sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2863fb8b8073aad25265845fda88765cb54289cdd56baa280c22724b30927e9a | Python | 2,197 | 63 | """ Full assembly of the parts to form the complete network """
import torch.nn.functional as F
from .unet_parts import *
import torch.nn as nn
class UNet(nn.Module):
def __init__(self, output_c = 1, bilinear=True):
super(UNet, self).__init__()
self.output_c = output_c
self.bilinear = bi... |
10b2b3b9109c2bbe61b6d81ee975f2e7290625eb09b05ce0951b063ae43346c6 | Python | 2,198 | 72 | _base_ = [
'../../_base_/datasets/urpc2020/urpc2020-test-A_coco_detection.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... |
3580a3703be0270dfe89a9546cd84672ddd022903ad1df8350d325d737a6942b | Python | 2,198 | 54 | import os
import glob
import pdb
import torch
import numpy as np
import torch.nn as nn
import nibabel as nib
import pytorch_lightning as pl
from pytorch3dunet.unet3d.model import UNet3D
from torch.utils.data import Dataset, DataLoader
from nibabel.orientations import axcodes2ornt, ornt_transform, inv_ornt_aff
model_pa... |
7de3decf18be47c419845494f82fdc41423fc0da5bcd348d52df5644e394856b | Python | 2,198 | 72 | _base_ = [
'../../_base_/datasets/urpc2020/urpc2020-test-B_coco_detection.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
... |
49c63c8c312f0bfae9b3aa56c6b883185384f5cc7ffda232ca68f9e97ea02d09 | Python | 2,199 | 72 | import torch
from torch import nn
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, bn_momentum=0.1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm... |
b6a3d32b800a4d19f0cf92bb0529ce1eb3385c2f8b22d89341010d559ea61be3 | Python | 2,199 | 64 | import sys
import json
import pickle
import argparse
from lop.utils.miscellaneous import *
from lop.utils.plot_online_performance import generate_online_performance_plot
def add_cfg_performance(cfg='', setting_idx=0, m=2*10*1000, num_runs=30):
with open(cfg, 'r') as f:
params = json.load(f)
list_param... |
aa92ef881f5acf3caf497991bbc2ae8337dd75762db85795e0f52b60ec2f64dd | Python | 2,202 | 72 | _base_ = [
'../../_base_/datasets/urpc2020/urpc2020-validation_coco_detection.py',
'../../_base_/schedules/schedule_1x.py', '../../_base_/default_runtime.py'
]
# model settings
model = dict(
type='ATSS',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],... |
cf44eef564403a70d35bd207e64e7085eda7996d4f9c326d978f2b1f141ae88d | Python | 2,203 | 57 | import torch
import torch.nn.functional as F
from torch import optim
class Backprop(object):
def __init__(self, net, step_size=0.001, loss='mse', opt='sgd', beta_1=0.9, beta_2=0.999, weight_decay=0.0,
to_perturb=False, perturb_scale=0.1, device='cpu', momentum=0):
self.net = net
s... |
8ef52e69e6aac25116346af786a4190997af6288637b763c246505d3c34041fe | Python | 2,205 | 59 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
286c522e15bf8874df7cfc9b7d5c3b7947a44e0bab8807b2f2ed625bbbc5f0c2 | Python | 2,206 | 63 | #
# CEBRA: Consistent EmBeddings of high-dimensional Recordings using Auxiliary variables
# © Mackenzie W. Mathis & Steffen Schneider (v0.4.0+)
# Source code:
# https://github.com/AdaptiveMotorControlLab/CEBRA
#
# Please see LICENSE.md for the full license document:
# https://github.com/AdaptiveMotorControlLab/CEBRA/bl... |
62e0a2e0b671495361771c68910c3b9d4f373ae84389463f34c26f96b2176245 | Python | 2,206 | 67 | '''
Registering statistical maps to anatomical data
'''
import glob
import os
import subprocess
subs = ['sub-06']
# Define data dir
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti/derivatives'
for sub in subs:
# Defining folders
funcDir = f'{DATADIR}/{sub}/ses-01/func' # Locati... |
78536ae1bb32ae24d5dbde6bed2913c2e11eae3c8d1dda76195a6fcaaa06d217 | Python | 2,206 | 68 | import pytest, torch
import ecg_augmentations as ea
from ecg_augmentations.augmentations.leads import INDEPENDENT, KORS, LEAD_SETS, LEADS
def vcg(x):
return torch.tensor(KORS, dtype=x.dtype) @ x[..., INDEPENDENT, :]
def test_synthetic_limb_leads_consistent(ecg):
assert torch.allclose(ea.DeriveLimbLeads()(e... |
bffb8aba376be86f44427886a8330f9cd13034a3c6c7f907d5210386c9eb9c05 | Python | 2,206 | 59 | # Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
ef3f1d72511c1549e8b2acde85f9c55f7b218e2b7292b48e782ce3d8bdfc4af8 | Python | 2,206 | 72 | import pytest, torch
import ecg_augmentations as ea
@pytest.mark.parametrize(
"make",
[
lambda: ea.Invert(p=1.5),
lambda: ea.GaussianNoise(snr_db=(30, 10)),
lambda: ea.HighPassFilter(100, cutoff=60),
lambda: ea.LowPassFilter(100, cutoff=(20, 50)),
lambda: ea.PowerlineN... |
3415b9c9e39f5a6683e94e573b739ae5ea561c4da701350b3f87a561c9ac88d2 | Python | 2,208 | 79 | """
Test of issue13 `spike_buffer get full very fast due to dumb spike target
routing`
"""
import os
from spinnaker2 import hardware, snn
def run_model(n_neurons, neurons_per_core=None, sys_tick_in_s=1.0e-3):
"""
create a model with a stimulus `spike_list``and a receiving population
`lif` with one-to-one... |
48276be4069dc9460947ccbdf2025425f113191b755aef59dbd21acab6f8fbb3 | Python | 2,209 | 53 | import math
from collections import defaultdict
def log_add(args) -> float:
if all(a == -float('inf') for a in args):
return -float('inf')
a_max = max(args)
lsp = math.log(sum(math.exp(a - a_max) for a in args))
return a_max + lsp
def ctc_beam_search(ctc_probs, beam_size = 5):
maxlen = ct... |
4b65d8052002d0b84d623709bc70505dbb1178552f4f368d9eda6a633925bf68 | Python | 2,209 | 61 | import torch
import numpy as np
import scipy.io as sio
import h5py
import os
import sys
torch.multiprocessing.set_start_method('spawn')
torch.set_num_threads(16)
# be sure the current path is `hstnn/data` and have downloaded the matrix data in `hstnn/data/N-MNIST`
def nmnist_numpy_gen(dt=3):
folder = "./N-MNIST/... |
50124071e50180c55d7f5bf850ae42760fe2fa06f86fb2c0fc1ed7b6b5e50674 | Python | 2,209 | 55 | from glob import glob
from os.path import join
import sys
from setuptools import setup, find_packages
import setuptools
import ngs_mapper
from ngs_mapper import util
# Run setuptools setup
setup(
name = ngs_mapper.__projectname__,
version = ngs_mapper.__version__,
packages = find_packages(),
scripts ... |
708ac4a84336403a722f5b2798fbe278303e8dee4fee076fcd6ed92318540e60 | Python | 2,209 | 67 | import torch
import torch.nn as nn
from torchvision import models
def make_decoder_block(in_channels, middle_channels, out_channels):
return nn.Sequential(
nn.Conv2d(in_channels, middle_channels, 3, padding=1),
nn.ReLU(inplace=True),
nn.ConvTranspose2d(
middle_channels, out_cha... |
b9377ceba961315e96fe49b468ab5644869d704c45bb206cbecbce1e41462b72 | Python | 2,210 | 65 | import math
import torch
from ..utils import loss_and_miner_utils as lmu
from ..miners.base_miner import BaseTupleMiner
# mining method used in Hard Aware Deeply Cascaded Embeddings
# https://arxiv.org/abs/1611.05720
class HDCMiner(BaseTupleMiner):
def __init__(self, filter_percentage=0.5, **kwargs):
su... |
e6b12c54e275bcd246ff4d7eb99136795ed297db40b97ff9f77c70a4b14a6cfb | Python | 2,210 | 75 | import numpy as np
import torch
import pdb
import matplotlib.pyplot as plt
from latent_time_stepping.oracle import ObjectStorageClientWrapper
class FNOTimeSteppingDataset(torch.utils.data.Dataset):
"""Dataset"""
def __init__(
self,
oracle_path: str = None,
local_path: str = None,
... |
3f5ef5111a91cfe134375b0ef9fe83d84285b59d1ba5105806b9f6d84155b554 | Python | 2,212 | 47 | import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
# Load your CSV dataset
data = pd.read_csv("D:\electrognathograph.csv")
# Calculate the difference between truth values and experimental values
dat... |
4cf7a2753e58157f8261279aabfc2c700ba8a0133403df371fecc93eed76db77 | Python | 2,212 | 55 | import math
import torch
from .fourier import get_3d_fft_freqs_on_grid
from .rotation import rotate_vol_around_axis
def get_missing_wedge_mask(grid_size, mw_angle, device="cpu"):
"""
Produces a 3D binary mask with shape 'grid_size', which can be used to zero-out Fourier components that lie inside a missing ... |
0c16a12420870cf8ad801bdcf6449000f1b8d3645b6bd7fc9e4def165342e237 | Python | 2,213 | 42 | from .utils import *
class TxEval:
def __init__(self, model):
self.df, self.df_train, self.df_valid, self.df_test, self.data_folder, self.G, self.best_model, self.weight_bias_track, self.wandb = model.df, model.df_train, model.df_valid, model.df_test, model.data_folder, model.G, model.best_model, mode... |
171deac8628a84a5a4015b1be71d73a0c5730abfb8c08c381fda72a24beeeecc | Python | 2,213 | 69 | # encoding: utf-8
"""
The *pathspec* package provides pattern matching for file paths. So far
this only includes Git's wildmatch pattern matching (the style used for
".gitignore" files).
The following classes are imported and made available from the root of
the `pathspec` package:
- :class:`pathspec.pathspec.PathSpec... |
977772b30b1f132b4038beefb0b4a3c2ec41f874b63387e64e9cc4a6d3836683 | Python | 2,213 | 82 | from dataclasses import dataclass
from typing import Annotated, Any, Sequence
from pydantic import BaseModel
__all__ = [
"FloatParameter",
"IntParameter",
"CategoricalParameter",
"TrialParameters",
]
class FloatParameter(BaseModel):
name: str
low: float
high: float
step: float | None... |
ef891d94ba1442b5755fe3e576f4a854b85fd4ef147672d230023c21c1b91aec | Python | 2,213 | 51 | r"""Molecular dynamics for ``ov.mol`` — OpenMM engine, MDTraj analysis.
This subpackage gives the otherwise-static ``ov.mol`` stack a time dimension:
a structure (fetched, predicted, or docked) becomes a solvated system, is
minimised and equilibrated, runs production dynamics on the GPU, and is then
analysed — up to a... |
1e6a5468fb2a3f013049483904ec18edb88c1de62c78ae2a59606d264a0e49d2 | Python | 2,214 | 69 |
import argparse
from matplotlib import cm, pyplot
import numpy
import tifffile
from pysted import base, utils
parser = argparse.ArgumentParser(description="""Simulate the acquisition of an
image serie with bleaching.""")
parser.add_argument("pixelsize", type=float,
... |
3c624219d76c14f57232ef184a0e4206bb5cb601d2488c4453d39b0c5f494d38 | Python | 2,214 | 51 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This library is fre... |
b934a97f212c6802e8a11848dbc5a77c3c3f9f69a31b6290a87b9a83733f4909 | Python | 2,214 | 69 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
# Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
#... |
1c7c5e486142477ea1ca8e3009bdd5790d481aaf176f1f5425fabe0f5dcbfa49 | Python | 2,215 | 60 | from typing import Union
import numpy as np
import torch
from mmengine.structures import PixelData
class BatchPixelData(PixelData):
"""Data structure for batched pixel-level annnotations or predictions.
Different from parent class:
Support value.ndim == 4 for batched tensor.
All data items in `... |
f1396e38ff9604323d5a8fef7e4499358114b349e2de62bb4861f4ebf11fb4f9 | Python | 2,215 | 49 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
4b777d7854d575c0628d271b562609440a9f47f38747664b21a9adb7190ac86b | Python | 2,217 | 85 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""Configurations presets for resistive processing units."""
from .configs import (
# Single device configs.
ReRamESPreset,
ReRamSBPreset,
Capac... |
d687494af9de42225f18ed98e47d63e11628e00383b71fa172a61d4171eb74b1 | Python | 2,217 | 25 | from fr import *
from scipy.stats import chi2
import statsmodels.api as sm, statsmodels.formula.api as smf
# LRTs for age x SP interaction, egg and receptivity separately (NB/binomial GLMs)
d=v.copy(); d['agef']=d.age.astype(str)
nb0=smf.glm('eggs ~ C(agef)+sp',d,family=sm.families.NegativeBinomial(alpha=1/np.exp(json.... |
40c1b7d3e0680bbd2af89052b253a9cd9f0d46f5421a3b50ac28375adfaa6b8c | Python | 2,220 | 62 | from torch import nn
from pfrl.nn.recurrent import Recurrent
from pfrl.utils.recurrent import (
get_packed_sequence_info,
is_recurrent,
unwrap_packed_sequences_recursive,
wrap_packed_sequences_recursive,
)
class RecurrentSequential(Recurrent, nn.Sequential):
"""Sequential model that can contain s... |
6dc033f995cfc1def06ea535a3a0245844b57675e3c8ea7ebd71aeaa28c14838 | Python | 2,221 | 61 | from urllib import request
import gzip
from lisa.core.data_interface import DataInterface
from lisa.core.genome_tools import Region, BadRegionError
from lisa.core.utils import Log, LoadingBar
import argparse
from scipy import sparse
import os
from scipy.stats import gamma
import numpy as np
from sys import stdout, stde... |
75a115e1dd6946e1f4c07877c137951de605033afcb2e6a9bfc92eb69fe2d6b5 | Python | 2,222 | 75 | from utils.tokenlizer import smi_tokenizer
from utils.chemistry_parse import canonical_smiles, clear_map_number
import os
import pandas
from tqdm import tqdm
import json
from rdkit import Chem
import sys
import multiprocessing
def get_all_smiles(mol, random_time=20):
atms, answer = [x.GetIdx() for x in mol.GetAto... |
4b1d6d9a6842407b009872949d8442110f63bcab9b70a1854293f200e2b6be1d | Python | 2,223 | 84 | # flake8: noqa
# isort: skip_file
# __tf_train_start__
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1"
import ray
import tensorflow as tf
from ray import train
from ray.train import ScalingConfig
from ray.train.tensorflow import TensorflowTrainer
from ray.train.tensorflow.keras import ReportCheckpointCallback
# ... |
77be597a78db34f43efffc83a922efc36f4373c2073db72ee273fda881d842cc | Python | 2,223 | 58 | ####################################################################################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
097d081bac9fe60c7824035a838c36355eecd0f71b2698193caf71bf195d97ae | Python | 2,224 | 75 | import logging
from dataclasses import dataclass
from typing import Any, Optional
import torch
from pokeformer.config_wrapper import load_class, obj_from_config
from pokeformer.models.base_model import BaseModel
from pokeformer.models.metrics_wrapper import MetricsWrapper
from torch import nn
from torchmetrics.classif... |
1e1bb8c2f461ffbe94fee51814dc3ebfddb63e4426939d0350e3bce99451e5f5 | Python | 2,225 | 49 | import numpy as np
import pickle
import pandas as pd
import sklearn.model_selection as md
import matplotlib.pyplot as plt
import os
import seaborn as sb
# Load and separate the dataset
with open('./model_optimisation/balanced_epochs.pickle', 'rb') as handle:
balanced = pickle.load(handle)
trainset = balanced[balan... |
1fd42b334d34681bee5a8c2cdb7d6fe86bf82e5a5e733e7ef1189e8cc6d96185 | Python | 2,226 | 75 | # fmt: off
# __doc_import_begin__
from ray import serve
import os
import tempfile
import numpy as np
from starlette.requests import Request
from typing import Dict
import tensorflow as tf
# __doc_import_end__
# fmt: on
# __doc_train_model_begin__
TRAINED_MODEL_PATH = os.path.join(tempfile.gettempdir(), "mnist_model.... |
d66ddd281659373762e97287a6f8eb3c1b432c8ed4bc2bebe1220e001e05bbc8 | Python | 2,227 | 65 | import pandas as pd
from data_profiling.model.pandas.discretize_pandas import (
DiscretizationType,
Discretizer,
)
def test_discretize_quantile():
example_data = {
"height": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
"weight": [60.0, 70.0, 70.0, 80.0, 80.0, 80.0, 90.0, 90.0, 90.0... |
ed3faa44423d328e46124c39de216e8347d271b638d6f86e8e838cd987191a6d | Python | 2,231 | 81 | # fmt: off
# __doc_import_begin__
from ray import serve
import pickle
import json
import numpy as np
import os
import tempfile
from starlette.requests import Request
from typing import Dict
from sklearn.datasets import load_iris
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.metrics import mean_... |
32e1782febb98b7fc9fdf97bac3ccd2af618454221afe586d8b6c351caae47c3 | Python | 2,232 | 66 | """Detect voxels that does not decay over time."""
import os
import nibabel as nb
import numpy as np
import glob
# =============================================================================
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti'
# Set subs to work on
SUBS = ['sub-07']
# ========... |
0ad073e226aa3f1bf8892fc0510b7ac79643d8d3790e43dc71797cb49eb292e5 | Python | 2,233 | 63 | # Configuration file for the Sphinx documentation builder.
#
# This file only contains a selection of the most common options. For a full
# list see the documentation:
# https://www.sphinx-doc.org/en/master/usage/configuration.html
# -- Path setup --------------------------------------------------------------
# If ex... |
2c8e428ec82b7f9711ce3c1dd7ab76c1d32249fb3d2ceed7096dd0ec59da029a | Python | 2,237 | 63 | import sys
import json
import copy
import argparse
import subprocess
from tqdm import tqdm
from lop.utils.miscellaneous import get_configurations
def main(arguments):
parser = argparse.ArgumentParser(
description=__doc__,
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argum... |
97a7a1efead67e24a2571a4f91390f70131c3f6d126237ab5f26ef48ef3ccc5e | Python | 2,237 | 66 | def test_extract_volume(tmp_path):
from samri.pipelines.extra_functions import extract_volume
import nibabel as nii
import nibabel as nib
in_file = '/usr/share/samri_bidsdata/preprocessing/sub-4007/ses-ofM/func/sub-4007_ses-ofM_task-JogB_acq-EPIlowcov_run-0_bold.nii.gz'
out_file_name = f'{tmp_path}/test_extract_... |
2daf1e252c34ae6593147f573e35392cbd89787519eb7abd0574f022a062303c | Python | 2,238 | 56 | ####################################################################################################
# Copyright (c) 2020 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is fre... |
91d6db0f0f4cf8418915686eb9d76195b2fd04596b346a7204993f0c073fb77d | Python | 2,240 | 24 | import numpy as np, pandas as pd, statsmodels.api as sm, statsmodels.formula.api as smf, json, warnings
warnings.filterwarnings('ignore')
import os
F=os.environ.get('FRICKE_XLS', os.path.join(os.path.dirname(os.path.abspath(__file__)), '..', 'data', 'Fricke_et_al_combined_raw_data.xls'))
m=pd.read_excel(F,sheet_name='2... |
b3c4bdc4e9bdf5581da6b7b2be47f0f7fb04c1a1347233f921c7eb3d1974cbee | Python | 2,240 | 68 | import pdb
import numpy as np
import pandas as pd
import torch
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
from data_assimilation.particle_filter.base import BaseObservationOperator
class TrueSolution():
def __init__(
self,
state: np.ndarray,
pars: np.ndarray,
... |
28f291864a85ed5f0c4e4f04a944b53d7b99e5d99d96962740d8f2d1d6de6e8d | Python | 2,241 | 81 | """Functionality related to displaying the profile report in Jupyter notebooks."""
import html
import uuid
from pathlib import Path
from typing import TYPE_CHECKING, Union
if TYPE_CHECKING:
from IPython.display import HTML
from IPython.lib.display import IFrame
from data_profiling import ProfileReport
from da... |
4fbd49833a6a193d8c55467fc562ba40bad625b86d20750f2c6261bb21d5e6a9 | Python | 2,241 | 38 | def paras(args):
para = {}
para["encoLen"], para["decoLen"] = args.enco, args.deco
para["lr"], para["epochs"] = args.lr, args.epochs
para["patience"] = args.patience
para['HVAC_module'] = args.HVAC_module
para["encoder_external_in"], para["encoder_external_h"], para["encoder_external_out"] = 5, ... |
8509a07b3f78196b6e70b57e2ce764d9f620248dac756436435ec7d7c703839c | Python | 2,241 | 75 | #!/usr/bin/env python
"""
Preprocess GENCODE basic GTF to extract canonical protein-coding transcripts for functional consequence annotation.
"""
import argparse
import gzip
CHROM_FIELD = 0
ELEMENT_FIELD = 2
ATTRIBUTES_FIELD = 8
TRANSCRIPT_TYPES = {"protein_coding", "nonsense_mediated_decay"}
CANONICAL = {"MANE_Plu... |
9495947790409f4a0900ce9ca602af5501d9f33705b85511019b1cc0e719ba48 | Python | 2,241 | 77 | from pathlib import Path
import argparse
import logging
import os
import pandas as pd
from StabilityOracle.augment import DataColumns, ThermodynamicPermutationPipeline
logging.basicConfig(
format="%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
datefmt="%Y-%m-%d:%H:%M:%S",
... |
7b74e27f3ce5133dd1832fc21a66f9c300dcf8130efe6fe29a31636d6169c932 | Python | 2,242 | 78 | #!/usr/bin/env python
"""
Useful utilities for testing.
"""
def test_sets_equal(iterable_a, iterable_b, item_str="item", name_a="set A", name_b="set B"):
set_a = set(iterable_a)
set_b = set(iterable_b)
a_not_in_b = set_a - set_b
if len(a_not_in_b) > 0:
raise ValueError("One or more %s(s) foun... |
c4f1511d52a2855e1e6e54d326d6d4abc4a1214f0a2ac545a79f9aa789e26957 | Python | 2,242 | 70 | from termcolor import cprint
from pyfiglet import figlet_format
import datetime as dt
import threading
import logging
import sys
class MyFormatter(logging.Formatter):
"""
Code for microseconds logging found at: https://stackoverflow.com/questions/6290739/python-logging-use-milliseconds-in-time-format
"""... |
8666cfce5b290881634c9a923954d130d0fd2322c7170061611f809aee77aaa4 | Python | 2,243 | 64 | import numpy as np
def select_best(data, c, int_measure, select='max', nclust_range=None):
"""
Select the best number of clusters that minimizes/maximizes
the internal measure selected.
:param data: dataset.
:type data: array-like
:param c: clustering algorithm class.
:type c: obj
:pa... |
1f336da9acc487f68e2968f5e9bb57855fb9ec123c1627ec1fcb8bbb0dd41bb7 | Python | 2,244 | 81 | """Shared test utilities for ci/ray_ci/automation tests."""
import os
import platform
import random
import shutil
import subprocess
import tempfile
import threading
import time
import pytest
import requests
import runfiles
def _registry_binary():
"""Get the path to the local registry binary."""
r = runfiles... |
57a9a6584cef2804d8c1545fb3022d01f47bcf11b2b62d261e8b8226bf4ea5e2 | Python | 2,244 | 53 | ###################################################################Pre################################
# Copyright (c) 2016 - 2024, EPFL / Blue Brain Project
# Author(s): Marwan Abdellah <marwan.abdellah@epfl.ch>
#
# This file is part of NeuroMorphoVis <https://github.com/BlueBrain/NeuroMorphoVis>
#
# This program is f... |
da70d47fa0f3e340e44a6f1343592d441bd8213bb2ecdc8ec9711661344cf494 | Python | 2,244 | 85 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
"""
import argparse
import sys
import pysam
import svtk.utils as svu
from svtk.famfile import parse_famfile
def get_denovo_candidates(record, fam, max_parents=10):
"""
Obtain list of samples which are putatively called de novo
"""
called = svu.ge... |
ec8180c9e28271d72f2a22b99532d0e42f4b22c3752f06b0d61fa4d6989320dd | Python | 2,244 | 66 | from typing import Dict
from ray.dag import (
ClassMethodNode,
ClassNode,
DAGNode,
FunctionNode,
InputAttributeNode,
InputNode,
MultiOutputNode,
)
class _DAGNodeNameGenerator(object):
"""
Generate unique suffix for each given Node in the DAG.
Apply monotonic increasing id suff... |
0ccaf3e8fb85fb369383032ac1daca159d58ccf565d2e6db5ca1ffa397065958 | Python | 2,245 | 59 | """Cross-species example datasets.
Currently ships the **frog + zebrafish embryogenesis** pair used by the SATURN
cross-species tutorial (Briggs et al. 2018, *Science*, Xenopus tropicalis
GSE113074; Wagner et al. 2018, *Science*, zebrafish). The two AnnData objects
carry **raw counts** with gene *symbols* as ``var_nam... |
c5be0054c84ce42605c083825d3cdc5da6019f6e3e90b5d6acd12cbe2681aa66 | Python | 2,245 | 70 | """Combine for flow artifact mitigated average (composite) image."""
import os
import numpy as np
import nibabel as nb
import glob
# =============================================================================
DATADIR = '/Users/sebastiandresbach/data/neurovascularCouplingVASO/Nifti'
# Set subs to work on
SUBS = ['su... |
0d5007404495a0a18fec3af00f648f7d989e84bec30b7376a82677c4bb59bfcf | Python | 2,248 | 56 | import numpy as np
import deepdish as dd
from skimage import io
from pathlib import Path
from skimage.external.tifffile import imsave as tiffimsave
from fimpy.core.split_dataset import H5SplitDataset
from processing.roi_extraction import correlation_map, correlation_flooding, extract_traces
from processing.roi_extracti... |
1e74c3618beed3908c3d0816ed0d87477ce10aa74b5c116bcc291ff4d64d4d3b | Python | 2,248 | 71 | # Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... |
202b95c6eab7189522631a5137f61627bed595ef138f6c30152e0398dbc83598 | Python | 2,248 | 72 | # -*- coding: utf-8 -*-
### ------------------------------- Imports ------------------------------- ###
import os
import tqdm
import numpy as np
import pandas as pd
from helper.array_helper import find_szr_idx
### ----------------------------------------------------------------------- ###
def get_seizure_prop(setting... |
705b20bbbea88e4b981118f2837b6fac639f60b89dce0e06c355c664c9057cfe | Python | 2,248 | 68 | import numpy as np
import tifffile
import h5py
import os
import scipy.io as sio
import cv2
median_area_target = 377
if 1:
dir_parent = r'D:\data_TENASPIS\added_refined_masks'
list_Exp_ID = [ 'Mouse_1K', 'Mouse_2K', 'Mouse_3K', 'Mouse_4K', \
'Mouse_1M', 'Mouse_2M', 'Mouse_3M', 'Mouse_4M']
median_area = 228 # 3... |
0a1ab5b121e8d56371ea1d45d75c56a2b4b3aa71892fa7867b4a388f7b13cc08 | Python | 2,249 | 66 | from typing import Sequence
import numpy as np
import pandas as pd
from omicverse.external.scTenifold.core._utils import timer
from tensorly import decomposition
import tensorly as tl
__all__ = ["tensor_decomp"]
@timer
def tensor_decomp(networks: np.ndarray,
gene_names: Sequence[str],
... |
1bd475e07159e9c70c3125f8dfc7596881d8918e9c2bb6eacd2dd6b6dd069be3 | Python | 2,251 | 100 | from pathlib import Path
import logging
import pandas as pd
import numpy as np
from StabilityOracle.model.dataloader import AA_3to1
_amino_acids = {
"ALA": 0,
"ARG": 1,
"ASN": 2,
"ASP": 3,
"CYS": 4,
"GLU": 5,
"GLN": 6,
"GLY": 7,
"HIS": 8,
"ILE": 9,
"LEU": 10,
"LYS": 11... |
90415be5002944e06a6ee789846090747ddfa5b5352a88b9b6f4e65b9d0f9161 | Python | 2,251 | 66 | import pandas as pd
import numpy as np
import matplotlib as plt
import time
def extract_longitudinal_info(version):
datatype = {'PATNO':int, 'diagnosis':int}
patient_df = pd.read_csv("../"+version+"/patient_info.csv", dtype=datatype)
datatype = {'PATNO':int}
data_df = pd.read_csv("../"+version+"/data.... |
bd09690a207f1d55a894e54dad5c4b32d981d0834eb0776a6ef0ad7afd83e65b | Python | 2,251 | 60 | import unittest
from neuron_morphology.feature_extractor.mark import Mark
from neuron_morphology.feature_extractor.marked_feature import (
MarkedFeature, specialize, nested_specialize)
from neuron_morphology.feature_extractor.feature_specialization import \
FeatureSpecialization
class TestSpecialization(unit... |
a5dbb573b2fbe433468832fa61669224bf115db57dfe4f7b9475dea8e872577b | Python | 2,252 | 70 | from typing import Any, Optional
import torch
from monai.inferers import Inferer
from src.networks.transformers.transformer import TransformerBase
class TransformerTrainingInferer(Inferer):
def __init__(self) -> None:
Inferer.__init__(self)
def __call__(
self, inputs: torch.Tensor, network:... |
e1cfb9f015fc877f3d89313feec3ee5529f984b7321453d76b6f4460041a4407 | Python | 2,252 | 72 | from typing import Union, Optional, Tuple
from numbers import Number
import jax
import jax.numpy as jnp
import jax.tree_util as jtu
from ..utils import to_array_shard
@jtu.register_pytree_node_class
class SamplerStatus:
def __init__(
self,
spins: Optional[jax.Array] = None,
wave_function: ... |
217bd86d8e343dc9c16b55afeefc2c97e28b7ae7050a3cab13409c1e5b37b767 | Python | 2,253 | 85 | """Get the dates of the functional scans"""
import os
import glob
import datetime
import calendar
import time
import numpy as np
subs = ['sub-05', 'sub-06', 'sub-07', 'sub-08', 'sub-09']
root = '/Users/sebastiandresbach/github/neurovascularCouplingVASO/code/stimulation'
ROOT = '/Users/sebastiandresbach/data/neurovas... |
3326d16dd4396cfb4e4e086f1a1b6c583b8726c6468d2110e0f05f0019499796 | Python | 2,253 | 77 | """
This script ensures that some dependencies are _not_ installed in the
current python environment.
This is to ensure that tests with minimal dependencies are not tainted
by too many installed packages.
It also ensures the correct Python version.
"""
import argparse
import sys
from typing import List
# These are ... |
96552831fb7b7d26ec62b050437cf16a677040e6feffcc5fe65d6ccc9ba5199d | Python | 2,254 | 68 | from numpy.random import seed
seed(1)
import tensorflow as tf
tf.random.set_seed = 1
import random
random.seed(1)
import numpy as np
np.random.seed(1)
import pandas as pd
import tensorflow as tf
import SimpleITK as sitk
from tensorflow.keras.utils import to_categorical
import sys
class DataGenerator(tf.k... |
863b5a633342f8cfb2ae07b1579419414db84ec4365bb7f4ce3693c5e360e457 | Python | 2,255 | 71 | import sys
from parameters import ParameterSet
from mozaik.models import Model
from mozaik.connectors.meta_connectors import GaborConnector
from mozaik import load_component
from mozaik.space import VisualRegion
import mozaik
class ModelGaborConnector(Model):
required_parameters = ParameterSet(
{
... |
df656c3e3d7eeb34fbc4d7b944c1cb003fa0ab378bf0675e9bad1ce73d7b191a | Python | 2,256 | 75 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
#
"""
Remove calls from Pilot pass VCF that were added in the raw overlap with Phase1
"""
import argparse
import pysam
def records_match(A, B):
return (A.chrom == B.chrom and
A.pos == B.pos and
A.stop == B.stop and
A.info['STRANDS... |
2749603479ba1eb5219a9c83b0d0179f2ad65bb14c5cad8c3c8630f0bd5751c1 | Python | 2,257 | 76 | #!/usr/bin/env python3
import argparse
from glob import glob
import sys
import numpy as np
import matplotlib.pyplot as plt
import pysam
import math
import pandas as pd
import haplotagging_stats
import os
import collections
import seaborn as sns
MAT_QV="mat_qv"
PAT_QV="pat_qv"
# cat Y1_assemblies_v2_genbank_QC.csv | ... |
6695c93898e4329aff0140ffc7fa6eee6a2e277d48305fc7e5bdd0c74788e49f | Python | 2,258 | 77 | # -*- coding: utf-8 -*-
# (C) Copyright 2026 IBM. All Rights Reserved.
#
# Licensed under the MIT license. See LICENSE file in the project root for details.
"""aihwkit example 7: simple network with one layer using other devices.
Simple network that consist of one analog layer. The network aims to learn
to sum all t... |
6d0d4f72a7a6f0e1f5df29852fe708a107d86d7d86b40b5c9bd35b8d617ce3ed | Python | 2,260 | 76 | """Contains all templates used for generating the HTML profile report"""
import shutil
from pathlib import Path
import jinja2
from data_profiling.config import Settings
from data_profiling.report.formatters import fmt, fmt_badge, fmt_numeric, fmt_percent
# Initializing Jinja
package_loader = jinja2.PackageLoader(
... |
f8bc494a86c06055840f3855033448074e7cf49df6a6827782e3205bba7b3664 | Python | 2,262 | 74 | import os
import glob
import pickle
import torch
# import numpy as np
CKPT_FTEMP = 'ckpt-ep-%d.pt'
SC_FTEMP = 'sc-ep-%d.pkl'
def pickle_load(data_path):
loaded_obj = pickle.load(open(data_path, 'rb'))
return loaded_obj
def pickle_save(input_obj, save_path):
with open(save_path, 'wb') as handle:
p... |
00e21453f8c5ae4047c343bcbda6bdec496a0aecad15cf373a15ac810cfe84cb | Python | 2,263 | 85 | # -*- coding: utf-8 -*-
"""
"""
import numpy as np
import os
from pathlib import Path
import pytest
import vtkplotlib as vpl
from vtkplotlib.unicode_paths import PathHandler
from tests._common import TEST_DIR
pytestmark = pytest.mark.order(1)
NAMES = ["name", "Ñ mé"]
NAMES.append(np.arange(0x100, ... |
7ccc99939b0b2d0a95dbd6d508c03db125f25930c50cf1dfe9fddb90019909f2 | Python | 2,263 | 59 | import numpy as np
from skimage.util import view_as_windows
def patch_selection(img, n_patches, patch_size=32, overlapping=False):
shape = (img.shape[0] - patch_size, img.shape[1] - patch_size)
idx = np.arange(np.prod(shape))
if not overlapping:
starting_points = np.ones(shape, dtype=bool)
... |
9c25beff683f45450bed9e01d5e90c9dd1933419e67d863e2da923f392d73ec8 | Python | 2,263 | 92 | import numpy as np
import collections
import seaborn as sns
import matplotlib
matplotlib.use('Agg')
from matplotlib import pyplot as plt
from numpy import genfromtxt
from os import path
from pandas import read_csv
def fix_labels_mapping_txt(labels,
):
ret = {}
for idx, item in enumerate(labels):
if(idx<14):
... |
babc191f1c4ec65e3864199b4ff2bcb8f04e54c5169ee975f0fd6c0a5d4cfc59 | Python | 2,264 | 65 | # --- START OF FILE model_selector.py ---
import os
import logging
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
import google.generativeai as genai
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_communi... |
36dc04a8593d0d75894c2b3c9c5e3132e845959c5884b7c338b242e3faa3165f | Python | 2,266 | 48 | import numpy as np
import os
import json
from scipy.stats import norm
from ..io import SAVED_DIR
from ..analysis import binarize_inputs, binarize_outputs, confusion_matrix, all_inputs
def F1_score(cm, prior=np.array([[1,1], [1,1]])):
precision = (cm[1,1] + prior[1,1]) / (cm[1,1] + cm[0,1] + prior[1,1] + prior[0,1]... |
97e5cb15b02a703523885d91cef7c4b414aa64a9df70f67b1d35654873d26eb5 | Python | 2,266 | 59 | import numpy as np
import matplotlib.pyplot as plt
import os
import utils
import pandas as pd
def parse_accuracy(filename, query):
with open(filename) as f:
for line in f.readlines():
if query in line:
tr_acc = float(line.split()[5])
te_acc = float(line.split()[1... |
b3fd2b2505b7a4813248f547b145000d9d39ede44312b2ecce4cefaac6b75744 | Python | 2,268 | 69 | import torch
import numpy as np
import exputils as eu
import rlexp.agent as agents
import rlexp.env as env
config = eu.AttrDict(
seed = 1984 + <repetition_id>,
agent_class = agents.<agent>,
env_class = env.Barreto2018TwoDObjectCollection,
env_config = eu.AttrDict(),
n_tasks = <n_tasks>,
n_ma... |
538816f1fe258f03b80baac4d58b2bb1f309d531a2ede5cbb7a7074465193366 | Python | 2,270 | 35 | import hail as hl
hl.init()
mt = hl.import_matrix_table('TE-900Genomes.tsv', no_header = False, row_fields={'Chr':hl.tstr,'Gene_ann':hl.tstr,'X1KGP_ann':hl.tstr,'X1KGP_AF':hl.tstr,'X1KGP_strand':hl.tstr,'EuL1db_ann':hl.tstr,'EuL1db_AF':hl.tstr,'EuL1db_strand':hl.tstr,'Evrony_ann':hl.tstr,'Evrony_strand':hl.tstr,'KNR_a... |
ad85f416cc49d90bf872c17aac4f14c5d5211f148f68e9bc3f40bf824106fa57 | Python | 2,271 | 81 | """
Copyright (c) Meta Platforms, Inc. and affiliates.
This source code is licensed under the MIT license found in the
LICENSE file in the root directory of this source tree.
"""
from __future__ import annotations
import glob
import os
from pymatgen.io.vasp.outputs import Oszicar
"""
This script provides utility fu... |
e160b868bd69edb459f0b38ffe142ef14b738f4386f2f64b829d0d36a4b0d77b | Python | 2,271 | 52 | import unittest
from neuron_morphology.constants import *
from neuron_morphology.morphology import Morphology
from neuron_morphology.feature_extractor.feature_extractor import FeatureExtractor # for individual cell features
from neuron_morphology.feature_extractor.data import Data
from neuron_morphology.feature_extrac... |
06a1b1effbbe859bacdac4e6855e67d6e685b8effdc77f5ef4885a8cf45e9107 | Python | 2,272 | 62 | import itertools
from shapely.geometry import MultiPoint, Point
from shapely.prepared import prep as shapely_prep
from siapy.core.exceptions import InvalidTypeError
from siapy.entities import Shape, Signatures, SpectralImage
def get_signatures_within_convex_hull(image: SpectralImage, shape: Shape) -> list[Signature... |
9bff2c1ac1d7431cdc023b092bd551f01c76b721cd81ea8d509c5f5a38b52150 | Python | 2,272 | 51 | import numpy as np
import visionloader as vl
import argparse
import lib.data_utils.contamination as contamination
import spikebinning
from lib.dataset_config_parser.dataset_config_parser import read_config_file, SettingsSection
import lib.dataset_config_parser.dataset_config_parser as dcp
from typing import List, D... |
5ec1776cda0472c3a779d0e412c31898061035860a1bd26c60117c35d104c051 | Python | 2,273 | 57 | # updated 19/11/2022
import logging
from datetime import datetime
from agent.classifiers.transformer_classifier import transformer_classifier
from agent.classifiers.transformer_classifier_text_trainer import train_loop, eval_loop
from agent.classifiers.data.transformer_text_cw_dataset_mgmt import get_datasets, annot... |
69d7d722d87cfc670e22f94ad158282fd19fb7ddc24f90d14e96f0dabd5ae52a | Python | 2,274 | 76 | import struct
import numpy
import warnings
import sys
import glob
import serial
import logging
def packIntAsLong(value):
"""Packs a python 4 byte integer to an arduino long
Parameters
----------
value : int
A 4 byte integer
Returns
-------
packed : bytes
A 4 byte long
"... |
453e6e71445d80f64ceb9206e81fee6d232c9e7f89cd46afe1f653466c103de5 | Python | 2,275 | 64 | # -*- coding: utf-8 -*-
"""
Created on Fri May 10 14:39:43 2024
@author: nlancaster
"""
agc100_file1 = 'P:/Projects/NML_TPC_2023_ThermoFisher_Phosphoproteomics_q-OT-Astral/20240306_HeLaMethodDev_Astral/20240306_NML_EGFHeLa_15min_2Th_1e4_AGC_01.raw'
agc500_file1 = 'P:/Projects/NML_TPC_2023_ThermoFisher_Phosphoproteomi... |
950b44204d0f1e7f742ddc8d974fe9a4f11f45cf98bd7e5f6a52077ce6e9f299 | Python | 2,275 | 102 | import os
import random
import time
import ray
@ray.remote
class Actor:
def __init__(self, init_value, fail_after=None, sys_exit=False):
self.i = init_value
self.fail_after = fail_after
self.sys_exit = sys_exit
self.count = 0
def _fail_if_needed(self):
if self.fail_a... |
1ab594746110adbf910b845806b9597c86484c42690857d15cbc7aba59c5799d | Python | 2,276 | 58 | from tensorflow.keras import models, layers, Model
from tensorflow.keras import metrics
from tensorflow.keras import optimizers
from tensorflow.keras import losses
from tensorflow.keras import regularizers
from tensorflow.keras import initializers
from tensorflow.keras import backend as K
from tensorflow.keras import ... |
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