sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
eaeddcfdbaf2fbdb8d5e0966f2b928c31b3154f9e7e6fb7ed82340a6050eabc0 | Python | 1,185 | 36 | # 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... |
d610c96377d99d02a24184b9a38624cedc3cb8523a3dd07c8e6378fc6316563b | Python | 1,186 | 43 | #!/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():
screen = Screen(fullscreen=False, server_number=0, id=0, vsy... |
97f852c40e103ea6216280a82b11011b8e42c505b10af73bd0079f04fe0fb6e2 | Python | 1,187 | 41 | #!/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_... |
35e213bc31f4ac4924d44a47b7234e2f4703ca01b752d751a3fa44c590441415 | Python | 1,189 | 30 |
from sklearn import preprocessing
import pandas as pd
import numpy as np
def fit_data_matrix_to_network_input(data_matrix: pd.DataFrame, features, feature_column="Protein") -> pd.DataFrame:
nr_features_in_matrix = len(data_matrix.index)
if len(features) > nr_features_in_matrix:
features_df = pd.DataF... |
6d08e9938276938a59305694876fda1106456d09a9927fb3084ec5852b381882 | Python | 1,189 | 28 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from metstab_shap.config import utils_section, csv_section
from metstab_shap.config import parse_data_config, parse_representation_config, parse_task_config
from metstab_shap.data import load_data
# load data (and change to clas... |
2a327d5b21ff20911a495da1a89def997ce5b54fe1f0f61641cc22f1436eeadb | Python | 1,191 | 45 | read("d.nex")
read('sets.nex')
a = var.alignments[0]
a.setCharPartition('p1')
d = Data()
t = func.randomTree(taxNames=d.taxNames)
t.data = d
pNum=0
t.newComp(partNum=pNum, free=1, spec='wag')
t.newRMatrix(partNum=pNum, free=0, spec='wag')
t.setNGammaCat(partNum=pNum, nGammaCat=4)
t.newGdasrv(partNum=pNum, free=1, val=... |
70c4c51988c8de9d544259018a22c0869700d005bdb10f221e6ae05fcbb4b490 | Python | 1,192 | 32 | """Handles location of output files"""
import pathlib
def initialize_results_dir(results_dir=None):
"""Determine where to save results for a pipeline run
The output directory will be resolved in this order:
1. if results_dir is provided, results will be to results_dir/query_name
2. results wi... |
9c0b9eacfef473206ec042436e9bea0dc8a3628fdfe8f8f7865cf510c7bd44ae | Python | 1,192 | 28 | import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from metstab_shap.config import utils_section, csv_section
from metstab_shap.config import parse_data_config, parse_representation_config, parse_task_config
from metstab_shap.data import load_data
# load data (and change to clas... |
f3ccef2e2d8dac26e7d408ba5bfcf4ae56b85b75719d8ccb919a6ec1c84d0d4a | Python | 1,193 | 27 | from base import BaseDataLoader
from dataset.acdc_miccai_dataset import AcdcMiccaiTrainDataset
class AcdcMiccaiDataLoader(BaseDataLoader):
def __init__(self, data_root, batch_size, shuffle=True, validation_split=0.0, num_workers=1, training=True,
transforms=None, type="sax_15p"):
# if tra... |
19bac8fa818c668cfc883a8a3407fae636fec4ed23c62338bfbfea6012f1bdd1 | Python | 1,194 | 39 | 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... |
7c3891876ef79696aa02b409135863ea6cb1c0546e8f7471c7ee7cfc4feaa7f8 | Python | 1,194 | 40 | """
This module provides a comprehensive set of filters designed to evaluate the sequence properties of oligonucleotides, ensuring their suitability for various applications.
"""
from ._filter_base import BasePropertyFilter
from ._filter_experiment_specific import DetectionOligoFilter, PadlockArmsFilter
from ._filter_... |
138146454aa869c4307ad1ee65e8ee2517b42be5a92d98130343b1ca9c4e26a0 | Python | 1,196 | 37 | import numpy as np
# import torch
def one_cls_dice(pred, target, label_idx):
"""
Calculate Dice metrics for one channel
:param output:Output dimension: Batch x X x Y (x Z) int
:param target:Target dimension: Batch x X x Y (x Z) int:[0, Channel]
:param label_idx:
:return:
"""
eps = 0.00... |
178a3de9c5c12171aad8d706aa67a7f250ea880b0dfaf197d113c36220948573 | Python | 1,197 | 36 | import torch
import torchvision.transforms as transforms
'''
preprocess reference : https://github.com/google-research/federated/blob/master/utils/datasets/cifar100_dataset.py
'''
def cifar100_transform(img_mean, img_std, train = True, crop_size = (24,24)):
"""cropping, flipping, and normalizing."""
if train:... |
83a0188af9c3607106c9233646789c781ce85fd4d631993da43fb98fa45d3858 | Python | 1,197 | 31 | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from statistics.clustered_mlp_output_wrappers import \
load_metadata_pathology_mlp_pivot
# Desired columns
COLS = ('ClusterIdx', 'PROGRESSES', 'BRAAK', 'CERAD', 'NIA_ADNC')
PATH = ['BRAAK', 'CERAD', 'NIA_ADNC']
# for pat... |
527094826a889a4f3df6278d9afd8e5d1637b53ca606d880b88c41d213d560a6 | Python | 1,200 | 38 | import numpy as np
from .plane_alignment_functions import plane_alignment
def calculate_brain_center_depth(section):
"""
Calculates the depth of the brain center for a given section
:param section: the section coordinates as an array consisting of Oxyz,Uxyz,Vxyz
:type section: np.array
... |
7ada0896ac67fe8550f44ff7259addb2686d1e0069f0ef53d99aa4cdc7ac4bfe | Python | 1,201 | 42 | import numpy as np
from scipy.signal import argrelmax, find_peaks
import inspect as isp
def min_len(func):
def wrapper(*args):
pred = func(*args)
if len(pred) <= 1:
return np.array([0, 0])
return pred
return wrapper
class Combination:
@min_len
def curve(self, p... |
534d5d3129453cc24de557e34426166b29627628f6244a9793216036442e8763 | Python | 1,202 | 49 | # -*- coding: utf-8 -*-
"""
Created on Mon Mar 21 13:20:49 2022
@author: Daniel
"""
import numpy as np
import os
import shelve
import pandas as pd
import sys
dirname = os.path.dirname(__file__)
path = os.path.join(
dirname, 'data', 'tempotron')
files = [x for x in os.listdir(path) if 'grid-seed' in x and '.dat' ... |
83c15862418ed866433f28e62741fcdbda9a0a4afa69c81f990b5fdf0072b2aa | Python | 1,202 | 38 | # 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 torch import ones, zeros
from sbi.simulators.linear_gaussian import diagonal_linear_g... |
e7bdb29d05e6802ee914b45c27f42ed2a6a55caef3409ae51c879820fd3f90e2 | Python | 1,204 | 49 | import eagerpy as ep
from ..types import Bounds
from .base import Model
from .base import T
class ThresholdingWrapper(Model):
def __init__(self, model: Model, threshold: float):
self._model = model
self._threshold = threshold
@property
def bounds(self) -> Bounds:
return self._mo... |
030acf2c7b5d8867d8ebeae8027b7abcc5354ebf574d041b9ea44ffcdd476d89 | Python | 1,205 | 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.npe.mnpe import MNPE # noqa: F401
from sbi.inference.trainers.npe.npe_a import NPE_A # noqa: F... |
39416068c5c17a18652f51980cdde26dd160deb125db910876675122a951e0ad | Python | 1,206 | 44 | import os
import torch
import numpy as np
from torch.nn import functional as F
import config_bayesian as cfg
# cifar10 classes
cifar10_classes = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
def logmeanexp(x, dim=None, keepdim=False):
"""Stable co... |
2815b9c628ed6708f144167f4be6e2bfb0cc6693089877cb4d3fce8e76e1cd70 | Python | 1,208 | 28 | 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
c1 = t.newComp(partNum=0, free=1, spec='specifi... |
5deb2d1a6a27006d783cb5a6977cf295543e22fed8a993fca275a54a3057b542 | Python | 1,209 | 48 | # -*- coding: utf-8 -*-
"""
Created on Thu Jan 20 12:51:20 2022
@author: Daniel
"""
import sqlite3
import os
import numpy as np
dirname = os.path.dirname(__file__)
db_path = os.path.join(
dirname, 'data', 'tempotron_thresholds_mean.db')
con = sqlite3.connect(db_path)
cur = con.cursor()
seed = 0
np.random.seed... |
c72e97e2a2d51002c04e88c02cd4b3662f271a26869200521727013d8d82c2d9 | Python | 1,209 | 35 | def pepper(self, proportion=0.1, andAmbiguities=1):
"""Pepper the alignment (self) with random gaps and ambiguities."""
import random
import math
ambigs = 'rymkswhbvdnnn'
#ambigs = 'n'
for s in self.sequences:
s.sequence = list(s.sequence)
for i in range(self.length):
... |
cffdc6c0b5bd7dccfc92a3c79e73a2065743769bb7cb40026f1b66adec3f554d | Python | 1,209 | 35 | import pandas as pd
from importlib import reload
from typing import Dict
def test__map_old_to_new_index():
index_to_lobe = (
pd.read_csv("metadata/data_processed/index_to_lobe_map.csv")
.set_index("ID")
.to_dict()["Lobe"]
)
index_to_lobe = _map_old_to_new_index(index_to_lobe)
def _... |
eb037fbf4e4dbea3bea285c0105e90ac4459fd3fd60320a4dd83eccc141a2d15 | Python | 1,210 | 30 | # Copyright (c) OpenMMLab. All rights reserved.
from .topdown_aic_dataset import TopDownAicDataset
from .topdown_coco_dataset import TopDownCocoDataset
from .topdown_coco_wholebody_dataset import TopDownCocoWholeBodyDataset
from .topdown_crowdpose_dataset import TopDownCrowdPoseDataset
from .topdown_h36m_dataset import... |
0fff6ed7c600a7b3b0cc84def10a44678a8e72cfb7f2883613b7d79c2ddb594b | Python | 1,212 | 37 | '''
common format we use for SV project
we do not include translocation in the data.
'''
class SV:
chrom=0 # chromsome
start=1 # starting position [start, end] is the call
end=2 # ending position [start, end] is the call
calltype=3 # call type, DEL/DUP/INV/INS
calllength=4 # length of th... |
c227b45464ddb12607b0f90776043dfd8f318bd0b5f0340499f7cc80409af75a | Python | 1,212 | 44 | """
Setup file for first-time users
This file sets up the following directories and subdirectories:
benchtop_wdir:
dsets:
tmp:
settings_files:
results:
analysis_dir:
analysis_files
results
"""
import os, shutil
if __name__ == "__main__":
#Create the directo... |
f11c4bb24c7d72fa78089bf01e9b87d0650f6f7c6637cbc6144edd6b7f5642e3 | Python | 1,212 | 45 | """Type validators for CLI arguments."""
from pathlib import Path
from typeguard import typechecked
@typechecked
def valid_file(path_str: str | Path) -> Path:
"""Validate that a path exists and is a file."""
path = Path(path_str)
if not path.exists():
raise IOError(f'File does not exist: {path}'... |
54846e24da5c37a548b14c29c68fc289a1c88a90ae9783e8a2bc48c031eb1339 | Python | 1,213 | 27 | #!/usr/bin/env python3
# Author: Francois Aguet
import argparse
import os
import subprocess
parser = argparse.ArgumentParser(description='')
parser.add_argument('fastqtl_pairs_file', help='FastQTL output from nominal pass')
parser.add_argument('--tmp_dir', default='', help='tmp directory for sorting')
parser.add_argu... |
8b9218ede1ed0eb63f502acf3c3d636e17fcc66fd18e3c4275aa4a00bfa8adfa | Python | 1,214 | 37 | class MyMessage(object):
"""
message type definition
"""
# server to client
MSG_TYPE_S2C_INIT_CONFIG = 1
MSG_TYPE_S2C_SEND_FAKE_DATA_TO_CLIENT = 2
MSG_TYPE_S2C_STOP = 3
# client to server
MSG_TYPE_C2S_SEND_LABEL_TO_SERVER = 4
MSG_TYPE_C2S_SEND_GRAD_TO_SERVER = 5
MSG_TYPE... |
ef3548dc8a1e7f20c65874f234d8d6366c96ca148998c720ffd9dc7a4db155a8 | Python | 1,217 | 39 | # 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/>
# NOTE: This is inspired by the sbibm-package <https://github.com/sbi-benchmark/sbibm>
from .base_task import Task
from .gaussian_linear import GaussianLi... |
48ea4f16e317818ceab968f6e64c2d31792f816ec1314dc44c748895f73cef9d | Python | 1,222 | 34 | # 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 pytest
import torch
from sbi.utils.diagnostics_utils import get_posterior_samples_on_batch
class DummyPosterior:
"""Posterior returning zero... |
3da0745fe1ad1cdcb492bddd92f9ada181de040959503a64d48654e78b6d2553 | Python | 1,227 | 37 | """
Turns the cleaned h5 file dataset into a pickle file such as the one used in DFTBML.
"""
# %%
from h5_converter import h5_handler
import pickle
# %%
if __name__ == "__main__":
k = h5_handler.h5_to_dict('cleaned_dsets/new_ANI1ccx_clean.h5')
p_file = []
for name in k.keys():
#p_entry = {}
... |
bfec3853e6102368ab02d9c15b2681785323947cf3737f7865063206ae36f494 | Python | 1,229 | 56 | import argparse
import json
import multiprocessing.dummy as mp
import os
from pathlib import Path
import numpy as np
import tqdm
from scipy.signal import stft, istft
def do_irm(gt, mix, alpha=1):
"""
Computes and applies the Ideal Ratio Mask
gt: (n_voices, 1, t)
mix: (1, t)
... |
6aaa6c95b819d089a549ef89e7b44b39528690b9ce1e582f615c286bf67bac7e | Python | 1,230 | 49 | # 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.analysis.conditional_density import (
ConditionedMDN,
conditional_corrcoeff,
conditional_potential,
eval_conditional_density,
)
f... |
a9d0f2136deebf9235802b79d09d4fbf3d8dbd983a1626998f2e32acc5a11fcd | Python | 1,230 | 48 | import os.path
import random
import sys
import cv2
import h5py
import numpy as np
import scipy.ndimage as ndimage
from PIL import Image
from data.base_dataset import BaseDataset, get_params, get_transform
from data.brats_dataset import BratsDataset
class BratsSplitDataset(BaseDataset):
def __init__(self, opt):... |
7dcdb1d8bb7752f7328c912246b06592e9876fcb9b330e17ae7ab7e918c2d563 | Python | 1,231 | 42 | from typing import TypeVar, Callable, Optional
import eagerpy as ep
from ..types import Bounds
from ..types import BoundsInput
from .base import Model
T = TypeVar("T")
class NumPyModel(Model):
def __init__(
self, model: Callable, bounds: BoundsInput, data_format: Optional[str] = None
):
se... |
88e4a404c45830c1d628c7f712a2f20b85e6c653b7b55b17d13fb1638755faed | Python | 1,231 | 37 | import pytest
import foolbox as fbn
from conftest import ModeAndDataAndDescription
def test_dataset_attack(
fmodel_and_data_ext_for_attacks: ModeAndDataAndDescription,
) -> None:
(fmodel, x, y), _, _ = fmodel_and_data_ext_for_attacks
x = (x - fmodel.bounds.lower) / (fmodel.bounds.upper - fmodel.bounds.... |
980f3c55b847feaa657c91c0030cfe58f17d9d5ac0634ea6f844195c934f9b1a | Python | 1,231 | 45 | 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 TestC3Out(object):
def test_simple_c... |
87b2d2aabdec4ac432cc451b9975f3086e4975b41bc1e9b6aeacc8314cc0a2f8 | Python | 1,232 | 37 | ############################################
# imports
############################################
from abc import ABC, abstractmethod
############################################
# Property Filter Class
############################################
class BasePropertyFilter(ABC):
"""
An abstract base class ... |
d31f2602c8cfdfbbfefe2ffacfaec6389186952775c8ac02e39223bf6b3119fa | Python | 1,232 | 44 | # -*- coding: utf-8 -*-
import os.path as op
import pkg_resources
import numpy as np
import pytest
from snf import compute, metrics
rs = np.random.RandomState(1234)
test_dir = pkg_resources.resource_filename('snf', 'tests/data/sim')
data1 = np.loadtxt(op.join(test_dir, 'data1.csv'))
data2 = np.loadtxt(op.join(test_di... |
8c75830dfd547c4e84541b3274d358edc6b2287f4fa862942b77bea3c3918405 | Python | 1,233 | 42 | import eagerpy as ep
import numpy as np
import pytest
from typing import Any
from foolbox.attacks.gen_attack_utils import rescale_images
def test_rescale_axis(request: Any, dummy: ep.Tensor) -> None:
backend = request.config.option.backend
if backend == "numpy":
pytest.skip()
x_np = np.random.un... |
fffa9d623bb57cac0e997efbd004ebab828fc42a724d3df5076f22c8e3b49685 | Python | 1,233 | 39 | # Copyright (c) OpenMMLab. All rights reserved.
from unittest import TestCase
import numpy as np
from mmpose.datasets.datasets.base import (Kpt2dSviewRgbImgTopDownDataset,
Kpt2dSviewRgbVidTopDownDataset,
Kpt3dSviewRgbImgTopDownDatas... |
fe39d5c0e2642586641aa3c616852224c2393251742f5477c9ac3a79e0e70b0b | Python | 1,241 | 52 |
import os
import SV
class RDXplorer:
calltypes=['DEL', 'DUP']
segStart=0
segEnd=1
state=2
Length=3
copyEst=4
segMedian=5
zstat=6
chrom=7
posStart=8
posEnd=9
@staticmethod
def doparse (rdx_line):
E=rdx_line.rstrip('\n').split('\t')
chrom=E[RDXplorer.chrom]
start=E[RDXplorer.posStart]
end=E[RDXp... |
0cf0a8c9dfec8edd00058d5a3f6f813cc6795065c4d7b8f2ada96bf42e10d1fe | Python | 1,242 | 35 | import argparse
def write_increasingLUT(full_path_original):
originalLUT = full_path_original.split('/')[-1]
path = full_path_original.strip(originalLUT)
new_doc = []
counter = 1
with open(full_path_original) as fr:
for line in fr.readlines():
if (not line.startswith(('#', '\n... |
a3c07725a76e2c7ef3b2ebbfa981c0be6d9b136be0ae1fc86d2a9e3348f7e258 | Python | 1,242 | 45 | #!/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... |
a51968a3b3653419fa127980f915a3f3064c5fe92bd4db0c29ae44baad6c2144 | Python | 1,248 | 43 | #!/usr/bin/env python
# coding: utf-8
# # Generating box files for each micrographs
import glob
import os
import pandas as pd
import numpy as np
#Change this according to your structure of your CryoSPARC project
project_dir = '/home/*/*/CryoEMData/'
empiar_ids = ['10081','10289','11057'] #Example EMPIAR IDs
#Put ... |
b97afcf19d8a80ba3444363aa6e6f6065d05767906ef01a529757ea2bd1b2c87 | Python | 1,248 | 41 | import numpy as np
def aji(pred, target, istrain):
""" target should be instance level label"""
assert pred.shape == target.shape
aji = 0.0
for k in range(pred.shape[0]):
aji += _AJI_fast(pred[k], target[k])
aji /= pred.shape[0]
return aji
def _AJI_fast(pred_arr, gt):
gs, g_areas... |
7a666c34685436bb2c5a04538ac4bc8d760898956c702b675e1ba241c4cfe8f8 | Python | 1,249 | 38 | # Copyright (c) OpenMMLab. All rights reserved.
import torch
from mmdet.core import build_assigner, build_sampler
def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
"""Create sample results that can be passed to BBoxHead.get_targets."""
num_imgs = 1
feat = torch.rand(1, 1, 3, 3)
assign_co... |
9a472c4164e2986fbaa4c9206547d64848455c7f84c226fd92290ca11c096b9d | Python | 1,249 | 41 | from torch.utils import data
import os
import numpy as np
import time
import glob
class Dataset_training(data.Dataset):
'Characterizes a dataset for PyTorch'
def __init__(self, data_dir, train_T, seg_num=100):
self.data = np.load(data_dir) # T*bs*dim
self.seg_num = seg_num
... |
9d1242309365cddb7925974392c5b6aaad57d9a2ced552d5edec16b937a59534 | Python | 1,249 | 40 | # from keras.preprocessing.text import Tokenizer
# from keras.preprocessing import sequence
import logging
from transformers import AutoTokenizer
def truncate(x, n):
ret = [x[0]] + x[-n:]
return ret
class BERTTokenizer():
def __init__(self, model_name, truncation, padding, max_length=None):
... |
ff3295f0067d2414037de05a60c16a511cbb863bd970b15f86f5f8280a409a28 | Python | 1,249 | 39 | from base.results.base_result_tracker import BaseResultTracker
class BaseEvaluator:
"""
Abstract base class for model evaluators.
Evaluators run inference on a dataset and track metrics using
result trackers. Concrete implementations should override the
evaluate() method.
Args:
name ... |
51ea685e7005cb27879030f3be1851b9bb9ecf6e5707ef6a150822b9929cb0c6 | Python | 1,250 | 34 | # Copyright (c) OpenMMLab. All rights reserved.
from ..builder import HEADS
from .standard_roi_head import StandardRoIHead
@HEADS.register_module()
class DoubleHeadRoIHead(StandardRoIHead):
"""RoI head for Double Head RCNN.
https://arxiv.org/abs/1904.06493
"""
def __init__(self, reg_roi_scale_factor... |
eae8920fff9721e155eeda1d64171196f369e3ef14b4fb1bd25c199d6ea50fba | Python | 1,250 | 33 | # Copyright (c) OpenMMLab. All rights reserved.
from .inference import (collect_multi_frames, inference_bottom_up_pose_model,
inference_gesture_model, inference_top_down_pose_model,
init_pose_model, process_mmdet_results,
vis_pose_result)
from .inf... |
82b323e3b3815616946d33f42fb01561a142eb1071bc6e8b08a34e87e571e227 | Python | 1,253 | 37 | #!/usr/bin/env python3
from argparse import ArgumentParser
from flystim.dlpc350 import make_dlpc350_objects
def main():
parser = ArgumentParser()
# FPS argument
parser.add_argument('--fps', type=float, default=115.06)
# RGB currents
parser.add_argument('--red_current', type=float, default=0)
... |
6c1ef7e8466b5e7b76120172f87448029bbf06ca64ecdd42bebc498482752a49 | Python | 1,260 | 45 | import tensorflow as tf
from keras import layers as kl
from keras import Model, Layer
from genomad.neural_network import igloo
class OneHotLayer(Layer):
def call(self, x):
return tf.one_hot(x, depth=257, axis=-1)
def create_encoder():
inputs = kl.Input(shape=(5_997,), dtype="int64")
embedded =... |
3cc59e87644140407b6684b37e47736f1460e5bcf0c97a85c1897f34699da605 | Python | 1,261 | 38 | # Copyright (c) OpenMMLab. All rights reserved.
import numpy as np
import torch
from mmpose.models import MultiModalSSAHead
def test_multi_modal_ssa_head():
# substantialize head
train_cfg = dict(ssa_start_epoch=10)
head = MultiModalSSAHead(
num_classes=25, modality=('rgb', 'depth'), train_cfg=t... |
82a5dd5c88d601bd1a8a44945269a2c54cbc1ad54a63a2f58b58c78e3e503d43 | Python | 1,262 | 44 | import sys
# Add gumpy path
sys.path.append('gumpy')
from gumpy import signal
import numpy as np
def preprocess_data(data, sample_rate=160, ac_freq=60, hp_freq=0.5, bp_low=2, bp_high=60, notch=False,
hp_filter=False, bp_filter=False, artifact_removal=False, normalize=False):
if notc... |
dda68662c5117219cc9ce52a77bed40bb21ca39d24d72f3b3d41489f46e90b24 | Python | 1,262 | 44 | import torch
from torch import nn
from torchvision import models
class vgg16_feat(nn.Module):
def __init__(self):
super().__init__()
self.vgg_layers = models.vgg16(pretrained=True).features
self.layer_name_mapping = {
'3': "relu1_2",
'8': "relu2_2",
'15'... |
816c015d3e5d39b8ebfb20f5ce5f34a8779bb47eb84343677c93e6d11eba4b2b | Python | 1,265 | 37 | import json
import matplotlib.pyplot as plt
from dtor.utilities.utils import set_plt_config
set_plt_config()
x = lambda p, t: f'results/res_{p}_totfolds_{t}.json'
files = [
{'prefix': "tumor_nonzero_weighted_scaled_aug3_resnet", 'tot_folds': 3},
{'prefix': "tumor_nonzero_weighted_scaled_aug5", 'tot_folds': 2}... |
f8040576b0a5ccf70a348d06de03a3a0737d097db42aa0fdbba424fae4db023a | Python | 1,267 | 24 | from .base_options import BaseOptions
class TestOptions(BaseOptions):
"""This class includes test options.
It also includes shared options defined in BaseOptions.
"""
def initialize(self, parser):
parser = BaseOptions.initialize(self, parser) # define shared options
parser.add_argum... |
fa79e80d1d628b3091138cd7ab3afee43a414f72d8beef7b60c44a0b3ee7389a | Python | 1,274 | 57 |
import SV
import os
import re
class CNVnator:
match_calltype={'deletion':'DEL', 'duplication':'DUP'}
CNVtype=0
coordinates=1
CNVsize=2
normalizedRD=3
eval1=4
eval2=5
eval3=6
eval4=7
q0=8
@staticmethod
def parse_coordinates (coordinates):
coord=re.sub (':', '-', coordinates)
E=coord.split ('-')
re... |
5e0a36ccac3312855f08c4f4a870e648f0a0b5a35f19b52c93a69fcd6d3b1b98 | Python | 1,275 | 42 | """
Build hdf5 files based on MURA datasets.
"""
import csv
import os
import h5py
import imageio
import re
def readlist(path, file):
with open(os.path.join(path, file)) as csvfile:
csv_reader = csv.reader(csvfile, delimiter=',')
list = {}
h5_file = h5py.File(os.path.join(path, 'mura.hdf... |
0de268811224312e5bb2b6a7fdc387f8b66531490b39e9e21fd2152bc2359f63 | Python | 1,276 | 36 | import numpy as np
from collections import deque
class CDIIS:
def __init__(self, overlap, maxNumFock=20):
self._overlap = overlap
self._fockQue = deque(maxlen=maxNumFock)
self._commQue = deque(maxlen=maxNumFock)
def NewFock(self, fockList, comm):
self._fockQue.append(fockList)... |
61eccb712afeafbd036bbbc57e6fbf8e84a004c72fc4167142728606a5a0acca | Python | 1,276 | 44 | import torch
from torch import nn
from torchvision import models
class vgg16_feat(nn.Module):
def __init__(self):
super().__init__()
self.vgg_layers = models.vgg16(pretrained=True).features
self.layer_name_mapping = {
'3': "relu1_2",
'8': "relu2_2",
'15'... |
dbb2fdd038203f58135e112634d070924575fd7da13aaa9f32a85f76b1831aea | Python | 1,281 | 38 | import numpy as np
import torch
def one_cls_dice(output, target, label_idx):
"""
Calculate Dice metrics for one channel
:param output:Output dimension: Batch x Channel x X x Y (x Z) float
:param target:Target dimension: Batch x X x Y (x Z) int:[0, Channel]
:param label_idx:
:return:
"""
... |
3cdfcdb1a3c7184c33e937e6cc57a4fc33791e6240a2598b78bde4e96eaa2ca3 | Python | 1,282 | 39 | # Copyright (c) OpenMMLab. All rights reserved.
from mmcv.cnn import ConvModule
from mmdet.models.builder import HEADS
from .fcn_mask_head import FCNMaskHead
@HEADS.register_module()
class HTCMaskHead(FCNMaskHead):
def __init__(self, with_conv_res=True, *args, **kwargs):
super(HTCMaskHead, self).__init_... |
4d14134b1c866e4faf3cbdb0a36d4e54bcc727a5c49b3e1683212322dbc60f9b | Python | 1,283 | 41 | # Copyright (c) OpenMMLab. All rights reserved.
import torch.nn as nn
import torch.nn.functional as F
from ..builder import LOSSES
@LOSSES.register_module()
class BCELoss(nn.Module):
"""Binary Cross Entropy loss."""
def __init__(self, use_target_weight=False, loss_weight=1.):
super().__init__()
... |
2eddc8adbdb377f750ad78e363931bfbda691bd5fd863447865f4df1971deac7 | Python | 1,287 | 38 | read('t.nex')
t = var.trees[0]
a = func.newEmptyAlignment(dataType='dna', symbols=None, taxNames=t.taxNames, length=1500)
b = func.newEmptyAlignment(dataType='standard', symbols='xyz', taxNames=t.taxNames, length=1000)
read('setsForSim.nex')
a.setCharPartition('cp1')
d = Data([a,b])
t.data = d
# Model for Part 0
pNum... |
f32680578d4f17615212040305cbc8203178598869fc570012a49e1f922ec57f | Python | 1,288 | 49 | from pybrid import utils
default_cfg = {
"exp": {
"log_dir": "results/test",
"seed": 0,
"num_epochs": 20,
"num_batches": 500,
"batches_per_epoch": 50,
"test_every": 1,
"test_hybrid": False,
"test_pc": False,
"test_amort": False,
"log_b... |
7f4a5ef7bee6f3668824c2204ff30e04f1b193e37ff97a83bf8fb1e51e6dfd44 | Python | 1,290 | 52 | # -*- coding: utf-8 -*-
"""
Created on Mon Mar 21 13:20:49 2022
@author: Daniel
"""
import numpy as np
import os
import shelve
import pandas as pd
import sys
dirname = os.path.dirname(__file__)
path = os.path.join(
dirname, 'data', 'tempotron')
path = '/home/baris/results/tempotron/adjusted_weight/disinhibited... |
200922dbe2d80b82e920cbff5a63c30a5e3b5cdbfea68db3e9abac63cc73d557 | Python | 1,293 | 36 | import os
import subprocess as sp
from typing import Callable
from Bio import AlignIO
from .shrinked_force_permutations import ShrinkedForcePermutationsDataset
class ZwdDataset(ShrinkedForcePermutationsDataset):
def __init__(self, root: str, transform: Callable = None):
super().__init__(transform=transf... |
c587738b67766159fea9934699227540824bd1ab4cf0e4fde7189c1bf0754959 | Python | 1,293 | 58 | # Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import tempfile
import mmcv
import pytest
from mmdet.datasets import CocoDataset
def _create_ids_error_coco_json(json_name):
image = {
'id': 0,
'width': 640,
'height': 640,
'file_name': 'fake_name.jpg',
}
... |
95c65cdbd557b5f6ad50b0ee819e66da6116f3465daeebba6d1d0a58d2092c26 | Python | 1,294 | 31 | import argparse
import warnings
from argparse import ArgumentParser, Namespace
from typing import Union, Text, Sequence, Any, Optional, Tuple
class DeprecatedStoreAction(argparse.Action):
def __call__(self, parser: ArgumentParser, namespace: Namespace, values: Union[Text, Sequence[Any], None],
*a... |
deafbcd80db018d087177f65a1fc2f00beaec4a5a26335b792ca5c9677a6cd3e | Python | 1,300 | 34 | #!/usr/bin/env python3
from flystim.stim_server import launch_stim_server
from flystim.screen import Screen
import numpy as np
from time import sleep
def main():
manager = launch_stim_server(Screen(fullscreen=False, server_number=0, id=0, vsync=True))
manager.load_stim(name='ConstantBackground', color=[0.5,... |
70ecd0a357436f6487b560da4ed87d620dcc115455dc25cbebc3e2d0e1ed4f71 | Python | 1,301 | 43 | # Copyright (c) OpenMMLab. All rights reserved.
import argparse
import subprocess
import torch
def parse_args():
parser = argparse.ArgumentParser(
description='Process a checkpoint to be published')
parser.add_argument('in_file', help='input checkpoint filename')
parser.add_argument('out_file', h... |
3a72d0d233345014fa1a89b09b7c65b108598256337467b1013735fd1cf2cc6f | Python | 1,302 | 66 | from importlib import reload
import numpy as np
import functions
reload(functions)
import functions as f
# Parameters
# Feedforward network
tau_r = 0.001 # s
inhibitory_weight = -1700. #
inhibitory_rate = 100. # Hz
n_inputs = 50 # number
slope = 1e-4
# Stimulus
min_stimulus = -np.pi/2
max_stimulus = np.... |
4e14784f4f8863bbd2693def7a9885e7f0352813f77396b80bd5e1463e83c9ea | Python | 1,302 | 34 | import setuptools
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setuptools.setup(
name="gradient-accumulator",
version="0.4.0",
author="André Pedersen and David Bouget and Javier Pérez de Frutos",
author_email="andrped94@gmail.com",
description="Package for ... |
f3e271cea6e54342c6899e6c352105ce34f865113ae12c40fb6b2e1257a8ae50 | Python | 1,302 | 33 | # 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
from torchmdnet import models
from torchmdnet.models.model import create_model
fr... |
8df3b50a77e3611b61cae29b796215bbaf7f2e95db98eb30f8fdff0a571b1c33 | Python | 1,306 | 36 | """
This module provides a comprehensive set of filters designed to assess and mitigate the off-target and cross-hybridization potential of oligonucleotide sequences, ensuring high specificity and low off-target effects.
"""
from ._filter_base import AlignmentSpecificityFilter, BaseSpecificityFilter, ReferenceSpecific... |
e317ff2e09319fbc66cf18d5a11b863ae54dd4754b12324eb0ccd187565e6ee4 | Python | 1,312 | 37 | import sys
import os
# os.system("module purge")
cmd = "../../install/x86_64/bin/nrnivmodl ../../src/all_mechanisms"
os.system(cmd)
# generate model with fully distributed spines
cmd = "./x86_64/special gen_model.py 1.3"
if not os.path.isdir("./coredat/coredat_1150cell_400_1.0hz_back_noise_1.3spine"):
os.system(... |
295b5378aede0415c506ee7f2b5a2d69eee9849584d51ea3ea9ef6c293dc604c | Python | 1,313 | 35 | # 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
# -- Project information -----------------------------------------------------
# https://www.sphinx-doc.org/en/master... |
2f554ef4d90311362b457b41d8df04e5a0f8f654cee328e1386dedc02cef10e8 | Python | 1,313 | 34 | # 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 torch
from torchmdnet.models.utils import rbf_class_mapping
@mark.parametrize("name,rb... |
9b92c23d77c0d1e7be7083d3385973c830b86667270a468605ff026cbdab6bd0 | Python | 1,315 | 67 | #!/usr/bin/env python
import sys
import numpy as np
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-i",
"--bedpe",
dest="bedpe",
help="BEDPE file")
parser.add_option("-n",
"--name",
default="LUMPY BedGraph",
dest="name",
help="Name")
(options, args) = pars... |
c69ed5a81312919e507ef7f001e60792a399a9a98f6eb4a0f618d43617dbced5 | Python | 1,319 | 32 | """Helper function to load area-wise metrics, such as reconstruction errors.
"""
import pickle
def load_metric_areawise(filename):
"""Takes metrics that was recorded areawise, but stored in a single file and returns the recorded values per area.
See the metric class for which metrics are stored in this manner... |
667e67c8cc023610b6f8145d92972c1fa9996fb69efe813954f2b709fabfada3 | Python | 1,320 | 26 | # Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
from .cityscapes import CityscapesDataset
from .coco import CocoDataset
from .coco_panoptic import CocoPanopticDataset
from .custom import CustomDataset
from .dataset_wrappers import (ClassBalancedD... |
7708b3c11a64560fb2fd3447be4e93d6bcb65c0da628ebef61d62fdb42784cc0 | Python | 1,320 | 42 | import torch.nn as nn
from layers.misc import FlattenLayer
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 ThreeConvThreeFC... |
5908bfd76de02e540658d5f96422d63208a9da442f56d8c33c89b8aa8b1d2c01 | Python | 1,321 | 32 | import torchvision.transforms.functional as F
class NormalizeBoth(object):
"""Normalize both image and label (with channel dim) tensors given the mean and standard deviation.
:param mean: mean value.
:param std: standard deviation value.
"""
def __init__(self, mean, std, training=True):
i... |
e1a0dfa8c4abf3a686179bf394528932cc43dc0bbe24771873cc42ba5563de42 | Python | 1,323 | 47 | """
Class for holding data about a given model under evaluation.
Includes the model name (String), trial type (0 - Executed or 1 - Imagined),
the layers to disable during transfer learning (List), the Tensorflow model object and
if the model uses multiple branches.
After the experiment has been run on the model,... |
003658f0efac475216af028d5d2e5714608b14bf07943992f97fe49823845792 | Python | 1,327 | 41 | from setuptools import setup, find_packages
import versioneer
short_descr = ("Tools for parsing, manipulating, and replaying QIIME 2 "
"analyses leveraging the framework's decentralized provenance "
"data tracking.")
setup(
name='provenance-lib',
version=versioneer.get_version(),... |
459a0864023bfd11c82ac98c3d6c713ca0908fffc85b29ea8e4afab15cc464e2 | Python | 1,327 | 25 | # -*- coding: utf-8 -*-
"""
Created on Sun May 29 12:57:04 2022
@author: fhu14
This is a rework of the dataset_gen_script.py used in previous versions of
PaperPackage. That code was inelegant and messy, and it is intended that this
packaged approach is more comprehensive, concise, and maintainable
The first set of ... |
e08544f6fde1bf47cd0a278932e0aca7aa2a285917b7bb06774314b2eaa087fe | Python | 1,328 | 47 | import json
from pathlib import Path
import requests
import os
def load_config() -> dict:
"""
Loads the config file
:return: the config file
:rtype: dict
"""
path = str(Path(__file__).parent) + os.sep
with open(path + "config.json", "r") as f:
config = json.loads(f.... |
a7c148dbe7e01d8a38c2a27f4e9c68334f3c0c3b616e4d05a91d01d7190d2348 | Python | 1,332 | 33 | #!/usr/bin/env python
import argparse
import os
import torch
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Convert MAE Pre-Traind Model to DEiT')
parser.add_argument('--input', default='/data/code/deit/pretrained_model/pretrain_mae_vit_base_mask_0.75_400e.pth', type=str, metavar='P... |
0c1c6fe1ba48a26069f4d54022e61b7e9c9f6b46ca438a20156634de8cb1ffe4 | Python | 1,336 | 50 | import os.path
import random
import sys
import cv2
import h5py
import numpy as np
import scipy.ndimage as ndimage
from PIL import Image
from data.base_dataset import BaseDataset, get_params, get_transform
from data.brats_dataset import BratsDataset
from data.brats_hgglgg_dataset import BratsHgglggDataset
from data.nu... |
9a37f1d837eb9a4d3304c9402c8de8e4cbd9f1164a301f1fecaddf61ee3e3554 | Python | 1,337 | 46 | import argparse
import pysam
import numpy
def print_sex(bam):
"""
Print sex based on chr x ratio
Args:
bam (str): Path to bam file
"""
idxstats = pysam.idxstats(bam)
chr_ratio = []
# Calculate read / chromosome length ratio per chromosome
for chr in idxstats[0:24]:
chr = chr.st... |
1bbe293267ee87cc03bb6d4a5c4e577c6c8f959d44cae29a851b21a7b6cc067d | Python | 1,338 | 46 | read('../A_NavidiAlignFromYang/navidiSSRNA.nex')
d = Data()
read('t.nex')
#readFile('t31.WithRates.nex')
t=var.trees[0]
t.data = d
t.newComp(free=1, spec='empirical')
t.newComp(free=1, spec='empirical')
t.newComp(free=1, spec='empirical')
t.newComp(free=1, spec='empirical')
t.newComp(free=1, spec='empirical')
t.newCom... |
478cd77588d99ca2c1a2160631b40a9989e073e8e60295edeb64e62b12b583b8 | Python | 1,340 | 47 | import torch.distributed as dist
import torch.multiprocessing as mp
import os
import os.path as osp
import sys
import numpy as np
import copy
from lib.cfg_holder import cfg_unique_holder as cfguh
from lib.cfg_helper import \
get_command_line_args, \
cfg_initiates
from lib.model_zoo.sd import version
from lib... |
52e4b5c2e88a0e0ef5c67c749ca6c9ff5cde0f8a874a9a76fdc118aac2c7d34c | Python | 1,342 | 43 | # Use the mb cons tree as the main tree, and add supports from the paup boot tree.
# We need an ordered list of taxNames.
read('dB.nex')
a = var.alignments[0]
# a.taxNames is the list we want.
# Read in the two trees. The mrbayes con file has 2 trees; we only
# want the first one.
read('mbout.con')
tMB = var.trees[0... |
a63c0b9af51aec8a70e88ce48f4c1b2a944a8b05201c23c63876ccb8ffb779a4 | Python | 1,349 | 28 | import manager_0_user_params as upar
from A_config import a10_config
from B_preprocess import b50_yield_data_analysis
from E_viz import e50_yield_data_analysis
if __name__ == '__main__':
"""
Main function for analysing yield data
"""
#############################################################... |
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