sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
09ec62da5b57b7a4d4052c8ba6680e426a11b7511d9ae237a29e558806b6c406 | Python | 3,851 | 123 | from collections import defaultdict
from stanza.server import CoreNLPClient
from agent.data.sql.sql_mgmt import get_connection, select_fields_where, execute_read_query
TABLE = "annotate_sentence"
FIELDS = "id, sentence, metamap_extraction, big_subject, predicate, big_object, manually_identified_predicate"
def get_s... |
ea0f89043e6c3390ff6a8a0dd870f555c7d02a7ba94b6ee0a7aa1b069ef23a31 | Python | 3,852 | 92 | # -*- coding: utf-8 -*-
# @Time :2024/6/6 11:04
# @Author :MENGLong Li
# @File :ReadTxtDataFromOdbFile.py
# @Software:PyCharm
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
import os
class ReadTxtDataFromOdbFile:
def __init__(self, num_group, num_index, LengthX, ... |
b2643902f6d3a10197f125bf7e3486061712968d86dd5f48cf0f7a5050340e34 | Python | 3,853 | 119 | import matplotlib
matplotlib.use("Agg")
from mozaik.analysis.analysis import *
from mozaik.storage.datastore import *
from mozaik.storage.queries import param_filter_query
import mozaik
import logging
import os
import pytest
class TestDatastore:
ref_path = "tests/full_model/reference_data/LSV1M_tiny"
@clas... |
74d40fec7db88a73da1245523fe74d97d2d034603e3a9bc517a6db7820a20d66 | Python | 3,857 | 136 | #!/usr/bin/env python
"""
Collect ped file metrics. Writes stats to stdout.
Metrics:
ped_file_count : Number of records
ped_file_<family> : Number of families of each type
"""
import argparse
import sys
import pandas as pd
import svtest.utils.IOUtils as iou
KEY_PREFIX = "ped_file_"
SINGLETON_STR = "singletons... |
87d24b78c767adca08bdc6eea331678399c1eb8ed8d8445604fc340c48b3dd79 | Python | 3,857 | 124 | import time
from pathlib import Path
import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets
from sinabs.backend.dynapcnn import DynapcnnNetwork
from sinabs.backend.dynapcnn.chip_factory import ChipFactory
from sinabs.backend.dynapcnn.specksim import from_sequential
from sinabs.from_tor... |
9c1a51773c79c8c5e170ee1fe693a5b8236770105f280c74c49b9e9a2df56495 | Python | 3,857 | 134 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Merge variants that were linked by per-sample bedtools merge
"""
import argparse
import sys
from collections import deque
import numpy as np
from scipy import sparse
from scipy.sparse import csgraph
import pysam
import svtk.utils as svu
def slink(record_links, rec... |
cecd9dfd97248689422299fe98ea07d8e234dc52836c45b58ad3a155bc1f22d9 | Python | 3,858 | 102 | import torch
from ..utils import common_functions as c_f
from ..utils.module_with_records import ModuleWithRecords
class BaseReducer(ModuleWithRecords):
def forward(self, loss_dict, embeddings, labels):
self.reset_stats()
assert len(loss_dict) == 1
loss_name = list(loss_dict.keys())[0]
... |
d9c9307391ea72fbef9b40eee050e5721b781afe206c6c9e96bf302b1ec01928 | Python | 3,859 | 120 | #!/usr/bin/env python3
"""Calculate statistics for deployments on categorical targets."""
from pathlib import Path
import pandas as pd
import scipy.stats as st
from sklearn import metrics
__author__ = "Marko van Treeck"
__copyright__ = "Copyright 2022, Kather Lab"
__license__ = "MIT"
__version__ = "0.1.0"
__maintain... |
1cfa49c498dbf7ad511dd4d944f894ed306ad9e1e7bfd01c280e33933106c085 | Python | 3,863 | 103 | import os
import numpy as np
import matplotlib.pyplot as plt
import pyvips as Vips
NP_DTYPE_TO_VIPS_FORMAT = {
np.dtype('int8'): Vips.BandFormat.CHAR,
np.dtype('uint8'): Vips.BandFormat.UCHAR,
np.dtype('int16'): Vips.BandFormat.SHORT,
np.dtype('uint16'): Vips.BandFormat.USHORT,
... |
8a1032b7afb08815f35af2158a4e3ef7cfe85ab6f837601dd4b40a00c673accb | Python | 3,863 | 113 | from typing import Any, Iterable, Sequence
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from numpy.typing import NDArray
from siapy.entities.pixels import CoordinateInput, Pixels, validate_pixel_input
__all__ = [
"map_affine_approx_2d",
"affine_matx_2d",
"align",
"transform"... |
63f14a853d6baf882c4f2d74d41e974c16ad7d71587a94cec9f62f07d9d21fe7 | Python | 3,864 | 94 | import tensorflow as tf
from models.deep_learning.deep_learning_models import DLRegressor
from utils.constants import is_spline
class BiLSTMRegressor(DLRegressor):
"""
This is a class implementing the BiLSTM model for time series regression.
! Remember to use regularization techniques such as dropout, wei... |
1fee27d521c42223dffd20dbc5316109e99f0bb5be2d76f8266d33cc08510ffb | Python | 3,865 | 133 | import logging
import warnings
from datetime import date, datetime
import numpy as np
import pandas as pd
from matplotlib import MatplotlibDeprecationWarning
from pyspark.sql import SparkSession
from data_profiling import ProfileReport
from data_profiling.config import Settings
logging.basicConfig(level=logging.INFO... |
3e4f5c74009dcbcd67c4a9c19afa38e8f50486aa306565256301dc44a934e4ae | Python | 3,867 | 53 | #!/usr/local/bin/ipython -i
from mozaik.experiments import *
from mozaik.experiments.vision import *
from mozaik.sheets.population_selector import RCRandomPercentage
from parameters import ParameterSet
import mozaik
def create_experiments(model):
return [
#Spontaneous A... |
07b5bd20db61f4924d7e629f18fb7171160f1c70c2cae2b0dde845a3442f624a | Python | 3,869 | 83 | import numpy as np
import visionloader as vl
import argparse
import pickle
import lib.data_utils.matched_cells_struct as mcs
from lib.data_utils.interaction_graph import InteractionGraph, VertexInfo
import lib.data_utils.cell_curation as curate
from lib.dataset_config_parser.dataset_config_parser import read_config_f... |
38fb1935e2b3e4d68f76049209e1c51cfaf192fb51cc89ff6c42e5c035ce3e56 | Python | 3,869 | 102 |
import os
import yaml
import h5py
import numpy
import random
import matplotlib
from tqdm.auto import tqdm, trange
from stedopt.configuration import Configurator
from stedopt.articulation import PreferenceArticulator
from stedopt.defaults import obj_dict, regressors_dict
from stedopt.tools import create_dataset, crea... |
0f0b9ded4d7344cb8702863fd922f2195617f21cec67a217ae4ea58a3b1d563e | Python | 3,870 | 119 | #!/bin/python
#------------------- Description & Notes --------------------#
#------------------- Dependencies ---------------------------#
# Standard imports
import argparse
import itertools
from pathlib import Path
# Third-party imports
from tensorflow.keras import layers
from tensorflow.keras import metrics
from... |
d5b22e63378a9968a820a90227399b0a31b3735f8cae1793a3e762d8d9e21a82 | Python | 3,870 | 110 | # -*- coding: utf-8 -*-
import os
import pandas as pd
from nilearn.datasets.utils import _fetch_files, _get_dataset_dir
def fetch_ibc_subjects_contrasts(subjects, data_dir=None, verbose=1):
"""Fetch all IBC contrast maps for each of subjects.
After downloading all relevant images that are not already cached,... |
c3184e224205ecebbce866cc174a48792956515ebcb5e097cc281c32182a3a3e | Python | 3,871 | 131 | """Compute statistical description of datasets."""
from typing import Tuple
import numpy as np
from pyspark.sql import DataFrame
from pyspark.sql.types import (
ArrayType,
BooleanType,
ByteType,
CharType,
DataType,
DateType,
DecimalType,
DoubleType,
FloatType,
IntegerType,
L... |
099b021df370ea17862042b82f8f581e7ac1fcbdce4ebfb5f45a033c615e0728 | Python | 3,872 | 93 | import os
import pandas as pd
import numpy as np
import pcntoolkit as ptk
from pcntoolkit.util.utils import create_design_matrix
# globals
root_dir = '/project_cephfs/3022017.02/projects/hansav/Run8_f/'
data_dir = os.path.join(root_dir,'data/')
mask_nii = ('/opt/fsl/6.0.3/data/standard/MNI152_T1_2mm_brain_mask.nii.gz... |
a707787382584b3549568dc4002e018011c28278533281acee16a8b7fac625b5 | Python | 3,872 | 103 | import numpy as np
import re
import stanfordnlp
import nltk
class Sequencer():
def __init__(self, text, max_words, tokenizer="re", lang="en", oov_token="<UNK>", min_word_lenght=1, lower=True):
self.lang = lang
self.lower = lower
self.min_word_lenght = min_word_lenght
self.tokenizer ... |
40df5383933551998b0e1fbedbc075f93b4260c37482297d94f5b247a022b3f9 | Python | 3,873 | 122 | #!/usr/bin/python
# --------------------------------------------
# Load multiregion and write each as mtx files
# - write raw chuks, merge after
# Updated 09/08/2023
# --------------------------------------------
import re
import gc
import pandas as pd
import numpy as np
import scanpy as sc
from tqdm import tqdm
import... |
58d5d4cfa9afbdfaff8e26c99291428da8a02fec14cafb4d7e91554f941850c9 | Python | 3,873 | 87 | import numpy as np
import tensorflow as tf
class IgAttn:
def __init__(self,
input_array,
mymodel,
baseline_freq,
myvalue_full,
my_pre_input_full,
current_i,
label,
m_steps=100,
batch_size=64):
self.input_array=input_array
sel... |
a802fc7e9f02f2e2a8eaacffe302c4ba7f823eceb3a2c938d88d9adc4a4d1c4d | Python | 3,873 | 117 | import numpy as np
import warnings
import os
import argparse
import pickle as pk
from math import ceil
from sklearn.metrics import r2_score
from src.data.load_data import load_params, load_data, make_input_labels
def predict_model(model, params, data_file, task_filter):
"""Use trained model to predict on data an... |
0e7a85ec053e5e770fb788ca3628e0e6edf1c0e816a9e75ed2f862aecd36d690 | Python | 3,874 | 103 | # from torchsummary import summary
from thop import profile
from model.swinir import swinir, swinir2dto3d, swinirup, swinirProj_stage2, UNetA, swinirhugecnn, torch
import argparse
from model.Unimodel import UniModel
def Unimodel():
model = UniModel(args) # FLOPs: 1305.23647G, Params:174.83787M
... |
250f2c1591579dca359b064b83500e2ef7ab8a8896b89eb23f9991313617f1c8 | Python | 3,874 | 94 | 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... |
7eba4c3d3f0a42d3e5f4e18cdffe234c261eb6e341aa7ba1c45de499f3db0eb9 | Python | 3,874 | 84 | import torch as th
import numpy as np
import scanpy as sc
import scipy as scp
from sklearn.model_selection import train_test_split
from .utils import normalize, sparse_mx_to_torch_sparse_tensor
from .dataloader_utils import handle_adata_view, ensure_csr_csc
class ATACDataset(th.utils.data.Dataset):
def __init__... |
18e2c8def6e034b06917c0aea79b2aab4d2340148d9a31d1e8bb04499a147fd6 | Python | 3,875 | 100 | from __future__ import annotations
import logging
import os
import joblib
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from .. import settings
from ._base_algorithm import BaseAlgorithm
class Random_Forest(BaseAlgorithm):
"""
Class to compute Random forest clas... |
214c21361edeb88839d8621dd04d4ffc352658c4ab2953e411c49dcca2a6a885 | Python | 3,875 | 97 | # Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# 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 L... |
2a40e0dd0b40499cf67f78ce4673f58bb0f2179f12bb62fba012d8f079d3eb69 | Python | 3,875 | 116 | # -*- 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.
#... |
a9df0902de2909eacb0f759cb9c5c2d394c2e4d53165b5e834227a961c9e5e4b | Python | 3,875 | 130 | from ast import dump
from email.policy import default
import pandas as pd
import yaml
import os
import sys
# import pickle
from simulator import SimulatedPhenotypes
import logging
import click
from torch.utils.data import DataLoader, Dataset, Subset
logging.basicConfig(
format="[%(asctime)s] %(levelname)s:%(name... |
c26273d5a16217c475da9565fae9af2f497b874e5e98d382be66db6b3dba0eb2 | Python | 3,875 | 96 | 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... |
28154d34ccd83cfad17d9bef2da4d6959468a11488072a94e42c1da232a9a9f1 | Python | 3,876 | 106 | from general_utilities.path_helper_function import list_subjects
from pathlib import Path
import os
import scipy.io
import mne
import numpy as np
import pandas as pd
ses = "1"
data_type = "eeg"
task = "object_processing"
category_dict = {
1: "human_body",
2: "human_face",
3: "animal_body",
4: "animal_... |
60320b9395fca03a81169e64574178a58337393ad9b1f6df9443cb7dca32927a | Python | 3,876 | 125 | import copy
import logging
import os
import queue
import threading
from typing import Optional
import numpy as np
from ray.air.constants import _ERROR_REPORT_TIMEOUT
logger = logging.getLogger(__name__)
def is_nan(value):
return np.isnan(value)
def is_nan_or_inf(value):
return is_nan(value) or np.isinf(v... |
893d0dcdcf0bc6510330e316b06ee801a8f21cc9fecf6ff69b41e6102b35abde | Python | 3,876 | 123 | # 3rd party dependencies
import matplotlib.pyplot as plt
# project dependencies
from deepface import DeepFace
from deepface.commons.logger import Logger
logger = Logger()
# some models (e.g. Dlib) and detectors (e.g. retinaface) do not have test cases
# because they require to install huge packages
# this module is ... |
fda3d05fbb709fecc401c01cbba16c5c2a9f814106f16f1e7aa9c117513c673b | Python | 3,876 | 134 | import numpy as np
from numba import njit
@njit
def isotonic_l2(y, sol):
"""Solves an isotonic regression problem using PAV.
Formally, it solves argmin_{v_1 >= ... >= v_n} 0.5 ||v - y||^2.
Args:
y: input to isotonic regression, a 1d-array.
sol: where to write the solution, an array of the sam... |
34997d5b9e2d34f358f846809dc730229f3185ad2e1a6fc095457ea4a49ec743 | Python | 3,877 | 98 | import torch
import torch.nn as nn
import torch.nn.functional as F
thresh = 0.15
lens = 0.5
probs = 0.5
decay = 0.6
cfg_cnn = [(2, 64, 3, 1, 3),
(64, 128, 2, 1, 3),
(128, 256, 2, 1, 3),
(256, 256, 1, 1, 3),
]
cfg_fc = [512, 512, 200]
def mem_update(opts, x, mem, spike):
... |
893fb07eb25a121ea9f293e200ddf6b703b722dbc169d8303c2cce8bda39765e | Python | 3,878 | 94 | import abc
import numpy as np
from projectmetis.python.models.model_dataset import ModelDataset
from projectmetis.proto import metis_pb2, model_pb2
class ModelOps(object):
def __init__(self, model, encryption_scheme=None, *args, **kwargs):
self._model = model
self._encryption_scheme = encryptio... |
a428c9e02963f474f0369583f645d78ef83f573041b99fb81ec26114ab5542e6 | Python | 3,879 | 135 | import numpy as np
from numba import njit
@njit
def isotonic_l2(y, sol):
"""Solves an isotonic regression problem using PAV.
Formally, it solves argmin_{v_1 >= ... >= v_n} 0.5 ||v - y||^2.
Args:
y: input to isotonic regression, a 1d-array.
sol: where to write the solution, an array of the sa... |
56a9248a0bd8e69e631d2220a54ecb042b23bb3a57b3f304f52df7ef3c9cbbc0 | Python | 3,880 | 110 | import csv
import os
import matplotlib.pyplot as plt
import wandb
class Losses(object):
def __init__(self, dataloader, losses="px"):
self.dataloader = dataloader
self.names = losses if isinstance(losses, list) else [losses]
self.losses = {phase: {loss: [0.0] for loss in self.names} for ph... |
91d02335b8c124f5d19b8c9f24dd0dddf06a141d2b9ab823f2bb72fe40a41ff0 | Python | 3,881 | 110 | import re
import h5py
import torch
import argparse
import numpy as np
import os
from tqdm import tqdm
from darts import TimeSeries
from sklearn.metrics import r2_score
from src.data.load_data import load_params, load_darts_timeseries
from src.tools import load_model
SUBS = [f"sub-0{i}" for i in range(1, 7)]
def mai... |
7f42ff54f864969346e59276acd086c76359ae755147fec9fe742a58c1750c8b | Python | 3,883 | 101 | from loguru import logger
import os
import sys
import json
from deltasplice.constant import CL, EL, Fapath
from bisect import bisect_left
import numpy as np
from pyfasta import Fasta
def parse_line(line, i):
line = line.replace("\n", "").split("\t")
try:
Name, _, Chr, Strand, Ts, Te, Js, Je, Psi, delt... |
2720e1b774a3ccabf6463b39efddef6d4a3b4b909b0dd5d6e344788eb52ae52d | Python | 3,884 | 108 | from dataclasses import dataclass
from datetime import datetime, timedelta
from typing import Any, Dict, List, Optional, Union
from pandas import Timedelta
@dataclass
class BaseAnalysis:
"""Description of base analysis module of report.
Overall info about report.
Attributes
title (str): Title of... |
aa26c6e475effb56319c58d3efe1f342f032be80ae68e1f2a48714c7e32b8a9d | Python | 3,884 | 99 | import unittest
from copy import deepcopy
import torch
from rising.transforms import BoxToSeg, DoNothing, FilterKeys, InstanceToSemantic, PopKeys, SegToBox
class TestUtilityTransforms(unittest.TestCase):
def setUp(self) -> None:
torch.manual_seed(0)
def test_do_nothing_transform(self):
inp ... |
b5368c16c9f147cc13903d17df7c67dfc7eb30eb85bd87276d43d5b5156e095d | Python | 3,884 | 106 | import yaml
import scipy
import pickle
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def get_max(x, m=100):
lex_x = x.shape[0]
max_x = np.zeros(lex_x//m)
for i in range(max_x.shape[0]):
max_x[i] = np.max(x[i*m: (i+1)*m])
return max_x
def bootstrapped_val(x, stride,... |
b97842992e19f8f043e38fc602c2d026e9487e871b990f4566523a7ae510d9e4 | Python | 3,884 | 98 | import os
import numpy as np
import matplotlib.pyplot as plt
import ivim
root = '/path/to/root'
input_root = os.path.join(root,'data')
results_root = os.path.join(root,'results')
subs = [sub for sub in os.listdir(results_root) if sub.startswith('sub')]
runs = ['run-{}'.format(r+1) for r in range(2)]
scans = ['sIVIM'... |
d6542531b4828a6af569178b2f5e8cc2250537fe5cb60f0d1eef0b4697d92bc9 | Python | 3,884 | 64 | from nnunetv2.model_sharing.model_download import download_and_install_from_url
from nnunetv2.model_sharing.model_export import export_pretrained_model
from nnunetv2.model_sharing.model_import import install_model_from_zip_file
def print_license_warning():
print('')
print('####################################... |
32d5b8976a833867adbb0bf7be471a3df0b2a32b8a090b71aa80de2ed4f97905 | Python | 3,885 | 139 | from typing import Literal, Tuple
import numpy as np
import torch
from flyvis.utils import hex_utils
def rotation_matrix(angle_in_rad: float, three_d: bool = False) -> torch.Tensor:
"""Generate a rotation matrix.
Args:
angle_in_rad: Rotation angle in radians.
three_d: If True, generate a 3D... |
15e348a093596bbcba40cbb4c6bce970651ffbeac148fafab590f00b93a4b223 | Python | 3,886 | 132 | """
Test of spikes_from_array_latency_code
This population creates input spikes based on array values and the following
translation of values to time:
t = t_max*(x_max-x)/x_max
The approach is tested by generating random input data of a given size.
The stim population is connected in a one-to-one fashion to an eq... |
89b80d9fd9d640e95cd3f7aba54387dff2c718e576f7f9482b956a7ebb381798 | Python | 3,886 | 100 | """ Parts of the U-Net model """
import torch
import torch.nn as nn
import torch.nn.functional as F
# import cv2
# import numpy as np
# import os
class DoubleConv(nn.Module):
"""(convolution => [BN] => ReLU) * 2"""
def __init__(self, in_channels, out_channels, mid_channels=None):
super().__init__()
... |
13339b0a3c5e5465152e6a5496a56194f6d9eeaea3bd1aab13ba86bb35f43dc1 | Python | 3,887 | 92 | import sys
import logging
from typing import Optional
import pandas as pd
import pickle
import yaml
import click
logging.basicConfig(
format="[%(asctime)s] %(levelname)s:%(name)s: %(message)s",
level="INFO",
stream=sys.stdout,
)
logger = logging.getLogger(__name__)
@click.command()
@click.option("--exp_di... |
fbc64ab2cb1c24fd2c3bb14a9c4cddaca0de61e9ac54d188cc40c7dc1fd0fc57 | Python | 3,887 | 103 | # Copyright 2021 Division of Medical Image Computing, German Cancer Research Center (DKFZ)
# and Applied Computer Vision Lab, Helmholtz Imaging Platform
#
# 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 L... |
499f4b17258ea95d733e1ed2de84d450ac9025a71f59ec7c1e97c8ca1eea9d17 | Python | 3,889 | 113 | # coding=utf-8
# Copyright 2023 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... |
60e9a50fb64c590a485f1fb7dc61b1d507ed9efd894d1c1ef84b4bee001def60 | Python | 3,889 | 96 | ####################################################################################################
# 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... |
0d5e7db332fef92e6d4dfb41239a98251638e8089b8792691760d870488e5702 | Python | 3,890 | 106 | from abc import ABC, abstractmethod
from typing import ContextManager, Iterator, List, Optional
from .execution_options import ExecutionOptions
from .physical_operator import PhysicalOperator
from .ref_bundle import RefBundle
from ray.data._internal.stats import DatasetStats
class OutputIterator(Iterator[RefBundle],... |
3950664621b252ef2071e1d66e34d569e5d1a226ffba86b58bb2e037d84c8f17 | Python | 3,892 | 108 | """Code in this file is heavily based on the code from the following repository: https://github.com/CompVis/invariances"""
import numpy as np
import torch
import torch.nn as nn
from ldctinv.cinn.blocks import ConditionalFlow, FeatureLayer
from ldctinv.vae.blocks import ActNorm, DenseEncoderLayer
class DenseEmbedder... |
61e00526d4cbb0d5e64776cf4692001f843a90472f092f4e1779e00798a8689d | Python | 3,893 | 123 | import time
from os.path import join
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from config import args
from models import GNN, GNN_graphpred
from sklearn.metrics import roc_auc_score
from torch_geometric.data import DataLoader
from util import get_num_task
f... |
8368e3c6008266c0a329b25bbcd4e0aa2face4e1a0dc390dbda1d677e945d3b7 | Python | 3,893 | 114 | # 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, ... |
c0d3b70ad42a33570c4577d80f9c761213939a594ca919f932b90fe8be27d140 | Python | 3,893 | 90 | """Read units: sub-file units of work.
A read unit is a piece of one file that a reader scans on its own and can
name, so anything downstream can refer to that piece again. A Parquet row
group is one example; a reader that scans a file in a single pass reports
the file itself as the unit.
Three small types share the ... |
db65133f0490d5f52b0963c4111383957eae312f7f0b2e925f15076304993a05 | Python | 3,896 | 116 | # Copyright (C) CVAT.ai Corporation
#
# SPDX-License-Identifier: MIT
"""
This script lists resources on the CVAT server
and saves them to JSON files in the `assets` directory.
Before running it, start the test instance by running:
pytest tests/python --start-services
The script determines which endpoints to quer... |
0e29adc639ccd5ea6e0daf5060b2ce4f0e29d777461c04fed5707b5c8bd187b5 | Python | 3,897 | 94 | import pandas as pd
import os
import sys
def get_hg38_chrom_size():
f_chrs = open("../hg38_chrom_size.tsv", "r")
lines = f_chrs.read().splitlines()
chrs = {}
for line in lines:
eles = line.split("\t")
chrs[eles[0]] = int(eles[1])
return chrs
def main():
chrs = get_hg38_chrom_... |
a643afb1f7cd834217fabecb777bb107e37568a1a435dc03e452fbc3f937f568 | Python | 3,899 | 95 | 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
import pandas as pd
import pickle
import concurrent.futures
import pyranges as pr
import json
import... |
496abe6f0cd153282a6820383bade70f6bc3e283f023e7d5e4f7a8d2e35503c3 | Python | 3,900 | 151 | # Author: Zachary Chiang, Buenrostro Lab, Harvard University
# Adds haplotype information to .bam file
import sys
import numpy as np
import math
group_file = open(sys.argv[1],'r')
sam_file = open(sys.argv[2],'r')
# create dict with all groups
groups = {}
for line in group_file:
column = line.rstrip().split()
col... |
f9094d44a0c62b24bafa3ed48b486fb5ab445a28634babcb3300115cb4bf96f8 | Python | 3,900 | 114 | from batchgenerators.dataloading.data_loader import SlimDataLoaderBase
from batchgenerators.dataloading.multi_threaded_augmenter import MultiThreadedAugmenter
import numpy as np
class DummyDL(SlimDataLoaderBase):
def __init__(self, num_threads_in_mt=8):
super(DummyDL, self).__init__(None, None, num_thread... |
95fd6c79efea544ae301ceb76e5cc3661d7918b66784d114d8335b4759b8cd31 | Python | 3,902 | 97 | import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import argparse
import os, glob
from sklearn.model_selection import train_test_split
from torch_geometric.loader import DataLoader
import socket
from src import AseReadDataset
from src import TrainerPreTrain
from equiformer_v2.nets import... |
2baa725377f2d77ace48d12f27681dbe1f6d2450b2df94a82c50c75f6cda6dbc | Python | 3,903 | 128 | #!/usr/bin/env python3
"""
OmicVerse 统计功能使用示例
这个示例展示了如何在生成报告时启用或禁用统计功能。
"""
import scanpy as sc
import omicverse as ov
import os
def example_with_analytics():
"""启用统计功能的示例"""
print("📊 示例1：启用统计功能")
# 假设我们有一个示例数据集
# adata = sc.datasets.pbmc68k_reduced()
# 生成报告时启用统计（默认行为）
# ov.generat... |
d1b567240d6c92ac9ca66b914b5e3c0a55c2c06dfbe40a7fd13030ebbf60c6bb | Python | 3,904 | 118 | import copy
import glob
from keras.models import load_model
import numpy as np
from numpy.random import seed
import os
import pandas as pd
import shutil
import sys
import tensorflow as tf
def use_available_image_version(file_paths):
results = []
for x in file_paths:
# It will only do this for eLife im... |
fb8453d3f3e2f557520f4a2fa0a3a89246bceebbb0369aa398c09dddfc00f17f | Python | 3,905 | 115 | __author__ = "Simon Andermatt"
__copyright__ = "Copyright (C) 2017 Simon Andermatt"
import numpy as np
from mdgru.helper import argget, compile_arguments, generate_defaults_info
import functools
import copy
import torch as th
from torch.nn import init
import logging
def init_weights(m):
if hasattr(m, 'initialize... |
70804c0e53c9fe8b72b4fad8ec2c3da57180c6dcfcba9bd2e9b3db6dbd4b46e8 | Python | 3,908 | 93 | import tensorflow as tf
from models.deep_learning.deep_learning_models import DLRegressor
from utils.constants import is_spline
class GRURegressor(DLRegressor):
"""
This is a class implementing the GRU model for time series regression.
! Remember to use regularization techniques such as dropout, weight d... |
7755252ff244da9f392bbff551d992a6fb422b93834393d29438778c61b947d8 | Python | 3,909 | 96 | 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... |
923e361a82ec4861f0207cc3837ae1150f445828e5bd87007cb717b565283e5b | Python | 3,909 | 77 |
class BazelMetisServicesCmdFactory(object):
@classmethod
def bazel_init_controller_target(cls,
hostname,
port,
aggregation_rule,
participation_ratio,
... |
bb21b177730e5b63e53641bd56ed2ffff975656ffe651fa9bf2d1fa69d5b27a5 | Python | 3,909 | 123 | import json
import matplotlib.pyplot as plt
from scipy.stats import spearmanr, pearsonr
import pandas
GlobalDict = {}
def load_json(Path):
with open(Path, "r") as f:
return json.load(f)
def load_txt(Path):
with open(Path, "r") as f:
content = f.readlines()
content = [list(map(float, x.st... |
885c59e74f3c2dd7a84b653f704eb1f368d67aa15fd2546c172e82603b5d98d3 | Python | 3,910 | 18 | from .index_tools import make_index, show_df
from .io import save_network_all, load_network_all, load_params, save_params, save_meta, load_meta, load_experiment_meta, load_experiment_results, save_outputs, load_outputs, save_activity, load_activity, RESULTS_DIR, SAVED_DIR, load_responsiveness, load_rates, load_weights,... |
383730c20f48d58a2b311d5a0e7656af9ccf1fe3ae8696f360941b9b9cf255f3 | Python | 3,911 | 122 | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pdb import set_trace as stop
def batch_product(iput, mat2):
result = None
for i in range(iput.size()[0]):
op = torch.mm(iput[i], mat2)
op = op.unsqueeze(0)
if(result is None):
... |
d1ae7e7ee4011d1da98bb2f8264c030f9dda5861da56d3aef94d6c2514b88ad5 | Python | 3,911 | 105 | ####################################################################################################
# Copyright (c) 2020 - 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... |
4fefe2fd51f773da229d2ee7f00f0bf826c2e3e1a2130b7a52e5e9c27aba95b8 | Python | 3,912 | 104 | """Parquet chunk helpers for DataSourceV2.
Maps the ``unit_ids`` of a ``FileChunk`` row (the explicit surviving row groups
a bin assigns to a file) to PyArrow ``ParquetFileFragment`` subsets for reading, and
names the read unit each subset stands for.
"""
from typing import Callable, Iterable, List, TypeVar
import py... |
0e5b9006fde369c5879be75f15313f0115e12efc2e4b7ebb7c937c054c499c24 | Python | 3,914 | 111 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Base class for an Experiment Runner."""
from typing import Any, List, Optional
from aihwkit.cloud.client.entities import CloudExperiment
from aihwkit.cloud.... |
afddbaa55e64df7a25b6565ae18985d59856075fc565dc704d4fbf28462c1cd7 | Python | 3,915 | 111 | import matplotlib.pyplot as plt
import numpy as np
import h5py
import library
# Generate distributions
gecis = ['jrgeco']
mouse_ids = ['6841']
recording_ids = [0, 1, 2]
sizelims = [1, 500]
durlims = [1, 50]
ks = list(range(1, 15))
xpt = 3
rows = 3
cols = 2
fig, axes = plt.subplots(rows, cols, figsize=(5*cols, 5*row... |
0c1556324b34ed9efe8224c8fdb16f3589380245563772c18a5c8fafa1dfb6bd | Python | 3,916 | 75 | proj_dir = "/path/to/main_project_folder/" # edit this line
import numpy as np
import pandas as pd
import xarray as xr
import dask.array as da
import glob as glob
from functools import partial
import sys
sys.path.append(proj_dir)
from project_fxns import load_region
from project_fxns import fxns
import importlib
impor... |
c1c5660f80eeca2efad7025087ac66aeb255490c568ebd2abbb50f5b790646e9 | Python | 3,916 | 125 | from collections import defaultdict
from scipy.signal import find_peaks_cwt
import numpy as np
from .utils import fill_tril_inplace
def call_boundary_peaks(Pb):
n = len(Pb)
peaks = find_peaks_cwt(
Pb,
np.array([0.5]),
wavelet=None,
max_distances=None,
gap_thresh=Non... |
0ac76d9be4a73a0d83f130b45f0edd38176511f3ea38bbefb1384be44cf8b51e | Python | 3,917 | 88 | import numpy as np
import matplotlib
#matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
import os
import utils
import pandas as pd
from scipy import stats
datadir = "./"
param = utils.parseparam(f"{datadir}/apps/reprlearn/reprlearn.par")
cij = utils.loadbin(datadir, "learn.cij.l1.bin", shape=(-1, param['H1'], pa... |
08a57317291452ed84ad6e91c720d85e5abcfb1013c8e1de448eb4850705a3ae | Python | 3,918 | 120 | # -*- coding: utf-8 -*-
#
"""
std_s.py
Standardize Lumpy records.
"""
from .standardize import VCFStandardizer
from svtk.utils import is_smaller_chrom, parse_bnd_pos
@VCFStandardizer.register('smoove')
class SmooveStandardizer(VCFStandardizer):
def standardize_records(self):
for record in self.filter_r... |
afb41919c9f13e621fb7ab1699045dc4b9513390c7bef7215d113a4fb47e6a98 | Python | 3,918 | 81 | import sys
import json
import pickle
import argparse
from lop.utils.miscellaneous import *
from lop.utils.plot_online_performance import generate_online_performance_plot
def add_cfg_performance(cfg='', setting_idx=0, m=2*10*1000, num_runs=30, metric='accuracy'):
with open(cfg, 'r') as f:
params = json.loa... |
1d32229b429a6d2d2c7887e4703b8663cad52f9761a94c431084d8f821fdf28a | Python | 3,919 | 88 | import os
import glob
import numpy as np
from pathlib import Path
import mne
def find_files(root, naming_pattern=None, extension=None):
"""
This function finds files matching a specific naming pattern recursively in directory from root
:param root: root of the directory among which to search. Must be a st... |
56fdc5435a99bfb2b3be6c5c46a9b83f4713c2b99520b737ca26b12b5423fce4 | Python | 3,920 | 99 | import glob
import hydra
import os
import pandas as pd
from omegaconf import DictConfig, OmegaConf
from pathlib import Path
from posebusters import PoseBusters
def resolve_input_protein_dir(data_dir: str, dataset: str) -> str:
"""Resolve the protein directory for a given method.
:param data_dir: The data d... |
08ea1d86702d1214b2b50677979e2cdc1a9aeeaac237c015ac023d2fe0455b72 | Python | 3,921 | 112 | import torch
import monai.transforms as mt
import numpy as np
from glob import glob
import sys
sys.path.append('./')
import torch
import numpy as np
import os
import monai.transforms as mt
from utils import threshold_at_one, jpg_to_ndarray
class ABUSsetNew(torch.utils.data.Dataset):
def __init__(self, args, fo... |
1eb91d4860a7fa66cc4ce3877aa0bfa7d5ba3ddd3b368fe43ce95919b405a667 | Python | 3,926 | 143 | #!/usr/bin/env python
# Copyright (c) 2017-present, Facebook, 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 a... |
3da15f1f6e27c313f59a6ad89022a1d34eb84a812735200557b11d86ef95a51f | Python | 3,926 | 122 | from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from pdb import set_trace as stop
def batch_product(iput, mat2):
result = None
for i in range(iput.size()[0]):
op = torch.mm(iput[i], mat2)
op = op.unsqueeze(0)
if(result is None):
... |
64f85822a751b441f022a3444a7ef54ee271b1cc98ba8cb0f2566f5956e9a847 | Python | 3,926 | 129 | from typing import Callable, Dict, Hashable, Mapping, Sequence, Tuple, Union
from rising.transforms.functional.utility import filter_keys, pop_keys
from .abstract import AbstractTransform
__all__ = ["MapToSeq", "SeqToMap", "PopKeys", "FilterKeys", "RenameKeys"]
class MapToSeq(AbstractTransform):
"""
Conver... |
adca38b5a549fd1c2e20c0070aef56b4aabe43c42c88fe5d82ae4fbaf7926982 | Python | 3,926 | 95 | from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
from scipy.signal import find_peaks, peak_widths
TIME_GRID = np.round(np.arange(6.0, 30.0 + 0.01, 0.01), 2)
MZ_GRID = np.arange(50, 600, 1)
EXPECTED_SHAPE = (TIME_GRID.size, MZ_GRID.size)
def _load_matrix_csv(path: P... |
5407f8d8d2674eae8f3f9703e1d2996215dddd772a96e12f253c4428c83f472a | Python | 3,927 | 108 | # Modified from https://github.com/open-mmlab/mmdetection/blob/main/mmdet/models/test_time_augs/det_tta.py # noqa
from typing import List, Tuple
import torch
from mmcv.ops import batched_nms
from mmdet.structures.bbox import bbox_flip
from mmengine.structures import InstanceData
from torch import Tensor
from lqit.co... |
244c7d5e63329aa27e6ac9a0deb264f4fe2960313b491a16fbff21f4c3cd76d9 | Python | 3,928 | 121 | import time
from collections import Counter
from typing import List, NamedTuple
from ray._raylet import GcsClient
from ray.autoscaler.v2.schema import ClusterStatus, Stats
from ray.autoscaler.v2.utils import ClusterStatusParser
from ray.core.generated.autoscaler_pb2 import (
ClusterResourceState,
GetClusterRes... |
35eed546d3b14c191eea98cdffd321ba57dcf2beb9f18a54d1464651f71eefda | Python | 3,928 | 125 | import json
import pickle
import pandas
import os
from utils.graph_utils import smiles2graph
from utils.chemistry_parse import clear_map_number
import random
import numpy as np
import torch
from tqdm import tqdm
import rdkit
from rdkit import Chem
import multiprocessing
from utils.tokenlizer import DEFAULT_SP, Tokenize... |
13edd102c83e50b38e9241ee7f6e94fcec871642bb4300c3bce49e99e1cb918c | Python | 3,929 | 115 | # 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... |
8a4e8c6ff235a9decbf9db7d727b053319d4904c701893e3d3d53da858a29aed | Python | 3,930 | 107 | """
EWH - used to start a thread for a specific valuelogger.
"""
from datetime import datetime
import os
import time
from PyQt5 import QtCore
def isnan(num):
return num != num
class valuelogger(QtCore.QThread):
timelog=None
valuelog=None
historylength=None
logfile=Non... |
4be3b77b3ffb537022210d61f919aadf06855fc6992b9933e68fcd6d87a1a1b3 | Python | 3,931 | 140 | """
Shared utilities for generating Docker image tags for Ray and Anyscale images.
"""
import logging
from typing import List
from ci.ray_ci.automation.crane_lib import (
CraneError,
call_crane_copy,
call_crane_manifest,
)
from ci.ray_ci.configs import DEFAULT_PYTHON_TAG_VERSION
from ci.ray_ci.docker_cont... |
eeac7eb020c6b266686801fc47a804d713b28e0ec151a56d5a46b4df29a6f88e | Python | 3,931 | 108 | import yaml
import scipy
import pickle
import argparse
import numpy as np
from tqdm import tqdm
import matplotlib.pyplot as plt
def bootstrapped_return(x, y, stride, total_steps, confidence_level=0.95, to_bootstrap=True):
assert len(x) == len(y)
num_runs = len(x)
avg_ret = np.zeros(total_steps // stride)
... |
0e8b69c4490ce6c216b0621a926aeda87d40e453526477c8f74fccf757723068 | Python | 3,933 | 103 | import os
import tempfile
from pathlib import Path
import numpy as np
import pytest
import torch
from PIL import Image
from flyvis import connectome_file
from flyvis.connectome import ConnectomeFromAvgFilters
SEED = int(os.environ.get("FLYVIS_TEST_SEED", 0))
"""Seed used to make the test suite deterministic.
Overri... |
62a88e7e4f78553ae0946343e4b544a5e14116d47086a8b623fbad2184459f23 | Python | 3,933 | 131 | # flake8: noqa
# __example_start__
from starlette.requests import Request
from typing import Dict
import ray
from ray import serve
from ray.serve.handle import DeploymentHandle
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
@serve.deployment
class Translator:
def __init__(self):
self.lan... |
fd09d8f9cd70dd666dd030ee001b87742a8da445a047a34ddbb1bef312b67038 | Python | 3,934 | 126 | from __future__ import annotations
from collections import defaultdict, deque
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Deque, Dict, Iterator, Optional
from typing_extensions import override
from .base import QueueWithRemoval
if TYPE_CHECKING:
from ray.data._internal.execution.int... |
8ccd044dcf926cc9cb27191856e31c5302b9cfa35706472d09d9938e489f3978 | Python | 3,935 | 124 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Run before 04_v2_genotype_batch
"""
import argparse
import os
import firecloud.api as fapi
from firecloud import fccore
from google.cloud import storage
fcconfig = fccore.config_parse()
def get_bucket_name(namespace, workspace):
response = fapi.get_workspace... |
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