sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a88ada240a4ee1c8c1172afc1b91b9a6dc6f5a3550fc3291645044a052de7de1 | Python | 1,031 | 30 | import torch
from rdkit import Chem
import pandas as pd
from serenityff.charge import Trainer, ChargeCorrectedNodeWiseAttentiveFP
print("imports done")
sdf_file = "../test146_combine/combined_multi.sdf"
trainer = Trainer(device="cpu")
trainer.model = ChargeCorrectedNodeWiseAttentiveFP()
trainer.optimizer = torch.optim... |
cb564ad794ba7fbe1e33f80d1f43af3d63fdb53d1621f7354665be0dd5ce5a09 | Python | 1,032 | 36 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 6 14:37:58 2021
@author: baris
"""
grid_seed = 1
output_path = (
f"{grid_seed}_{trajectories}_{poisson_seeds[0]}-{poisson_seeds[-1]}_{dur_ms}"
)
storage_old = shelve.open(
"/home/baris/results/poisson_seeds_seperate/grid-seed_trajectory_p... |
368de47f51820abb1f9c090e8016075b17af97b0ceadbd248a0d68263a03acca | Python | 1,033 | 33 | # 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/>
import warnings as _warnings
from sbi.inference.trainers.nre.bnre import BNRE
from sbi.inference.trainers.nre.nre_a import NRE_A
from sbi.inference.train... |
17596da5188a5b20a41ec2868d2195ac1537b7d27725980a089a49ad78d6b394 | Python | 1,038 | 34 | from setuptools import setup, find_packages
setup(
name="deepfastvision",
version="1.0.2.3",
description="A Python library for rapid prototyping of deep transfer learning vision models",
author="Fabi Prezja",
author_email="faprezja@fairn.fi",
url="https://github.com/fabprezja/deep-fast-vision",... |
134f05fd76ac5c00b5386cea9064df8f9a65b75faf5abd44a2d3a6f52bcb4560 | Python | 1,041 | 40 | var.verboseRead = 0
read('t.nex')
t = var.trees[0]
read('d.nex')
var.alignments[0].setCharPartition('cp1')
t.data = Data()
# Model for Part 0
pNum = 0
t.newComp(partNum=pNum, free=1, spec='empirical')
t.newRMatrix(partNum=pNum, free=1, spec='ones')
t.setPInvar(partNum=pNum, free=1, val=0.1)
t.setNGammaCat(partNum=pNum... |
f48207e19791de0b315183000f70a3c2c4fba7e374a2397d87ae7bac07e1605a | Python | 1,042 | 29 | from keras.preprocessing.text import Tokenizer
from keras.preprocessing import sequence
import logging
from keras.utils import pad_sequences
class KerasTokenizer():
def __init__(self, mode, vocab_size , pad_length):
self.mode = mode
self.vocab_size = vocab_size
self.pad_length = pad_length
... |
33c03fe6e1e364824dd1ed249132e86a140ecbb738912a33e29482af447e8b24 | Python | 1,046 | 27 | ### Functions for downloading from Google Drive ###
### Adapted from https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url
import requests
def download_file_from_google_drive(id, destination):
URL = "https://docs.google.com/uc?export=download"
session = requests.Sessio... |
b3969afc63b7cb3374f10d4aca8f21eaedcf76aa97300056fbff8dde47409192 | Python | 1,049 | 37 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen
from flystim.trajectory import Trajectory
import numpy as np
from time import sleep
def main():
manager = launch_stim_server(Screen(fullscreen=False, server_number=0, id=0, vsync=False))
manager.load_... |
27007a53dc516e9ab6ffe046b52f06273c2aa6b222c94cc94389ebfa5e9186bb | Python | 1,052 | 34 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Mar 1 14:27:20 2022
@author: baris
"""
import os
import shelve
#variables to change
shuffling = "non-shuffled"
network_type = "no-feedback" # netowrk type for the filename
path = "/home/baris/results/olli_data/"
folder = "no-feedback_adjusted" # fol... |
badcfe0950a6c1b8d55b568b7d1cefd4adc346ef0433cc0f078818d4c9b56421 | Python | 1,055 | 39 |
"""
Class for holding information about the experiment to be performed.
Includes trial type (0 or 1), experiment type (String), list of models to evaluate,
nr of epochs to train the classifiers, percentage of data to use for validation and
percentage of data to use for testing.
"""
class Experiment(object)... |
9e16aab78b8007f7deac6ba3a062e29f180b9222cb0e13cc54da6ddf64a6a26a | Python | 1,056 | 32 | import json
import numpy as np
import torch
from torch.utils.data import DataLoader
from torch.utils.data import ConcatDataset
from FOD.Trainer import Trainer
from FOD.dataset import AutoFocusDataset
with open('config.json', 'r') as f:
config = json.load(f)
np.random.seed(config['General']['seed'])
list_data = ... |
ad1fecbaf37dd48c43eda345761c721fec430d15a5f76d19fa8b486d5b4e0c61 | Python | 1,058 | 38 | from abc import ABC, abstractmethod
class ModelTrainer(ABC):
"""Abstract base class for federated learning trainer.
1. The goal of this abstract class is to be compatible to
any deep learning frameworks such as PyTorch, TensorFlow, Keras, MXNET, etc.
2. This class can be used in both server a... |
12ba213a97c73a724d0f3234bc5aa38dc30d8dd0e65d46907f00ba432c60f6ca | Python | 1,062 | 34 | #!usr/bin/env python
#coding:utf-8
"""
@author: Haidong Zhang
@contact: haidong_zhang14@yahoo.com
@time: 2021/1/17 16:02
@project: MalariaDetection
@description:
"""
import numpy as np
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from scipy.optimize import linear_sum_assignm... |
dd78130081c81b1e81fc371c6f2a529399f308712eeaf04657921fd2e52baddc | Python | 1,065 | 26 | """exsclaim_gui URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.1/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-... |
4524422a103671204ab793a10d0f78d442598dc2935513e234c2f6806324b0f5 | Python | 1,066 | 32 | import torch
import torch.testing as testing
from selbstaufsicht.models.self_supervised.msa import diversity_maximization as dm
def test_distance_matrix():
msa = torch.tensor([[1, 2, 3, 4, 5],
[5, 4, 3, 2, 1],
[1, 2, 3, 2, 1]])
distance_mat = dm.distance_matri... |
d315f3a77970da91eafd4e6771ee4c80d9b4983fb7858856946a60f89debc9bf | Python | 1,067 | 29 | import torchvision.transforms.functional as F
class Normalize(object):
"""Normalize a tensor volume given the mean and standard deviation.
:param mean: mean value.
:param std: standard deviation value.
"""
def __init__(self, mean, std, training=True):
if isinstance(mean, list):
... |
1919481fd6c7071fd9da4e3c90fe0c7ca86d3b970842aca221310bf9f9c8e3b7 | Python | 1,068 | 42 | # Copyright (c) OpenMMLab. All rights reserved.
from abc import ABCMeta, abstractmethod
class TemporalFilter(metaclass=ABCMeta):
"""Base class of temporal filter.
A subclass should implement the method __call__().
Parameters:
window_size (int): the size of the sliding window.
"""
# If t... |
a9dfd117510957c9a00fed8868c640e0ff2ffd17eee6f6454466e8df9b1dcd1b | Python | 1,068 | 55 | # 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 .ace import Ace, AceHF
from .ani import ANI1, ANI1CCX, ANI1X, ANI2X
from .comp6 import (
ANIMD,
DrugBank,
... |
e85f94e898192a38999bdbd4708465e89cae28a2fc265b7eab7aefda9a58db15 | Python | 1,071 | 31 | def setUp(self):
import os
import shutil
ov_binany_path=os.environ['OV_BINARY_PATH']
self.terminal = App.open("xterm -e " + ov_binany_path +"/designer --no-session-management --play-fast Test_ROCCurve.xml") # NOSONAR
while not self.terminal.window():
wait(1) # NOSONAR
#def takepict... |
1416c15509028d005f4c64c2219be7ae475f8e0eb04ab8cc69a63f7617a66269 | Python | 1,072 | 32 | from __future__ import annotations
from typing import Optional
class ToCEntryError(Exception):
"""Generic Exception for errors related to the table of contents and streams"""
...
class ToCStreamError(Exception):
"""Exception related to ToC streams"""
...
class ToCNoSuchChildError(ToCEntryError):
... |
cd915145f5afea1db54f8da2fcbf06528c877859936e7a2b8d0aa90b51423d8e | Python | 1,072 | 43 | # -*- coding: utf-8 -*-
"""
Extract
=======
Main extraction routine. Run from the command line to start extraction.
author: Damian Wilary
email: dmw51@cam.ac.uk
"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
i... |
9626d2f67e67aa37b8ab21b592e5797f3caa0d6eb46ab13cd0c278df4a763e30 | Python | 1,074 | 26 | import numpy as np
import dgl
import torch
## https://blog.csdn.net/znsoft/article/details/114515868
def x_message_func(edges): # 0.8
msg = (edges.dst['nodegrid'] - edges.src['nodegrid']) - (edges.dst['nodereg'] - edges.src['nodegrid']) ## h*norm 更新边权重 源头点的h*源头点的正则化参数
msg = np.clip(msg, -2, 2) ##
... |
cfb6bdd5cc6d291dc6e696a8ffb99b8933b5e5aa6218aa6b19cf98acf921f939 | Python | 1,074 | 34 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import DETECTORS
from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor
@DETECTORS.register_module()
class PanopticFPN(TwoStagePanopticSegmentor):
r"""Implementation of `Panoptic feature pyramid
networks <https://arxiv.org/pdf/1901.02... |
cffef3607195367f808af7fcb9b878a3355ae14f042b4a001ce07c7f37d9b24c | Python | 1,074 | 35 | import torch
class ToTensorScale:
def __init__(self, mask_type="long", maxv_im=None, maxv_lb=None, training=True):
"""
Convert numpy array to Torch.Tensor
"""
self.training = training
self.mask_type = mask_type
self.maxv_im = maxv_im
self.maxv_lb = maxv_lb
... |
20977c98de680ac52836724bb49e36ae603f2ac18b37a6cbec873673b6cd6070 | Python | 1,076 | 42 | import matplotlib.pyplot as plt
def remove_keymap_conflicts(new_keys_set):
for prop in plt.rcParams:
if prop.startswith('keymap.'):
keys = plt.rcParams[prop]
remove_list = set(keys) & new_keys_set
for key in remove_list:
keys.remove(key)
def multi_slic... |
0d0e5e8d98e2c6fc302c0787299b8ec373d174b2ba953be2f0d3be9f5cd245b9 | Python | 1,078 | 35 | from os import path
from setuptools import setup
here = path.abspath(path.dirname(__file__))
with open(path.join(here, "README.md"), encoding="utf-8") as f:
long_description = f.read()
CLASSIFIERS = [
"Operating System :: OS Independent",
"Intended Audience :: Science/Research",
"Programming Language... |
0315bed8339e0ff48e15bbeb59cc2df18fdea231181ca829cd418a29b72d897c | Python | 1,083 | 31 | from data.updated_labels.data_reader import UpdatedLabels
# data = ManualLabels(outcome = 'any_cancer', text='IMPRESS')
data =[{'id':'any_cancer_IMPRESS','type': 'updated_label' , 'params': {'outcome': 'response', 'text': 'NARR+IMPRESS', 'training_split':0 , 'cloud':True}}]
# data = ManualLabels(outcome = 'any_cancer... |
a05d2be14bdac9690135a2e93b61e655869182ea4183776d37b0cf9595354fcf | Python | 1,083 | 31 | """
This module provides different evaluation strategies for oligonucleotides and their sets based on various scoring criteria.
"""
from ._oligo_scoring import OligoScoring
from ._scorer_base import BaseScorer
from ._scorer_region_property import IsoformConsensusScorer, OverlapTargetedExonsScorer, OverlapUTRScorer
fro... |
c9878fc1c5a62d0f7679614437cece3b96f111bd7d9ce98568c32d20d03bc3fd | Python | 1,084 | 32 | import numpy as np
from importlib import reload
import sys
sys.path.insert(2,'support')
import functions
reload(functions)
import functions as f
import parameters
reload(parameters)
import parameters as p
direc = sys.argv[0].split('script')[0]+'data/'
noise_type = f.poisson_noise
tuning_curve = f.vonmises_tuning_curv... |
78a9e3c2ffc45e6b2b202f0e2d2bdd1b257c3ee6543b8f82a7da58a28c54dc86 | Python | 1,085 | 29 | import torchvision.transforms.functional as F
class Normalize(object):
"""Normalize a tensor volume given the mean and standard deviation.
:param mean: mean value.
:param std: standard deviation value.
"""
def __init__(self, mean, std, training=True):
if isinstance(mean, list):
... |
b8fbf6dbdd63a569755feaf5e2a537c45a1b7c482ec465242192cbf191912222 | Python | 1,086 | 30 | import pytest
import eagerpy as ep
import foolbox as fbn
def test_plot(dummy: ep.Tensor) -> None:
# just tests that the calls don't throw any errors
images = ep.zeros(dummy, (10, 3, 32, 32))
fbn.plot.images(images)
fbn.plot.images(images, n=3)
fbn.plot.images(images, n=3, data_format="channels_fir... |
e75cd1b11df119db003aa13357279003f8e36bfacdf8b9200f763035303bdf0f | Python | 1,086 | 26 | from torchvision import transforms
from base import BaseDataLoader
from dataset.mnist_split_dataset import MnistSplitDataset
from dataset.mnist_synthetic_dataset import MnistSyntheticDataset
class MnistDataLoader(BaseDataLoader):
"""
MNIST data loading demo using BaseDataLoader
"""
def __init__(self... |
2145371313e4d8827b75d10b40b0c18117e128c698e41f115fd40c7c42509ca1 | Python | 1,090 | 36 | #!/usr/bin/env python
# Copyright [1999-2017] EMBL-European Bioinformatics Institute
#
# 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
#
# Unl... |
a61b3ed912e5145dbc7f4fb3334cde53fcc673f50e15857136dc488edb837cbe | Python | 1,093 | 39 | from typing import List, Tuple
import pytest
import foolbox as fbn
import foolbox.attacks as fa
from conftest import ModeAndDataAndDescription
def get_attack_id(x: fbn.Attack) -> str:
return repr(x)
# attack
attacks: List[Tuple[fbn.Attack, bool]] = [
(fa.SpatialAttack(), False),
(fa.SpatialAttack(grid_... |
8c3a909262c63221c1633bcaf0f2c63579b2204f4ef193c651d1f133c86a29ba | Python | 1,095 | 28 | #!/usr/bin/env python
# Convert calls from Canvas, cn.MOPS, CNVnator, ERDS, Genome STRiP, or RDXplorer to a common format
# Usage example:
# convert_CNV_calls_to_common_format.py input_filename name_of_caller
# name_of_caller must be one of "Canvas", "cn.MOPS", "CNVnator", "ERDS", "Genome_STRiP" (note underscore), or ... |
0d26f9b3dc59e8292fa6f65604c5171bec3fc6e4988b187316c1b565eb6c7dff | Python | 1,096 | 40 | var.verboseRead = 0
var.warnReadNoFile = 0
read('d.nex')
d = Data()
d.compoSummary()
read('simTree.nex')
t = var.trees[0]
t.data = d
c1 = t.newComp(free=1, spec='empirical')
c2 = t.newComp(free=1, spec='empirical')
# Put the c1 comp on all the nodes of the tree. Then put c2 on the
# root, over-riding c1 that is alre... |
8816255e39a6bdc16c36519d737bb83390fcc0277246b55d787654f9e8e1944d | Python | 1,096 | 48 | import pandas as pd
import requests
df = pd.read_excel('EMPIAR.xlsx')
df.head()
df['Authors'] = df['Authors'].fillna('N/A')
df['Size'] = df['Size'].fillna('N/A')
df['Resolution'] = df['Resolution'].fillna('N/A')
df1 =df.dropna(axis=0)
df1 = df1.reset_index()
df1 = df1.drop(columns=['index'])
df1.to_excel('Processed_EM... |
5a583c349648a3a0b1e6101f3c8bf7e5cfb3ab98df5f9f7e50a179842dabff65 | Python | 1,098 | 37 | import numpy as np
import pandas as pd
import requests
from ftplib import *
import ftplib
import wget
import os
import time
#running for individual
empiar_id = '11139'
print(empiar_id)
data_download_path = f'/home/ad256/Ashwin/Projects/cryoEM/repo/data_downloads/444_data/{empiar_id}'
if not os.path.exists(data_downl... |
763972b9e5411f620fd73cd7ac5a9e136ceefdc52802e0cdaa60e2b045e46f1d | Python | 1,098 | 38 |
import metric.surface_distance.metrics as metrics
# import torch
import numpy as np
def hd95_and_sd_all(output, target, misc):
channel_num = output.shape[1]
pred = np.argmax(output, axis=1)
sds = []
hds = []
for i in range(1, channel_num):
hd, sd = h95_and_sd_calc(pred, target, misc, i)
... |
3cb8ae568705ff0665dbcfdbf6f83a33baee2fe7ca9582bac093816349139ce0 | Python | 1,100 | 31 | import numpy as np
import struct
import os
import sys
data_name = sys.argv[-2]
with np.load(data_name, allow_pickle=True) as f:
x_train, y_train = f['x_train'] / 255, f['y_train']
x_test, y_test = f['x_test'] / 255, f['y_test']
dest_dir = sys.argv[-1]
interval_train, interval_test = 5 / (x_train.flatten() +... |
73ea8d611b4b7987b90e8a6718a040eff7d547c8ccff6661e2c7d2eb35d6d7cf | Python | 1,100 | 49 |
'''
Convert ERDS output to a common format
'''
import myvcf
import SV
import os
class ERDS:
@staticmethod
def doparse ( erds_data ):
E=erds_data
#if type (erds_data) is str:
E = erds_data.rstrip('\n').split('\t')
chrom=myvcf.vcf.get_chrom (E)
start=myvcf.vcf.get_pos (E)
length=abs(int(myvcf.vcf.get... |
9effd709c54c650e1f45dcabb0716a394df31456701a2243ad6e9f6e5493bb1e | Python | 1,100 | 35 | # Imports
from Classes.Survey import SurveyBase
from kivy.app import App
import pathlib
import json
# Survey Protocol Class
class SurveyProtocol(SurveyBase):
"""
A demographic survey protocol for collecting participant information.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)... |
af375c1ddc0c10ae643478e5cedf00d7ed6d1c839b0051aa850edcf570fcdcfa | Python | 1,100 | 26 | from pydapsys.neo_converters.helper import DapsysToNeoHelper
from pydapsys.neo_converters.interface import INeoConverter
from pydapsys.neo_converters.ni_pulse_stim import NIPulseStimRecordingConverter
class ExtraDependencyNotFoundError(Exception):
package_name = "PyDapsys"
def __init__(self, missing_name: str... |
cf5ea2fc3df52242ba8d0cad06429f6364e1474393d5b9e81aeac0805fbf0091 | Python | 1,100 | 33 | # Simulate data with more than one data partition.
nTax = 5
taxNames = list(string.ascii_uppercase[:nTax])
dnaAlign = func.newEmptyAlignment(dataType='dna', taxNames=taxNames, length=300)
read(r"#nexus begin sets; charpartition p1 = first:1-100, second:101-.; end;")
dnaAlign.setCharPartition('p1')
d = Data([dnaAlign])... |
8700c41506313f3bf44f22e466c0fa92fa47ffa20441e72ca2c9c5ea608c936e | Python | 1,102 | 28 |
#Model
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import datasets, layers, models
def top_k_categorical_accuracy(y_true, y_pred):
return tf.keras.metrics.top_k_categorical_accuracy(y_true, y_pred, k=2)
#Build model
def model_build(num): # num = number of categories
input_f = l... |
a6e4db956891d1015c085e0c6f756bdeca41b5ebcdd5118e372881ef12ce1e85 | Python | 1,102 | 25 | from base import BaseDataLoader
from data_loader import my_transforms
from dataset.brats_dataset import BratsDataset
class BratsDataLoader(BaseDataLoader):
def __init__(self, h5_filepath, batch_size, shuffle=True, validation_split=0.0, num_workers=1, training=True,
img_size=224):
if train... |
b41222b803dc2920b7187b504710874e4407a77ed2caacf8042527d9198826cd | Python | 1,103 | 36 | import tensorflow as tf
import numpy as np
class ConvUnit(keras.layers.Layer):
def __init__(
self,
filters,
activation,
normalization,
convolution,
kernel=3,
name="conv3d",
**kwargs
):
super(ConvUnit, self... |
69c87c2c5d21bcb16d05a613045af2a3e8b1f3ae664c360be0c1d9aed4f42504 | Python | 1,104 | 41 | import matplotlib.pyplot as plt
import json
import numpy as np
root = "/home/wangbowen/PycharmProjects/mmpose/work_dirs/hrnet_w32_coco_tiny_256x192/None.log.json"
df = [json.loads(line) for line in open(root, 'r', encoding='utf-8')]
loss_epoch = []
epoch_record = []
for i in range(len(df)):
current_dict = df[i]... |
1fdc29670c979c541aec99c9b9ab025e2d0a1b39e71dee1da1f35c891bfd6e93 | Python | 1,105 | 33 |
import metric.surface_distance.metrics as metrics
import torch
import numpy as np
def surface_distance_avg(output, target, misc):
channel_num = output.shape[1]
sds = []
for i in range(1, channel_num):
sd = surface_distance_calc(output, target, misc, i)
sds.append(sd)
return np.average... |
327f4174b102600d344c61b3ee7df2d68175e5d4dddcda682a30c24d718eeeeb | Python | 1,105 | 47 | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
n = 36
start_end = 0.1
peak = 0.2
center = (n - 1) / 2 # 17.5
x = np.arange(n)
a = (start_end - peak) / (center ** 2)
y = a * (x - center) ** ... |
49625d679ee47517963af3fba21c15aceff4f880823d4607652fa047f9a9c948 | Python | 1,105 | 41 | import json
import numpy as np
class NumpyEncoder(json.JSONEncoder):
"""
JSON encoder that handles numpy types.
Converts numpy arrays to lists and formats numpy floats with 4 decimal
places for compact, readable output.
"""
def default(self, obj):
if isinstance(obj, (np.ndarray,)):
... |
25cf5f5f346896e0fd3ae51df4eecc8cd0b85651c9bcb31da66af186b7dc7576 | Python | 1,108 | 26 | import numpy as np
import torch
class ToMask(object):
"""
Convert a 3 channel RGB image into a 1 channel segmentation mask
"""
def __init__(self, palette_dictionnary):
self.nb_classes = len(palette_dictionnary)
# sort the dictionary of the classes by the sum of rgb value -> to have ... |
959b3194539750cb8f85fba692fe57e25665aef0b38db5d4c944aec9a8afc4c9 | Python | 1,114 | 39 |
def hook(self, theTree):
"""Write stuff during the MCMC.
You get the stuff from the Mcmc (which is self) or from the
current tree. Write whatever you like, however you like it. You
will need to know how to get it, tho ...
In this demo, I write out the gen number+1, the composition, and
the ... |
2a0c25a1eaf5438fd0d6ae8340ce7b8ceeec70afe9791e86c9c14c81eee8c1e3 | Python | 1,115 | 40 | from os.path import join as _join
from os.path import dirname as _dirname
with open(_join(_dirname(__file__), "VERSION")) as _f:
__version__ = _f.read().strip()
# internal modules
# ----------------
from . import devutils # noqa: F401
from . import tensorboard # noqa: F401
from . import types # noqa: F401
# ... |
cdfa685c3d2691161c920a42b36bc9622f7fb32cad8ea87553b14ce2f1bcad1b | Python | 1,115 | 39 |
from tqdm import tqdm
import requests
import grequests
import urllib.parse
import datetime
import os
SERVER_URL = os.environ.get("SERVER_URL", "http://dorresteintesthub.ucsd.edu:6541")
def test():
request_url = "{}/classify?smiles={}".format(SERVER_URL, "CC1C(O)CC2C1C(OC1OC(COC(C)=O)C(O)C(O)C1O)OC=C2C(O)=O")
... |
77c183e64f99e5256869b6fffad244fd3c6d0bae5151e9c75fc24237bb9ef3cd | Python | 1,117 | 29 | # -*- 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... |
9756683cfc2592a5998bbe7bc5ce58b9f8cef2a4aabaf6a703e9dc0eee200962 | Python | 1,117 | 46 | #!/usr/bin/env python3
# Copyright 2021 Tomoki Hayashi
# MIT License (https://opensource.org/licenses/MIT)
"""Test code for Mel-spectrogram loss modules."""
import numpy as np
import torch
from parallel_wavegan.bin.preprocess import logmelfilterbank
from parallel_wavegan.losses import MelSpectrogram
def test_me... |
d8f3c1b11df45ba67ff8e5563b8a808ddbed658e8c565af5653bafdd7e2a523f | Python | 1,119 | 34 | """
Software to support scientific reproducibility, attribution, and
collaboration on the QIIME 2 platform.
Core commands:
- replay_supplement
- replay_provenance
- replay_citations
These take a payload including QIIME 2 provenance information,
and write replay documentation to disk.
Core objects:
- ProvDAG: A direc... |
4cac105a6f08951ab7e40d3b81a501e67357548d5064aafe5f3491e036328ee0 | Python | 1,120 | 31 | import torch
import torchvision.transforms.functional as F
from data_loader.my_transforms.normalize3d import normalize_common
class NormalizeLabel3d(object):
"""Normalize a label rather than the image volume given the mean and standard deviation.
:param mean: mean value.
:param std: standard deviation val... |
e00bd0ab2a3ca09c3672df2c23b99405caedcd2363276ed64aa9e02783ec0906 | Python | 1,121 | 32 | # 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_new... |
1d8386882bb1d84f7334aa0f2a605fd2015cb629955b1400360c1f2509f5507e | Python | 1,123 | 32 | import logging
class BaseCentralWorker(object):
def __init__(self, client_num, args):
self.client_num = client_num
self.args = args
self.client_local_result_list = dict()
self.flag_client_model_uploaded_dict = dict()
for idx in range(self.client_num):
self.fla... |
06f50c93b5683dec390eed41f591aec838b4ae0a8f9ba471d22e3450a344c220 | Python | 1,124 | 46 | #!/usr/bin/env python
import argparse
import CNVworkflowlib
####################################
### Parse command-line arguments ###
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("samtools_depth_filename", type=str, nargs="?", default="-")
args = parser.... |
3efba740ad1853ef4eee7705f32073fee9df8592d8437752a74c0403aeec9c77 | Python | 1,124 | 40 | from concept_mapping import UMLSAPI
import utils
_umls_api_key = '148475b7-ad37-4e15-95a0-ff4d4060c132'
def do_compute_subconcept(c, umls, container):
try:
subconcepts = umls.get_narrower_concepts(c)
container.append((c, subconcepts))
except ValueError:
print 'no subconcepts found for... |
508148d6f83f4584d5fbe4b0e5cc999a7d593b5ee2f9db88128a932344fbfd82 | Python | 1,124 | 52 | import pickle
import os
def saveVar(var, filePath):
"""
saveVar saves a var to a specified filePath
INPUT:
var: the variable to be saved
filePath: the filepath you want to save the var, for example data/.../var.pckl. If the path does not exist, it creates it
"""
directory = os.path.dirnam... |
cfeab4baede85e703d1ba8c587a030ee060e0f25b798f3bd71c562fb6a72774d | Python | 1,125 | 30 | import dftd4
from dftd4 import interface
import numpy as np
import pandas as pd
from rdkit import Chem
import matplotlib.pyplot as plt
from tqdm import tqdm
print("start dftd4 calculation", flush=True)
mol_sup = Chem.SDMolSupplier("combined_multi.sdf", removeHs=False)
sd_writer = Chem.SDWriter("mols_comb_dftd4.sdf")
... |
433e2ca0be595c3cb34a3c0d8122f2ad6dd310c60ab234cca17464efd4ff7336 | Python | 1,128 | 39 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestBlockGappyMode(object):
def test_simple_alignment(self):
input_file = f"{here.paren... |
926a000065fff3a9ea7719a8181023f8b0fd88062c7d8e4c3aacb2c22eea677d | Python | 1,129 | 40 | import glob
import torch
import numpy as np
from torch.utils.data import Dataset
import torch
# Ignore warnings
import warnings
warnings.filterwarnings("ignore")
def curvature(pulse):
curves = []
for line in pulse:
diff1 = np.array(np.gradient(-line * 100))
diff2 = np.gradient(diff1)
... |
6434bf266c9560238b1953a19ed219fad424c6c8b6f216a2fc9bc67779f1f28d | Python | 1,135 | 39 | import numpy as np
import pandas as pd
import requests
from ftplib import *
import ftplib
import wget
import os
import time
#running for individual
empiar_id = '10970'
print(empiar_id)
data_download_path = f'/home/ad256/Ashwin/Projects/cryoEM/repo/data_downloads/444_data/{empiar_id}'
if not os.path.exists(data_down... |
9adfa906e521e16c583959c51d840631493b420e5be2eaa0e2bc63ac4b1bda4d | Python | 1,136 | 41 | from typing import Union, Any, Optional
import eagerpy as ep
from ..criteria import Criterion
from ..models import Model
from .base import FlexibleDistanceMinimizationAttack
from .base import T
from .base import raise_if_kwargs
from .base import verify_input_bounds
class InversionAttack(FlexibleDistanceMinimizatio... |
b3783661742e30cd7bb5c1123fa34ee3160cf72c1b40d415b57b26ed0e68e0f1 | Python | 1,136 | 54 | from setuptools import find_packages, setup
# Package meta-data.
NAME = "consbi"
DESCRIPTION = "simulation-based inference for computational connectomics"
URL = "https://github.com/mackelab/synaptic-rule-inference"
AUTHOR = "Jan Bölts, Philipp Harth"
REQUIRES_PYTHON = ">=3.6.0"
REQUIRED = [
"matplotlib",
"num... |
ae05f24675d95c80b3d070a7a042834db396a08d0a3d75bf725410eb8fc3a67e | Python | 1,137 | 27 | from nipype.pipeline.engine import Node, Workflow
from shivai.interfaces.datasource import Direct_File_Provider
def graft_swomed_infiles(workflow: Workflow):
'''
Changes the datagrabber from a processing workflow to and identity interface that gets the
file paths directly givent to the pipeline in SWOMed
... |
d641abd14f22d17c6399a127777ec8aa5329a5746e6742493ba5a3ae77886ac4 | Python | 1,137 | 38 | #!/usr/bin/env python
# Copyright [1999-2017] EMBL-European Bioinformatics Institute
#
# 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
#
# Unl... |
bd346a5aae2f6bdcba88a3409ba00085c03b22b484c9e88175b349301d752ac7 | Python | 1,139 | 32 | import os
import json
from torch.utils.data import Dataset
from torchvision.datasets.utils import download_url
from PIL import Image
class nocaps_eval(Dataset):
def __init__(self, transform, image_root, ann_root, split):
urls = {'val':'https://storage.googleapis.com/sfr-vision-language-research/datase... |
c31fb8de9c9897ca780138e6e9d151e755b89b0186395a15f02fffc00e578600 | Python | 1,139 | 33 | # construct 3D matrices as nodextsfeaturesxsubj
import h5py
import numpy as np
import time
import glob
datapath = '/path/to/hctsaMEGoutputs/';
# MEG files
tsID = np.loadtxt(datapath +
'HCP_MEG_outputs/Schaefer100/sharedIDall_segments_pca_Schaefer100_fullSet_80.txt')
tsID = np.array(tsID).astype(int)... |
25ed1ed880f1925375bc8f50ff7649c00b624545d4dce9043eaac3179c343c1b | Python | 1,140 | 44 | from setuptools import setup, find_packages
setup(
name='PytorchTemplate',
version='0.5.0',
description='Annotation tool for Medical Image',
author='Qi Chang',
author_email='tommy.qichang@gmail.com',
license='GPL',
home_page='',
packages=find_packages(),
include_package_data=True,
... |
b388cee0fc8922714e90f4303ee50f329401e7964617a4d8b6f4c514fcd1a91d | Python | 1,141 | 50 | '''
Author: ----
Date: 2022-04-06 10:57:29
LastEditors: GhMa
LastEditTime: 2022-10-02 19:24:00
'''
import torch
import torch.nn as nn
import numpy as np
import math
class Seq2ANN(nn.Module):
r"""
Forked from spikingjelly
"""
def __init__(self, *args):
super().__init__()
if len(args) ==... |
c011d7801c700da33a90c02ad9778b2e0a71406790991bcf1924a6961fd3b038 | Python | 1,144 | 27 | from distutils.core import setup
from distutils.extension import Extension
import glob
mySources = glob.glob('*.cpp')
toRemove = [
'main.cpp',
'testMatrix.cpp',
'testQDist.cpp'
]
for fN in toRemove:
mySources.remove(fN)
setup(name="PackageName",
ext_modules=[
Extension("scqdist", ... |
0633d574b50909859adb6c1d3a412e53846e84c5862ed250aed68446a3bd102c | Python | 1,146 | 15 | # Copyright (c) 2023 IBM Research. All Rights Reserved.
#
# Code accompanying a manuscript entitled:
# "On the visual analytic intelligence of neural networks"
NUM_IMAGES = 6
MAX_COLOR_INTENSITY = 60
NUM_RIDDLES = 45
RIDDLE_IDS = ['color', 'orientation', 'straight_line', 'curve_line', 'alignment_point_on_line'... |
8231da8e9b9101adf7d94ae33f8c1b8735445fbff8edeb9288bae146587ad268 | Python | 1,147 | 39 | from mpi4py import MPI
from .central_manager import BaseCentralManager
from .central_worker import BaseCentralWorker
from .client_manager import BaseClientManager
from .client_worker import BaseClientWorker
def FedML_init():
comm = MPI.COMM_WORLD
process_id = comm.Get_rank()
worker_number = comm.Get_size... |
3a03cab0efa38ea406b8cd8421fef0e21f0b53e1ef440868c94425a38dcfea89 | Python | 1,148 | 38 | import sys
import os
sys.path.append(os.path.abspath("tqDist-1.0.2/tqDist"))
import pytqdist
if 1:
# This does not need p4
# spaces in the string are not allowed.
# eg taStr = "(A, B, (C, (D, E)));"
taStr = "(A:0.1,B:0.3,(C:0.1,(D:0.1,E:0.1)thisName:0.1,F:0.1):0.1)theRoot;"
tbStr = "(B:0.2,A:0.55,(... |
aa3404642b4449c076aa06990701187c4708969aaa34f9ed4f96807c601b2719 | Python | 1,150 | 34 | #!/usr/bin/env python
""" Test Measures Script """
import logging
import pandas
pandas.set_option("display.max_rows", None)
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _summarize(summary_df, measures_df, column, threshold):
measures_df = measures_df.... |
a75c21c56e944d16e68de883271deffb719655fbf6ea7f3f2f683ad69ecb7ad2 | Python | 1,151 | 34 | import numpy as np
from .common_func import cropImage2DAt
class CropPadding:
def __init__(self, load_size, training=True):
"""
Padding the image: C x H x W, if H or W less than load_size, and will keep the overflow side.
"""
# TODO: Not just support static filling of the padding, ... |
7e3a79cedaaa1d4bccc44ea8eca2ce947ab11e8e6f51727af0df00d3d4117ba8 | Python | 1,152 | 27 | 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.
leave the array empty if don't want select the channel
"""
assert isinstance(img_ch, list), "Image channels should be a... |
fced695efb46c9c5a1ba56bddd8c0090df58a118a929e66b36af6090fa197095 | Python | 1,154 | 39 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestGappyoutMode(object):
def test_simple_alignment(self):
input_file = f"{here.parent}... |
06ed97ab57ab96bd465d893711c70b9543cff5734183ac3ce260e773d349c397 | Python | 1,155 | 29 | import logging
class DecentralizedWorker(object):
def __init__(self, worker_index, topology_manager):
self.worker_index = worker_index
self.in_neighbor_idx_list = topology_manager.get_in_neighbor_idx_list(self.worker_index)
logging.info(self.in_neighbor_idx_list)
self.worker_resul... |
d5db6cdc214dd71384fe6e00c3a4b66a141cb5e392d92df61bc659de9cff3946 | Python | 1,156 | 36 | import numpy as np
import random
class RandomSwitchxy:
def __init__(self, training=True):
self.training = training
def __call__(self, sample):
"""
Randomly switch X-Y axis of the numpy image
Args:
sample: {'image':..., 'mask':...}
image size: [c, h, w]
... |
1644bb2919b477bdc371c67153eb18e9ed08c724abd7a890dbed738eb3b17aaa | Python | 1,157 | 40 | # Copyright (C) 2024-2025 ETH Zurich, Niels Maeder and other DASH contributors.
"""Pytest configuration file."""
from collections.abc import Sequence
import pytest
def pytest_addoption(parser: pytest.Parser) -> None:
"""Add cli option to include slow tests.
Args:
parser (pytest.Parser): pytest par... |
785c0546df81a79f415e8505810887d8d33951deee028bdc8e803613a15537c2 | Python | 1,157 | 35 | # Author: Fabi Prezja <faprezja@fairn.fi>
# Copyright (C) 2023 Fabi Prezja
# License: MIT License (see LICENSE.txt for details)
import os
import sys
sys.path.insert(0, os.path.abspath('../../'))
# Configuration file for the Sphinx documentation builder.
#
# For the full list of built-in configuration values, see the d... |
e1009084a1aaa8d32c6d4dc4289d92dc944a0b145d8bad892591410204d9f1cf | Python | 1,158 | 29 | import logging
class TA_DecentralizedWorker(object):
def __init__(self, worker_index, topology_manager):
self.worker_index = worker_index
self.in_neighbor_idx_list = topology_manager.get_in_neighbor_idx_list(self.worker_index)
logging.info(self.in_neighbor_idx_list)
self.worker_re... |
daa94542a43a8df8d96113b92104ddcd925a43f6e45465f67a78f3d7361088f5 | Python | 1,159 | 45 | from typing import cast, Any
import eagerpy as ep
from ..types import BoundsInput, Preprocessing
from .base import ModelWithPreprocessing
def get_device(device: Any) -> Any:
import tensorflow as tf
if device is None:
device = tf.device("/GPU:0" if tf.test.is_gpu_available() else "/CPU:0")
if is... |
52df9822110882e8cbfb1a52342552f2ae12ebd90f28d2b3bbb48bfa7f6a809c | Python | 1,163 | 38 | import unittest
import pandas as pd
import numpy as np
from weibull_mlp import datas, make_imaging_sheet_full
class WeibullTest(unittest.TestCase):
def test_datagen(self) -> None:
df2 = pd.read_csv("./metadata/data_processed/weibull_model_adni.csv").set_index(
"RID", drop=True
)
... |
7d57694620b9baa9fd012b9d57817066086c8b594a479c38d44f8cf5482e66e1 | Python | 1,166 | 39 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestHeterotachyMode(object):
def test_simple_alignment(self):
input_file = f"{here.pare... |
b8c198dfd6ccb17c4030af47574368abb47b1924ca761f9e3ec4ca3d278018b9 | Python | 1,166 | 40 | #!/usr/bin/env python3
"""
Pushes a 4-channel sinusoidal signal to LSL for testing the LabStreamingLayer
acquisition-server driver.
Requires pylsl: pip install pylsl
"""
import math
import time
from pylsl import StreamInfo, StreamOutlet
CHANNEL_COUNT = 4
SAMPLING_RATE = 250.0
CHANNEL_NAMES = [f"Sin {i + 1}" for i in... |
19a88f2194aff71fa56568ab6b8a375eff01123262693627bc62350a4d2ad362 | Python | 1,168 | 28 | import numpy as np
from skimage.transform import resize
class MoveAxis:
def __init__(self,from_axis, to_axis, move_label=False, training=True):
"""
Resize the image: C x H x W to the C x load_size[0] x load_size[1]
order will apply to image and mask separately. 0: nearest-neighbor, 1: bi-l... |
a30558f47722539cb66f95c710c1c89388babd48e315cf77adb3149204ba585b | Python | 1,170 | 57 |
import SV
import os
import re
class cnMOPS:
seqnames=0
start=1
end=2
width=3
strand=4
sampleName=5
median=6
mean=7
CN=8
@staticmethod
def parse_CN (CN):
i=re.sub ('CN', '', CN)
if int(i) > 2: return 'DUP'
else:
return 'DEL'
@staticmethod
def doparse ( input_line ):
E=input_line.rstrip('\n').... |
f3e9dcbceffe91a9575881543af88a60225e9911e5e920b4867f7c7e6ee3dbb1 | Python | 1,173 | 32 | from sklearn.mixture import GaussianMixture
from sklearn.cluster import KMeans, AgglomerativeClustering
import numpy as np
def cluster_gmm(A, B, num_clusters):
"""Gaussian Mixture clustering"""
estimator = GaussianMixture(n_components=num_clusters,
covariance_type='tied', # c... |
9cc8c012748ea0095bceed7949df9bbca6fdd2c75e0e981a997f14c0422f01f3 | Python | 1,181 | 42 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmdet.utils.util_random import ensure_rng
def random_boxes(num=1, scale=1, rng=None):
"""Simple version of ``kwimage.Boxes.random``
Returns:
Tensor: shape (n, 4) in x1, y1, x2, y2 format.
References:
ht... |
8de2ef3b978c4b43d688ed860ee9bcdf7549b0df3eb083f97e4b126e7e39131b | Python | 1,184 | 59 | '''
Author: ----
Date: 2022-04-08 11:09:07
LastEditors: GhMa
LastEditTime: 2022-10-02 19:25:10
'''
import torch
import torch.nn as nn
from .snn_modules import tdLayer, BN
class ToyNet(nn.Module):
r"""
"""
def __init__(
self,
num_classes = 10,
hidden = 64,
norm_layer = Non... |
30a68fadba4602779538494d3c077fff8a53af0813eb14757ce4b160abb51e8a | Python | 1,185 | 44 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen
from flystim.trajectory import Trajectory
import numpy as np
from flystim.draw import draw_screens
import os
from time import sleep
def main():
"""."""
save_dir = '/home/mhturner/CurrentData/'
scre... |
8d2244c327dd1e953d4e7b880fbf89c2052505cd6322490637c1c7304786a130 | Python | 1,185 | 39 | import pytest
from pathlib import Path
from clipkit.clipkit import execute
from clipkit.modes import TrimmingMode
from clipkit.settings import DEFAULT_NT_GAP_CHARS
here = Path(__file__)
@pytest.mark.integration
class TestCompositionBiasMode(object):
def test_simple_alignment(self):
input_file = f"{here.... |
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