sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9c5b24cd323e569e6f1005dda96df5e69cc28237f19d7323c4a6ece5962b080b | Python | 909 | 47 | """
A collection of constants that probably should not be changed
"""
import numpy as np
# Universal Constants
SPEED_OF_SOUND = 343.0 # m/s
FS = 48000
# Project constants
MAX_SHIFTS = [2, 4]
ROOM_DIM = 6
MAX_SPEAKER_RELATIVE_HEIGHT = 0.8
NEG_SAMPLE_INTIAL_CANDIDATES = 30
# Robot constants
CHANNELS_PER_MIC = 1
CODEC... |
0994a9151466a03ea21cf29630adebbe4f10b377cadb48513bd866a469c484ba | Python | 911 | 33 | # Restart an MCMC from a checkpoint.
# Settings made to the var module in the original MCMC are not in
# the pickled file, so you will probably need to set them again ---
var.PIVEC_MIN = 1.e-6
var.RATE_MIN = 1.e-6
var.BRLEN_MIN = 1.e-5
var.GAMMA_SHAPE_MIN = 0.15
read("d.nex")
d = Data()
# Assuming more than one run,... |
47bb6b0edf0f99c6a5d44f08cbd449b48d455b5a9dbb747d9d2039ed7f4c503d | Python | 911 | 9 | from .attention import MultiHeadSelfAttention2d
from .attention import AxialSelfAttention2d
from .attention import TiedAxialSelfAttention2d
from .attention import Transmorpher2d
from .attention import TransmorpherBlock2d
from .loss import NT_Xent_Loss, SequenceNTXentLoss, EmbeddedJigsawLoss, SigmoidCrossEntropyLoss, Bi... |
8b9db7c54d223f29cc23b5162065a40c78f8fa3f1804373beeeb31415545c979 | Python | 911 | 35 | from torch import nn
class ModuleWrapper(nn.Module):
"""Wrapper for nn.Module with support for arbitrary flags and a universal forward pass"""
def __init__(self):
super(ModuleWrapper, self).__init__()
def set_flag(self, flag_name, value):
setattr(self, flag_name, value)
for m in ... |
3b52675bc0008a4891d8197e6c082eb3913b186909e54136be1556dd025ea2f4 | Python | 912 | 26 | import pickle
from rdkit import Chem
from serenityff.charge.tree.dash_tree import DASHTree
sdf_file_path = "sdf_qmugs500_mbis_collect.sdf"
mol_sup = Chem.SDMolSupplier(sdf_file_path, removeHs=False)
tree = DASHTree()
node_path_storage = {}
last_dash_idx = ""
for mol in mol_sup:
dash_idx = mol.GetProp("DASH_IDX")
... |
b56de6ea01510372a251c7fba922abd70e5113f5780e68b8c33b0bc763c8a782 | Python | 912 | 22 | """
The module provides a collection of comprehensive oligonucleotide design pipelines, each equipped with specific functionalities to facilitate efficient and targeted oligo synthesis for diverse applications.
"""
from ._cycle_hcr_probe_designer import CycleHCRProbeDesigner
from ._genomic_region_generator import Geno... |
e61ff0ac442cf01330e68dfe07eb97dfb1290131c95020945e098ff0cfa95cbc | Python | 912 | 37 | from dataclasses import dataclass
from typing import TYPE_CHECKING
from Bio.Align import MultipleSeqAlignment
if TYPE_CHECKING:
from .msa import MSA
@dataclass
class TrimmingStats:
msa: "MSA"
@property
def alignment_length(self) -> int:
return self.msa.original_length
@property
def... |
f1e9a61654dda768b919fb54a460062ce548d7070477dce7f93e71a356e86b62 | Python | 915 | 36 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
from time import sleep
def main():
stim_duration = 2
iti = 2
screen = Screen(... |
9f0e26aaf98f992eeb2396c95475cf38521613b794dd1ea96cc954bd982ce3f5 | Python | 916 | 18 | # 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... |
f800474dcd763cd99ef2a17098cd2bee242abbd259041e7b2e1dcd222277697c | Python | 916 | 31 | nTax = 5
taxNames = list(string.ascii_uppercase[:nTax])
a = func.newEmptyAlignment(dataType='protein', taxNames=taxNames, length=200)
b = func.newEmptyAlignment(dataType='protein', taxNames=taxNames, length=133)
d = Data([a,b])
t = func.randomTree(taxNames=taxNames)
t.data = d
pNum = 0
t.newComp(partNum=pNum, free=0,... |
f523f4bbff68aedf98069ce968570862fe094f29f049ada09c05d06729293f3b | Python | 917 | 30 | import torch
import torch.nn as nn
class ModelB(nn.Module):
def __init__(self, inmodel, base_output_shape=3172, output_classes=2, fix_inmodel=14):
super(ModelB, self).__init__()
self.modelA = inmodel
if fix_inmodel:
assert isinstance(fix_inmodel,int), "Tell me how many layers ... |
32167355c096dc180a7b7674beb31a6537b53de1bbb589d9249f44fa98cd45f2 | Python | 920 | 29 | import pandas as pd
import urllib.parse
import requests
from tqdm import tqdm
import grequests
import time
#SERVER_URL = "http://localhost:6541"
#SERVER_URL = "http://dorresteintesthub.ucsd.edu:6541"
SERVER_URL = "http://npclassifier.gnps2.org"
def test_speed():
df = pd.read_csv("test.tsv", sep=",")
iteratio... |
3e8e69608db49bfe932a80306f6db6005b06a5a577ddeb76272a95ce85e89658 | Python | 920 | 39 | import pandas as pd
import moses
import argparse
import selfies as sf
parser = argparse.ArgumentParser()
parser.add_argument('--input', default='selfies_sample.txt',
help='path to selfie token file')
parser.add_argument('--valid',
help='path to validation fil... |
f0d2eb2d086ee9aae0810ee9ed9b461d79cce9a130d5dfa43e5acc4f095187c8 | Python | 922 | 37 | import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import pandas as pd
def to_var(var):
if torch.is_tensor(var):
var = Variable(var)
if torch.cuda.is_available():
var = var.cuda()
return var
if... |
8fc073413c81f9a055e2347afc129f81bea4005872021f491d2b38a3a90cb173 | Python | 925 | 27 | # 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, 'split_path':'splits'}}
... |
7a360c9f0d24e39d1b584f2d22ab50d7f521d3bd3fbe3c7e47d5d43f3f35692c | Python | 926 | 32 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .two_stage import TwoStageDetector
@DETECTORS.register_module()
class GridRCNN(TwoStageDetector):
"""Grid R-CNN.
This detector is the implementation of:
- Grid R-CNN (https://arxiv.org/abs/1811.12030)
- Grid R-CNN Pl... |
b7f91335c991b7b56131421309271dd3c8bf6ae5f44d26cb4d114ec022d50937 | Python | 926 | 26 | from Bio.Align import MultipleSeqAlignment
from Bio.Phylo.BaseTree import Tree
from Bio.Phylo.TreeConstruction import (
DistanceCalculator,
DistanceTreeConstructor,
NNITreeSearcher,
ParsimonyScorer,
ParsimonyTreeConstructor,
)
def build_parsimony_guide_tree(alignment: MultipleSeqAlignment) -> Tree... |
74482f517e4926c4ed02dab328d457a521f00b2505bd28210f44f51503d02f43 | Python | 927 | 23 | import numpy as np
def get_dff(flu, cell_index, demean_frames, frames=None):
if type(demean_frames) is slice:
f0 = flu[:, demean_frames].mean(axis=1) if cell_index is None else flu[cell_index, demean_frames].mean()
else:
f0 = demean_frames
frames_slice = None
if type(frames) is slice o... |
11cf7f3b781ddeb3d2aace6381668feb487f70578f4158f913ad772226aa79ab | Python | 929 | 36 | import numpy as np
from trefide.pmd import batch_decompose
from trefide.utils import psd_noise_estimate
def test_psd_noise_estimate():
n, t = 100, 5
Y = np.random.randn(n, t)
res = np.asarray(psd_noise_estimate(Y))
print(res)
def main():
test_psd_noise_estimate()
X = np.load("/home/sy2685/t... |
0ad6d32c4de228ed83503061dc269de841e9b2f8bb1375bdd614feaab02433ca | Python | 933 | 57 | #!/usr/bin/env python
import sys
import numpy as np
from operator import itemgetter
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-b",
"--bedpe_file",
dest="bedpe_file",
help="BEDPE file")
parser.add_option("-g",
"--genome_file",
dest="genome_file",
help="Genome... |
87074d085cdc793651f204e7f22ee89e5ad7fd50cb05101472e6392034d677e3 | Python | 936 | 32 | import torch
# from imblearn.metrics import sensitivity_score
from sklearn.metrics import confusion_matrix
def sensitivity(output, target, misc):
"""
Sensitivity = TP / (TP + FN)
:param output: Batch x Channel x ....
:param target: Batch x ....
:return:
"""
with torch.no_grad():
i... |
22ef463c5e0d41795ddee11f732f36cb4efdcd381fbf293f8243ad078818f80f | Python | 938 | 41 | import numpy as np
def gen_labels(in_annot,
out_annot,
in_pulse,
out_pulse,
size):
peaks = np.load(in_annot).astype(int)
pulses = np.load(in_pulse)
idx = range(len(peaks))
p1, p2 = peaks[:, 0], peaks[:, 1]
div = pulses[idx, p1] / pu... |
d2a82453e5053ed4a817286ec72995bec3dda0a85b9b446596c7fe402212fd21 | Python | 938 | 34 | import argparse
import typing
import models.keras
# Import bez importu :)
if False:
import tensorflow.keras as keras
class LinearModelMeta(models.keras.KerasModelMeta):
def define_parameters(cls, parser: argparse.ArgumentParser):
pass
@property
def custom_objects(cls) -> typing.Dict[str, ty... |
79254701c801b5d53af6fc0ce28fbe9ff7767f60f5db2fed739404f2a04aff73 | Python | 940 | 40 | import SimpleITK as sitk
import numpy as np
import argparse
import os
import glob
import sys
import pandas as pd
def main(args):
image_info = []
for fn in glob.glob(os.path.join(args.dir, args.ext)):
print("Reading:", fn)
img = sitk.ReadImage(fn)
spc = img.GetSpacing()
size = img.GetSize()
image_in... |
85d08d345d261632914953b1a7d27c55402e295ad29dcf2440ca9d876645d21d | Python | 940 | 24 | # Copyright (c) OpenMMLab. All rights reserved.
from .csp_darknet import CSPDarknet
from .darknet import Darknet
from .detectors_resnet import DetectoRS_ResNet
from .detectors_resnext import DetectoRS_ResNeXt
from .hourglass import HourglassNet
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .pvt im... |
2a7e04b57c449aeadb971b4e833f9b8e496b6b948d5922d2ee26fcbfc9a194d1 | Python | 941 | 23 | class SelectChannel:
def __init__(self, img_ch=[0], label_ch=[0]):
"""
Just select image channel(s) and label channel(s) by given img_ch and label_ch array.
"""
assert isinstance(img_ch, list), "Image channels should be a list."
assert isinstance(label_ch, list), "Label chan... |
f5459c7f0e0f151cdabfc541f4676b1902cd43fb94ac90dca8f4ce484a85e5fb | Python | 942 | 37 | class Error(Exception):
"""
This is the base class on which all local errors
and exceptions are based.
"""
pass
class FermiEnergyConflict(Error):
"""
Exception to be raised when asking for a cost function which uses the fermi energy
but not using finite the temperature which is used to ... |
d23b6c220d44195aa7e865423d092d542b182aee5fe976eb565c7417647615ec | Python | 944 | 29 | var.warnReadNoFile = 0
var.verboseRead = 0
nTax = 5
taxNames = list(string.ascii_uppercase[:nTax])
a = func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=100)
d = Data([a])
read('(B:0.5, ((D:0.4, A:0.3), C:0.5), E:0.5);')
t = var.trees[0]
t.taxNames = taxNames
t.data = d
c1 = t.newComp(free=1, spec='sp... |
ec6d18d92e5bcdc42212a5f5e25bad7ddf56cf7ffd9d69320e7664e62c5618d0 | Python | 946 | 40 | # -*- coding: utf-8 -*-
"""
Created on Mon Jun 7 20:08:53 2021
@author: fhu14
"""
ELEMENTS = {1: 'H', 6: 'C', 7: 'N', 8: 'O', 16: 'S', 79: 'Au'}
GROUP_S = [1]
GROUP_SP = [6, 7, 8]
GROUP_SPD = [16, 79]
NUMSHELL = {'s': 1, 'sp': 2, 'spd': 3, 'spdf': 4}
NUMBASIS = {'s': 1, 'sp': 4, 'spd... |
2ade6b441d7b9fd0c086e49cc9d2bcce8ec9834acb1928af4415bf2e4f72cca8 | Python | 947 | 38 | ''' adapted from https://github.com/all-umass/ManifoldWarping '''
''' miscellaneous utilities '''
import numpy
import scipy
import sys
import time
# Change:
from .distance import L2
def pairwise_error(A, B, metric=L2):
''' sum of distances between points in A and B, normalized '''
return metric.pairwise(A /... |
98583b34be027ef921bb9ee39a9b844912fa84ff0c2b50199694cdbda3978613 | Python | 948 | 35 | nTax = 10
taxNames = list(string.ascii_uppercase[:nTax])
a = func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=96)
d = Data([a])
t = func.randomTree(taxNames=taxNames)
t.data = d
t.newComp(free=0, spec='specified', val=[0.1, 0.2, 0.3])
t.newRMatrix(free=0, spec='specified', val=[2., 3., 4., 5., 6., 7.])
... |
fd594e3df384f5ab44cc7b0134c46cc8fd965031f2b46dc3b3daecdbbbcfed0e | Python | 948 | 31 | import pandas as pd
import numpy as np
def filter_array(array, w):
filtered_array = pd.Series(array).rolling(w, center=True).mean()
filtered_array[filtered_array.isna()] = 0
return np.array(filtered_array)
def compute_absolute_change(array, w):
array_f = filter_array(array, w)
output = [False]+[... |
d60c60d8b74cce184db0c28d64a82b69b56144f87a323c9df4ba84a0a7170004 | Python | 953 | 25 | import matplotlib.pyplot as plt
import pathlib
import os
import numpy as np
from dtor.viewer import multi_slice_viewer
p_ltp = 1013
abl_ltp = 19
p_nltp = 1002
abl_nltp = 4
o_liver_post = lambda p, a: os.path.join(pathlib.Path(os.environ["DTORROOT"]),
f"data/processed/{p}_{a}_liver_post.npy... |
bc6eddfc334ff3495d90928a81c8e969c13599f4dd13b7b8fe596f5cb70c6994 | Python | 955 | 29 | import multipagetiff as mtif
import argparse
import logging
import os
from . import utils
def main(*args, **kwargs):
parser = argparse.ArgumentParser(description="Unpad one stack by removing zero-valued lines and columns from every page.")
parser.add_argument("input_path", help="The path of the volume to reg... |
7b5da0968babb52d5573bc156c216b65b6f399ed81a6c712296d69a4a73d5339 | Python | 957 | 29 | #!/usr/bin/env python
from setuptools import setup
from glob import glob
__license__ = "GPL"
__version__ = "2.6.4"
__maintainer__ = "Langille Lab"
long_description = ("Douglas et al. 2020. PICRUSt2 for prediction of "
"metagenome functions. Nature Biotechnology. doi: "
"10.103... |
c83b3703ec4a24051eb0e297dcf347bf123fdd27a912bed36da2a16dfd44e3f7 | Python | 957 | 41 | # Do an MCMC.
# The defaults for these values are much smaller. These suggested
# changes may not be needed all the time, but seems to help for
# difficult data.
var.PIVEC_MIN = 1.e-6
var.RATE_MIN = 1.e-6
var.BRLEN_MIN = 1.e-5
var.GAMMA_SHAPE_MIN = 0.15
# Assuming more than one run, we set the run number by calling ... |
0c2a42d11039bda9ca5f1bdf266dc4710dd3210cf90f3074bcd7b90566717281 | Python | 959 | 31 | """
subsample slices for MICCAI 2020 Reconstruction project.
"""
import numpy as np
import random
from scipy.ndimage.interpolation import shift
class ZUpsampler:
"""
Upsample the 3D numpy label [c, h, w, d] to more dense volume. [c, h, w, d*10].
"""
def __init__(self):
self.sample_size_propab... |
384ffe647bdcede43e98c2f979b3000c2d92f09067bcc935d0e50c758623eff0 | Python | 959 | 30 |
import h5py
import numpy as np
import time
import glob
datapath = '/path/to/hctsaMEGoutputs/';
# MEG files
fileNames = sorted(glob.glob(datapath +
'HCP_MEG_outputs/Schaefer100/dataSegments/fullSet_80/*_N.mat'))
IDList = []
for n, inMat in enumerate(fileNames):
start = time.time()
... |
486788b1c4f5285ff58f760370208f8bbad16ced4d452a9173b35cac086a1c21 | Python | 959 | 18 | import os
#os.system('ls -l')
# Download Experiment Infos
os.system('aws s3 cp s3://natural-scenes-dataset/nsddata/experiments/nsd/nsd_expdesign.mat nsddata/experiments/nsd/')
os.system('aws s3 cp s3://natural-scenes-dataset/nsddata/experiments/nsd/nsd_stim_info_merged.pkl nsddata/experiments/nsd/')
# Download Stimul... |
c66c475e85981c119b687076fcb9e0dbb43deac56bfb1386d62943f97cbe73c3 | Python | 959 | 33 | '''
#python将某文件夹下的文件名存储到excel中
'''
#导入所需模块
import os
import xlwt
#定义要处理的文件路径（文件夹）
file_dir = "D:\Tuo\yolov5-cell\detect/Positivate_sp"
#将文件名列出并存储在allfilenames里面
allfilenames = os.listdir(file_dir)
#打印看是否符合预期
print(allfilenames)
#创建工作簿
workbook = xlwt.Workbook()
#新建工作表，并且命名为fileNames
worksheet... |
c87ba69590c5e3c5bff9bef1a5f6d3a5e26bda270f554675b0348f768453b3ff | Python | 960 | 39 | _base_ = ['hrnet_w48_coco_640x640.py']
model = dict(test_cfg=dict(
nms_dist_thr=0.1,
max_pool_kernel=11,
))
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='BottomUpGetImgSize',
base_length=32,
test_scale_factor=[0.5, 1, 2]),
dict(
type='BottomUpResizeAl... |
99bc0339c6dbb966ca8fbc78a46cb1152b06bdb28442a3e06719bd9ee4ebb0e1 | Python | 963 | 33 | import numpy as np
import pandas as pd
from sklearn.metrics import confusion_matrix
def eval(pred_path, annot_path, out_path, out_matrix, threshold):
#chargements
preds = np.load(pred_path, allow_pickle=True)
peaks = np.load(annot_path, allow_pickle=True).astype(bool)
df_error = pd.DataFrame()
... |
aa04a125b5b239170590c7ce234ae400b71b6be904998dbf0c23704dc3b5c478 | Python | 964 | 43 | import argparse
from pathlib import Path
import numpy as np
import tqdm
from scipy.signal import stft, istft
def do_ibm(gt, mix, alpha, theta):
"""
Computes and applies the Ideal Binary Mask
gt: (n_voices, 1, t)
mix: (1, t)
"""
n_voices = gt.shape[0]
nfft = 2048
eps = np.finfo(np.fl... |
155a65e260abc7f943a066463e3468e54f4b2cedc74b6e55f12ddf39b0e6e682 | Python | 965 | 37 | import pytest
import foolbox as fbn
import eagerpy as ep
@pytest.mark.parametrize("k", [1, 2, 3, 4])
def test_atleast_kd_1d(dummy: ep.Tensor, k: int) -> None:
x = ep.zeros(dummy, (10,))
x = fbn.devutils.atleast_kd(x, k)
assert x.shape[0] == 10
assert x.ndim == k
@pytest.mark.parametrize("k", [1, 2, ... |
1ec6c8d39e521414e133fec15e26bf33b091c59830ff25074658f3368076294b | Python | 970 | 31 | import torch.nn as nn
import torch.nn.functional as F
import numpy as np
def conv_init(m):
classname = m.__class__.__name__
if classname.find('Conv') != -1:
#nn.init.xavier_uniform(m.weight, gain=np.sqrt(2))
nn.init.normal_(m.weight, mean=0, std=1)
nn.init.constant(m.bias, 0)
class LeN... |
338bc986145abccabe1c0a8299ae0baf7133091b1ecf25162aab4651fa81b02d | Python | 970 | 30 | # 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 pytest import mark
import yaml
import glob
from os.path import dirname, join
from torchmdnet.models.model import c... |
ce3e1a242611c8663ab46863529e86d236d4e692f69de0d68c0b4eddbc749c2f | Python | 970 | 55 | from dataclasses import dataclass
from enum import IntEnum
class PointStyle(IntEnum):
"""
Plot point styles
"""
Simple_Dot = 0x15
Solid_Circle = 0x0A
Empty_Circle = 0x09
Solid_Square = 0x02
Empty_Square = 0x01
NA = 0xFFFFFFFF
class LatencyPlotUnit(IntEnum):
"""
Units used... |
25bdfebc0730ada3d0949c7b531b09003d86def609da3e529dc7e9f9a2629f9b | Python | 974 | 25 | # -*- coding: utf-8 -*-
"""
Created on Tue May 31 18:39:26 2022
@author: fhu14
"""
'''
Separate module for precomputing with a lock from if __name__ == "__main__":
'''
#Taking no changes, putting the imports into the if __name__ == "__main__" block too
if __name__ == "__main__":
from precompute_driver import ... |
989b4ff1e961f789476911d29f06630af3bac386e1fdbab36cd076692421e872 | Python | 974 | 28 | import numpy as np
class HistNorm:
def __init__(self, bd_low=0.1, bd_up=99.9):
self.bd_low = bd_low
self.bd_up = bd_up
# self.mean_i = mean_i
# self.std_i = std_i
def __call__(self, sample):
image, mask, misc = sample['image'], sample['mask'], sample['misc']
n... |
1abb2984d50b59bfbf305113674464ed356eec741387c159273139a1eb8ed6d4 | Python | 975 | 26 | # 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 sbi.neural_nets.embedding_nets.SC_embedding import SpectralConvEmbedding
from sbi.neural_nets.embedding_nets.causal_cnn import CausalCNNEmbedding
fro... |
bf264e3d3dda5e1cb62280afbcf5eb5205f70ef9d67e4027a2c92862c18a03bb | Python | 975 | 20 | # Copyright (c) OpenMMLab. All rights reserved.
from .ckpt_convert import pvt_convert, tcformer_convert
from .geometry import batch_rodrigues, quat_to_rotmat, rot6d_to_rotmat
from .misc import torch_meshgrid_ij
from .ops import resize
from .realnvp import RealNVP
from .rescore import DekrRescoreNet
from .smpl import SM... |
12568950f61abd5533615d77f1aef82041480c82bbcdbce0d374c12912ea7e57 | Python | 976 | 31 | import pkg_resources
import _webrtcvad
__author__ = "John Wiseman jjwiseman@gmail.com"
__copyright__ = "Copyright (C) 2016 John Wiseman"
__license__ = "MIT"
__version__ = pkg_resources.get_distribution('webrtcvad').version
class Vad(object):
def __init__(self, mode=None):
self._vad = _webrtcvad.create()... |
349e23dec2462efd87a69333ff2c9a8f71cc2eb553d6fe5af68f91093ffd0d31 | Python | 977 | 20 | import torch.nn.functional as F
from loss.bce_loss import bce_loss
from loss.bce_loss_with_logits import bce_loss_with_logits
from loss.cross_entropy_loss import cross_entropy_loss
from loss.dice_loss import dice_loss
from loss.nll_loss import nll_loss
if __name__ == '__main__':
import torch
torch.manual_seed... |
0307912ac33eadd4feec484ac251a81fe463b3466edc796961061568c0a9a11b | Python | 978 | 40 | var.PIVEC_MIN = 1.e-6
var.RATE_MIN = 1.e-6
var.BRLEN_MIN = 1.e-5
var.GAMMA_SHAPE_MIN = 0.15
rNum = 0
read('d.nex')
#read('simTree.nex')
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
pNum = 0
# comp for each node
for n in t.iterNodes():
c = t.newComp(partNum=pNum, free=1, spec='empirical', symbol... |
d9835abc7fd2e4dd0c0e344607a8fe370fdef86b193254fa5184b27591fd691e | Python | 978 | 26 | # 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': 'response', 'text': 'NARR+IMPRESS', 'training_split':0, 'split_path': 'splits_new'}}
... |
bb28afaac0a5493f97c37f0edb4d6a66b2f54b635c75b804fe90d642180ef064 | Python | 979 | 28 | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class SCNetMaskHead(FCNMaskHead):
"""Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
... |
ba5e3cebcc8909c59a340952e6fbf89b7422cc99b4f780cf28d7e2a871875812 | Python | 980 | 17 | from distutils.core import setup
from distutils.extension import Extension
setup(name="p4",
ext_modules=[
Extension("fastReducedRF", ['fastReducedRF.cpp'],
# Adjust the following to be able to find pyublas, numpy, and boost. Maybe need library_dirs as well?
#include_dirs = ["/usr/l... |
bd4ab1ede9551430785f2bb6031f4ca27a3e8a0180397f73218587592262554b | Python | 980 | 30 | #!/usr/bin/env python
import sys
valid_spliced_reads=0
problem_reads=0
for ln in sys.stdin:
if ln[0] == "@": continue
ln_split=ln.split()
cs = ln_split[5]
if "N" in cs:
quality_score=int(ln_split[4])
if quality_score < 10: continue # quality score
try:
... |
e50a26e89792a95b61b9a330559e7b47e9ac39b275e4aa1ad54c48775d97b9a2 | Python | 980 | 37 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
from time import sleep
def main():
stim_duration = 2
iti = 2
screen = Screen(... |
ac9f5c0990e09953d4e797cb4f28dc54eacfc34e20cd7615fa9fa468d832d5ec | Python | 981 | 27 | import numpy as np
class NormalizeHist:
def __init__(self, bd_low=0.1, bd_up=99.9, mean_i=0, std_i=1):
self.bd_low = bd_low
self.bd_up = bd_up
self.mean_i = mean_i
self.std_i = std_i
def __call__(self, sample):
image, mask, misc = sample['image'], sample['mask'], sampl... |
0f7cd20a02987ba5513d6bbdb8b44aa0190c3afbd295c6091801648dd1a17a2a | Python | 983 | 33 | import os
import gzip
import urllib
import urllib.request
def get_family_ids(rfam_file):
"""
extracts Rfam family IDs from a stockholm formatted multiple sequence alignment file
"""
ids = []
with gzip.open(rfam_file, 'rt', encoding='latin1') as f:
for line in f:
line = line.spl... |
460831ece088c3d686a1b2603bb6e7a40db7e4f081b33ae0a84643021e625f97 | Python | 984 | 32 | import torch
import torch.nn as nn
class ModelBSingle(nn.Module):
def __init__(self, inmodel, base_output_shape=2359296, output_classes=2, fix_inmodel=True):
super(ModelBSingle, self).__init__()
self.modelA = inmodel
if fix_inmodel:
for param in self.modelA.parameters():
... |
8d171d9c19b49f6ab1f58d63e77dac501d8edf16dbea676eaaffc216049c895c | Python | 985 | 35 | #!/usr/bin/env python3
import torch
import torch.nn as nn
import os
from foolbox.models import PyTorchModel
from foolbox.utils import accuracy, samples
def create() -> PyTorchModel:
model = nn.Sequential(
nn.Conv2d(1, 32, 3),
nn.ReLU(),
nn.Conv2d(32, 64, 3),
nn.ReLU(),
nn.M... |
e03527e8f0d4b05cb3bd052acb573b790dc64649f2a3ea14ff911ebc1f3f1e1a | Python | 986 | 33 | import os
import json
import time
import platform
import logging
def logging_config(args, process_id):
# customize the log format
while logging.getLogger().handlers:
logging.getLogger().handlers.clear()
console = logging.StreamHandler()
if args.level == 'INFO':
console.setLevel(logging.... |
1ff178946cd823accca10212abd976bd92c00c114963bb3ec80eab62401bf46b | Python | 989 | 25 | read('t.nex')
t = var.trees[0]
var.alignments.append(func.newEmptyAlignment(dataType='dna', symbols=None, taxNames=t.taxNames, length=1000))
var.alignments.append(func.newEmptyAlignment(dataType='dna', symbols=None, taxNames=t.taxNames, length=700))
d = Data()
t.data = d
t.newComp(partNum=0, free=1, spec='specified', ... |
6f8590a8077cb9de0eb6f6613b74a66a83cd3f4eb10697ac31c4d2af2b96960d | Python | 989 | 27 | # 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, 'split_path': 'splits_ne... |
eaa81145131f6eeb9b011e299e26902aa324d26f1100bddcd19bb8ab3f6ecfe7 | Python | 991 | 26 | import cv2
import numpy as np
def mask(img_c, img, lower_val, method):
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
# =============================================================================
# lower_val = np.array([105,0,0])
# ========================================================================... |
35585cbbac019935f047e2e2cf8521fb8741b94934f34c90f94a6e6d5805c55c | Python | 993 | 26 | import torchvision.transforms.functional as F
from data_loader.my_transforms.normalize3d import normalize_common
class NormalizeInstance3d(object):
"""Normalize a tensor volume with mean and standard deviation estimated
from the sample itself.
:param mean: mean value.
:param std: standard deviation v... |
69a395cef71c0227ba3e49cb3da930490b9a26f5174b629e14413aa4eb95027f | Python | 993 | 17 | import os
import pandas as pd
root = os.path.dirname(os.getcwd())
kinase_mapping = pd.read_csv(root + "/data/kinases.csv")
sel_kinases = [ "LCK", "KDR", "EGFR", "SRC", "MAPK14", "FGFR1", "MAP2K1", "CDK2", "GSK3B", "MAPK10", "ABL1", "CHEK1", "ITK", "PDPK1", "NTRK1", "SYK", "INSR", "IGF1R",
"MAPK1", "H... |
8f5ef17a739ed663bcb76d4c942199cfe300e97a36b71241c290ede97e4ed6e9 | Python | 994 | 40 | _base_ = ['hrnet_w48_crowdpose_640x640.py']
model = dict(
test_cfg=dict(
multi_scale_score_decrease=0.9,
nms_dist_thr=0.1,
))
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='BottomUpGetImgSize',
base_length=32,
test_scale_factor=[0.5, 1, 2]),
di... |
e886034c0921f882595f7d102bbb0b2fae6568e443d7d29005441ce8b04addcd | Python | 994 | 40 | _base_ = ['hrnet_w32_crowdpose_512x512.py']
model = dict(
test_cfg=dict(
multi_scale_score_decrease=0.9,
nms_dist_thr=0.1,
))
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='BottomUpGetImgSize',
base_length=32,
test_scale_factor=[0.5, 1, 2]),
di... |
e32d92aa410fe9a04f721a623d206095c37a602ef12257bd22c7258727339693 | Python | 995 | 28 | import pickle
from rdkit import Chem
from serenityff.charge.tree.dash_tree import DASHTree
sdf_file_path = "sdf_qmugs500_mbis_collect.sdf"
mol_sup = Chem.SDMolSupplier(sdf_file_path, removeHs=False)
tree = DASHTree()
# tree = DASHTree(tree_folder_path="/cluster/work/igc/mlehner/test192_DASHtree/")
node_path_storage =... |
524b26fd3a6071405bababcf36bd147aab644350ff727fd50e983931114ca525 | Python | 996 | 22 | # 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 sbi.neural_nets.estimators.base import (
ConditionalDensityEstimator,
ConditionalVectorFieldEstimator,
UnconditionalDensityEstimator,
)
f... |
dbcbf50bbc208cc4838f5e65a0c3e5236ad0b2bbd9906e64631fd541149a7d33 | Python | 996 | 37 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen, SubScreen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
from time import sleep
def main():
stim_duration = 2
iti = 2
screen = Screen(... |
7fbdcb7895248417d24945bfd8269d7342dd03188a3dc7db29979bf80dc5bb27 | Python | 998 | 28 | # Copyright (c) OpenMMLab. All rights reserved.
from mmdet.models.builder import HEADS
from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
from .fused_semantic_head import FusedSemanticHead
@HEADS.register_module()
class SCNetSemanticHead(FusedSemanticHead):
"""Mask head for `SCNet <https://arxiv.org/ab... |
f4fe9f07760f5f829b074089f6dbcac8882d93e03a26e54932d4888495697f71 | Python | 998 | 26 | from mpi4py import MPI
from fedml_api.distributed.decentralized_framework.decentralized_worker import DecentralizedWorker
from fedml_api.distributed.decentralized_framework.decentralized_worker_manager import DecentralizedWorkerManager
from fedml_core.distributed.topology.symmetric_topology_manager import SymmetricTop... |
cb1047f3ee445900519b0e2b6cf823efb9a43fa591e7184c524cddfdf8c674b1 | Python | 1,000 | 34 | # import torch
# from imblearn.metrics import specificity_score
def specificity(output, target, misc):
"""
Specificity = TN / (TN + FP)
:param output: Batch x Channel x ....
:param target: Batch x ....
:return:
"""
tn = misc['tn']
fp = misc['fp']
score = tn / (tn + fp)
del misc... |
989913c295253eff3f4116c5fd42b942b1a6d56f31c28ff6afe4e2ec73cdaea9 | Python | 1,001 | 39 | from enum import Enum
from typing import TYPE_CHECKING
from .logger import log_file_logger
if TYPE_CHECKING:
from Bio.Align import MultipleSeqAlignment
from .msa import MSA
class TrimmingMode(Enum):
gappy = "gappy"
block_gappy = "block-gappy"
gappyout = "gappyout"
entropy = "entropy"
comp... |
40ffd3874ab4f5a6cd6f66b381c170e4aa935cae88a6192d13fefbf638cdf21c | Python | 1,002 | 28 | import numpy as np
class HistNorm:
def __init__(self, bd_low=0.1, bd_up=99.9, mean_i=0, std_i=1):
self.bd_low = bd_low
self.bd_up = bd_up
self.mean_i = mean_i
self.std_i = std_i
def __call__(self, sample):
image, mask, misc = sample['image'], sample['mask'], sample['mi... |
a1ce23b70a8f0e9d2f7f93de429b85a265318148569581e43b52497f6ddf401d | Python | 1,002 | 31 | """Functions for downloading from Google Drive
Adapted from `this StackOverflow <https://stackoverflow.com/questions/38511444/>`_
"""
import requests
def download_file_from_google_drive(id, destination):
URL = "https://docs.google.com/uc?export=download"
session = requests.Session()
response = session.ge... |
b6f9c6365f7b6a695b2f489274eefcd8324777663f483c898a37e45ff9eea6bd | Python | 1,003 | 31 |
import metric.surface_distance.metrics as metrics
import torch
import numpy as np
def hausdorff_distance95_avg(output, target, misc):
channel_num = output.shape[1]
sds = []
for i in range(1, channel_num):
sd = hausdorff_distance95_calc(output, target, misc, i)
sds.append(sd)
return np... |
6cb27318567688b359d662e3e4d74163ea89dfb1ced508c5ce096742586c0524 | Python | 1,004 | 35 |
import sys
import numpy as np
# default values
def_allele_freq = 0.2
def_effect_size = 1.2
def_sample_size = 50
sys.stdout.write("ID REF.ALLELE.FREQ EFFECT.SIZE SAMPLE.SIZE\n")
sim_id = 0
# range of sample sizes
samp_size_step = 10
for samp_size in range(10, 60 + samp_si... |
b966ec62576862544d1bbb50ddf9a26ab85450060279b5d2042d1b7041ac2c00 | Python | 1,004 | 34 | #setup.py for neuronmusic extension
from distutils.core import setup, Extension
nrn_srcdir = "."
instdir = "/raid/zyc/code_for_dhs_paper/install"
if nrn_srcdir[0] != '/' :
nrn_srcdir = '../../' + nrn_srcdir
mpicc_bin = "mpicc"
mpicxx_bin = "mpic++"
import os
os.environ["CC"]=mpicc_bin
os.environ["CXX"]=mpicxx_bin... |
8ed88e83f5e509ff0ae6a061cac179fd4c31907432767faa5dc6f9a32e0ea5b3 | Python | 1,005 | 40 | # Copyright (c) OpenMMLab. All rights reserved.
from typing import Union
import cv2
import numpy as np
from .misc import load_image_from_disk_or_url
class ImageCapture:
"""A mock-up of cv2.VideoCapture that always return a const image.
Args:
image (str | ndarray): The image path or image data
"... |
f6d417e15624f35f13cab625caaf3e083bfd595e04093ce9519b2473dfd6a409 | Python | 1,005 | 38 | nTax = int(var.argvAfterDoubleDash[0])
nTrees = int(var.argvAfterDoubleDash[1])
#taxNames = list(string.ascii_uppercase[:20])
t = func.randomTree(nTax=nTax)
#t.draw()
a = func.newEmptyAlignment(dataType='dna', taxNames=t.taxNames, length=10)
t.data = Data([a])
t.newComp(spec='equal')
t.newRMatrix()
t.setPInvar()
t.set... |
b6ced034497893042432b814c1b0685cc88b96ff4154f407cceba7d889b94257 | Python | 1,006 | 30 | # Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import sys
# -- Project information -----------------------------------------------------
# https://www.sph... |
7b8d413b785a064ceb42308c227dcd45fe1320c4512e3da355614de2008bc20d | Python | 1,007 | 43 | import argparse
import math
import os
import numpy as np
import cv2
from tqdm import tqdm
import glob
def main(args):
fourcc = None
out = None
for filename in tqdm(glob.glob(args.dir + args.ext)):
img = cv2.imread(filename)
height, width, layers = img.shape
if fourcc is None:... |
6f81ee01ccaa2518436e99f529f51aed2bed94f18cd3c852a5f227162a26f266 | Python | 1,008 | 27 | import numpy
class MyOVBox(OVBox):
def __init__(self):
OVBox.__init__(self)
self.signalHeader = None
def process(self):
for chunkIdx in range( len(self.input[0]) ):
if(type(self.input[0][chunkIdx]) == OVSignalHeader):
self.signalHeader = self.input[0].pop()
outputHeader = OVSignalHeader(self.... |
d20563e5c327d0564ebe0c8aecafb89318e6bfe234e0bf1320813bea036c3a23 | Python | 1,011 | 23 | import torch
from .shrinked_force_permutations import ShrinkedForcePermutationsDataset
class CombinedDataset(ShrinkedForcePermutationsDataset):
"""
Dataset that combines several datasets to a single one.
Prerequisits: Passed datasets contain their data in the list attribute \"samples\" and inherit from \... |
f5b537bec9fbcb05fda1881ca0cdadfb02d5d0f268075bfa320f7b9b46e10c4c | Python | 1,015 | 31 | import os
import cv2
from csutil import util
if __name__ == '__main__':
root_dir = 'E:/Zhu/ETreg/newdata/HUA3/'
TOM_fine_dir = root_dir + '/TOM/fine/'
stackdeformationdir = root_dir + '/TOM/stack/deformation/'
resultdir = root_dir + '/TOM/stack/result/'
util.mkdir(resultdir)
tl_cut, br_cut ... |
ad105011a6613e6fe2fea38814d4f0e1b992ed148afce00c985f17598e19edca | Python | 1,016 | 41 | _base_ = ['hrnet_w32_coco_512x512.py']
model = dict(
test_cfg=dict(
multi_scale_score_decrease=1.0,
nms_dist_thr=0.1,
max_pool_kernel=9,
))
val_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='BottomUpGetImgSize',
base_length=32,
test_scale_facto... |
998c570d960935f11ebf075ae6df5c10980552073b642242088427c6154614ac | Python | 1,017 | 37 | # October 22nd 2015
# Modifying these. I think that the ECC stuff uses some of these but called from a local directory
# so should be fine.
# Actually haven't modified these yet but should. Rename load_file and save_file to
# mention text, and then add binary functions.
# Some general functions for housekeeping, l... |
741fd8db44fcb488851d18066dd18314a50f981cae72b51d3eafd48431926313 | Python | 1,018 | 39 | import requests
import sys
import argparse
import json
import os
import os.path
import re
ap = argparse.ArgumentParser()
ap.add_argument('-i','--input', help = 'Input file')
ap.add_argument('-o','--out', help = 'Output file')
args = vars(ap.parse_args())
input = ""
out = ""
if (args['input']):
input = args['inp... |
3bfea4910c2df7760176754d599da18c32721b5cf5418af0d573ab332121aff0 | Python | 1,019 | 28 | import cv2
import numpy as np
def fullpixcount(n, cx, cy, full_pix, img):
num_wpix_full = np.sum(img == 255)
#print("fullwpix"+str(n), num_wpix_full)
img_contour = cv2.putText(img, str(n), (cx,cy), cv2.FONT_HERSHEY_SIMPLEX, 3, (255,0,255))
#cv2.imshow("fullpixcount", img_contour)
#... |
21f860cba5726f70a2db6478166a6201d7cbe3b0f48768ebf7a3d262db1dde56 | Python | 1,021 | 29 | import numpy as np
"""
@input - model (Object); testing data (List); testing labels (List); model name (String); optional args
Method that predicts target values with given model and calculates the accuracy of the predicitions by mean value of correct answers.
@output - Accuracy value (float), truth ... |
dba8a43395de77beda1651d831f2d03e6a4e300267777890f5acd3611c3d09e2 | Python | 1,021 | 20 | # Copyright (c) OpenMMLab. All rights reserved.
from .buffer import BufferManager
from .event import EventManager
from .image_capture import ImageCapture
from .message import FrameMessage, Message, VideoEndingMessage
from .misc import (copy_and_paste, expand_and_clamp, get_cached_file_path,
get_confi... |
4caab129b976a93abaf37761cf5bbaa090e335ecdf181f625d10905d907a7733 | Python | 1,023 | 32 | from dash import Dash, CeleryManager
from flask_caching import Cache
import dash_bootstrap_components as dbc
from app_main import layout
from app_main.callbacks import register_callbacks
from celery import Celery
REDIS_URL = "redis://localhost:6379"
celery_app = Celery(__name__, broker=REDIS_URL, backend=REDIS_URL)
ba... |
d238ceabfc2b18fed5f91af48964e404c756adbabf3eca9c8156c879726190a8 | Python | 1,026 | 32 | # Copyright (c) OpenMMLab. All rights reserved.
from torch.nn.modules import GroupNorm
from torch.nn.modules.batchnorm import _BatchNorm
from mmdet.models.backbones.res2net import Bottle2neck
from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
from mmdet.models.backbones.resnext import Bottleneck as Bottl... |
7a42a37f01718e949f354df88fd21f7dbc3887490d94d59b04d45b1c05726efb | Python | 1,030 | 30 | #!/usr/bin/env python
from setuptools import setup, find_packages
import subprocess
import os,sys,re
from pathlib import Path
def get_git_describe():
version_file = Path(__file__).parent / "checkmysequence" / "version.py"
version_vars = {}
with open(version_file, "r") as f:
exec(f.read(), version_... |
81c23f5942ce73a09d9b1099d7e81bb90ea1d8de66524a10ca398977ff6d3290 | Python | 1,030 | 39 | # -*- coding: utf-8 -*-
#
# This file is part of the taxopy package, available at:
# https://github.com/apcamargo/taxopy
#
# Taxopy is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of... |
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