sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c385ad2dc948d7ffab0035ffb1c36cf03153d4aa871c988c5cf844ece59e3739 | Python | 19,819 | 510 | from pathlib import Path
from typing import Dict, List, Optional, Union
import numpy as np
import torch
from pytorch_lightning import LightningDataModule
from torch.utils.data import random_split
from torch_geometric.loader import DataLoader
from torch_geometric.data import InMemoryDataset, Data
from sklearn.utils im... |
f8a3ae3ce75d3aa8810511e0bfaf15b98077cb37e22ce13bbfeb40a6da7355bf | Python | 19,821 | 338 | """Early validation, subject isolation and reuse after cache cleanup."""
from contextlib import nullcontext
import json
import os
from pathlib import Path
import signal
import subprocess
import sys
import tempfile
import time
import unittest
from unittest.mock import patch
import torch
import run_ddsurfer_pipeline a... |
9c9550aaf986db4bba69241b6f4a57a984bb7825a807ae76edec0fd2f19889f8 | Python | 19,858 | 418 | import copy
import numpy as np
import pandas as pd
import torch
from transformers import AutoTokenizer, AutoModel
import spacy
class TransformerRSM(object):
def __init__(self, stimulus_name, model_name, file_path=None, verbose=False):
self.stimulus_name = stimulus_name
self.model_name = model_n... |
029dc0724603e0765fd1959d186a3d6aa49dafe3c04c3db78bf6ccd0a362c9f1 | Python | 19,868 | 547 | #!/usr/bin/env python3
import os
import pickle
import json
import logging
import multiprocessing as mp
import polars as pl
from enrichm.annotate import Annotate
################################################################################
def parse_genomes(path):
genome = pickle.load(open(path, 'rb'))
retu... |
1ffd039890a3a5198ccdaf8b3d7f6d542e46b51521f026661e454f529a16f347 | Python | 19,956 | 502 | from typing import TYPE_CHECKING, Any, NoReturn
import numpy as np
import pandas as pd
from numpy.typing import ArrayLike
from skbase.utils.dependencies import _check_soft_dependencies, _safe_import
BaseDistribution = _safe_import("skpro.distributions.base.BaseDistribution")
if TYPE_CHECKING:
from matplotlib.axe... |
4f4c0dd6d86f356a43fd0fc2db93a93993a9c2d7647448d24a8069e8e6122e25 | Python | 19,957 | 545 | import os
import argparse
import pickle
import math
import random
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
import scipy.stats
from Bio.PDB.Polypeptide import one_to_index
def seed_all(seed):
torch.backends.cudnn.deterministic = True
torch.manual... |
db3def212435d21000bfbc7d38fe85321d77e55135434f923ed46e761fc04c5b | Python | 19,961 | 655 | import pytest
from langchain_core.messages import AIMessage, AIMessageChunk, HumanMessage
from langchain_core.messages import content as types
from langchain_core.messages.block_translators.openai import (
convert_to_openai_data_block,
)
from tests.unit_tests.language_models.chat_models.test_base import (
_con... |
6ab38011e8185d73928bf14b624aa3a1ba5b870918d9b0cfebefd6739407ca61 | Python | 20,035 | 514 | """
Wrapper class providing a minimal consistent interface to `AmberTools <http://ambermd.org/AmberTools.php>`_.
"""
__all__ = ("AmberToolsToolkitWrapper",)
import functools
import subprocess
import tempfile
from collections import defaultdict
from shutil import which
from typing import TYPE_CHECKING
import numpy as... |
ea0ed941c5823e4e969fd2e2c186abc8ba474269d66d4f6c7dc7124fae63893c | Python | 20,039 | 410 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
# :noTabs=true:
# (c) Copyright Rosetta Commons Member Institutions.
# (c) This file is part of the Rosetta software suite and is made available under license.
# (c) The Rosetta software is developed by the contributing members of the Rosetta Commons.
# (c) For more infor... |
92d8692cf3e659370acd10f909c8a89243e99c2528a386c4c909a041f1122849 | Python | 20,057 | 429 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
POCIF-BAT — Préenregistrement du cycle R2.
Ce fichier fixe, AVANT toute exécution de test comparatif, les hypothèses, les
critères de réfutation, les seuils numériques, les partitions et les règles de
décision. Il est haché ; le condensat est reporté dans le manifeste... |
ae65b34afee3cceb32fa81b1db45c8d4001d53716e9cf309bb5baef37d3d27e3 | Python | 20,059 | 515 | """Destination resolution for LangSmith tracing."""
from collections.abc import Iterator
from contextlib import AbstractContextManager, nullcontext
from itertools import product
from typing import Any
from unittest.mock import MagicMock
from uuid import uuid4
import pytest
from langsmith import Client, address, confi... |
017c66e8f555f69af45539e7a56a1ae7e0d9480270800f2ebcef59f2ebdb2811 | Python | 20,076 | 621 | '''tracks.py - Working with sample names in pipelines
=============================================================
Motivation
----------
A pipeline typically processes the data streams from several
experimental data sources. These data streams are usually processed
separately (processing, quality control) and as agg... |
438b33f1828b8b5379d546808000ef963d1c7576f96bed939d3899fd979da895 | Python | 20,101 | 467 | import asyncio
import os
from collections import defaultdict, OrderedDict
import torch.nn.functional as F
from torch.cuda.amp import autocast, GradScaler
from core.runner import runner_register
from dataset.utils.load_data import load_data_omics
from utils.my_containers import AverageMeter, deep_dict_get
from utils.t... |
88d98a77849f479131cb4991a5a4124a44ecc03e46ed7f2e2d8e773ef826fdf7 | Python | 20,142 | 517 | import torch
import copy
from .so3 import SO3Grid
def check_activation_name(act_name):
assert act_name in [
'gate',
's2',
'sep_s2',
's2_swiglu',
's2_swiglu_mem',
'sep_s2_swiglu',
'sep-merge_s2_swiglu',
'sep_s2_swiglu_mem',
'sep... |
8099776934da915e562b3c211c8eeb77ca0997b44e1a90fd46205a73d2d9df40 | Python | 20,153 | 435 | import os
import glob
import numpy as np
import matplotlib.pyplot as plt
from utils.constants import ALL_MODELS
from paths import TRAINING_DATA_PATH, TEST_DATA_PATH
from utils.distribution_distance import compute_jsd
import matplotlib.gridspec as gridspec
from matplotlib.patches import Rectangle
from matplotlib_scaleba... |
e82abe2e99282fcd811aad7ab8c29374b53d2e36da86124aee87ac18af4adb3f | Python | 20,153 | 455 | import numpy as numx
import pydeep.base.numpyextension as numxExt
from pydeep.base.activationfunction import Sigmoid
from scipy.signal import convolve2d
class Weight_layer(object):
''' This class implements a weight layer that connects one unit
layer to another.
'''
@classmethod
def gene... |
046f3ef1a46e45c4bd7ce6083b9335c7aceffba778b045d8c928e7693a061bbb | Python | 20,162 | 328 | import sys
import os
import shutil
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
sys.path.insert(1, os.path.join(sys.path[0], '../../../'))
from Articles.DMS_DLS.Code.FiguresDMS import *
ratio = 0.4
name = 'article'
name_dir = 'main'
P_stim_by_pattern_list_noise = [[10, [3, 5], [10, 15], [0.0... |
14a55eb023ba4fd87ae2025d69658c9a54c8220b64fd6020b7ed2958a518ed1b | Python | 20,163 | 539 | """
Created on Mon Dec 3 17:53:24 2018
@author: Nick Anthony
"""
from __future__ import annotations
import typing
from dataclasses import dataclass
from numbers import Number
from typing import Union
import numpy as np
import copy
import matplotlib.pyplot as plt
import matplotlib as mpl
from misc.PropertyMap import P... |
0bf587d4663d73d8256eb7540bec011a5ce5cd94a136ad1c05a02683622007ed | Python | 20,171 | 389 | import importlib
from enum import Enum
from importlib.util import find_spec
import numpy as np
import pandas as pd
import pytest
from scipy.stats import norm
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import LinearRegression
import pgmpy.parameterization
from pgmpy.parameterization import... |
efe41bdeb65b7a19132fb0d7a51b5f3a444d5646d0a259f3112c987f68360296 | Python | 20,180 | 568 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde import NavierStokes_CoordTransformed
i... |
c85368eeae65628fd742f79a80a6ff68bc63967e53184c3c8f6fb8ef1f670e91 | Python | 20,184 | 661 | """Implementation based on the template of ALIGNN."""
import imp
import random
from pathlib import Path
from typing import Optional
# from typing import Dict, List, Optional, Set, Tuple
import os
import torch
import numpy as np
import pandas as pd
from jarvis.core.atoms import Atoms
from matformer.graphs import PygG... |
9706fd7a458a630c83f87df2da650f43809b5b8696de4ef5fdd8b75741c363ac | Python | 20,193 | 414 | # %%
"""
==============================================================
Study of the features chosen for the classification - ATM & ImCoh + SVM
===============================================================
"""
# Authors: Marie-Constance Corsi <marie.constance.corsi@gmail.com>
#
# License: BSD (3-clause)
import gzi... |
af4714ccfc27eb76051817b5a7fd8e6e814bdc0fad11c771f5327ea765b9e3d7 | Python | 20,199 | 378 | import os
import numpy as np
import pickle
from scipy.stats import mannwhitneyu
import params
def test_stats_pre_post(all_flies, i_beh, GAL4, beh_name,measured="", i_0=500, i_1=750,file=None,per_fly_avg=True):
v_pre = []
p_pre = []
v_post = []
p_post = []
if not per_fly_avg:
for fly in a... |
a1a84a44358a4696ce3ba58501393481794e99a64e1640a36f04b7f595a8d0ef | Python | 20,202 | 529 | import functools
import time
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.nn import init
from torch.optim import lr_scheduler
###############################################################################
# Functions
############################################... |
14a149174bd8191c76a24000a1666222cf0781e4ae643cffbf49ebf73e2d40e5 | Python | 20,206 | 539 | """Module tests interaction of chat model with caching abstraction.."""
from typing import Any
import pytest
from typing_extensions import override
from langchain_core.caches import RETURN_VAL_TYPE, BaseCache
from langchain_core.globals import set_llm_cache
from langchain_core.language_models.chat_models import _cle... |
d6bebc73d3d71eaae9a50dd2621d7056b7a78517e3e9d2828387f2ffeacd58e7 | Python | 20,217 | 300 | import argparse
import os
import warnings
import torch.cuda
from predmoter.core.constants import PREDMOTER_VERSION
class BaseParser:
def __init__(self, prog="", description=""):
super(BaseParser, self).__init__()
self.parser = argparse.ArgumentParser(prog=prog, description=description, add_help=... |
883a6693d0e20bf1cafc07bb6e927617e6fcace950ac1544367aea15b4e22ed9 | Python | 20,219 | 406 | import copy
import torch
import os
from tqdm import tqdm
import numpy as np
from CEL.utils.register import register
import torch.nn as nn
import optax
import equinox as eqx
import jax
from CEL.data.data_manager import MyDataLoader
from CEL.networks.models.meta_model import ModelClass
import pathlib
from munch import M... |
20803855a1f9ba880c405ecd5395558ed37da83ed0a2ac9c44d16c2fc2d7e505 | Python | 20,223 | 517 | try:
import cPickle as pickle
except:
import pickle
import numpy as np
import logging
import os
from collections import namedtuple
from nowcasting.config import cfg
from nowcasting.hko_iterator import get_exclude_mask
from nowcasting.helpers.msssim import _SSIMForMultiScale
def pixel_to_dBZ(img):
"""
... |
d6decf329f859bc62511e432c2ac27d804560c4f53c04a07e993c211d6ab08e0 | Python | 20,236 | 422 | """
Prepare all SARS-CoV-2 Mpro structures for docking and simulation in monomer and dimer forms and desired protonation states.
"""
# Configure logging
import logging
from rich.logging import RichHandler
FORMAT = "%(message)s"
from rich.console import Console
logging.basicConfig(
level=logging.INFO, format=FORMA... |
5b8168e8bdb6ee1b5d99aa9c0c33ccc34b6b017f747645ec5d4175e4f30207f7 | Python | 20,259 | 606 | '''
Created on Oct 12, 2011
this code is for reading input parameters.
@author: Ying Jin
@status:
@contact: yjin@cshl.edu
@version:
'''
# python modules
import sys
import os
import re
import logging
import time
import gzip
import collections
from math import log
from TEToolkit.Constants import *
from TEToolkit.Shor... |
4676c9fffefd6f2f9fb8985b7e418aa9e8ed97f90aee13c4229f997ee0d0aee9 | Python | 20,261 | 643 | #!/usr/bin/env python3
'''
Interpolate label between sparse sections using alpha shape
- Speeds up manual labeling for larger label volumes
Usage
----
interp_labels.py <label image> <index list>
interp_labels.py -h
Example
----
>>> interp_labels_a3.py -i labels.nii.gz -l 1,3,4
Authors
----
Mike Tyszka and Wolfgang P... |
cdfddd9aa719895be5e02ddcd1b49d279de892e9f96bad9d70e14dbe3085fb46 | Python | 20,281 | 596 | # supervised autoencoders
import mdtraj as md
import multiprocessing as mp
import numpy as np
import os
import itertools
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
class split_ae(nn.Module):
"""Unsupervised autoencoder with a split input (i.... |
31392a0b0d08d9cd6473f62a50acbb19d677fbfa14244695fd38e5baad872b40 | Python | 20,290 | 572 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
c7670eb5a47aee1fb45501dc49ca7d3d581a043526c413f7cebdb899572d6aab | Python | 20,305 | 578 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, FullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde impo... |
c8e9fb374c1b6ab269682ccdf6eb1f1893f4a54e5e071a845713e9af95f58357 | Python | 20,311 | 631 | """Utilities for pydantic."""
from __future__ import annotations
import inspect
import textwrap
import warnings
from contextlib import nullcontext
from functools import lru_cache, wraps
from types import GenericAlias
from typing import (
TYPE_CHECKING,
Any,
TypeGuard,
TypeVar,
cast,
overload,
... |
1fb915fe2ddedc7d42255f4ee418607dc3036f9958557f42b4d4846df13b617d | Python | 20,315 | 472 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri May 28 2021
@author: Simon Pelletier
"""
import warnings
import pickle
import numpy as np
import pandas as pd
import os
import csv
import json
import matplotlib
import xgboost
import xgboost as xgb
from sklearn.feature_selection import SelectKBest
from... |
7069aee9b39482e6df2c4f08a0d8b7aad74d15988c029e41f087fc6f12bca922 | Python | 20,324 | 468 | # TEMP copy of processing/paq.py to use for experimenting with and creating paq as child of temporal cellsdata.
import os.path
from dataclasses import dataclass
from typing import List
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.ticker as mticker
import matplotlib.pyplot as plt
fro... |
f4c78a94a6fde6951ffb764dc3668920e74d44d3b65116248457e1b5a657acec | Python | 20,329 | 548 | # -*- coding: utf-8 -*-
"""
Created on Wed Apr 10 17:27:13 2024
@author: Shencong Ni
"""
'''
Simulation for spontaneous activity
'''
import brian2.numpy_ as np
from scipy import sparse
from brian2.only import *
import time
import mydata
import os
import datetime
import connection as cn
import poisson_stimuli as ... |
b956751c0ad745634a2a89ef82251c767a9f3565e18a826ee909a0670c746657 | Python | 20,337 | 478 | """Simulation tests derived from the examples/ notebooks.
Covers end-to-end physics behaviour: directional activation, numerical
stability under prolonged stepping, environment step dynamics, and
policy forward pass / training loop correctness.
"""
import numpy as np
import torch
from motornet.effector import (
E... |
4d5012286119fdf1557170db890ca167948e92844a56b10b3b25ac0a9e47f942 | Python | 20,345 | 534 | """Classes and methods for using a trie to query for similar DNA/RNA strings.
The trie here is intended to store relatively short strings (e.g., k-mers where
k is about 28). It is expected that many of the strings will be highly similar
to other strings (i.e., there are dense regions in the space of strings) --
for ex... |
d11088c761d6e9eacc6ad5d6bd41f293ed550bfea5ec28934c8fa1c471e24cc7 | Python | 20,346 | 457 | # Copyright (c) DP Technology.
# 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 absolute_import, division, print_function
from ast import Not
import torch
import torch.nn as nn
import torch.nn.functional as F
from unicor... |
6cedb194f474ef01eb2bcea1c621926dbc3b3f52e632e5b3cdc334d5e335bae6 | Python | 20,382 | 458 | import math
import numpy as np
from Bio import Phylo
from pycircos.pycircos import Garc
from pycircos.pycircos import Gcircle
class Tarc(Garc):
def __init__(self, arc_id=None, tree=None, format="newick", interspace=3, raxis_range=(900, 950), facecolor=None, edgecolor="#303030", linewidth=0, label=None, labelposi... |
8e8e0ce0a57df779d881762c6aebf46d89158a5082adedbfd7c526bab376bf6d | Python | 20,383 | 523 | #!/usr/bin/env python3
from enrichm.sequence_io import SequenceIO
import logging
import os
###############################################################################
class Genome:
'''
A genome object which collects all the attributes of an input genome,
including protein sequences and their annotatio... |
1834378895959c057725269445e1262b15fb141a5cc6c757f5761bb4b5961e71 | Python | 20,397 | 558 | '''counts2counts.py - perform transformations on counts tables
==============================================================
:Tags: Python
Purpose
-------
This scripts provides transformations for counts tables
The methods implemented are
filter
Output the counts table after applying filtering.
spike
Outpu... |
870bc66f6dc5107437699444cd7262d3d0ea0881cf7ee532e7b3028302cd1a48 | Python | 20,405 | 442 | # -*- coding: utf-8 -*-
# @title Generate configuration for organoid emulation
# @file OrgStructures.py
# @author Romain Beaubois
# @date 17 Feb 2022
# @copyright
# SPDX-FileCopyrightText: © 2022 Romain Beaubois <refbeaubois@yahoo.com>
# SPDX-License-Identifier: GPL-3.0-or-later
#
# @brief... |
31f8099ed2ace00ff67aadf9ce934efab32a7ecebcc19ec6286b894c7cd50e96 | Python | 20,414 | 606 | from functools import partial
# from pathlib import Path
from typing import Any, Dict, Union
import ignite
import torch
from ignite.contrib.handlers import TensorboardLogger
try:
from ignite.contrib.handlers.stores import EpochOutputStore
except Exception as exp:
from ignite.handlers.stores import EpochOutpu... |
2ee1f1707906b8f177d29e487ad4f507789f84c11e6f35e77e494b10d17700aa | Python | 20,436 | 578 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, FullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde impo... |
8d7f74303fb05374d4aefd77877259e7f26df6c3bd321375c2a3ffa96972027b | Python | 20,436 | 468 | # new copy of processing/paq.py to use for experimenting with and creating paq as child of temporal cellsdata.
import os.path
from dataclasses import dataclass
from typing import List
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.ticker as mticker
import matplotlib.pyplot as plt
from... |
c61f919f31e7dafb68dc42b5d917387f558822e15292b9aceefd5e3d9022db89 | Python | 20,437 | 578 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="3"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, FullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde impo... |
fdfe9cfc7137985eae31dbb4512a2596099d2e2b54b15cdf3d963a0d6c668be7 | Python | 20,443 | 596 | import mne
import torch
from torch import nn
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import mean_absolute_error, r2_score
from sklearn.preprocessing import StandardScaler
from sklearn.compose import TransformedTargetRegressor
from joblib.parallel import Para... |
94a40bd43a22e808beb2e44c39fa6c8ca045175edcd0224968ee1f1f87210c8b | Python | 20,460 | 263 | import pandas as pd
import numpy as np
import sklearn
from scipy.stats import rankdata
from sklearn.metrics import roc_curve, roc_auc_score, auc, precision_recall_curve, average_precision_score, f1_score, \
accuracy_score
import argparse, sys, os, errno
def plot_roc(prob, label):
fpr, tpr, _ = roc_curve(label,... |
08bc2db4b88088126c095aa6d9ac46936e6c7eec3406a440bb971eae51ef9bc5 | Python | 20,475 | 539 | """**Document Loaders** are classes to load Documents.
**Document Loaders** are usually used to load a lot of Documents in a single run.
"""
from typing import TYPE_CHECKING, Any
from langchain_classic._api import create_importer
if TYPE_CHECKING:
from langchain_community.document_loaders import (
Acre... |
2ba47f09157dfe5f9fe37c5668e2e58681fb87cc9338c957923528f372fa929f | Python | 20,491 | 562 | from abc import ABC, abstractmethod
from itertools import product
from pathlib import Path
from typing import List, Union
from MRdataset import logger
from MRdataset.config import VALID_DATASET_FORMATS
from MRdataset.utils import valid_dirs, convert2ascii
from protocol import BaseSequence
# class Run(UserDict):
# ... |
8dab24d01c876426812a3cccdd6eaf1a146563ebc0692b53d83588fac6d45afa | Python | 20,492 | 619 | from functools import partial
# from pathlib import Path
from typing import Any, Dict, Union
import ignite
import torch
from ignite.contrib.handlers import TensorboardLogger
try:
from ignite.contrib.handlers.stores import EpochOutputStore
except Exception as exp:
from ignite.handlers.stores import EpochOutpu... |
f4a6dbd50ee9532ada4787ae569f2f8575e105b88089968ffa8ce525e99db8d4 | Python | 20,501 | 664 | """=================================================
pipeline_testing - automated testing of pipelines
=================================================
This pipeline executes other pipelines for testing purposes.
Overview
========
This pipeline implements automated testing of cgat pipelines. The
pipeline downloads... |
09dac04356c2ae3f5f679f88f367ccf4997ac7ffd2c1d331b4136fb27e2cec23 | Python | 20,504 | 546 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
0b0834988a86b42c98fcd27c1f3f11c21ce2cb1fa76501b13a978dd1cea5b0dc | Python | 20,521 | 562 | """
===================
Project 20 pipeline
===================
:Author: David Sims
:Release: $Id: pipeline_proj020.py 2900 2013-06-03 14:38:00Z david $
:Date: |today|
:Tags: Python
The project 020 pipeline processes reads from a gene trap screen experiment to identify trapped genes:
* Align reads to the gene tr... |
c6b30df62194ea75f0ddacbfe27d7d1ed071235777d465de90b3f568f017b87f | Python | 20,521 | 513 | # Copyright Generate Biomedicines, Inc.
#
# 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
#
# Unless required by applicable law or agreed to ... |
4dc9dcc38b3c6e256a821124e2d982953a7054dc4e6a2958dae89b9130700c17 | Python | 20,528 | 508 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.
from abc import ABC, abstractmethod
from typing import Dict, List
import torch
from torchmetrics import MetricCollection
from torch_geometric.data import Batch
from pytorch_lightning import LightningModule
from pytorch_lightning.cli im... |
b25e64136e8a98d513f6b0d1f5a741641e38f01d5848780a99e746c02a8435b4 | Python | 20,529 | 511 | # MCNN model
import tensorflow.keras as keras
import tensorflow as tf
import numpy as np
import time
from utils.utils import calculate_metrics
from utils.utils import create_directory
class Classifier_MCNN:
def __init__(self, output_directory, verbose, build=True):
self.output_directory = output_directo... |
f41a15da7511c2c20599da3a695f818b1d9a9cb900ce7afafe16785425d2ddde | Python | 20,552 | 522 | import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
from scipy.stats import hypergeom, mannwhitneyu
import pandas as pd
import numpy as np
import pickle
pd.set_option('display.max_rows', 400)
pd.set_option('display.max_columns', 10)
import sys, os
import random
from pathlib import Path
f... |
80f3c8fa57b7fdc90e88467904c125ba3b918355e5456256c1131600093f01c9 | Python | 20,553 | 549 | import threading
from collections import OrderedDict
from random import Random
from typing import Dict, Iterator, List, Optional, Union, Tuple
import numpy as np
import torch
from torch.utils.data import DataLoader, Dataset, Sampler
from rdkit import Chem
from rdkit import DataStructs
from .scaler import StandardScale... |
805415f5dc3500ece1406287f7a3467953892d2eec2b8a04eef04269ff28bc2b | Python | 20,556 | 538 | import os
import multiprocessing as mp
import functools
import glob
from .utils import *
import pickle
from collections import defaultdict
import numpy as np
import mdtraj as md
from scipy.linalg import inv, sqrtm
import torch
class ImproperlyConfigured(Exception):
'''The given configuration is incomplete or othe... |
0d6e36bb0df8645fd2dda785c844245af1a1088c0d11714434f66173f8dbda3f | Python | 20,561 | 576 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde import NavierStokes_CoordTransformed
i... |
be486e2801e2948bb426622209893203a7b31b20724d0d9d4e4c5a63a8906fb4 | Python | 20,563 | 576 | import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import CustomModuleArch, InheritedFullyConnectedFlexiLayerSizeArch, SingleInputInheritedFullyConnectedFlexiLayerSizeArch, CaseIDtoFeatureArch
from modulusDL.eq.pde import NavierStokes_CoordTransformed
i... |
af4e8133c6fad33f07fc7ea20f744907ec234b78c5ad2e865c47fd220506884b | Python | 20,585 | 565 | # Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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
#
# U... |
d4d85dea54073e958060b190c7c0c11eb5fbf158460544dce59ae56e37222f62 | Python | 20,597 | 569 | '''
runMEDIPS.py - wrap MEDIPS analysis
===================================
:Tags: Python
Purpose
-------
Runs MEDIPS from the commandline.
Note that the appropriate UCSC genome file has to have been downloaded
previously in Bioconductor::
source("http://bioconductor.org/biocLite.R")
biocLite("BSgenome.Hsapi... |
aa904fb6bbef4b05e2cfaca0a5c781ca62293383bb213eb88ef701d334b8454f | Python | 20,612 | 621 | #!/usr/bin/env
"""
# Author: Kai Cao
"""
import torch
import numpy as np
import os
import anndata
import scanpy as sc
from anndata import AnnData
import scipy
import pandas as pd
from scipy.sparse import issparse
from .model.vae import VAE
from .model.utils import EarlyStopping
from .logger import create_logger
fro... |
4474f89c021c2d96a2394922049fbc69aedb1f3fa40912146cfd36008887f90e | Python | 20,635 | 434 | import unittest
from EPI_MRI.LinearOperators import *
class TestLinearOperators(unittest.TestCase):
def setUp(self):
""" Initialize any common data or setup needed for the tests. """
torch.manual_seed(81) # reproducibility with randomness
self.device = 'cuda' if torch.cuda.is_available() ... |
ad1c0ea13ac36baa6340d2670fd93d658ce00351f5331788374e4da9db94a7d4 | Python | 20,683 | 657 | """A chain for comparing the output of two models using embeddings."""
import functools
import logging
from enum import Enum
from importlib import util
from typing import Any
from langchain_core.callbacks import Callbacks
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForChainRun,
Callback... |
eaa04f705e8f5fdb57366b64a213453abae04f29d8f0a3a08b37b0bc9dc97060 | Python | 20,683 | 550 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
29722c95580a0fcc121c029efacb62ca193bc68265af03c0c09a5ab3e58ca7f1 | Python | 20,684 | 460 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
4d21d0cc8c4eabcf71a545d3703963c7b87ffaa7a1fa63b173d48d113a20c936 | Python | 20,689 | 461 | """
python
Load_and_Test_GhmCN.py
Purpose: Load trained networks and test how given datasets predicts gene expression.
Usage: python ./Load_etTest_EGA.py [-c <str>] [-rf <int>] [-mo <str>]
Arguments:
'-c', '--cell_line', default='E116', type=str
'-rf', '--regression_flag', default=1 (1 = regression; 0 = clas... |
ced157fa3c99fc1b7d08207a98d60672f247a6d9cbb9e2eb2350d7e9f76310f9 | Python | 20,695 | 485 | import os
import numpy as np
from typing import Dict, List, Union, Tuple, Callable, Optional
from omegaconf import DictConfig
import warnings
from modulus.solver import Solver
from modulus.domain import Domain
from modulus.loss.aggregator import NTK, Sum
import time
import torch
from torch.utils.tensorb... |
3ab622b61aa618b776dbce58a38c9afabbe5b5f1cdf52d6b15f801eea251b9f1 | Python | 20,711 | 392 | import os
from .data_helper import DataHelper
from .normalizer import Normalizer
class FindStems():
def __init__(self):
self.dir_path = os.path.dirname(os.path.realpath(__file__)) + "/"
self.noun_lex_path = self.dir_path + "resource/stemmer/stem_lex.pckl"
self.verb_lex_path = ... |
7bbd6ba1144d0fd546e3b74d93d7dc5aa3d03c93a079b6185012da40a3734879 | Python | 20,713 | 513 | from collections import deque
from collections.abc import Hashable, Iterable
from itertools import pairwise
import networkx as nx
from pgmpy.utils.types import Self
class _GraphAlgorithms:
"""Graph-algorithm methods for ``_CoreGraph``-based classes (inherited, not instantiated alone)."""
def get_ancestral_... |
a0f176c73cde123526027919284bd82dd1bce365cbf2bad955f949c900d9d94f | Python | 20,732 | 382 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
65f0bc9db0b647b711ec50a73678fad7c3480a3174635959db38f5f8a89d3a92 | Python | 20,742 | 536 | # This file takes all available input data and trains the regressors
# using the prescribed preprocess methods.
# It displays time domain estimates for all data files as well as
# sensitivity plots for sensor channels and estimates
import time
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot ... |
2048644764c77b5a5f71661ae5b21e2a771171bc1a78d8ad6b2fd0fbf009eb48 | Python | 20,767 | 500 | """ This module contains the weight layer for DBMs.
:Implemented:
- DBM model
:Version:
1.0.0
:Date:
26.05.2019
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2019 Jan Melchior
This file is part of the Py... |
47a6407d32a3e10aaf98695e714490333b9606c143dea7f1cf4f7ebb46b35028 | Python | 20,770 | 443 | """Functions for preparing an alignment for input.
This includes downloading, curating, and aligning sequences.
"""
from collections import Counter
from collections import OrderedDict
import logging
import os
import random
from easyDesign.prepare import align
from easyDesign.prepare import cluster
from easyDesign.pr... |
1654569480d141523c5b9ffe65943c1f80f0b8939721bd5d5485fe94db1583e8 | Python | 20,848 | 522 | #!/usr/bin/python
#############################################################################
#Package: stamp
#Updated by Yiqin Wang
#Usage:
#generate mtDNA alignments for whole-genome sequencing data
#
##############################################################################
from argparse import Argum... |
413ab48432285ccbfab7ba4666ab74d26a96732a58839ef06a962e9188ef4835 | Python | 20,891 | 529 | from collections.abc import Hashable, Iterable
from itertools import combinations
from typing import Any
import networkx as nx
import numpy as np
import pandas as pd
from pgmpy.base import PDAG
from pgmpy.causal_discovery._base import BaseCausalDiscovery, _ScoreMixin
from pgmpy.structure_score import BaseStructureSco... |
bb62deaa1bb6141c646974df19b4cd87a16801f3e3e58534819e01cb5f0dbf08 | Python | 20,896 | 440 | "plotting utils"
import os
import numpy as np
import matplotlib.pyplot as plt
import torch as T
import src.utils as utils
import src.plotting as plot
FIG_SIZE = (6, 6)
FONTSIZE = 16
LABELSIZE = 16
LEGENDSIZE = 14
def hist_in_bins(*distributions, conds, binning, style={"range": [0,1], "bins": 20},
l... |
f2ce6514242f75d8833112159436a6993665278ca478f7a6d95fc1bbc831a073 | Python | 20,907 | 537 | import os
import torch
import numpy as np
import pandas as pd
import networkx as nx
from rdkit import Chem
from rdkit.Chem import AllChem
from torch_geometric.data import Data, InMemoryDataset
# allowable node and edge features
allowable_features = {
'possible_atomic_num_list': list(range(1, 119)),
'possible_... |
8a835d1738e4012f415c7e416b4b25bf2ef4424686801a6dba1f58ac357d61a4 | Python | 20,953 | 566 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
a5bb3d76ad22516aabaceabb4b245ca0d2d799e56b37955f76bee9d3cc0dede0 | Python | 20,956 | 573 | import numpy as np
from queue import PriorityQueue
import math
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.signal import argrelextrema
import os
from pensa.features import *
# -- Functions to cluster feature distributions into discrete states --
def _smooth(x,window_len,window=N... |
d3e8e1ad3ace6ada774713192b114f7f622a16cdee28866da940834bbf48d8e4 | Python | 20,962 | 545 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Aug 12 14:51:24 2020
@author: shni2598
"""
import numpy as np
import pywt
from scipy import signal as spsignal
import matplotlib.pyplot as plt
import scipy.signal.windows as sp_windows
import mydata
#import pdb
#%%
def myfft(data, Fs, amp=True, power=F... |
45c72bcc430738c2373bd0175a22a6c13c930d1654b570518fc0549bf830cfe8 | Python | 21,010 | 495 | '''
The MSN class defining the cell
'''
from neuron import h
import numpy as np
import json
# Distributions:
'''
T-type Ca: g = 1.0/( 1 +np.exp{(x-70)/-4.5} )
naf (den): (0.1 + 0.9/(1 + np.exp((x-60.0)/10.0)))
'''
def calculate_distribution(d3, dist, a4, a5, a6, a7, g8):
'''
Used for setting the maximal c... |
f69c5f6bb820a63c9dc447a393fafbbe75a6eb4a64019171687768604d77968b | Python | 21,012 | 642 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
b6e2f59b166a428d8ff0b7c3afd0a9c61a104e26e0eb3bf9e5fdb569fecf7b9a | Python | 21,033 | 495 | import sys;sys.path.append('../src')
from matplotlib import pylab
import pandas as pd
import analyse_experiment as analyses
import joe_and_lili
from scipy.stats import wilcoxon
import pickle as pickle
import analyse_model
from reaction_times_functions import (
reaction_time_plot, get_reaction_time_analysis)
import ... |
6a8d8229e5ad9e145979ac70853531861670439790cbe63ed1345272bb251e9d | Python | 21,043 | 501 | """PDB dataset loader."""
import math
from typing import Optional
import torch
import torch.distributed as dist
import tree
import numpy as np
import torch
import pandas as pd
import logging
import random
import functools as fn
from torch.utils import data
from data import utils as du
from openfold.data import data_... |
f11abb853d0eed0a683c7ba157d2bee8ece34d879036bfc4cbe3bfa5ccc9bc2a | Python | 21,054 | 606 | import pickle
from multiprocessing import Pool
from sklearn.utils import shuffle
import torch
import os, json
import os.path as osp
from itertools import repeat
import numpy as np
from rdkit import Chem
from torch_geometric.data import InMemoryDataset, Data
from tqdm import tqdm
import networkx as nx
from torch_geometr... |
023b5c30589b34f64d1a8b62f2b698497de2a06428b225455ade33d9401004cd | Python | 21,058 | 586 | #!/usr/bin/env python
import xml.etree.ElementTree as etree
from io import BytesIO
from itertools import chain
import numpy as np
from pgmpy.factors.discrete import TabularCPD
from pgmpy.models import DiscreteBayesianNetwork
from pgmpy.readwrite._base import BaseReader, BaseWriter
from pgmpy.utils import compat_fns
... |
eb153cd55d240a6a3d41377727ec3f9a1950d685381245eb6f303d61197f04b5 | Python | 21,063 | 763 | #!/usr/bin/env python3
"""
Create a report of intra and inter-observer atlas label statistics
Expects a label directory organized as follows:
<label_dir>/
<observer A>/
<template 1>
<observer B>/
Usage
----
atlas.py -d <observer labels directory>
atlas.py -h
Authors
----
Mike Tyszka, Caltech Brain Imaging C... |
c483dad0c9f917c93eaa4dddc8ed53f48a68af9d5e1626a79822b32e01a34fba | Python | 21,080 | 532 | import copy
import warnings
import numpy as np
import pandas as pd
import pytest
from skbase.utils.dependencies import _check_soft_dependencies
from pgmpy.parameterization.distributions.nominal import NominalDistribution
pytestmark = pytest.mark.skipif(
not _check_soft_dependencies("skpro", severity="none"), rea... |
cd1b3979e3bccec2333260ca11d9fa4b2ba06925cc0ac96c566c7439401882e0 | Python | 21,090 | 537 | #pylint: disable=E1101
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class create_classifier(nn.Module):
def __init__(self, latent_dim, nhidden=16, N=2):
super(create_classifier, self).__init__()
self.gru_rnn = nn.GRU(latent_dim, nhidden, batch... |
9d220cc5f899e8ea2d7a74469dc085211fed49adb19aa79fbda05ccdaaff90ba | Python | 21,130 | 578 | """Chain for chatting with a vector database."""
from __future__ import annotations
import inspect
import warnings
from abc import abstractmethod
from collections.abc import Callable
from pathlib import Path
from typing import Any
from langchain_core._api import deprecated
from langchain_core.callbacks import (
... |
ed57b57b91f059da905ce7fd23f878cd5261d7c383a71fe86b33c2a20d4ad0dd | Python | 21,133 | 531 | "general plotting"
import matplotlib.pyplot as plt
import numpy as np
import copy
from sklearn.metrics import roc_curve, roc_auc_score
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
import matplotlib.colors as mcolors
import src.utils as utils
# from plot_utils import get_atlas_internal_str
np.sete... |
ab4f893bc4d54e7884eba024c1916d58f7e0c3e51b5502b3811f8db3db2e09e4 | Python | 21,139 | 456 | # Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# 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://w... |
19ffc75614fa2ecb4642e368f85c101364595b572919137652d477a0b3518fdc | Python | 21,158 | 532 | """Implementation of a record management layer in SQLAlchemy.
The management layer uses SQLAlchemy to track upserted records.
Currently, this layer only works with SQLite; hopwever, should be adaptable
to other SQL implementations with minimal effort.
Currently, includes an implementation that uses SQLAlchemy which ... |
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