sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6e8f2c028361cf1108c46dc1bfd2e4eb08ae9bbae6d2cde1f39760d96be3d44e | Python | 2,009 | 61 |
import commot as ct
import scanpy as sc
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import os
import seaborn as sns
import sys
import gc
import ot
import pickle
import anndata
from scipy import sparse
from scipy.stats import spearmanr, pearsonr
from scipy.spatial import distance_matrix
impor... |
b417a450430f2e01d9419e92cb34942ccd87cb7010815fb39390f46def0575b5 | Python | 2,009 | 55 | ## Script for simulating data according to the distance based rule at the synapse level.
# See main paper and tutorials/1_simulation for details.
import pickle
from pathlib import Path
import torch
from sbi.inference import prepare_for_sbi, simulate_for_sbi
from consbi.simulators import RuleSimulator, peters_rule_su... |
ec030cf8eaf2128fe7e4c16bcc603aa8b3db7996ad0ccdb15af1392b159656cb | Python | 2,011 | 67 | #!/usr/bin/env python3
import re
import sys
from pathlib import Path
ROOT = Path(__file__).resolve().parents[1]
def read_text(path: Path) -> str:
return path.read_text(encoding="utf-8")
def extract_package_version() -> str:
text = read_text(ROOT / "clipkit" / "version.py")
match = re.search(r'__versio... |
f97dc2a4b8647a3a4f808406c765b52037421074828a6b27c603182d96fe1a70 | Python | 2,011 | 56 | # from keras.preprocessing.text import Tokenizer
# from keras.preprocessing import sequence
import logging
from collections import Counter
from torchtext.data.utils import get_tokenizer
import torch
from torchtext.vocab import Vocab
from torch.nn.utils.rnn import pad_sequence
class TorchTokenizer():
def __init__(... |
c2ee45548c5f4e7dd187be5a85560f7b1a7af175ea5c82e8a657ff984f0293f9 | Python | 2,015 | 55 | # -*- coding: utf-8 -*-
"""
Created on Thu May 19 13:39:59 2022
@author: fhu14
"""
'''
This module handles high level operations related to parsing through and analyzing a large
number of results. This includes os and shutil level operations
'''
#%% Imports, definitions
import os, shutil, json
from .table_ops import... |
ebaee951d58b8eefd23b6984654876a80d948e9b73792c528b60f4caaa2ac171 | Python | 2,022 | 57 | import sys
sys.path.append('versatile_diffusion')
import os
import numpy as np
import torch
from lib.cfg_helper import model_cfg_bank
from lib.model_zoo import get_model
from torch.utils.data import DataLoader, Dataset
from lib.model_zoo.vd import VD
from lib.cfg_holder import cfg_unique_holder as cfguh
from lib.cfg_... |
af64019e5e5d948d6aff8c557142a654edac9d23f964b225ce52f015fcad61bf | Python | 2,023 | 72 | from model.builders.JAMA_builders import any_cancer_JAMA
import os
from copy import copy, deepcopy
filename = os.path.basename(__file__)
data_base ={'id':'response','type': 'updated_label' , 'params': {'outcome': 'response', 'text': 'NARR+IMPRESS', 'training_split':0}}
training_splits = [0,1,4,6,7,9]
number_reports = ... |
227558d477b3a68780d309a784ea7fef7168e1e10b8dabda1a2c2dd087537f37 | Python | 2,027 | 58 | ## Script for simulating data according to the distance based rule at the neuron level.
# See main paper and tutorials/1_simulation for details.
import pickle
from pathlib import Path
import torch
from sbi.inference import simulate_for_sbi
from sbi.utils import BoxUniform
from consbi.simulators import DistanceRuleSi... |
9eaf4b6bdbc7312e3fbcd5c3d194a320972e48b3676101c890cab57dc26c565b | Python | 2,029 | 53 | import csv
import os
import pandas as pd
def compute_IOU(rec1,rec2):
"""
计算两个矩形框的交并比。
:param rec1: (x0,y0,x1,y1) (x0,y0)代表矩形左上的顶点，（x1,y1）代表矩形右下的顶点。下同。
:param rec2: (x0,y0,x1,y1)
:return: 交并比IOU.
"""
left_column_max = max(rec1[0],rec2[0])
right_column_min = min(rec1[2],... |
44d503266236ba280b59b92f56042d4a3ca9ea853b9861607afce607c324118c | Python | 2,030 | 60 | import os
import cv2
import h5py
import numpngw
import numpy as np
import scipy.ndimage as ndimage
def save_pairs_segmentation(data_path, save_root):
h5_data = h5py.File(data_path, "r")
test_data = h5_data["test"]
train_data = h5_data["train"]
_save_pairs(test_data, save_root, "test")
_save_pairs... |
5d42713e2052227b2bd046347f089b363b7f0d9e66f849d40f74f93201297453 | Python | 2,030 | 53 | import numpy as np
import random
class ExpCentralCrop2d:
"""
Crop the 2D numpy [c, h, w] and mask [h, w] in a sample
The d should be randomly sampled
"""
def __init__(self, output_size, padding=0, include_lax_tensor=False, training=True):
assert isinstance(output_size, (int, list, tuple))... |
49692e9bd5d949c5f8f3d263f0cf1fa3aa5f3407bc292940f5c9ec28a0be8eda | Python | 2,032 | 56 | import numpy as np
import torch
from torch.utils.data import DataLoader
def random_worker_init_fn(worker_id):
"""
Initialize random state for each DataLoader worker process.
This function is called when a worker process starts. It sets up the
dataset's random state based on the worker's seed to ensur... |
b680c2bbea3d21bc802e67daaa4f650b07229a79deddf920364e321bbc22525a | Python | 2,032 | 66 | import multipagetiff as mtif
import numpy as np
import logging
log = logging.getLogger(__name__)
try:
import iocbio_deconvolve as iocbio # IOCBIO deconvolve
except ModuleNotFoundError as e:
msg = f"{e.msg}\n\n" \
"The iocbio deconvolve python wrapper was not found.\n"\
"Probably, you need ... |
ceb4f106748b54a5604d4cd9db18260a3d77b05343687d10cfbd0a63081ecab0 | Python | 2,034 | 81 | # -*- coding: utf-8 -*-
"""
Functions for loading test data setss
"""
import os.path as op
from pkg_resources import resource_filename
import numpy as np
from sklearn.utils import Bunch
_res_path = resource_filename('snf', 'tests/data/{resource}')
def _load_data(dset, dfiles):
"""
Loads `dfiles` for `dset` ... |
55ef37b25eed789ed71d6d04310ad4bc87ebc5be2a5dbd4ebddaa92545d0b934 | Python | 2,035 | 70 | import pytest
import matlab.engine
import numpy as np
import scipy
from numpy.testing import assert_allclose
from pystoi.stoi import N_FRAME, NFFT, FS
ATOL = 1e-5
eng = matlab.engine.start_matlab()
eng.cd('matlab/')
def test_hanning():
""" Compare scipy and Matlab hanning window.
Matlab returns a N+2 si... |
aa5bbd99cb11401de3b8211691cc8e038073a610dc4d5cd7f1ee5a45bcc459ca | Python | 2,035 | 68 | import os
from pprint import pprint
def read_lists_from_file_separate(file_path):
with open(file_path, 'r') as file:
content = file.read()
lines = content.splitlines()
lists = []
current_list = []
for line in lines:
if line.strip() == '':
if current_list: # append on... |
b6ba1e0ef500f06959c51f4ef9484d682b13b36a0c4e774d0636174f65784d96 | Python | 2,042 | 69 | import numpy as np
import torch
import PIL
from PIL import Image
def imresize(im, size, interp='bilinear'):
"""
Function for image resizing with given interpolation method
"""
if interp == 'nearest':
resample = Image.NEAREST
elif interp == 'bilinear':
resample = Image.BILINEAR
... |
138dea1cbf8b1f0861234b926fc8094e970ecd6b0570a6001cab6d17d40bc308 | Python | 2,048 | 86 | import numpy as np
import pytest
import torch
from Bio.PDB import Atom, Chain, Model, Residue, Structure
from Bio.Seq import Seq
from Bio.SeqRecord import SeqRecord
from Bio.Align import MultipleSeqAlignment
from selbstaufsicht.utils import rna2index
from selbstaufsicht.models.self_supervised.msa.transforms import MS... |
b19aae0ca2357cb757f514ce84f3f22d5d2ec04a67e42683423eb75f80b3f92b | Python | 2,048 | 51 | import h5py
import numpy as np
import torch.utils.data as data
class StackOverflowDataset(data.Dataset):
"""StackOverflow dataset"""
__train_client_id_list = None
__test_client_id_list = None
def __init__(self, h5_path, client_idx, datast, preprocess):
"""
Args:
h5_path (s... |
82412dd937568e5c0ee2c3adb6e6403702c15ca7d3c0ca53eb08d22c38665e6b | Python | 2,050 | 56 | # Copyright (c) 2020-present Joindbre.com
# Authors: Patricia Maraver.
#
# This program is free software: you can redistribute it and/or modify
# it under the GNU Affero General Public License version 3
# as published by the Free Software Foundation.
# You should have received a copy of the Server Side Public Lic... |
a824f29b837ff2d86c4d347b3d7095647eb3535541ed62c60345a1a9ff0ba1dd | Python | 2,050 | 38 | import numpy as np
def tabGeneration(resfile,Yhat_pred,list_ids,window,threshold_presence):
'''
This function writes the tabular file for the output
'''
classes = {3:"RT",4:"GAG",5:"ENV",6:"INT",7:"AP",8:"RNASEH"}
longNormal = {3:1000,4:3000,5:2000,6:2000,7:1000,8:1000}
print("Wri... |
f509d20d26b8f10288eb2b7e5c880b15408e80814ebc32d2e77f06db4f5a739c | Python | 2,056 | 62 | import numpy as np
class Uniform:
def __init__(self, rand_min, rand_max):
self.rand_min = rand_min
self.rand_max = rand_max
def get_random_values(self, output_shape):
rand_values = np.random.uniform(self.rand_min, self.rand_max, size=output_shape)
return rand_values
class Ga... |
894563268ed38fb2a9cfe1b51048473a80622d22aaeeddf6ba8179942f95f7f7 | Python | 2,059 | 69 | # run with
# python setup.py build_ext --inplace
import sys
nrn_srcdir = "."
#mingw = 1
mingw = 0
instdir = "/raid/zyc/code_for_dhs_paper/install"
if nrn_srcdir[0] != '/' :
# TODO: is this right?
nrn_srcdir = '../../../../../../' + nrn_srcdir
from distutils.core import setup
from distutils.extension import Ex... |
cd49584b4e792e8f0f9194dd797d858df1c4a5975911fab99fd018de25bd5367 | Python | 2,063 | 69 | # run with
# python setup.py build_ext --inplace
import sys
nrn_srcdir = "."
#mingw = 1
mingw = 0
instdir = "/raid/zyc/code_for_dhs_paper/install"
if nrn_srcdir[0] != '/' :
# TODO: is this right?
nrn_srcdir = '../../../../../../' + nrn_srcdir
from distutils.core import setup
from distutils.extension import Ex... |
bd3889ec5a45c7dd0273fa2f4a9bd4354bb2ce3dd48013cffc6bcc337678265b | Python | 2,064 | 54 | import math
import torch
import torch.nn as nn
from functools import partial, reduce
from operator import mul
import torch.nn.functional as F
import numpy as np
from timm.models.layers import trunc_normal_
from models.vit_3d import VisionTransformerND, ConvStem3D
from models.layers.stmixer import STMixer
class LNGrow... |
677b77392b11f33aa9ff292be0ed2f28474d6b261894f0727025e78934c64a70 | Python | 2,068 | 53 | ############################################
# imports
############################################
from abc import ABC, abstractmethod
from typing import Any
from oligo_designer_toolsuite.database import OligoDatabase
############################################
# Scorer Base Class
#################################... |
a27207b46c02b0e31034369ac03737db85168fa84e8b6b57fef25414f3f172a5 | Python | 2,068 | 54 | """
subsample slices for MICCAI 2020 Reconstruction project.
"""
import numpy as np
import random
from scipy.ndimage.interpolation import shift
class RandomSample:
"""
Subsample the 3D numpy [c, h, w, d] by slice max number.
"""
def __init__(self, perturbation=False):
self.sample_size_propabi... |
2fae701d4b2667e409ebd94d92b60d20b82c3fb8bfba6059e78b738e10e3279f | Python | 2,069 | 52 | import discharge_summary_parser as dsp
from semquery import SemEHRES
import utils
def analyse_corpus(q, doc_type, full_text_field, reg_exp, output_file):
es = init_es_inst()
dsp.analyse_discharge_summaries(es, q,
doc_type=doc_type,
full_t... |
b0eb7449e1c3a3b58975a90b0dc0ff0de9ae160ccb19fba5b3fe363c7f3607a2 | Python | 2,074 | 55 | #!/usr/bin/env python
#
# The MIT License
#
# Copyright (c) 2011 Seoul National University.
#
# 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 limitati... |
2919fb5b6c234d063efdabea6ba991ebd71339dcf33e55625880f2de01bb673c | Python | 2,077 | 56 | import jax
import jax.numpy as jnp
from functools import partial
'''
u: [usize, udim]
lb: [udim]
rb: [udim]
out: [usize+2, udim]
'''
roll_db_funs = {2: jax.jit(lambda u, lb, rb: jnp.concatenate([jnp.tile(lb, [3,1]), u[:-1,:]], axis = 0)),
1: jax.jit(lambda u, lb, rb: jnp.concatenate([jnp.tile(lb, [2,1]... |
0c70bf58578290b9c04e418e9862602fd9a68780a1190cff7cad513b0237e281 | Python | 2,079 | 63 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
from mmpose.datasets.builder import PIPELINES
from .top_down_transform import TopDownRandomFlip
@PIPELINES.register_module()
class HandRandomFlip(TopDownRandomFlip):
"""Data augmentation with random image flip. A child class of
TopDownRandomF... |
41af9ad108d558fb5ba173ea0fe3a7cf83bd585514f25b94a4c061586e3f3400 | Python | 2,079 | 57 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
7c14f783fc7012cee11b015735e71f3dc6e75dcdd4da3488e5a14be4beb4aa72 | Python | 2,080 | 70 | """Custom formatting for CLI help and error messages."""
import argparse
import sys
import textwrap
class ArgumentParser(argparse.ArgumentParser):
"""Enhanced argument parser with better formatting."""
def __init__(self, **kwargs):
super().__init__(
formatter_class=HelpFormatter,
... |
5fc926a3fcbaba4722bdfcdea783cbffb3fc906059bb7eae4ff6051ffa2d44c3 | Python | 2,082 | 63 | import argparse
import os
import librosa
import numpy as np
def process_utterance(wavscp, utt2spk, audio_dir, wav_dumpdir, segment, tgt_sr=24000):
uid, _, phns, notes, syb_dur, phn_dur, keep = segment.strip().split("|")
utt2spk.write("{} {}\n".format(uid, "opencpop"))
# apply bit convert, there is a kn... |
0bf310ff752bd85c1b1cacd29576465a6585bd400b0e4b1d7abdb6b8ba08f506 | Python | 2,084 | 45 | from fedml_api.distributed.split_nn.message_define import MyMessage
from fedml_core.distributed.server.server_manager import ServerManager
from fedml_core.distributed.communication.message import Message
class SplitNNServerManager(ServerManager):
def __init__(self, arg_dict, trainer, backend="MPI"):
super... |
f12a51bd6f9aba069f4162d9e47db6f4fc90bcf538722887d62aa48119f4319f | Python | 2,086 | 75 | '''
Author: ----
Date: 2022-04-09 14:24:09
LastEditors: ---a
LastEditTime: 2022-09-05 14:46:08
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
class Gramian(nn.Module):
r"""
Enforcing weight matix to be orthogonal by restricting Gramians
"""
def __init__(self) -> None:
s... |
9c487bc01f069866ca4c4f6344a38bdca1d088b6d624325deaa1ce5015099e46 | Python | 2,090 | 55 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from torch import Tensor, nn
class FCEmbedding(nn.Module):
"""Fully-connected multi-layer neural network to be used as embedding network."""
de... |
a0bc48430e7532562d37e8816ebc8a4d793306822fbcd13f77880944cb79e246 | Python | 2,090 | 63 | import numpy as np
from torch.utils.data import DataLoader
from torch.utils.data.dataloader import default_collate
from torch.utils.data.sampler import SubsetRandomSampler
class BaseDataLoader(DataLoader):
"""
Base class for all data loaders
"""
def __init__(self, dataset, batch_size, shuffle, validat... |
aae0b37d86858e69ac36e5779c5268672f3e8986c36e7c9c4f3c953e3ffedd91 | Python | 2,090 | 78 | import pickle
from pathlib import Path
import torch
from sbi.inference import simulate_for_sbi
from sbi.utils import BoxUniform
from consbi.simulators import (
RuleSimulator,
default_rule,
default_rule_constrained_two_param,
default_rule_linear,
default_rule_linear_constrained_2p,
dso_linear_t... |
3a88e67d0ba84a460de987876349ff17291370ff7b251d7fe6a109ad8fe7574e | Python | 2,091 | 61 | #!/usr/bin/env python
from __future__ import division
from __future__ import print_function
import os
import sys
import vcf
import argparse
TUMOR_SAMPLE = "TUMOR"
THRESHOLD = 1.3
def filter_strelka(input_fh):
input_vcf = vcf.Reader(input_fh, strict_whitespace=True)
output_vcf = vcf.Writer(sys.stdout, templ... |
dc26e4f4e65426ddad52083b6ffccd3ed0135b20039b369f52b8ca1057e648e4 | Python | 2,091 | 47 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# McNemar's test source: https://machinelearningmastery.com/mcnemars-test-for-machine-learning/
import statsmodels.stats.contingency_tables as sm
"""
@input - truth values of the predictions of the first model (List); truth values of the predictions of the second m... |
47ae83eb983f8c9cd71ba0b96bfd4c178b561fc141fb4c55b22a7a8b68b3134b | Python | 2,097 | 61 | #------------------------------------------
# Compute all folial widths and perimeters
# 1 Feb 2023
#------------------------------------------
'''Compute and save gyral measurements for all subjects in the dataset'''
import json
import numpy as np
import pandas as pd
import gyri as gy
from convert_polygons_to_shapel... |
f4b021fd6964dfedc4244599a6b104bcdd64589d8ca119bf7efdf761e083e70d | Python | 2,098 | 58 | from skimage import filters
from skimage.io import imread
from flystim import util
import numpy as np
class Image:
"""Image class."""
def __init__(self, image_name):
self.image_name = image_name
self.image_path = util.get_resource_path(image_name)
def load_image(self):
""" Return... |
326e9cd82823540355aa67d5e74021d357a72e6d1df14ed0bb400630ae48e0a1 | Python | 2,099 | 81 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 18:23
@project: MalariaDetection
@description:
"""
from keras.layers import * #For adding convolutional layer
from keras.layers import Dense, ZeroPadding2D, BatchNormalization, Activation... |
a7a52aa7911e001bb919e6c101e11014f238601e80cf08b11416a21534df46b8 | Python | 2,100 | 53 | import cv2
import numpy as np
def picdil(n, stencil, img):
kernel = np.ones((25,12), np.uint8)
# =============================================================================
# kernel2 = np.ones((0,1), np.uint8)
# img_dilated = cv2.erode(img.copy(), kernel, iterations = 1)
# img_dilated = cv2.dil... |
e7dd3a1748b9ec4ed72bfb4f5c89285989f89686fbb3a5cdc35a8c2df2fe3825 | Python | 2,106 | 59 | import sys
import numpy as np
import sklearn.linear_model as skl
import argparse
parser = argparse.ArgumentParser(description='Argument Parser')
parser.add_argument("-sub", "--sub",help="Subject Number",default=1)
args = parser.parse_args()
sub=int(args.sub)
assert sub in [1,2,5,7]
nsd_features = np.load('data/extract... |
0ca420ce3e73d1d4b16f4b7cc436448c078df6fa75ad7e93ac69580e9b623bd3 | Python | 2,111 | 53 | import argparse
import logging
import os
import sys
import numpy as np
import torch
import wandb
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
from fedml_api.standalone.hierarchical_fl.trainer import Trainer
from fedml_experiments.standalone.fedavg.main_fedavg import add_args, load_data... |
73c8ba3329d60e5a10a3d793b83b67bd416390ddb709bd41481de01f521e0206 | Python | 2,115 | 64 | # Copyright (c) 2023 IBM Research. All Rights Reserved.
#
# Code accompanying a manuscript entitled:
# "On the visual analytic intelligence of neural networks"
from random import randint, choice
from PIL import Image, ImageDraw
from constants import NUM_IMAGES, MAX_COLOR_INTENSITY
class BaseGenerator():
... |
5a73d33a921f527064df302587e047bb468c5fff1b140d564e7d63efab9092fe | Python | 2,119 | 47 | # Copyright (c) 2020-present Joindbre.com
# Authors: Patricia Maraver.
#
# This program is free software: you can redistribute it and/or modify
# it under the GNU Affero General Public License version 3
# as published by the Free Software Foundation.
# You should have received a copy of the Server Side Public Lic... |
239042d3964c701daae3bfda2617c131d7bb631f936930d8630d6919500c0947 | Python | 2,121 | 55 | # Module containing functions used to validate input parameters
#
# Author: Fernando García Gutiérrez
# Email: fgarcia@fundacioace.org
def checkInputType(input_var_name: str, input_var: object, valid_types: list or tuple):
""" Check if the input variable is in the allowed types ("valid_types"). """
if n... |
c280cdff61f57481d5113d97cabd47976598d152527aac6747fddc2b167eb7f4 | Python | 2,122 | 56 | class VerticalMultiplePartyLogisticRegressionFederatedLearning(object):
def __init__(self, party_A, main_party_id="_main"):
super(VerticalMultiplePartyLogisticRegressionFederatedLearning, self).__init__()
self.main_party_id = main_party_id
# party A is the parity with labels
self.pa... |
3c6872d0e3ac3b09a85e5dbc54a9a167ef42efbe138868460738ad9ac178f7c8 | Python | 2,124 | 62 | import json
from pathlib import Path
import numpy as np
import pandas as pd
from DeepSlice import DSModel
EXAMPLE_DIR = Path("examples/example_brain/GLTa")
REFERENCE_PREFIX = EXAMPLE_DIR / "MyResults"
def test_example_brain_predictions_match_reference(tmp_path):
output_prefix = tmp_path / "PR80Results"
m... |
ddb1f12084f5dff852a9c8f5598936921fe75873c13753fa872aabee23b09143 | Python | 2,125 | 60 | import logging
from abc import abstractmethod
from mpi4py import MPI
from ..communication.mpi.com_manager import MpiCommunicationManager
from ..communication.mqtt.mqtt_comm_manager import MqttCommManager
from ..communication.observer import Observer
class ServerManager(Observer):
def __init__(self, args, comm=... |
fd581aa4a1888c6bc835dad2945050788c62ad989f7718e3a9ccbe861963c88d | Python | 2,126 | 61 | import math
from basicstat import *
def readbf(runnames, min_ess = 10):
logz_idx = 2
meanlog_idx = 3
varlog_idx = 5
npoint_idx = 6
ess_idx = 7
runs = []
for run in runnames:
with open(run, 'r') as infile:
x = [[float(y) for y in line.rstrip('\n').split()] for line in i... |
99b87d26b15be95e24f3aacc8c73796f5ea03bbef5f4fa19d91dc7873dc06aa5 | Python | 2,127 | 66 | # Copyright (c) OpenMMLab. All rights reserved.
import pytest
import torch
from mmpose.models.backbones.tcformer import TCFormer
def test_tcformer():
with pytest.raises(TypeError):
# Pretrained arg must be str or None.
TCFormer(pretrained=123)
# test load pretrained weights
model = TCFor... |
ae100da6e95f58097543388730628a9c2410948c876abe119ce83e535e005e31 | Python | 2,127 | 71 | import argparse
import matplotlib.pyplot as plt
import pandas as pd
from graphrepr.wilcoxon import (
get_best_models_for_reprs,
get_validation_results,
load_all_preds,
mse,
plot_pval,
test_representations_df,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_a... |
2198a728e322dc4d0d0926bd53e15570fbf8cd7b4aa09af1bdaf1944b07b3bdb | Python | 2,129 | 65 | #!/usr/bin/env python3
"""A setuptools based setup module.
See:
https://packaging.python.org/guides/distributing-packages-using-setuptools/
"""
import setuptools
from brainstat import __version__ as brainstat_version
with open("README.rst", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.... |
e9ffa193aed7249d4d6f40ecbf3dce2d421f5d46ced40c5a3e46cb4e8da15ffa | Python | 2,129 | 56 | from model_v0 import *
from deprecated.solver_v1 import Solver
from fold import *
from util import Timer
dataset_path = "/home/francishe/Downloads/ANI-1ccx_clean_shifted.h5"
gammas_path = "/home/francishe/Downloads/gammas_50_5_extend.h5"
cv_root = "/home/francishe/Downloads/cv_inflect/"
target_type = ['cc', 'pe']
fol... |
37fd6d552d6b5d1566324b7549200dff520f2623fe525134b4dc1a71272933ba | Python | 2,131 | 64 | from model.builders.builders import build_full_network
import os
filename = os.path.basename(__file__)
from copy import copy, deepcopy
vocab_size = 20000
data_base ={'id':'response','type': 'updated_label' , 'params': {'outcome': 'progression', 'text': 'NARR+IMPRESS', 'training_split':0}}
training_splits = [0,1,4,6,... |
0380277b12a906d551cfa5e6119ed864115b427d85b7f818280d60431b69e927 | Python | 2,132 | 45 | from mpi4py import MPI
from fedml_api.distributed.turboaggregate.TA_Aggregator import FedAVGAggregator
from fedml_api.distributed.turboaggregate.TA_Trainer import FedAVGTrainer
from fedml_api.distributed.turboaggregate.TA_decentralized_worker_manager import TA_ClientManager
def FedML_init():
comm = MPI.COMM_WOR... |
eb7f3780490a13d3a2ba2b1a8945f4ff83dd5d60b86b903cd9dc3d81f34ae29f | Python | 2,132 | 72 | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Vector quantize codebook modules.
This code is modified from https://github.com/ritheshkumar95/pytorch-vqvae.
"""
import torch
from parallel_wavegan.functions import vector_quantize, vector_quantize_str... |
adf04bcb11224d80bf2f48dbc3663d902b3c2b581d2d94c065b5cf23e893d6e1 | Python | 2,133 | 51 | import multipagetiff as mtif
import argparse
import logging
import os
from . import utils
import numpy as np
from ..deconvolution import deconvolve
# =========================================
def main(*args, **kwargs):
parser = argparse.ArgumentParser(description="Deconvolve one stack.\
Example: pycro... |
1f8f36e2c5386a5352c7ac7a3ce8d60e05e0664563e9e7ddaa5ce103ef7adec2 | Python | 2,134 | 75 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 17:53
@project: MalariaDetection
@description:
"""
from keras.layers import * #For adding convolutional layer
from keras.layers import Flatten #For flattening max pooled layer values... |
932b4d59b063e5c31f6f3ecff0b27cca207983638dcc5425481bed3ac8674bae | Python | 2,134 | 64 | from model.builders.builders import build_full_network
import os
filename = os.path.basename(__file__)
from copy import copy, deepcopy
vocab_size = 20000
data_base ={'id':'progression','type': 'updated_label' , 'params': {'outcome': 'progression', 'text': 'NARR+IMPRESS', 'training_split':0}}
training_splits = [0,1,4... |
e5104d8897dfc229d045cab64fdfeac6c45071c215f949268be4dd7984600bec | Python | 2,135 | 55 | import torch
def vectorize_weight(state_dict):
weight_list = []
for (k, v) in state_dict.items():
if is_weight_param(k):
weight_list.append(v)
return torch.cat(weight_list)
def load_model_weight_diff(local_state_dict, weight_diff, global_state_dict):
"""
load rule: w_t + clip... |
3f33e45ce8272761b4660aa9c7c05c172b5ac98d003e96b8a47a7b10ecadc82c | Python | 2,138 | 75 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2020/12/10 17:53
@project: MalariaDetection
@description:
"""
from keras.layers import * #For adding convolutional layer
from keras.layers import Flatten #For flattening max pooled layer values... |
484945d11c9c2cd5dd63406d30fd19dfc7f9ed92f327bd87114652fc3c8d8385 | Python | 2,140 | 58 | #######################################################################
# Software License Agreement (AGPL-3 License)
#
# OpenViBE SDK Test Software
# Based on OpenViBE V1.1.0, Copyright (C) Inria, 2006-2015
# Copyright (C) Inria, 2015-2017,V1.0
#
# This program is free software: you can redistribute it and/or modify
... |
99710e8852585395f53d61cc931f61aa4481065acf89a56770edc0eec08fc28f | Python | 2,140 | 66 | import os
import time
import ujson as json
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader
class MySet(Dataset):
def __init__(self):
super(MySet, self).__init__()
self.content = open('./json/json').readlines()
ind... |
110cad2abd7e71d318c56487f0c4d0931d8854f6c7f02d5fddd9d4d8da92596a | Python | 2,142 | 52 | #!/usr/bin/env python3
"""
The spatial attack is a very special attack because it tries to find adversarial
perturbations using a set of translations and rotations rather then in an Lp ball.
It therefore has a slightly different interface.
"""
import torchvision.models as models
import eagerpy as ep
from foolbox import... |
21e9bba263e88fdbc2563fe35e81083396b6e28e1da48fc9887a3972cce009ba | Python | 2,142 | 76 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.files import FileFormat
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_AA_GAP_CHARS, DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestCodonOut(object):
def test_simpl... |
8abc02cb7793722918ca93b3d2964d9985ff243243f9b532dd240f571ac55df1 | Python | 2,146 | 76 | # -*- coding: utf-8 -*-
"""MNIST dataset for testing"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
import torch
from torch.utils.data import Dataset
import h5py
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import resample_poly
_factors = [(3, 1), (3, 1), (3, 1)]
def array_to_color(arra... |
aedfa3f9d9b3ce6c405d04b7fe9894364e385636ac7238a7ec39bac9789c16df | Python | 2,146 | 47 | import argparse
import pathlib
from exsclaim.pipeline import Pipeline
# Path to/ Name of Query JSON, used if no command line argument is supplied
# If no file extension (.json) is supplied, path will be assumed to be:
# path/to/exsclaim/query/<QUERY>.json
QUERY = "nature-haadf-ag-nanoparticles"
# First letter of tool... |
7be93d3cc1f7aab19531f4c01756acee6c05e026b900dd24cf1201260d7b519c | Python | 2,147 | 77 | # -*- coding: utf-8 -*-
"""MNIST dataset for testing"""
__author__ = "Sean Benson"
__copyright__ = "MIT"
import torch
from torch.utils.data import Dataset
import h5py
import matplotlib.pyplot as plt
import numpy as np
from scipy.signal import resample_poly
_factors = [(3, 1), (3, 1), (3, 1)]
def array_to_color(arr... |
9ca8707c1b6d246f37db0747d7a2c7c36deaf45e9f4270f6ca4561d7ac4247b6 | Python | 2,150 | 55 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import tabulate
def load_gmv_corr_with_ad() -> pd.DataFrame:
return pd.read_csv("./metadata/data_processed/gmv_corr_with_ad_stats.csv")
def load_risk_table(seed, risk) -> pd.DataFrame:
df_ = pd.read_csv(f"./metadata/d... |
a33450875af9cb9d438ec79772d710a1b412c76e2befbfd9c986f137c3eebf91 | Python | 2,161 | 56 | import numpy as np
class ExpRandomCrop3d:
"""
Crop the 3D numpy [c, h, w, d] and mask [d, 2] in a sample
"""
def __init__(self, output_size, padding=0, training=True):
assert isinstance(output_size, (int, list, tuple))
if isinstance(output_size, int):
self.output_size = (o... |
cc52e0b8d91c4b2daa26d898df0a143806f901245e30c8e5f014d0222570daa0 | Python | 2,162 | 78 | # -*- coding: utf-8 -*-
# Copyright 2020 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Causal convolusion layer modules."""
import torch
class CausalConv1d(torch.nn.Module):
"""CausalConv1d module with customized initialization."""
def __init__(
self,
in_channels,... |
e92c308932206c03a93eff83ba6fc65608b47084baff152739315fdbd6b3cba9 | Python | 2,162 | 56 | import numpy as np
import pandas as pd
def get_cluster_statistics(words: pd.DataFrame) -> dict:
"""
Calculates some general clustering features (see return-description).
:param words: pd.DataFrame containing a column "cluster" with np.NAN for words without a cluster and an integer for
words within a c... |
33a718cff9853298621daa702c22b7689684ab1079eb9d4310f7f0cbe5b6d354 | Python | 2,164 | 55 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class FastRCNN(TwoStageDetector):
"""Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
def __init__(self,
backbone,
... |
821dcf4d73a608792a4fdc734a62c0f14843a2f3a3d7caf1278c15c7ca1c52dc | Python | 2,164 | 69 | # Copyright (c) OpenMMLab. All rights reserved.
import tempfile
from unittest.mock import MagicMock
import pytest
from numpy.testing import assert_almost_equal
from mmpose.datasets import DATASETS
from tests.utils.data_utils import convert_db_to_output
def test_deepfashion_dataset_compatibility():
dataset = 'De... |
7e2205b97f9fd275586c6182ff5367db4501296953116438dd547bc85b7f841a | Python | 2,167 | 77 | """
This module provides property calculation functionality for oligonucleotides.
"""
from ._property_base import BaseProperty
from ._property_calculator import PropertyCalculator
# Export calculation functions for use by filters and scorers
from ._property_functions import (
calc_detect_oligo,
calc_dg_second... |
321526e7fef12ff37428f34bead59f22c9ba23f09ffdd983aca456159a619ccc | Python | 2,169 | 67 | # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org
# Distributed under the MIT License.
# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
from abc import abstractmethod, ABCMeta
from typing import Optional, Tuple
from torch import nn, Tensor
class BaseWra... |
2db4a12623d662e5c83d051a2c65660cd5292c4cf3a3e996588800aa52409c7e | Python | 2,170 | 70 | # Copyright (c) OpenMMLab. All rights reserved.
from unittest.mock import MagicMock
import pytest
from mmcv import Config
from numpy.testing import assert_almost_equal
from mmpose.datasets import DATASETS
from tests.utils.data_utils import convert_db_to_output
def test_deepfashion_dataset():
dataset = 'DeepFash... |
a41197c3c9fbe6a3bc6844efe281636c6794c1ef7794d04e25b80583dcd5786a | Python | 2,171 | 71 | import sys
import os
import pandas as pd
import numpy as np
import logging
from tqdm import tqdm
from utils import setup_custom_logger
def getThresholdValue(matrix_Arr, quantile):
flatten = []
for row in matrix_Arr:
for val in row:
flatten.append(float(val))
#print("Data size : ", len(f... |
4ed80eb06eb25d074b3942a60e9a4582c6e094f0b77934f21a496e29cd68e04c | Python | 2,175 | 58 | # Copyright (c) OpenMMLab. All rights reserved.
import mmcv
import torch.nn as nn
from mmcv.cnn import ConvModule
from mmcv.runner import BaseModule
class SELayer(BaseModule):
"""Squeeze-and-Excitation Module.
Args:
channels (int): The input (and output) channels of the SE layer.
ratio (int):... |
dcc1ba1d8c1c79d7361b737f261ab9422c057b2ebd987b2a2914c2eeccb3255b | Python | 2,176 | 70 | import torch
import torch.nn as nn
import torch.nn.functional as F
# Swish https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------
class Swish(nn.Module): #
@staticmethod
def forward(x):
return x * torch.sigmoid(x)
class Hardswish(nn.Module):... |
1d0e6734eaa5e1cf4715060df5d8799d11d0424d239087515754862132bbd00b | Python | 2,178 | 66 | import torch
import torch.nn as nn
import torch.nn.parallel
import numpy as np
from torch.nn.parameter import Parameter
class rational(nn.Module):
def __init__(self):
super(rational, self).__init__()
print('rational!!!!!!!!!!!!!!!!!!!!!!!!!!!!')
self.a0 = Parameter(torch.FloatTens... |
72dfc21f6a407f6d2e906d92a3d29fee2c6f7416bc1d2c1638eea6997b785bdd | Python | 2,179 | 85 | import os.path as osp
import numpy as np
import numpy.random as npr
import PIL
import cv2
import torch
import torchvision
import xml.etree.ElementTree as ET
import json
import copy
import math
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
... |
3b2805b943c6e0889d9588eb1ad4d74d4949c6ea0bae3578f01508d2ca700b61 | Python | 2,180 | 79 | #!/usr/bin/env python
# coding: utf-8
# In[ ]:
#Dataaugmentation
import copy
#rdkit
import rdkit
from rdkit import Chem
from rdkit.Chem import rdMolDescriptors
from rdkit.Chem import Descriptors
from rdkit.Chem import AllChem
from rdkit import DataStructs
#List of hydroxyl groups in the molecule
def list_of_hydroxy... |
2a7133c392da6be3175d45469a7aa78afbc7004afce0a630dfea474f622e755b | Python | 2,181 | 64 | import argparse
import logging
import os
import socket
import sys
import numpy as np
import psutil
import setproctitle
import torch
sys.path.insert(0, os.path.abspath(os.path.join(os.getcwd(), "../../../")))
# add the FedML root directory to the python path
from fedml_api.distributed.base_framework.algorithm_api imp... |
6ff90968119916d3e45a56b62d92fbe418335bda25444b59420c31416fc6f95b | Python | 2,181 | 68 | # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org
# Distributed under the MIT License.
# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
import torch
from torch_geometric.transforms import Compose
from torch_geometric.datasets import QM9 as QM9_geometric
f... |
e369d7c89e328123c826e6d26b0ef16e9d4792b7e919b501ef254ecd7fdface4 | Python | 2,182 | 59 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
"""
Builder for ODE solvers.
"""
from torch import Size, Tensor, nn
from sbi.samplers.ode_solvers.base import NeuralODE, NeuralODEFunc
from sbi.samplers... |
e252cd3908c5a7f6717c99b10ac2cb2bd2177b17516b7612492f41dd6ec11165 | Python | 2,184 | 74 | import json
import sys
class Message(object):
MSG_ARG_KEY_OPERATION = "operation"
MSG_ARG_KEY_TYPE = "msg_type"
MSG_ARG_KEY_SENDER = "sender"
MSG_ARG_KEY_RECEIVER = "receiver"
MSG_OPERATION_SEND = "send"
MSG_OPERATION_RECEIVE = "receive"
MSG_OPERATION_BROADCAST = "broadcast"
MSG_OPER... |
7301ef9d9eafa6c304d5903fbff1c1c2a276be18b6df89e664f96fe3ad582534 | Python | 2,188 | 56 | import numpy as np
class RandomCrop3d:
"""
Crop the 3D numpy [c, h, w, d] and mask [h, w, d] in a sample
"""
def __init__(self, output_size, padding=0, training=True):
assert isinstance(output_size, (int, list, tuple))
if isinstance(output_size, int):
self.output_size = (o... |
62242566ec9cf5f1d522cec0d8eeeb902dff2b8c9899ffe720169931d813f23d | Python | 2,190 | 46 | import logging
from fedml_api.standalone.hierarchical_fl.client import Client
from fedml_api.standalone.fedavg.fedavg_trainer import FedAvgTrainer
class Group(FedAvgTrainer):
def __init__(self, idx, total_client_indexes, train_data_local_dict, test_data_local_dict, train_data_local_num_dict, args, device, model)... |
a655feeff5becf6fc2c09bfba01c91d0549577a333a4ce27fed04b2d173063c1 | Python | 2,192 | 53 | import logging
from .message_define import MyMessage
from ....fedml_core.distributed.communication.message import Message
from ....fedml_core.distributed.server.server_manager import ServerManager
class BaseCentralManager(ServerManager):
def __init__(self, args, comm, rank, size, aggregator):
super().__i... |
c0afe0573d46f55b5d19245e14db99bc8c805c3750feb1270f164875c9bb5ac9 | Python | 2,192 | 69 | # This file is part of sbi, a toolkit for simulation-based inference. sbi is licensed
# under the Apache License Version 2.0, see <https://www.apache.org/licenses/>
from __future__ import annotations
import pytest
import torch
from sbi.simulators.linear_gaussian import diagonal_linear_gaussian, linear_gaussian
@py... |
a05c6ac216cfd6541d5a8650a918a836458a3c1b56f8ade3e9f4452f86568dfa | Python | 2,193 | 60 | import torch
"""
FedNova Optimizer comm_helper implementation cited from https://github.com/JYWa/FedNova/tree/master
"""
def flatten_tensors(tensors):
"""
Reference: https://github.com/facebookresearch/stochastic_gradient_push
Flatten dense tensors into a contiguous 1D buffer. Assume tensors are of
sa... |
9ea5eef4668f0e4df57f0e161411793418684d38a54389edf7ebc2a4314198ee | Python | 2,198 | 69 | # Copyright Universitat Pompeu Fabra 2020-2023 https://www.compscience.org
# Distributed under the MIT License.
# (See accompanying file README.md file or copy at http://opensource.org/licenses/MIT)
import pytest
from pytest import mark
try:
import NNPOps
nnpops_available = True
except ImportError:
nnpo... |
02ed42bf8ec9b562ef14acb8a216d1f802ae324931eb868c338cc2ee78125661 | Python | 2,200 | 76 | from model.builders.JAMA_builders import any_cancer_JAMA
import os
from copy import copy, deepcopy
filename = os.path.basename(__file__)
data_base ={'id':'progression','type': 'updated_label' , 'params': {'outcome': 'progression', 'text': 'NARR+IMPRESS', 'training_split':0}}
training_splits = [0,1,4,6,7,9]
number_repo... |
40fed9e29fe1cc693148f54a774cf1332aa63225fcd202170c2611eddd0ea3ba | Python | 2,200 | 76 | from model.builders.JAMA_builders import any_cancer_JAMA
import os
from copy import copy, deepcopy
filename = os.path.basename(__file__)
data_base ={'id':'progression','type': 'updated_label' , 'params': {'outcome': 'progression', 'text': 'NARR+IMPRESS', 'training_split':0}}
# training_splits = [0,1,4,6,7,9]
# number_... |
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