr
from .terminal_them... |
fb137ace9c4331ce408c66e2ea53e4032f806e5b8d09f22eb6eb4da2fe0be19f | Python | 27,088 | 625 | # Copyright 2020 The HuggingFace Team. All rights reserved.
#
# 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 applicabl... |
36771fa2b08e7e6cca99d61320ea86a7bc291615017b069b0de8315026f28de2 | Python | 27,099 | 625 | """
Generate a synthetic set of microsomes with different membrane bound proteins
Input: - Data set parameters:
+ Number of tomograms (one microsome each)
+ Tomogram size
+ Resolution (pixel size)
+ SNR range
+ Missing wedge (sem... |
50309246ad4a14e967766f268d4a728eeafe00f492d49c4a2bdf77a0fc2bedf4 | Python | 27,109 | 683 | from . import get_submodules_from_kwargs
__all__ = ['load_model_weights']
def _find_weights(model_name, dataset, include_top):
w = list(filter(lambda x: x['model'] == model_name, WEIGHTS_COLLECTION))
w = list(filter(lambda x: x['dataset'] == dataset, w))
w = list(filter(lambda x: x['include_top'] == incl... |
1e363f2797ae0259540cb9bd5ac7f552dbf8c50f6c145e503647a6a8ae6f953d | Python | 27,113 | 603 | # -*- coding: utf-8 -*-
################################################################################
## Form generated from reading UI file 'dgn_preproco_ctrl.ui'
##
## Created by: Qt User Interface Compiler version 6.8.0
##
## WARNING! All changes made in this file will be lost when recompiling UI file!
#... |
f619427aa90a77be3eb88fe1a74c49a7242181581b87c544dfd0117cb1371494 | Python | 27,122 | 718 | # 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... |
70fe826e9e563abf667847fda94401b964bcd4e477fec2bcd3f864759c2cdf2a | Python | 27,132 | 494 | import math
from e3nn import o3
import torch
from torch import nn
from torch.nn import functional as F
from torch_cluster import radius, radius_graph
from torch_scatter import scatter, scatter_mean
import numpy as np
from e3nn.nn import BatchNorm
from utils import so3, torus
from datasets.process_mols import lig_feat... |
95c5bbf53510bf80ea68cc6ca2a8e9aec31d1c15b91c8b5661619e1bbe257154 | Python | 27,141 | 726 | #
# 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,... |
e2fa4a7fad150dbb95314ca4e3b462ac70c1b6b03028b6bc12b13ad350ba92cc | Python | 27,154 | 615 | """
A simple GUI framework for the U01 stitching workflow.
Andy Thai
andy.thai@uci.edu
"""
# Import standard libraries
import glob
import os
import subprocess
import sys
import tempfile
import time
# Heavy third-party imports (loaded when tab is first accessed)
import cv2
import joblib
import numpy a... |
14d222d4a408aba5b502b70efc935f9ce19a3de0e15710883083db65d99206dc | Python | 27,226 | 617 | import numpy as np
import time
import unittest
import pyntbci
FS = 120
N_TRIALS = 11
N_CHANNELS = 7
N_SAMPLES = 2 * FS
N_CLASSES = 5
DECODING_LENGTH = 0.1
DECODING_STRIDE = 1 / 60
ENCODING_LENGTH = 0.3
ENCODING_STRIDE = 1 / 60
CYCLE_SIZE = 1.0
class TestCorrectLatency(unittest.TestCase):
def test_correction(se... |
ffc24e98c17025cdc9e2532a857ce48a217d966b8b5617d5f1cb2f044ce24d59 | Python | 27,245 | 448 | """Shadow-mode spatial dopamine field prototype (hypothetical; never fed back into the reservoir).
Reads recorded reservoir states, maps the 16 DA-classified neurons' activity to a nonnegative
release proxy and integrates a 3D reaction-diffusion field
dC/dt = D ∇²C − k C + S(r, t)
on a voxel grid around the CX51... |
9760e291c2cff6c6e53fd193ff8dd8af73192bd80e009696c1c3460119700efe | Python | 27,247 | 751 | import bpy
import MDAnalysis as mda
import numpy as np
import pytest
import molecularnodes as mn
from .constants import data_dir
@pytest.fixture
def canvas():
return mn.scene.base.Canvas()
@pytest.fixture()
def universe():
topo = data_dir / "md_ppr/box.gro"
traj = data_dir / "md_ppr/first_5_frames.xtc"
... |
1ca0f5cc3f8e2065ba447d9f19e836867d587322f9e1b5ccb1385b3e8e23f5d7 | Python | 27,248 | 825 | import uuid
import copy
import navis
import matplotlib.colors as mcl
import numpy as np
import pandas as pd
import plotly.graph_objects as go
import seaborn as sns
import scipy as scp
import scipy.cluster.hierarchy as sch
import scipy.spatial.distance as ssd
from dash import dcc, html
from dash.exceptions import Prev... |
e15fa0b09574eff2508bbb78cc9e2b36cc05267e9f27f1c899823793e585c187 | Python | 27,259 | 326 | import os.path
import sys
from ckg import ckg_utils
from ckg.graphdb_builder import builder_utils
from ckg.graphdb_builder.databases.parsers import *
from joblib import Parallel, delayed
from datetime import date
try:
ckg_config = ckg_utils.read_ckg_config()
log_config = ckg_config['graphdb_builder_log']
... |
51932a03800d3e66c91d4d74d1a619e2f640813266d0d1b1689be2a37a42066a | Python | 27,272 | 654 | """
Analyzes oriented membrane segmentations inter-distances from lists of tomograms
Input: - A STAR file with a set of ListTomoOMSegmentations pickles (SetListOMSegmentations object input)
- Settings for the measurements
Output: - Plots by tomograms
- Global plots
"""
########... |
04fa92ded8226b2d7e59c8a530a504394742ea90e475de587dba51f3ab3727b5 | Python | 27,331 | 664 | import argparse
import collections
import gzip
import math
import os
import random
import shutil
import subprocess
import sys
import time
sys.path.insert(0, '/home/jupyter/repos/genformer')
import numpy as np
import pandas as pd
import tensorflow as tf
import pybedtools as pybt
import tabix as tb
from datetime impor... |
cf642eda852cdd7915bb0edb15baffd9e22441b82760f5ba1db474ea0f8c9836 | Python | 27,340 | 946 | # 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... |
a267a0794f5cf4221acc640a8dec9a25ba42435b2b49987d61e0738b09f4b5ac | Python | 27,348 | 579 | # coding=utf-8
# Copyright 2020 The Google AI Language Team Authors and The HuggingFace Inc. team.
#
# 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/LICEN... |
ef7d6d77368f72ba80fe7d4f705e990282383c5ac064eba3da3e196c83d437a3 | Python | 27,353 | 946 | # 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... |
20decb0e3f84f3afc4859c7d6a1878b895d312a66ad829b4fc6535a3f3758cb0 | Python | 27,355 | 946 | # 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... |
96649951c75cc25ed15ef598a1927bbc3848141d7a296ae0278c39728ac045ed | Python | 27,379 | 605 | """
Classes to handle cell assemblies detected in the previous step of the pipeline
authors: Michael Reimann, András Ecker, Daniela Egas Santander
last modified: 10.2022
"""
import os
import numpy as np
from scipy.stats import binom, pearsonr, hypergeom
__io_version__ = "1.0"
__str_io_version__ = "_version"
__h5_str... |
cab2877f87d26260afecb3a605aa69dd26f41257e9ba84204b798d5beb362671 | Python | 27,456 | 604 | import os
import sys
import time
import json
import random
import argparse
import datetime
import numpy as np
import pandas as pd
import torch
import torch.distributed as dist
from torch.optim import Adam, AdamW, SGD
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader, ... |
698f63a5e169b6096420857e6c3681f188b5b10af08c13255edc6c8fa1af0565 | Python | 27,458 | 478 | import time
import os
import argparse
import wandb
from datetime import datetime
import tensorflow as tf
from tensorflow.keras import mixed_precision
import numpy as np
import sys
# Environment configuration for TensorFlow
os.environ['TF_ENABLE_EAGER_CLIENT_STREAMING_ENQUEUE']='False'
sys.path.insert(0, os.path.expa... |
12ca67b07341421daf36388ec93e863b42bdf678557eee20f7d360fda3caa59b | Python | 27,462 | 797 | # 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... |
f25b1532dfc502e88636c7ff7bb0bbfdc0923843bba47ca3815991371f965ebc | Python | 27,475 | 740 | """Support for installing and building the "wheel" binary package format.
"""
import collections
import compileall
import contextlib
import csv
import importlib
import logging
import os.path
import re
import shutil
import sys
import warnings
from base64 import urlsafe_b64encode
from email.message import Message
from i... |
48f9c2a433d3c8f22dddcfabc48c6ea580b5f403ec6a3b45ee423ecbe4ab85a7 | Python | 27,476 | 742 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Oct 26 09:28:25 2019
Collection of functions for preprocessing experimenal visual data.
@author: irisyoon
"""
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('/usr/local/lib/python2.7/site-packages/')
sys.path.append('/usr... |
935cbe727757c19ad49a888cfceadfd557092c405ddefea683333a61fb02390b | Python | 27,481 | 681 | #!/usr/bin/env python3
import logging
import warnings
from time import localtime, strftime
from typing import Union, Callable, Optional
import numpy as np
from ase import Atoms
from ase.optimize.optimize import Optimizer
from ase.utils import basestring
from ase.io.trajectory import Trajectory
from .restricted_step ... |
c4cc56d69852892d6151f79c2b311a12bf3f69cdeacf4509ff443cee89dac500 | Python | 27,496 | 549 | import io
import json
import os
import shutil
import unittest
import pandas as pd
from hed import Sidecar, SpreadsheetInput, TabularInput, load_schema_version
from hed.errors.error_reporter import ErrorHandler
from hed.errors.error_types import ErrorContext, ValidationErrors
from hed.models.column_source import ListC... |
b044106829fcaec859ca9a8e69bd80da1dabe02713c5cc4f983e40ee1b3c2c0d | Python | 27,500 | 1,088 | # parsers.py
import argparse
import sys
from fba import __version__
def coords(s):
try:
# from itertools import chain
# s = ''.join([i.strip() for i in chain.from_iterable(s)]).split(',')
# return [int(i) for i in s]
return [int(i) for i in s.split(",")]
except TypeError:
... |
d514ebfcfaf29d316697ee488958948ef1e8cf49377158f35dfd1d4f02829e46 | Python | 27,535 | 490 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
inference_prod_activate = True # Activate Inference Procedure?
data_path = 'Shallow_Grid_1_Rule_Kern'
acts = [0] # [0, 1, ...]
act = max(acts)
_observe = 1 # {0, 1, ...} # L ~... |
f28a48adf65b97cc9c81381a6fe6b784e117cd21f1c0c8559d60a3d02276ad5e | Python | 27,536 | 490 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
inference_prod_activate = False # Activate Inference Procedure?
data_path = 'Shallow_Grid_1_Rule_Kern'
acts = [0] # [0, 1, ...]
act = max(acts)
_observe = 1 # {0, 1, ...} # L ... |
85db2fd871a0cf7d793b65042efc111fc1eaac4d63cc36358431108dcaf8202c | Python | 27,539 | 587 | from __future__ import print_function
import argparse
import math
import numpy as np
import pybullet as p
from pr2_utils import TOP_HOLDING_LEFT_ARM, LEFT_ARM_LINK, LEFT_JOINT_NAMES, RIGHT_JOINT_NAMES, TORSO_JOINT_NAME, \
REST_RIGHT_ARM, \
load_inverse_reachability
from create_ir_database import create_invers... |
04197953da0f277b6898b8b4910810b729f1fea5ab7177a6c55a08a86df4fad8 | Python | 27,598 | 903 | #!/usr/bin/env python3
#******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
... |
39973eb068f3471d1e165e63b0403757743932404947940b6d9622cff1003cbd | Python | 27,598 | 847 | from __future__ import annotations
# built-in
from collections import defaultdict
from itertools import zip_longest
from typing import Any, Sequence, TypeVar
# app
from .base import Base as _Base, BaseSimilarity as _BaseSimilarity
from .types import SimFunc, TestFunc
try:
# external
import numpy
except Impo... |
479980bab3838d465260eaecdc584877a264a21d31e17e03809834e581344863 | Python | 27,629 | 729 | """
Classes for doing the spatial analysis of structures in membranes
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.06.15
"""
__author__ = 'martinez'
from pyto.segmentation.cluster import Cluster
from .globals import *
from pyorg import pexceptions
##### PACKAGE VARIABLES
ST... |
c6e4fe22e2e084f96a0d64db4ae91117d3279c1faf37363ba8dad60c09d5c745 | Python | 27,637 | 657 | # For fully-connected models
import torch
import torch.nn as nn
from abc import ABCMeta, abstractmethod
from typing import Union, Tuple, Dict
from torch.distributions.distribution import Distribution
from mnp.models.parents import SimpleModel, MetaModel
# The CNP should read the inputs for each batch
# and then outpu... |
f60b7a216f4a80e156f5ef35ddc3314394456f846bc064f462bcdbfe9bac563e | Python | 27,642 | 801 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Simulation and plotting script reproducing figure 2 of:
Multimodal modeling of neural network activity: computing LFP, ECoG, EEG and
MEG signals with LFPy2.0
Espen Hagen, Solveig Næss, Torbjørn V Ness, Gaute T Einevoll
bioRxiv 281717; doi: https://doi.org/10.1101/28171... |
94d582094c1bcfb0a9ea8dee8ee40d4689df5f8c39b203b691366259ac46ed43 | Python | 27,643 | 687 | """
Functions to create meshes for demos.
:func:`create_cubes` defines a 'cube-in-a-cube' mesh using the
built in :class:`dolfin.UnitCubeMesh()`, with subdomains defined and
marked by :func:`facet_topology` and :func:`cube_condition`
:func:`create_spheres`, :func:`create_ellipsoids`,
:func:`create_cylinders`, and :fun... |
f1e0eac333a1c18886e1b0166ea0e44361124b761a7e52ff57203f3982b3bbf0 | Python | 27,654 | 610 | # coding=utf-8
# Copyright 2020, The RAG Authors and The HuggingFace Inc. team.
#
# 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 r... |
d3b85db0384e6e3caf59149a1d8985ba6cbcc34783b17b95d117f2aab6b8a39d | Python | 27,661 | 719 | """
Base classes for representing signals.
"""
import logging
from copy import deepcopy
from functools import partial
import numpy as np
import xarray as xr
from ..models.model import Model
from scipy.signal import butter, detrend, get_window, hilbert
from scipy.signal import resample as scipy_resample
from scipy.sig... |
1101ae95d0493b76f078562d00252bc2ff334b7882cab0d8db575234eada3e89 | Python | 27,670 | 733 | """
Classes for doing the spatial analysis of structures in membranes
# Author: Antonio Martinez-Sanchez (Max Planck Institute for Biochemistry)
# Date: 12.06.15
"""
__author__ = 'martinez'
from pyto.segmentation.cluster import Cluster
from .globals import *
try:
import pexceptions
except:
from pyseg import... |
7881e7fd772fead2f450e78bc8b109d77a90f73fcd734faf568c557298caa42f | Python | 27,686 | 545 | from unittest import mock
import numpy as np
import unittest
import pyntbci
FS = 120
PR = 60
SHIFT = 2
v = pyntbci.stimulus.make_m_sequence()
SHIFTS = np.arange(0, v.shape[1], SHIFT)
V = pyntbci.stimulus.shift(v, SHIFT)
V = np.repeat(V, FS // PR, axis=1)
N_CLASSES = V.shape[0]
CYCLE_SIZE = V.shape[1] / FS
LAGS = S... |
1b9dceab5f852ef04fad33409114e6ff68abaf841d82b58a31a63fff1cccada2 | Python | 27,712 | 837 | import json
import os
import numpy as np
from numpy.random import default_rng
import random
import math
from dreamcoder.task import Task
from dreamcoder.type import arrow, tint, tlist
from dreamcoder.domains.arc.arcPrimitives import *
import dreamcoder.domains.arc.arcPrimitives as p
def make_rotation_tasks():
# ... |
cd90f82c54737c4b78bedbb807c7f3c2a6da6df49824dc893e7e6980e34310cb | Python | 27,713 | 783 | # Copyright (c) 2023-2024 Blue Brain Project/EPFL
# 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 applicable law or agreed ... |
d9b823a4fcbd239d5809a88da9c904771a87c6c915bb909410268b070f2e08ff | Python | 27,713 | 534 | # -*- coding: utf-8 -*-
# @title Generate configuration files for bioemum
# @file gen_conf_snn_hh.py
# @author Romain Beaubois
# @date 06 Oct 2023
# @copyright
# SPDX-FileCopyrightText: © 2022 Romain Beaubois <refbeaubois@yahoo.com>
# SPDX-License-Identifier: GPL-3.0-or-later
#
# @brief Sc... |
3c309c001963d16f36f41c3df35e9482ab466e5f2c316490b967ca45667d0924 | Python | 27,718 | 673 | import threading
from collections import OrderedDict
from random import Random
from typing import Dict, Iterator, List, Optional, Union
import numpy as np
from torch.utils.data import DataLoader, Dataset, Sampler
from rdkit import Chem
from .scaler import StandardScaler
from chemprop.features import get_features_gene... |
35edbbc5cce3103d37569aef138407646ea5fea5783e828963a77b8a01ba42a9 | Python | 27,775 | 460 | #---------------------------------------------------------------------------------------------------------
# Author: Brandon Coventry, PhD Wisconsin Institute for Translational Neuroengineering
# Date: 02/16/2023
# Purpose: This is an implementation of PyMCs implementation of Krusckes Bayesian Estimation Su... |
aa204a41e007135244e9d0abdea61361e810d9839abd5f201d4aec8d35eef779 | Python | 27,775 | 601 | """finds bad channels."""
from copy import copy
import mne
import numpy as np
from mne.utils import check_random_state
from scipy import signal
from pyprep.ransac import find_bad_by_ransac
from pyprep.removeTrend import removeTrend
from pyprep.utils import _filter_design, _mad, _mat_iqr, _mat_quantile
class NoisyCh... |
ef05045a53cee1cb321058ef07e8b1d0b746a786d5fb3acba5a52f7ba101582f | Python | 27,780 | 779 | # 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... |
cc8c2caa4665ba386b491745a3ad697fc9d80de5e98428db43ae2f597ba2badf | Python | 27,786 | 828 | import argparse
from argparse import RawDescriptionHelpFormatter
from pathlib import Path
import os
import numpy as np
from scipy import fftpack, ndimage
from skimage.filters import threshold_otsu
import tifffile
import pywt
import multiprocessing
import tqdm
from dcimg import DCIMGFile
from pystripe import raw
from .l... |
f67d8953290e451e163a1c6f3567b9c8ab3730cc68bd03cf0acc2728cfb41b0c | Python | 27,786 | 800 | """
Parses output files from fusion-finding algorithms
"""
import csv
import re
import sys
import pandas as pd
class _Parser:
"""Base parser."""
def __init__(self, logger):
self.fusions = []
self.iterator = 0
self.logger = logger
def __iter__(self):
return self
def... |
97270dd90a412e1e1e3de81daf1656c7952b4f11b3187c560e679494ab5f82ac | Python | 27,813 | 942 | # 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... |
dd1c36cabe88a7bf3049c45919323c00f63ee2fb88d6b4b0ad6318e54ad14873 | Python | 27,815 | 711 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import numpy as np
from pathlib import Path
from scipy.ndimage import gaussian_filter1d
from scipy.stats import zscore, spearmanr
from scipy.cluster.hierarchy import fcluster, linkage
from sklearn.decomposition i... |
0b5664ca97e67c789cdebad907475824c983a2b33a39b7fad97b3628894dec0c | Python | 27,819 | 580 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 16 08:00:51 2020
@author: daltonm
"""
#%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.me... |
899c112478c4fe79b5f4528e463ea64cc17a744f62e079cce8d9bf698d791ca9 | Python | 27,819 | 515 | from best_cut_node import best_cut_node
import numpy as np
from sklearn import datasets
from sklearn.neighbors import KDTree, NearestNeighbors
from sklearn.decomposition import PCA
import pandas as pd
from numpy.linalg import svd
from sklearn.preprocessing import StandardScaler, normalize
import pandas as pd
import ran... |
6e480ed7e42d6b966b992ef04dd74acf473dcf41c39d3f5c7d865e18f9463229 | Python | 27,838 | 624 | import os
import sys
import h5py
import xron
import toml
import torch
import pysam
import shutil
import inspect
import argparse
import numpy as np
from typing import Callable
from datetime import datetime
from itertools import groupby
from functools import partial
from fast_ctc_decode import beam_search as fast_beam_s... |
cb54367ec57518a0ca3e2b686a938b2c4b3be5637312977897f45ecc2b7ddbb7 | Python | 27,845 | 730 | import contextlib
import functools
import logging
from typing import (
TYPE_CHECKING,
Dict,
FrozenSet,
Iterable,
Iterator,
List,
Mapping,
NamedTuple,
Optional,
Sequence,
Set,
Tuple,
TypeVar,
cast,
)
from pip._vendor.packaging.requirements import InvalidRequiremen... |
3b3740ff4fe5997566e50a807642e1d4f141b4d2d09dbf5ce6a27b61f58e17e6 | Python | 27,854 | 522 | import torch
import torch.nn as nn
from torch_geometric.nn import global_add_pool
from utils.constants import LayerType
class GAT(torch.nn.Module):
"""
I've added 3 GAT implementations - some are conceptually easier to understand some are more efficient.
The most interesting and hardest one to underst... |
e0f9dce01fd01b1e733db150142531a5e431a72df82debfeca922970c4831598 | Python | 27,875 | 796 | #!/usr/bin/env python3
# ******************************************
#
# SHARC Program Suite
#
# Copyright (c) 2023 University of Vienna
#
# This file is part of SHARC.
#
# SHARC is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by... |
460f1107f5302ece27634b6e2549dfbd2b837fd8ef3f7600d4d2b210f146b952 | Python | 27,885 | 850 | # 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... |
6731fbedf6dcad28f1eb353f7ef94a7543c37f8595697df349c6ac1d1aff303a | Python | 27,915 | 443 | import os
import sys
from warnings import warn
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime
if __name__ == '__main__':
if len(sys.argv) < 2:
anatomy_subject_name = 'DTI004'
ecg_subject_name = 'DTI004' # Allows using a different ECG for the per... |
90534f53b59f7ff61383162c3437fdc7cf179a3bda27bb70cae5354ffd53d5b7 | Python | 27,926 | 749 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scanpy as sc
from scipy.sparse import issparse
from sklearn.preprocessing import normalize
import torch
from torch.utils.data import TensorDataset, DataLoader, WeightedRandomSampler
import torch.optim as optim
import torch.nn as nn
import tor... |
3141199971e874ee7bcae35e13874c4eece517004f363bd6be870a33544346e1 | Python | 27,929 | 451 | # coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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 applicable... |
34b60f5bcfd847934f151ccbd6971e24757025d8f9b835d52fab618b14ac2c3b | Python | 27,948 | 607 | #https://machinelearningmastery.com/image-augmentation-deep-learning-keras/
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from PIL import Image
import glob
from PIL import Image
from PIL import ImageOps
from PIL import ImageFilter
from PIL import ImageEnhance
from PIL ... |
080648315aa9421a7ac0f1fabd38df173ebc4b72f44f85f3ddfa5faa2a7827fc | Python | 27,961 | 901 | # Nucleus 2022.04
# Tiago Rito, The Francis Crick Institute
import os
from skimage import io
import numpy as np
import matplotlib.pylab as plt
from matplotlib import colors
#!pip install Faker
from faker import Faker
fake = Faker()
import pandas as pd
import cv2 as cv2
#!pip install networkx==2.5
#!pip install cupy-c... |
78511e7466afadd1bf7e8f268195f69514cb7c28f885ce4811259d358b79d45a | Python | 27,984 | 602 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 16 08:00:51 2020
@author: daltonm
"""
#%matplotlib notebook
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pickle
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.me... |
d7ba9f751c0ef9e877b49aa165790374501984af4d6907cbf971dbde8b745e30 | Python | 27,984 | 658 | """Format templates for HED schema error messages.
Add new errors here, or any other file imported after error_reporter.py.
"""
from hed.errors.error_reporter import hed_error, hed_tag_error
from hed.errors.error_types import (
ColumnErrors,
DefinitionErrors,
ErrorSeverity,
SidecarErrors,
TagQuali... |
d83d1ba0d513169f5e0ee131d499e976d205db60320350278c3ef4cf97ec445d | Python | 28,007 | 758 | import datetime
import multiprocessing as mp
import os
import sys
import time
from collections import OrderedDict
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from topaz.filters import AffineDenoise
from topaz.model.utils import load_state... |
16f42fe02d3da189c983781533058319789ccaed4c940ba4d7ebb5bc72fcbad2 | Python | 28,024 | 677 | # 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... |
d5ab7da4cad931ab6ec69596d9168e4a657dd1b9a3ed4c9b1cbe74e9474bb909 | Python | 28,033 | 758 | # set QT_API environment variable
import os
os.environ["QT_API"] = "pyqt5"
import qtpy
import pyqtgraph as pg
import pyqtgraph.dockarea as dock
from pyqtgraph.dockarea.Dock import DockLabel
import control.utils.dockareaStyle as dstyle
import numpy as np
from collections import deque
# qt libraries
from qtpy.QtCore ... |
635eb2db6016d644f31b11c4b52d70b7bc74b221b49841e18dcc0f0e228cbf49 | Python | 28,051 | 542 | '''
Implements the combinedFC connectivity method published in:
Sanchez-Romero, R., & Cole, M. W. (2020). Combining multiple functional connectivity methods to improve causal inferences. Journal of Cognitive Neuroscience, 1-15.
Toolbox to reproduce the results of the paper in https://github.com/ColeLab/CombinedFC.
Ver... |
6ce6dbb57e4e9d3b56562723d07aa266a8ec7813a7cae664c295865a49fa5eb1 | Python | 28,073 | 489 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
inference_prod_activate = True # Activate Inference Procedure?
data_path = 'Shallow_Grid_1_N_Link'
acts = [0] # [0, 1, ...]
act = max(acts)
_observe = 1 # {0, 1, ...} # L ~ {1... |
c329f4df1ca85b29e1602944d95967fde65b2fb17f94900a8ca9ecba31d5687c | Python | 28,074 | 489 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
inference_prod_activate = False # Activate Inference Procedure?
data_path = 'Shallow_Grid_1_N_Link'
acts = [0] # [0, 1, ...]
act = max(acts)
_observe = 1 # {0, 1, ...} # L ~ {... |
bc0999328a9f57019e06b4a011769aa049b7fde5c0697c005b3ff64184b247ee | Python | 28,084 | 542 | #%%
"""This script is used to gathering reads from a given sites file with a sam index file."""
import os
import sys
import h5py
import pysam
import numpy as np
import pandas as pd
from tqdm import tqdm
import random
import time
from typing import List
from multiprocessing import Pool,current_process,RLock
from multip... |
c54ee368b0ebcf9bb3a9111fe80843e85ac0489d54b8a5c84b4471c04d2cd787 | Python | 28,089 | 772 | import sys
import os
import time
import math
import shutil
import torch
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from torch.autograd import Variable
import struct # get_image_size
import imghdr # get_image_size
class AverageMeter(object):
"""Computes and stores the average and current value... |
4f51ab5989b563c01f54ee4b7af439e138982286c2e617383320236179e5f681 | Python | 28,098 | 501 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
cellulate = (0, 0) # (cell_layers, layer_cells)
reiterate = f'Reiterate_{cellulate[0]}_{cellulate[1]}'
data_path = 'Shallow_Grid_1_Rule_Kern'
acts = [3] # [0, 1, ...]
act = ma... |
6f8f8819b61d475084fbee4097358d8699f44d000533228b0fcd1a72c623ba3c | Python | 28,119 | 497 | ##############################
######## Shallow Grid ########
##############################
#%%# Catalyzer
sup_comp = False # Super Computer?
cellulate = (0, 0) # (cell_layers, layer_cells)
reiterate = f'Reiterate_{cellulate[0]}_{cellulate[1]}'
data_path = 'Shallow_Grid_1_N_Link'
acts = [7] # [0, 1, ...]
act = max(a... |
3709251d1291cc7263b15cf32d4a9c4a3e72f84c6c1847136effa5237608cf9d | Python | 28,131 | 762 | """
TODO: Check the commented code for modifying it and getting the appropriate plots; displaying all channels with offset
Some functions are modified from Gabrielle Schroeder's functions
"""
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn.metrics as sk_metrics
import scipy.st... |
b628da9dbe2d9aeb779a3e9d6a235a24ae2887803275f51fad3c79ab90daf826 | Python | 28,169 | 694 | import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import torch_geometric
import copy
from .activation import (
ScaledSiLU,
ScaledSwiGLU,
SwiGLU,
ScaledSmoothLeakyReLU,
SmoothLeakyReLU,
GateActivation,
SeparableS2Activation,
S2Activation,
)
from .layer_norm i... |
be5a4ad3a65e3913ab746dd932c05e7f3bbeb624612b834b63c5de4191153345 | Python | 28,189 | 676 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Main simulation script for setting up the model producing the data shown in
figure 7 and 8 in:
Multimodal modeling of neural network activity: computing LFP, ECoG, EEG
and MEG signals with LFPy2.0
Espen Hagen, Solveig Næss, Torbjørn V Ness, Gaute T Einevoll... |
870c5edffa539b4c2f97a631d3a6abc7cd26a01384a5f92017faf2303d69d4d6 | Python | 28,192 | 651 | """
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import sys, os
import numpy as np
import pyqtgraph as pg
from qtpy import QtGui, QtCore
from qtpy import QtWidgets as QtW
from qtpy.QtCore import QEvent
from qtpy.QtWidgets import QMainWindow, QApplication, QWidg... |
d07d053b8385c96cb26e92c1f467dc8fe391d5168a0def7c569b793ef6c2d8ea | Python | 28,192 | 632 | """the feature extraction module of deepsignal2.
output format:
chrom, pos, alignstrand, pos_in_strand, readname, read_strand, k_mer, signal_means,
signal_stds, signal_lens, cent_signals, methy_label
"""
from __future__ import absolute_import
import sys
import os
import argparse
import time
import h5py
i... |
ea691e2e33df91e21a202e97625560cd75455172d3a24afc31b53914fa8a1027 | Python | 28,192 | 641 | import os
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
import SimpleITK as sitk
import random
import nibabel as nib
from monai.transforms import \
Compose, Resize, AsChannelFirst, ScaleIntensity, ToTensor, RandRotate, RandFlip, RandZoom, EnsureChannelFirst
impor... |
fefbce95ef492e6d482821a23f3c96213f4da728a1907f70e0764064243ee9ab | Python | 28,207 | 752 | """
OXASL: Multi-step Bayesian model fitting for ASL data
Based on the BASIL shell command from FSL
"""
import sys
import math
import numpy as np
import scipy.ndimage
from fsl.wrappers import LOAD
from fsl.data.image import Image
from oxasl import __version__, __timestamp__, reg
def run(wsp, prefit=True, **kwargs)... |
960c807fe97c867b867ce2f5db2260d847d0276b5d3f8b2699fdbc771fa055f1 | Python | 28,220 | 1,566 | from __future__ import annotations
from pathlib import Path
from typing import Sequence
import matplotlib.colors as mcolors
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from matplotlib.gridspec import GridSpec
from mpl_toolkits.axes_grid1 import make_axes_locatable
from scipy.cluster.hierar... |
65de1e702eaaaf5700220bbe17ebe85efe699b62d58ebc867c17130916d15361 | Python | 28,222 | 451 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Feb 5 2024
@author: MegNParker
https://www.statsmodels.org/stable/gettingstarted.html
"""
#%%
import numpy as np
import pandas as pd
import statsmodels.formula.api as smf
import statsmodels as statsmodels
import math
import statsmodels.api as sm
impo... |
a835694592cc15a83f9f05334197d44928ac45f851fbc1463e1028d442afe735 | Python | 28,234 | 675 | """
@author: heavens
"""
from torch import nn
from torch import Tensor
import numpy as np
import torch
import xron.nn
import pandas as pd
from copy import deepcopy
from functools import partial,reduce
from itertools import permutations
from fast_ctc_decode import beam_search, viterbi_search
from numpy.random import def... |
9f33c62c10f0e7d735d89c3efa5b3360e180c0373ade7395ba6fcc5dde4ea954 | Python | 28,247 | 654 | #!/usr/bin/env python
"""
Script to visualise spatiotemporal evolution of activity
Based on the following modules:
Load npx data (load_npx.py) as module SpikeStruct with following variables:
- st spike times
- spikeTemplates template identity for each spike
- clu clusted ... |
785c483371d1a7f3a7482becdceb2ce605f566bebe4d908832b9607abeacb150 | Python | 28,264 | 643 | """
Performs Univariate 1st order analysis to a SetListTomograms object by tomograms
Input: - A STAR file with a set of ListTomoParticles pickles (SetListTomograms object input)
Output: - Plots by tomograms:
+ Plots by list:
* Univariate 1st order analysis against ran... |
3a647213e1160523194fcd8da43d5873d386fa41eee32cc7fc772190bb551699 | Python | 28,277 | 619 | import os
import unittest
from hed.errors.exceptions import HedFileError
from hed.tools import get_file_list, get_new_dataframe
from hed.tools.analysis.file_dictionary import FileDictionary
from hed.tools.analysis.tabular_summary import TabularSummary
class Test(unittest.TestCase):
@classmethod
def setUpClas... |
a6df3d405ebf4d782c9dabebe1e1074399e0ac35079bb24d22945cd86457c947 | Python | 28,281 | 714 | # pylint: disable=line-too-long,too-many-lines,missing-docstring
import os
import warnings
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
from torchvision import transforms
from . import video_transforms, volume_transforms
from .loader import get_image_loader, get_video_loade... |
8d25ff4c4787df872ea6c429a92543b03e4403e9d502a478b8044aa332c9f255 | Python | 28,294 | 552 | ###############################
######## Objective Fun ########
###############################
#%%# Catalyzer
# import re
import numpy as np
# import numba
# from scipy import interpolate
# import torch
# from sbi.inference import simulate_for_sbi
# import time
import matplotlib
import matplotlib.pyplot as plt
plt.... |
1a79abdfaae1cf8dc023f3c514cd8b38a0e826e6be2a807a08ab1a29119c8fd6 | Python | 28,302 | 741 | """
m6A_semi_supervised.py
Author: Anupama Jha <anupamaj@uw.edu>
Training and data generator code adapted from bpnet-lite
(https://github.com/jmschrei/bpnet-lite)
This module contains an implementation of a semi-supervised
CNN model for m6A calling. We implement a similar approach
to the percolator algorithm for a CNN... |
8329c0356bf254d14f01df13d1c353db97a323586e3bef7667a632592acc54be | Python | 28,308 | 940 | """
Nonparametric fiducial inference for interval-censored survival data.
This module implements Algorithm 1 of [1]_.
References
----------
.. [1] Cui, Y.; Hannig, J.; Kosorok, M. R. (2024). "A Unified Nonparametric
Fiducial Approach to Interval-Censored Data". Journal of the American
Statistical Association, 1... |
fa54634be21d153b3ad62b4af72d1e158ac04f0f05ef58671da97813d1a1b6d5 | Python | 28,339 | 817 | # 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... |
a96ad72ec8fbfce57dc0953606e1e4c073d10095aa022c48b7c659ba12d3cb65 | Python | 28,346 | 655 | # 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... |
7115f3b2574bbb405830f6e2fcc1afc3581b1ca52a920aae6e58ef368699ad38 | Python | 28,372 | 716 | """
Script for cleft segmentation
Input: - Pickle files with MbGraphMCF for post- and pre-synaptic membranes
- Membranes segmentation files
- xml file with mb_slice definitions
Output: - Cloud files
- Segmentations
"""
__author__ = 'Antonio Martinez-Sanchez'
######... |
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