sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
617c0e6749870e48b2d266c5057281a278a9c7ae46aef0aef1a8fe9b87ddb741 | Python | 5,597 | 146 | import torch
import numpy as np
import pandas as pd
import os
import torch.nn as nn
from .neural_net import get_loss
# def process_files(output_folder):
# smallest_loss = np.inf
# best_model_folder_path = None
# best_mod=None
# for folder_name in os.listdir(output_folder):
# folder_path = os... |
dc317a089b0778bd184d3f695e5616bd591c923e29e772281452ae137d9afd0e | Python | 5,602 | 174 | import unittest
from unittest import TestCase
from unittest.mock import MagicMock, patch
import tempfile
import shutil
import os
import sys
import pytest
import numpy as np
import imageio
import glymur
import pandas as pd
import matplotlib.pyplot as plt
try:
import rasterio
import neuron_morphology.snap_polyg... |
2eeb57163544b2b377ff75841f4f6a60a0457d620b06f557c2810c412505c5e7 | Python | 5,603 | 154 | """
An implementation of Context dependent GRU.
the gru uses the implementation from:
https://github.com/emadRad/lstm-gru-pytorch/blob/master/lstm_gru.ipynb
"""
import math
import torch
import torch.nn as nn
import numpy as np
class CGRU(nn.Module):
def __init__(
self, input_dim, hidden_dim, output_dim, c... |
dc9ed3b6289d5f219781d69aca582abd7fada1a932adc68f2ec65232773a7804 | Python | 5,605 | 180 | import argparse
import numpy as np
import torch
import pfrl
from pfrl import experiments, explorers, replay_buffers, utils
from pfrl.wrappers import atari_wrappers
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--env", type=str, default="BreakoutNoFrameskip-v4")
parser.add_argument(... |
9476bf3bd2c1d0f3771ba0ac4c3a8d89cdafb99c7a09a5886842d4767752d644 | Python | 5,606 | 116 | import copy
import numpy as np
import gym
from gym.spaces import Discrete, MultiDiscrete
from typing import Dict, List, Union, Optional
from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.misc import SlimFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.policy.rnn_se... |
47c3d98753835c78c616e06e65b1f4af457451c0c459c8a0166797252246f912 | Python | 5,607 | 133 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
7794f4c0f86b7016f2a27a44d26193ca5f58180ab398a9fe245fe8561377f959 | Python | 5,611 | 183 | '''
Estimate layer profiles across geometric "columns" in the calcarine sulcus
'''
import nibabel as nb
import numpy as np
import os
import glob
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('dark_background')
# Set subjects to work on
subs = ['sub-05','sub-06','sub-07','... |
a52a942c08f6d11ae6e61d878e7d5e930daa24f50e6c9dba0e43b2411144eb60 | Python | 5,612 | 195 | """Logging configuration and utilities."""
import logging
import sys
from pathlib import Path
from typing import Optional
from rich.console import Console
from rich.logging import RichHandler
from rich.traceback import install as install_rich_traceback
from ..config import settings
# Install rich traceback handler... |
cb55bdc9076bafd6dd9ba96b531569882fc12e4134ec44d29ceb96b72120093f | Python | 5,615 | 121 | # This file is part of the INTREPPPID programme.
# Copyright (c) 2023 Joseph Szymborski.
#
# This programme is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, version 3.
#
# This programme is distributed i... |
573d04235661dadace5e30ca3a8d95bc4667ed3727ea0530f876e54f5c308161 | Python | 5,617 | 157 | import torch.nn as nn
import torch
from torchvision import models
import torch.nn.functional as F
def initialize_weights(models):
for model in models:
real_init_weights(model)
def real_init_weights(m):
if isinstance(m, list):
for mini_m in m:
real_init_weights(mini_m)
else:
... |
c7164adf4fdc07beb59763c728e5651739e27bba0e0d65cc552911497d5b6be5 | Python | 5,617 | 142 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Convert a VCF to a BED.
"""
import argparse
import sys
import pysam
import pybedtools as pbt
import svtk.utils as svu
def vcf2bed(argv):
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.RawDescriptionHelpFormatter... |
857ae8c42f5d13683fe1392d32a3dc2f1152fb2bc7f5aad502c889f4b4519bfe | Python | 5,618 | 201 | # Ultralytics YOLO 🚀, GPL-3.0 license
from pathlib import Path
import cv2
import numpy as np
import torch
from PIL import Image
from ultralytics import YOLO
from ultralytics.yolo.data.build import load_inference_source
from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
MODEL = Path(SETTINGS['weights_... |
17d0c0e0b844323544d420c20438f6be9259369578e6ff46a1dbe20ecc7ea473 | Python | 5,621 | 103 | import numpy as np
import sys
import os
import scipy.io as sio
import time
from scipy import sparse
sys.path.insert(1, '../SUNS2') # the path containing "suns" folder
from suns.PostProcessing.combine import piece_neurons_IOU, piece_neurons_consume
from suns.PostProcessing.evaluate import GetPerformance_Jaccard_2
# da... |
ba26b823eb2cccfdaa9a599e2b399c3e91cb5f7871f6c8408a354aa85bddbedc | Python | 5,623 | 152 | import argparse
import numpy as np
import pickle
from datetime import datetime
import general.utility as u
import swap_errors.auxiliary as swaux
import swap_errors.analysis as swan
def create_parser():
parser = argparse.ArgumentParser(description="fit several modularizers")
parser.add_argument(
"-o",... |
dee7b80c9dcd994c84f2e36fa87f442c68005ccc125fce312c77f8c96787fa92 | Python | 5,623 | 154 | '''
Create TFRecordDataset for FBN project
Use in NeoCortex
Author: Xiaohui Zhang
Date Created: 03/07/2022
Data Updated: 09/06/2022
'''
# label wake=0, nrem=1, artifacts=2, rem=3
import os, glob, re, h5py
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from functools import partial
import sc... |
5e5a1925275f12377638a2a028bab84e6fc0e49218912aadcef4370f5978a413 | Python | 5,624 | 175 | # Copyright 2023 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
39d48b1481ab838e25d5a60838e8fffd34f8aa225c5b389485ed4f30e6cc88b4 | Python | 5,625 | 172 | from array import array
import os
from pathlib import Path
import pdb
import pickle
import numpy as np
import oci
from multiprocessing import Process
from multiprocessing import Semaphore
import ocifs
import torch
import yaml
# Number of max processes allowed at a time
concurrency= 5
sema = Semaphore(concurrenc... |
8bd672096a865587a6151c35473d5be3e446c74650027ee1149f1e5868acdc3c | Python | 5,625 | 134 | # coding=utf-8
from multiprocessing import Pool
from typing import Mapping, Optional
import numpy as np
import pandas as pd
from scipy import stats
from scipy.optimize import minimize_scalar
from sklearn import mixture
from tqdm import tqdm
from .diptest import diptst
def _derive_threshold_task(args):
regulon_... |
6c92b0eba5430f0459e824751b6fc4ba977a8cd1e79ab65b13cd73b2138a0d8f | Python | 5,626 | 202 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""aihwkit example 3: MNIST training.
MNIST training example based on the paper:
https://www.frontiersin.org/articles/10.3389/fnins.2016.00333/full
Uses learni... |
bf6c0e43c41273026d9de4b2221a7c646f25347f82e6ce4a82445d383ef95b9a | Python | 5,626 | 125 | # -*- coding: utf-8 -*-
"""
Created on Fri Oct 29 18:07:26 2021
@author: Nikhil
"""
#CWT analysis
#CWT analysis works better with smooth waveforms. So we will use a higher sigma gaussian filter
#to smooth pixel values
#reads image
image = io.imread(filename_full)
dim = rescale(image, image_scaling_factor... |
73929fe1adcc13f59e53240b4a89838120c0ffdfde4dcc7d07f759f64af32365 | Python | 5,627 | 207 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
# from SSN_model_funcs import *
import SSN_model_funcs as ssn
from tqdm import tqdm
import pickle
tqdm.pandas()
df = pd.read_feather("fit_result4.feather")
with pd.option_context("mode.use_inf_as_null", True):
df = ... |
d76f16425e08096094e7a6b947f988583a2ea17986ed8e88f69917544605ceef | Python | 5,630 | 155 | """Efficient MPI gathering for the regular recordings produced by Mozaik."""
import neo
import numpy
import quantities as pq
import mozaik
SPIKE_ANNOTATIONS = {"channel_id", "source_index", "source_population"}
SIGNAL_ANNOTATIONS = {"channel_ids", "source_population"}
def gather_recording(block):
"""Gather a r... |
60c664156be731936b5a480870f83936c35c3708e770cf36e3c4c53fbccb3d85 | Python | 5,631 | 158 | """Script to run a jupyter notebook for a particular model."""
import argparse
import logging
import os
import sys
import tempfile
import papermill as pm
import flyvis
from flyvis.utils.config_utils import HybridArgumentParser
if __name__ == "__main__":
parser = HybridArgumentParser(
hybrid_args={
... |
58976d8f02504a03045341f6f1537dbc6ffd62737e80be21e14958735127d89d | Python | 5,633 | 173 | import common
import fixtures
from nose.tools import eq_, raises
from nose.plugins.attrib import attr
from mock import MagicMock, patch, Mock, call
from os.path import *
import os
from ngs_mapper.compat import OrderedDict
class Base(common.BaseBamRef):
modulepath = 'ngs_mapper.stats_at_refpos'
def setUp( se... |
c1df18baf1ecb98a7c2bfdc9701e0c882a33536bef53a135eb1b00c168bbd8d5 | Python | 5,634 | 186 | from pathlib import Path
import pandas as pd
import numpy as np
from collections import defaultdict
import random
from scipy.stats import multivariate_normal
from scipy.stats import wasserstein_distance
from scipy.stats import pearsonr
import matplotlib
# matplotlib.use('Agg')
import matplotlib.pyplot as plt
from mat... |
fb4d3eeee8f851ceffd3837e162da9027c23624c24ebe267d7c9cb271d0b0c7c | Python | 5,636 | 146 | from typing import Tuple
import numpy as np
from scipy.linalg import eigh
import matplotlib.pyplot as plt
from pyriemann.utils.mean import mean_riemann
import mne
from mne.decoding.base import BaseEstimator
from mne.decoding.mixin import TransformerMixin
import warnings
class EventBasedArtifactProjector(Transformer... |
5b255b877cb8ed2c4f514098a30cbaf5d8b08df65db41c426829b236786152d8 | Python | 5,639 | 148 | #!usr/bin/python
"""Compute representations using just neurotransmitter-related genes."""
# --------------------------------------------------------------
# Compute a UMAP for each major celltype alone for visualization
# Updated: 09/03/21
# --------------------------------------------------------------
import scanpy a... |
84b2314edfb9728b43dea627e04758352d8b4e27f8d03e2a66178e35e05af58c | Python | 5,641 | 203 | # -*- coding: utf-8 -*-
"""
Created on Fri Apr 30 10:52:00 2021
@author: ju-bar
Functions and classes handling atom data
This code is part of the 'emilys' repository
https://github.com/ju-bar/emilys
published under the GNU General Publishing License, version 3
"""
import re
import numpy as np
import emilys.structure... |
6c83bcf83c99b56b3dc93717c946fbf8b6c504d08e85c8b52398235cd28c9d48 | Python | 5,643 | 154 | # -*- coding: utf-8 -*-
import os
import warnings
import nibabel as nib
import numpy as np
from nilearn._utils.niimg_conversions import _check_same_fov
from nilearn.image import index_img, new_img_like, smooth_img
from nilearn.masking import _apply_mask_fmri, intersect_masks
from nilearn.regions.parcellations import P... |
a5db5bcd0e0610af6ad6a69e00f72b296f899b725f3b53523eb33d5ef38c3e90 | Python | 5,646 | 149 | from collections import OrderedDict
from typing import Collection, Dict, Hashable, Mapping, Union
import numpy as np
from monai.config import KeysCollection
from monai.transforms import (
Compose,
RandGaussianNoised,
RandShiftIntensityd,
RandAdjustContrastd,
RandAffined,
RandFlipd,
RandRota... |
a1d9152b7a8d8a7dfffcf8ba50847794dd5e73ba859dae08c020b5c34912b3b3 | Python | 5,647 | 156 | from Bio import SeqIO
import re
import sys
def GC(seq):
"""
Calculate G+C content, return the percentage (float between 0 and 100).
Copes mixed case sequences, and with the ambiguous nucleotide S (G or C)
when counting the G and C content. The percentage is calculated against
the length of the se... |
a937a03fcf2325baf3b127184593c0828046938b3565dac17ee48a03a212747d | Python | 5,652 | 177 | import sys
from unittest.mock import AsyncMock
import pytest
import ray._private.ray_constants as ray_constants
import ray.dashboard.consts as dashboard_consts
from ray._common.ray_constants import (
LOGGING_ROTATE_BACKUP_COUNT,
LOGGING_ROTATE_BYTES,
)
from ray._raylet import JobID
from ray.core.generated imp... |
11effba83aab3c0348618a89094862bb36ca7b4bb537167c916abda84e5bf590 | Python | 5,654 | 147 | # built-in dependencies
from typing import List, Union, cast
# 3rd party dependencies
import torch
from torch import nn, Tensor
import torch.nn.functional as F
# project dependencies
from deepface.commons import weight_utils
from deepface.models.facial_recognition.pytorch.TorchFacialRecognition import (
TorchFaci... |
7a64041282d3b866122e9cc984a1693f8a92fe0506deabc04fd0b70c18bc7542 | Python | 5,656 | 221 | import json
import torch
import pandas as pd
import numpy as np
ELEMENT_MAP = lambda x: {
"H": 0,
"C": 1,
"N": 2,
"O": 3,
"F": 4,
"S": 5,
"P": 6,
"F": 7,
"Cl": 7,
"CL": 7,
"Br": 7,
"BR": 7,
"I": 7,
}.get(x, 8)
AA_INDEX = {
"ALA": 0,
"ARG": 1,
"ASN": 2,
... |
03d49c23d95c07b16aeb67dfaef08817cce1c1ae78ad6c3650a2fef3a0449507 | Python | 5,659 | 176 | # -*- coding: utf-8 -*-
# (C) Copyright 2020, 2021, 2022, 2023, 2024 IBM. All Rights Reserved.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#... |
7d3876541229bfb14c4cf329ca1e38686c3e950ef927e69dae91bd2a3ffc0268 | Python | 5,661 | 175 | from typing import Any, Dict, Tuple
import numpy as np
import pandas as pd
from data_profiling.utils.compat import pandas_version_info
if pandas_version_info() >= (1, 5):
from pandas.core.arrays.integer import IntegerDtype
else:
from pandas.core.arrays.integer import _IntegerDtype as IntegerDtype
from data_... |
c129412545536b0ce2fb5d91a55b20af1a76fe259ffe299f4d35fa83156d7190 | Python | 5,664 | 153 | """SEACells backend (Persad et al., Nat Biotech 2023)."""
from __future__ import annotations
import time
from typing import Optional
import numpy as np
from scipy import sparse
from .base import FitResult, MetaCellBackend
class SEACellsBackend(MetaCellBackend):
name = "seacells"
capabilities = {"soft", "l... |
d38aab66e1f8c1a8cbdd93f697cd2d277bf0967ce311b9e324c86b156cfd0bbc | Python | 5,665 | 133 | import pathlib
path = pathlib.Path.cwd()
if path.stem == 'tmb_survival':
cwd = path
else:
cwd = list(path.parents)[::-1][path.parts.index('tmb_survival')]
import sys
sys.path.append(str(cwd))
import numpy as np
from lifelines import CoxPHFitter
from lifelines.utils import concordance_index
import pandas as pd
i... |
37075b227f57160feacb1eec8dbf67d5d33fae7d5503a162f0d6080dd4245b21 | Python | 5,666 | 126 | """
This python file include all constants. Change them here, and the changes will apply to all python file that you run afterwards
"""
from scipy import signal
import numpy as np
##################################### BELOW ARE THE MOST IMPORTANT VARIABLES TO MODIFY#####################
#set debug to True to ... |
6265a1f4bd947d51aedbecbca1925c14fb6859fc858cf40860cc9dbb2e3af1fa | Python | 5,668 | 141 | from __future__ import division
import os
import sys
import cv2
import numpy as np
import torch
from torchvision.transforms import transforms
sys.path.append(os.path.join(os.getcwd(), 'models_', 'detectors', 'yolo'))
from .yolo.models import Darknet
from .yolo.utils.utils import load_classes, non_max_suppression
d... |
c8d1613584c0513c893898b01cb3ad322e648d15932745533f66c41b1ad32dc2 | Python | 5,668 | 136 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
e51a89483a19e318739b316fc885e07a52933aeb3c991173d76376f70bc6a645 | Python | 5,669 | 146 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
From Uchida Takumi https://github.com/uchida-takumi/CustomizedLinear/blob/master/CustomizedLinear.py
extended torch.nn module which cusmize connection.
This code base on https://pytorch.org/docs/stable/notes/extending.html
"""
import math
import torch
import torch.nn a... |
9851a6e8f8260fc721aea3c324c4d4cb71432fbd360198ac71bbdfdfee21c723 | Python | 5,670 | 128 | # Copyright 2023 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... |
a51e67871915e38bfd58bfc9c66fcf423c036bfbe4fce54d75f9e06fd53fa780 | Python | 5,671 | 150 | import os
from os.path import *
from Bio import SeqIO
import logging
log = logging.getLogger(__name__)
# Valid Read extensions
VALID_READ_EXT = ('sff','fastq')
def clip_seq_record( seqrecord ):
'''
Correctly trims sff Bio.SeqRecord.SeqRecord
@seqrecord - Bio.SeqRecord.SeqRecord object to trim phred_... |
d8c8200875f64921f90e1f01bcd18a28b23ad65b426e34e0894eb7f509ba97cd | Python | 5,671 | 175 | import pickle
import h5py
import numpy as np
import os
def parse_fasta_predict(dir, number=None):
"""
Load peptide sequences and labels from ACPXXX.txt
:param dir:
directory of ACPXXX.txt
:return:
acps: peptide sequences
"""
names = []
sequences = []
... |
bad8f2154fed57cb6503a2efc2c62c927ba3017f6c31a1732eecd7b05f7281ca | Python | 5,672 | 177 | """
QC metrics and checks.
Each QC step writes one or more QC result JSON files, typically from the rule
that computes the thing being checked, so the metrics are recorded without
failing that rule. A report rule then visualizes the results, and a separate
verify rule (which also requires the report, so it always exis... |
838756451f38fa03ed616c56eff4e174ea417f0fe192782f3c34c6ff7b492e85 | Python | 5,677 | 155 | """
the custom class, used to help customize settings for each
dependent variable for a future STAGG run
***
built as part of the Russell Ray Lab Breathing And Physiology Analysis Pipeline
***
Breathe Easy - an automated waveform analysis pipeline
Copyright (C) 2022
Savannah Lusk, Andersen Chang,
Avery Twitchell-He... |
bc00b694ed46919b8cb032b1efa4809cc995752ba50d6c5a22fb622c0db2a10e | Python | 5,677 | 162 | import argparse
import datetime
import json
import os
import pickle
import shutil
import subprocess
import sys
from binascii import crc32
import pfrl
def is_under_git_control():
"""Return true iff the current directory is under git control."""
return pfrl.utils.is_return_code_zero(["git", "rev-parse"])
def... |
c656264853facc5acdf3559021517815679896c0579211e3524600bbe45d2bdf | Python | 5,678 | 137 | import argparse
parser = argparse.ArgumentParser()
# about seed and basic info
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--runseed", type=int, default=0)
parser.add_argument("--device", type=int, default=0)
# about dataset and dataloader
parser.add_argument(
"--input_data_dir", typ... |
816070d3f3394eacfebd315c93761c5cb9cdb0e985d0cbec6c73455aeb0dd37f | Python | 5,679 | 142 | #!/usr/bin/env python3
import argparse
import numpy as np
import matplotlib.pyplot as plt
import pandas
from matplotlib.pyplot import cm
import os
import seaborn as sns
from collections import defaultdict
SEQUENCE_IDENTITY_IDX = 13
ALIGNMENT_IDENTITY_IDX = 14
SAMPLE = "Sample"
SEQUENCE_IDENTITY = "Sequence Identity"
... |
26a2eb262d619fe4ee8fa29640a13a4ae98d8579c08d9f07ac1b6c08facc319e | Python | 5,680 | 128 | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nibabel as nib
import json
import ivim
tissues = {'WM':3,'CGM':2,'DGM':4}
ylabels = {'WM':'White matter','CGM':'Cortical gray matter','DGM':'Deep gray matter'}
pars = ['D','f','vd']
plot_par = {'D':'$D$','f':' $f$ ','vd':'$v_d$'}
u... |
9877da21d784544af3aed696db83aec0a1d114af05b6ee4b76f031d9e280224d | Python | 5,681 | 133 | import logging
from datetime import datetime
from agent.data.entities.config import DEV_LABEL, TEST_LABEL, EXTERNAL_LABEL, CTT3_LABEL, CTT3FR_LABEL, CTT3HEALTH_LABEL
from agent.classifiers.ffnn_classifier import ffnn
from agent.classifiers.data.wrappers.count_features_fd_dataset_dev import get_datasets as get_dataset... |
a9dba8e54123f2e81096e1dbe0c6fd8e1706b3d9190ec3f5462b8339b688b23e | Python | 5,681 | 156 | import random
import unittest
from math import isclose
from unittest.mock import Mock, call
import torch
from rising.random import DiscreteParameter
from rising.transforms import (
Clamp,
ExponentialNoise,
GammaCorrection,
GaussianNoise,
Noise,
NormMeanStd,
NormMinMax,
NormRange,
N... |
b8c8443b9d03c5abb05adf1b3d4dd972bb5eb0e74c96f835efa0ca9aee60866b | Python | 5,683 | 195 | # -*- coding: utf-8 -*-
#
# Configuration file for the Sphinx documentation builder.
#
# This file does only contain a selection of the most common options. For a
# full list see the documentation:
# http://www.sphinx-doc.org/en/stable/config
# -- Path setup ------------------------------------------------------------... |
712bb1e31473d97adc61ecfc40fca7e49b3db6ffcfe6360db1c6b18ff282a893 | Python | 5,685 | 141 | import abc
import math
from dataclasses import dataclass
from typing import Optional
from ray.data._internal.average_calculator import TimeWindowAverageCalculator
from ray.data._internal.execution.resource_manager import ResourceManager
from ray.util.metrics import Gauge
@dataclass(frozen=True)
class ClusterUtil:
... |
733502e9fed6249acd09217f223a19f11b5ada6c545a788b35c2aef1f6eca1a0 | Python | 5,685 | 169 | import numpy as np
from contextlib import suppress
from math import ceil
from tensorflow import constant
from tensorflow.data import Dataset
from sklearn.cluster import KMeans
from copy import deepcopy
# All of this functionaliy has been taken from https://github.com/willtownes/nsf-paper/blob/main/utils/preprocess.py
... |
52357b5ed22be081c3266102f7b53990952a9a183f86be3937fdc8cbdbe20c12 | Python | 5,688 | 219 | import pytest
import torch
from torch import nn
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.from_torch import from_model
from sinabs.layers import SumPool2d
from sinabs.layers.iaf import IAFSqueeze
torch.manual_seed(0)
input_shape = (2, 16, 16)
input_data = torch.rand(1, *input_shape, requires_gra... |
57eb6de9a70c89a29060e94f92df4f3ec244960872a53df9c8fcb803a411692a | Python | 5,693 | 105 | #!/usr/bin/env python
# coding: utf-8
import warnings
import os
warnings.filterwarnings('ignore')
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # or any {'0', '1', '2'}
os.environ['TF_ENABLE_ONEDNN_OPTS'] = '0'
from tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint,TensorBoard
from tensorboard.plugins.hparams... |
a677bad17b3bdfd6b42df3eef5c772aa011daa7b030f1024bd008259ece37599 | Python | 5,693 | 170 | # Copyright 2023 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
71315d6573b5d250e211d0d3b3c7c29ae365a0583e7c9e61f237cee00703471a | Python | 5,695 | 145 | # run in /benchmark/src_compare_results/
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd
import os
def run_plotter(type):
# obtain the averaged data from POSITIVE sample
for db in ['GRCm39', 'Mmul_10', 'NHGRI_mPanTro3', 'TAIR10']:
avg_df = pd.read_csv(f'..... |
621d15a09c6431a83007e115d5116af7f6379418ccd7868337e3e650b0ad10ba | Python | 5,697 | 214 | r"""
Spatial transcriptomics analysis utilities.
This module provides comprehensive tools for analyzing spatial transcriptomics data
including spatial clustering, integration, trajectory analysis, and visualization.
Supported platforms include 10x Visium, Slide-seq, MERFISH, seqFISH, and others.
Spatial clustering an... |
04421cf4b886bdb07f73d19f0c5c9df51dd06887f9289ec4f18a5f4f549a22bc | Python | 5,698 | 172 | import numpy as np
from math import ceil
from sklearn.cluster import KMeans
from copy import deepcopy
# All of this functionaliy has been taken from https://github.com/willtownes/nsf-paper/blob/main/utils/preprocess.py
# and https://github.com/willtownes/nsf-paper/blob/main/utils/postprocess.py
def rescale_spatial_co... |
24c4165e1e5d6eb0a958c35e1793101f382d973ca98536698cc49f4af635f722 | Python | 5,698 | 177 | from typing import Dict, Any, Tuple, List, Set, Optional, Type
import inspect
from neuron_morphology.features.default_features import default_features
from neuron_morphology.feature_extractor.feature_extractor import \
FeatureExtractor
from neuron_morphology.feature_extractor.mark import Mark
import neuron_morphol... |
177d9223d7e99a6262c0fa704fb8a53c3698ba6953d61db64809af952b4701c5 | Python | 5,699 | 191 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 21 23:29:28 2021
@author: Saad Idrees, Joel Zylberberg's Lab, York University
idrees.sa@gmail.com
"""
# import needed modules
import numpy as np
import os
import models
from data_handler import prepare_data_cnn2d, load_h5Dataset, model_evaluate_n... |
e91f4171f0b265f5f9d4f40f1a83f6a3e3d782390a749c9104a79a5bfeae94ba | Python | 5,700 | 178 | import os
import subprocess
import spinnaker2.hardware
from spinnaker2.configuration import (
Experiment,
ExperimentConfig,
MemoryRegion,
PEConfig,
)
from spinnaker2.coordinates import (
PE,
QPE,
ByteAddr,
WordAddr,
align_addr_to_next_multiple_of_other,
)
from spinnaker2.helpers imp... |
ed8f17eecc8d18c59e92f92020bf0b03f3bac9d476f021db76ccb937d5fedb89 | Python | 5,701 | 151 | import copy
import logging
from typing import Dict, List, Optional, Set, Tuple
from ray.autoscaler.v2.instance_manager.storage import Storage, StoreStatus
from ray.core.generated.instance_manager_pb2 import Instance
logger = logging.getLogger(__name__)
class InstanceStorage:
"""Instance storage stores the state... |
592879768201d31c2cd2eaa7c39c88c2a2ec5d729ce10b059e84956e86d08130 | Python | 5,702 | 169 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from nilearn.plotting.cm import _cmap_d as nilearn_cmaps
import seaborn as sns
from ipywidgets import interact_manual, IntSlider
from neuromaps.transforms import mni152_to_fslr
from neuromaps.datasets import fetch_fslr
from surfplot import Plot
de... |
f2be8f674279e48ee3ba1332c87c4fcd6b9e0f923411701e34de6182c04440fb | Python | 5,707 | 104 | import numpy as np
import sys
import os
import scipy.io as sio
import time
from scipy import sparse
sys.path.insert(1, '../SUNS2') # the path containing "suns" folder
from suns.PostProcessing.combine import piece_neurons_IOU, piece_neurons_consume
from suns.PostProcessing.evaluate import GetPerformance_Jaccard_2
# da... |
9f02799ce07ad189a5e99c5e2bf39113bc6dba80e1e7ac5cefe81e49c94b181f | Python | 5,708 | 148 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Analog layers."""
from typing import Optional, Type
from torch import Tensor
from torch.nn import Linear
from aihwkit.nn.modules.base import AnalogLayerBase... |
0b3a9222eef4d886802204a3f0fa31f85243cc09fe1042afb87414443ffe992d | Python | 5,709 | 157 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
Basic stripped-down demo for running the CNMF source extraction algorithm with
CaImAn and evaluation the components. The analysis can be run either in the
whole FOV or in patches. For a complete pipeline (including motion correction)
check demo_pipeline.py
Data courtes... |
47b690e419d00ac1d616f5c3927d40857389e45e33a75d1f4551207012b90f6c | Python | 5,710 | 153 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
6808c67938562cc201825cb9604a9143326a089814e753ffb290bd69facc6060 | Python | 5,713 | 178 | r"""STPath backend — generative foundation model (npj Digital Medicine 2025).
Loads weights from HuggingFace ``tlhuang/STPath`` and runs zero-shot
inference on GigaPath features extracted from the WSI tiles. Predicts
log1p expression for up to 38,984 genes across 17 organs.
Reference
---------
Huang et al., STPath: a... |
782f98a0ccc0b536e148c582653691b8aebe3f011bf92ccd47e6806dc60900ec | Python | 5,719 | 120 | import pysam
import argparse
# Implementation note:
# This code implements the same python code in the CalculateCpxEvidences task without alterations.
def CPX_manual_readin(CPX_manual):
out = {}
fin = open(CPX_manual)
for line in fin:
pin = line.strip().split()
if not pin[0] in out.keys()... |
a8d18f1eef4b22898e1283208816fe708dd5807d66f6ed5811158feeb0cf3b04 | Python | 5,719 | 168 |
import pdb
import numpy as np
import torch
from data_assimilation.particle_filter.base import BaseParticleFilter
import matplotlib.pyplot as plt
class BootstrapFilter(BaseParticleFilter):
def __init__(
self,
**kwargs
) -> None:
super().__init__(**kwargs)
self._upda... |
fe7f3957b4dc9a1f7f0ba45a9f80ca247721064474a87f37f2c09932b39a32a7 | Python | 5,720 | 171 | # Copyright 2023 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
3ddd1b35e1023f002c0bf802ccbabb5765207c248487787afe77016f3f73107c | Python | 5,721 | 110 | import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
import random
import os
def main():
# for TOOL in ["SPLAM", "SPLICEAI"]:
COLORS = ["green", "blue"]
TOOLS = ["SPLAM", "SPLICEAI"]
TARGETS = ["Donor", "Acceptor"]
output_files = ["pos_MANE", "pos_ALTS", "neg... |
c541a41a26782263e9a9e65811c43158b0140614be3e8bc08e9de1da7a696fbb | Python | 5,725 | 206 | import abc
import operator
import time
from abc import abstractmethod
from collections import defaultdict
from typing import Dict, List, Optional, Tuple
import ray
from ray.autoscaler.v2.schema import AutoscalerInstance, ClusterStatus, ResourceUsage
from ray.autoscaler.v2.sdk import get_cluster_status
from ray.core.ge... |
55c115b007906db61b817957f60093fa501abca19c23f1d9b286525db3b8ed19 | Python | 5,727 | 161 | __author__ = "Simon Andermatt, Simon Pezold"
__copyright__ = "Copyright (C) 2017 Simon Andermatt"
import copy
import logging
import os
import subprocess
import sys
import numpy as np
from mdgru.helper import argget, compile_arguments, generate_defaults_info
class DataCollection(object):
'''Abstract class for all... |
784de60272dbdbc3252bd7277924a630a9dd254ff1e303c2df08225f79b52f61 | Python | 5,727 | 139 | import argparse
import json
import os
import glob
import re
import joblib as jl
import numpy as np
import sklearn
from examples.pi_vae import task
class piVAEEmission():
def __init__(self, path,save=False):
if not os.path.isdir(path):
raise ValueError(f"{path} is not existing")
... |
77ad8fb85ca8b6b9b350b7a05b2927349ac6b04685e15a1a0bf8f798ca6ef6d5 | Python | 5,728 | 144 | import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import LeaveOneOut
from sklearn.metrics import mean_squared_error
from scipy.stats import pearsonr
from sklearn.preprocessing import StandardScaler
import statsmodels.api as sm
from sklearn.svm import SVR
import matplotlib.pyplo... |
abeda534a32ce886569c063d6a293feb3c63d2574388f1af9ad6eeca4c3a6e97 | Python | 5,729 | 147 | from collections import defaultdict
import logging
from typing import Dict, List
from .predict import predict
from GR_pKa.data import MoleculeDataLoader, StandardScaler
from GR_pKa.models import MoleculeModel
from GR_pKa.utils import get_metric_func
import numpy as np
import pandas as pd
def evaluate_predictions(pre... |
8b5d48b611039cbbf902fbcb5fc4a95d636d76146eaa9bd424e38be2d4ed5aa6 | Python | 5,731 | 195 | """
Documentation example and test for custom tokenizer batch inference.
Demonstrates how to use vLLM's tokenizer infrastructure for models whose
tokenizers are not natively supported by HuggingFace (e.g. Mistral Tekken,
DeepSeek-V3.2, Grok-2 tiktoken).
This example uses a standard model to demonstrate the pattern. F... |
c2bddc1eb0d855b5eb813b0247ae2c1742788d1b1d2243572b54e283bb5bf000 | Python | 5,731 | 171 | from PIL import Image
import numpy as np
from ctypes import *
import ctypes
import copy
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import cv2
import time
import pdb
import socket
import pickle
import scipy
import scipy.ndimage
import matplotlib.pyplot as plt
from datas... |
e8e988a34cc75fd99dccc0f36a7729583589949d9965b374669d632b18aa9929 | Python | 5,731 | 135 | import numpy as np
import argparse
import pickle
from typing import List
import lib.data_utils.data_util as du
import lib.data_utils.dynamic_data_util as ddu
import lib.dataset_config_parser.dataset_config_parser as dcp
from lib.data_utils.sta_metadata import compute_convex_hull_of_mask
from lib.dataset_specific_hype... |
351653848f05ef8fcb9c8be15d5b309e48787d461ff932bcd1fd99b8e8445a76 | Python | 5,732 | 148 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import numpy as np
from ase import neighborlist
from ase.neighborlist import natural_cutoffs
class De... |
591846c114afcf742d3d585c38c6364ad3a4078957506fdcd27203adb7136d9c | Python | 5,732 | 171 | # Copyright 2023 Google LLC
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, sof... |
ad4895f413784c2b9e6d0a583ca4332e634703ab79d17d3db11509f9cb6c9007 | Python | 5,732 | 195 | #!/usr/bin/env python3
"""
Ray job to generate SigLIP text embeddings for tags and descriptions.
Usage:
ray job submit --working-dir . -- python jobs/generate_text_embeddings.py --bucket my-bucket
"""
import argparse
import asyncio
import io
import json
import time
import aioboto3
import numpy as np
import ray
i... |
fc206ea8a31bab5fc4cc8a9d3c00785b4d0f0ad619f8d4bf0232d532a7b1fe93 | Python | 5,732 | 106 | """Parameters corresponding to paramsPCAGaborTuning_v1.m plus execution controls."""
from dataclasses import asdict, dataclass
from pathlib import Path
import json
@dataclass
class PalmConfig:
image_size: int | None = None
pca_patch_size: int = 15
pca_filters: int = 15
retained_variance: float | None ... |
1712257906886f3957c22a8c822624086009d3270f7a4b14946a295f748ceee8 | Python | 5,733 | 154 | from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion ob... |
6885af4d1c97fc2ae5ae58ea357322d6968bedd272c57ef34ccc98f804141514 | Python | 5,733 | 133 | # coding: utf-8
r"""
Definition of the component interfaces. These interfaces are not currently directly checked or enforced.
"""
from mozaik import __version__
from parameters import ParameterSet
import parameters.random
from parameters.random import UniformDist
import mozaik
from mozaik.tools.distribution_parametriz... |
9552a2d493aa6055926933ad77556cfbc7aa87ae79b9356d263b0f228137fa53 | Python | 5,736 | 194 | import os
try:
print('SlURM_JOB_ID',os.environ["SLURM_JOB_ID"])
except:
print("no slurm id")
import matplotlib
matplotlib.use('agg')
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import tables
import gc
import scipy
import sklearn.metrics as skm
import sklearn.linear_model as skl
impor... |
6bf8161d6e1f6d0fb35b94eeb1085b62218e7b7e91327895bb7206a018e2756f | Python | 5,739 | 144 | #!/usr/bin/env python
from __future__ import print_function
from multiprocessing import Manager, Process, current_process
import random
import time
TOTAL_KEY = "total"
FAILURE_KEY = "failure"
MEM_USAGE_KEY = "mem_usage"
# here is an example of a class which will "perform work". this could also just be a function tha... |
0583ed2b2d7ca5e1c1951f35410adc9de01246b1f790cab555d5e129727183d5 | Python | 5,740 | 141 | import tensorflow as tf
from models.deep_learning.deep_learning_models import DLRegressor
from utils.constants import is_spline
class ResNetRegressor(DLRegressor):
"""
This is a class implementing the ResNet model for time series regression.
The code is adapted from https://github.com/hfawaz/dl-4-tsc des... |
ba6db9524e23e8fbec7ccdd1c6cfdaac635a93f6f186d507717ef38514b32665 | Python | 5,740 | 185 | # ------------------------------------------------
# Processing + plotting of specific cell datasets:
# Created: 04/04/21
# ------------------------------------------------
import glob
import h5py
import re
import gzip
import numpy as np
import pandas as pd
import time
import gc
import os
import sys
# For plotting:
im... |
c3b5d9782ed9c4bba35e0b4b2663cc879ebd273ede7216f5e88dae30a5ca8e94 | Python | 5,741 | 172 | from zaber.serial import AsciiSerial, AsciiDevice, AsciiCommand, AsciiReply, AsciiAxis
import time
class stages:
ctspmm = 10078 # counts per mm
zPosOffset = 0.5
stowPosition = [0, 0, 75]
pickupPosition = stowPosition
def __init__(self, axis):
self.port = AsciiSerial(... |
ffa1bbd96dfdcad5e1940b3a8d2ea7df3a2e7968cf46a67d0d2ab24c4bc472e5 | Python | 5,741 | 188 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
For the selected k and subject ID, find the average
transition distance between LE(t) for given clustering transitions within each
run, and average across runs.
Output:
statejump.h5 Contains all transition distances for each subject, across runs and within run.
Usag... |
2feea7c3860f75b8962959bcd995e2d72e7b4565466a0e282765b01a53127f22 | Python | 5,742 | 186 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import logging
import pytest
import torch
from ase import Atoms
from ase.build import molecule as get_... |
4b313c5e17ddd7178e552db98f9423afbf600e74c84cb39507543af5051d8941 | Python | 5,742 | 162 | # coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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 ag... |
2ec40d2d041d4ffcf64296316fc3f32a5da19e4ae3de60a824c3d02f83ebc5ac | Python | 5,743 | 160 | import os
from typing import Callable, Optional, Tuple
import pytorch_lightning as pl
import numpy as np
import tonic
from tonic import (DiskCachedDataset, MemoryCachedDataset, SlicedDataset,
datasets, slicers, transforms)
from torch.utils.data import DataLoader
class NMNISTFrames(pl.LightningData... |
63dce0da5fe53bddf6c03ac97b7755fe5e531e3bfd4fd403ed15a74965a382eb | Python | 5,744 | 204 | from __future__ import annotations
import importlib
from typing import Iterable
def normalize_torch_device(device=None, *, prefer_cuda=True, fallback_to_cpu=False):
"""Return a torch.device with bare CUDA normalized to the current index."""
import torch
if device is None:
device = "cuda" if pref... |
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