sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ee1a4eed9d3df09e38f0ccf937c1b162a5ff24c98d6aa06e5f2565ac87988b23 | Python | 45,524 | 1,538 | # -*- coding: utf-8 -*-
"""
Created on Fri Oct 23 12:31:28 2020
@author: Xingyan Liu
"""
import logging
from typing import Union, Mapping, Sequence, Optional, Callable
from pathlib import Path
import networkx as nx
import numpy as np
import pandas as pd
from scipy import sparse
from sklearn import metrics
import scan... |
7b1c354cd04ea374b7296dc43664cad59936ff7557a579051c5484332fe5a560 | Python | 45,550 | 976 | #!/usr/local/bin/env python
#=============================================================================================
# MODULE DOCSTRING
#=============================================================================================
"""
Test custom integrators.
"""
#=============================================... |
6a9672d479c67b3ff15169b2b1b161155c7ac78e35cc1fcc5624a16b4a01d875 | Python | 45,582 | 1,025 | """Structure(s) and functions for working with alignments of sequences.
"""
from collections import defaultdict
import logging
import statistics
import numpy as np
from math import log2
from easyDesign.utils import guide
from easyDesign.utils import lsh
from easyDesign.utils import predict_activity
logger = logging... |
d773b6be4093935e56b216b672fa3251b1675a2f03fa194cff57071844b8d572 | Python | 45,602 | 972 |
# !!
# * don't forget to get rid of the old gens/utils for syns (see the old skeleton)
# * make sure we export each "Reduced*" template somewhere
# * maybe replace everywhere:
# * exportOptions.isExportGapJuncs -> exportOptions.isExportAnyGapJuncSets()
# * exportOptions.isExportSyns -> exportOptions.isExportAnySyn... |
fccd930dba4efb17054471ff012536f8466a4f45607762c5a764b9e2ec13556d | Python | 45,617 | 1,194 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
a21d136af8c65e5632b10911e5b4f6821b264ba12abe1b86afaa01a422ad1d60 | Python | 45,620 | 1,194 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
19e6608c2ce6f4bd7268eed110f965a19be9bff96f91b961cf2f928abe726a48 | Python | 45,639 | 1,221 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
2d358818ec0841e350d514a13b29a045c91eae336917416d680a0854c6156c2f | Python | 45,668 | 1,221 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="1"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
cee47f171eeee4bfb99a2bb9153ebf3b3b0113206121d17b4e2515196340bbc2 | Python | 45,672 | 1,155 | from cgatcore import pipeline as P
from Bio import Entrez
import numpy as np
import httplib2
import sqlite3
from intermine.webservice import Service as SS
import string
import re
import os
import xml.etree.ElementTree as ET
import pandas as pd
import cgatcore.experiment as E
import cgatcore.iotools as iotools
from urll... |
f5258eba49d7baabb3eff46338f01dc7b90de0b00cc28514f6b27c7b80016989 | Python | 45,692 | 1,540 | # -*- coding: utf-8 -*-
"""
Created on Fri Oct 23 12:31:28 2020
@author: Xingyan Liu
"""
import logging
from typing import Union, Mapping, Sequence, Optional, Callable
from pathlib import Path
import networkx as nx
import numpy as np
import pandas as pd
from scipy import sparse
from sklearn import metrics
import scan... |
9c9543e82cea7f4f15f02c80f20f2b9397374a2bb4d65d2cf10e42ce97e67dae | Python | 45,746 | 1,176 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.autograd import Function
from .utils.stochastic import GaussianSample
from .utils.distributions import log_normal_standard, log_normal_diag, log_gaussian
from .utils.utils import to_categorical
import pandas... |
09205b07244f9ebccddcd7227276649c6e57d1ee1832c1aafcba8b2b818cd04a | Python | 45,770 | 1,226 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
29639578395a5d73c15c37aa50e6d3ba9e65ecd5b5ba7be1460e25cf959b5ef8 | Python | 45,975 | 1,454 | import sys
from collections.abc import AsyncIterator, Iterator
from typing import Any
import pydantic
import pytest
from pydantic import BaseModel, Field, ValidationError
from langchain_core.exceptions import OutputParserException
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessag... |
fe7ed69dc1675eb918f050c9d97527ce0eecb98ede430ce2c758d91c6f5f6dcc | Python | 46,023 | 1,061 | ''' This module provides restricted Boltzmann machines (RBMs) with different
types of units. The structure is very close to the mathematical derivations
to simplify the understanding. In addition, the modularity helps to create
other kind of RBMs without adapting the training algorithms.
:Implemented... |
a013c8d3d81cf45d2a4483e84c24e47cdaac2d5927bd4b92c92ee611e1230d3c | Python | 46,050 | 1,511 | """
===============================
Transcriptome analysis pipeline
===============================
:Author: Andreas Heger
:Release: $Id$
:Date: |today|
:Tags: Python
The transcriptome analysis pipeline examines various gene/transcript sets
and attempts to interprete and compare them. Among the quantities that it
com... |
22970746dce9d70ab65f15ce1e0c4a47a2a927a15d96ef060a1b5074de1de355 | Python | 46,066 | 1,234 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
5e07ead5549753b0e3a234cd9462cdf34ba8128be3fb08cd6cadc5680528c835 | Python | 46,067 | 1,352 | """========================================
RNA-Seq Differential expression pipeline
========================================
The RNA-Seq differential expression pipeline performs differential
expression analysis. It requires three inputs:
1. A geneset in :term:`gtf` formatted file
2. Mapped reads in :term:`ba... |
6709ef83c8bfd3cbfcfe50508bc0599f0b1b6f28f645cd1ad9bda7e325495ed3 | Python | 46,170 | 1,181 | # coding=utf-8
"""Defines the network test suite."""
import torch
from torch.utils import data
import math
import numpy as np
from sklearn import metrics as skl_metrics
import pandas as pd
from .utils import round_list, _get_probs, _filter_matched_subj
from ..plotters.visualization import display_confusion_matrix
fro... |
90371f5ff6e4fadebdb4d2cff7230a4f7acae79be857aafc1560b3b7f4c0cb4d | Python | 46,403 | 1,265 | from __future__ import print_function
import time, re, json, sys
from collections import Counter
from svtyper.statistics import mean, stdev, median, upper_mad
# ==================================================
# VCF parsing tools
# ==================================================
def confidence_interval(var, tag... |
61322c0c0bf15ae279c6393216efb59fc8eaf4c4b4ddb9bc9b96563f9d215d6f | Python | 46,471 | 1,213 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.autograd import Function
from bernn.dl.models.pytorch.utils.stochastic import GaussianSample
from bernn.dl.models.pytorch.utils.distributions import log_normal_standard, log_normal_diag, log_gaussian
from be... |
9def32a05c291b90e76903d0f0e6c30b9970af971e9014cbaa31e14d4eb0ab19 | Python | 46,535 | 1,644 | '''geneset.py - Tasks for processing gene sets
======================================================
Most of this tasks take a geneset (.gtf.gz) from ENSEMBL as input.
As of ENSEMBL release 75 the gtf file contains both transcripts but
also untranscribed features such as pseudo genes, for example::
1 pseud... |
c91c8b5ef05ec95c198c455db44058d5ef431465535aab36fb8bdccd5517dcc0 | Python | 46,573 | 1,487 | # 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... |
9972ee7c8bc7e65c305125e54e62c663b17d00472b5948ed8ceddb64279c4794 | Python | 46,656 | 1,169 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.autograd import Function
from bernn.dl.models.pytorch.utils.stochastic import GaussianSample
from bernn.dl.models.pytorch.utils.distributions import log_normal_standard, log_normal_diag, log_gaussian
from be... |
8514ca6a06cc66f82fabbd1ae31fdd8d813b0e5c9efdf53d23f9a653f9502204 | Python | 46,971 | 978 | # Implementation of the filters is borrowed from paper "Alias-Free Generative Adversarial Networks (StyleGAN3)" https://nvlabs.github.io/stylegan3/
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary righ... |
ce545dc62982219ecbe56460b9bd29acf694719164ea374b89fe56bf0d5dc8a6 | Python | 47,026 | 1,529 | """
================
Genesets Pipeline
=================
Overview
========
This pipeline generates a number of annotations that can be used with
downstream cgat pipelines. The user will download a GTF from ENSEMBL
and then the GTF is parsed and filtered. In addition to downloading an
ensembl GTF the user will need to... |
f4edbb623481f41e6aab639dab15e824a8084b7c5c797d4aa032d61755612b47 | Python | 47,173 | 1,240 | """Tests for the CMTK/elastix transform backends.
navis runs CMTK and elastix point transforms through navis-fastcore's in-process
Rust implementation. The deprecated `"binary"` backend shells out to the external
binaries (`streamxform`, `transformix`) instead, and is kept until 3.0.
Two kinds of test live here:
* B... |
732490e47729fca27a7bb41fae7907700f5c2d69551502c7f8b716eaf5e0798c | Python | 47,268 | 900 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
c9bd1275e7a434d192897aa4479b9b3fd9ed9fe18e6ad23c75c3015690228aa0 | Python | 47,342 | 983 | # Implementation of the filters is borrowed from paper "Alias-Free Generative Adversarial Networks (StyleGAN3)" https://nvlabs.github.io/stylegan3/
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary righ... |
205ebcd896b24d5aadd7d4404e832e9216eeb6461beab062f740f52e9833cbd7 | Python | 47,343 | 1,190 | # This script is part of pymaid (http://www.github.com/navis-org/pymaid).
# Copyright (C) 2017 Philipp Schlegel
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of ... |
a6aaebc2782a9181100c71183af76337e8db9e216fb71af700b394e5cd4c9ac2 | Python | 47,393 | 1,456 | import json
import pickle
import re
import tempfile
import time
import unicodedata
from collections import Counter
from datetime import datetime, timedelta, timezone
from email import encoders
from email.mime.base import MIMEBase
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from i... |
2a88767b7cea5fcd6dbaefa02c7a76dc211a87b850fc659d7ae88eaa4ae43533 | Python | 47,468 | 823 | import click
import logging
import os
from pathlib import Path
from data import DataLoad
from utils import Utils
from model import Models
import numpy as np
import matplotlib.pyplot as plt
from Download import downloader
from pathlib import Path
import seaborn as sns
from sklearn.model_selection import train_test_split... |
65202c42c2e43fe7ba3b008e74bf166cf02185d7d22f7d6e6880e02647424655 | Python | 47,630 | 1,214 | import torch
import numpy as np
from omegaconf import DictConfig, OmegaConf
from rfdiffusion.RoseTTAFoldModel import RoseTTAFoldModule
from rfdiffusion.kinematics import get_init_xyz, xyz_to_t2d
from rfdiffusion.diffusion import Diffuser
from rfdiffusion.chemical import seq2chars
from rfdiffusion.util_module import Com... |
a37ed51c2332318cf0f863fe51b5a20841839157c15216c8265f8b87a2d66591 | Python | 47,662 | 1,058 | # -- coding: utf-8 --
# @Time : 2022/10/17 15:32
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : pipeline.py
import os.path
import scanpy as sc, anndata as ad
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.metrics import adjusted_rand_score
import matplotlib.p... |
caf6db1ea17854dd727c6eba783ae055d1082872132678ca474a32bb7ded60fa | Python | 47,734 | 1,258 |
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
SumMultiplication... |
49aa86ff6679b024022cd4a3a0d3273a0ca815d916321624c4337a47a73b2a61 | Python | 47,839 | 972 | import torch
import numpy as np
from typing import Union, Any
from gymnasium.utils import seeding
from motornet.skeleton import TwoDofArm, PointMass
from motornet.muscle import CompliantTendonHillMuscle, ReluMuscle
class Effector(torch.nn.Module):
"""Base class for `Effector` objects.
Args:
skeleton: A :clas... |
98e83aa0996575a99aca1d8cc596156f8b68f4aa880aebeceabd44e29e56d0b9 | Python | 47,854 | 935 | #!/usr/bin/python3
# Allow direct execution from the repository root, e.g.
# ``python bernn/dl/train/train_ae_classifier_holdout.py``.
import sys
from pathlib import Path
_repo_root = Path(__file__).resolve().parents[3]
if str(_repo_root) not in sys.path:
sys.path.insert(0, str(_repo_root))
import os
import trace... |
187484995372d1f4372dc31ff67294b7bd3aa51674d6f437ecfff75e0ae411f7 | Python | 47,914 | 1,052 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
train_ae_head_sweep.py
======================
Optuna sweep: jointly optimise AE encoder hyperparameters **and** the
classification head type/hyperparameters.
Design
------
Each Optuna trial:
1. Trains the AE encoder with the trial's hyperparameters (layer sizes,
... |
60625e35deb2d525a91392ea8f8e3b0653b53ea8439fec4b62095a83611f1217 | Python | 47,950 | 1,517 | '''
PipelineChipseq.py - ChIP-Seq tasks associated with intervals.
==============================================================
.. note::
This module is obsolete, see PipelinePeakcalling.py instead
'''
import shutil
import random
import re
import glob
import os
import collections
import sqlite3
import pysam
im... |
4447c6860e7347fa622d269fedb8758e66fce341ea7bc786a058b6ece1752705 | Python | 47,972 | 1,242 | eval_latent=False
import os
os.environ["CUDA_VISIBLE_DEVICES"]="0"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
... |
5b11996e352b4176369a01939ab7b956bc58eeaa9d0c971b7d20bf8e8ddfb8fc | Python | 47,984 | 1,242 | eval_latent=False
import os
os.environ["CUDA_VISIBLE_DEVICES"]="2"
import sys
sys.path.insert(0,"/examples")
from modulusDL.models.arch import (
CustomModuleArch,
FullyConnectedFlexiLayerSizeArch,
CaseIDtoFeatureArch,
CaseIDArch,
FixedFeatureArch,
MaxPoolArch,
MatrixMultiplicationArch,
... |
20f7ab7e77033f3eda7e521a859d49422881898b25dbc8ab33fdeba6fe032a8e | Python | 48,115 | 1,339 | """
Write ``gt_coeffs`` into a **coeffs-only sidecar LMDB** (same shard keys as ``lmdb_in``).
The main LMDB is read-only. Each sidecar entry stores a pickled ``gt_coeffs`` tensor
(small) keyed ``"0"`` … ``"N-1"`` plus ``"length"`` per shard.
Resume: ``--skip_existing`` skips graphs (or whole shards) already present i... |
2b1c4468898f6be39b05ef3a70fc8b324dc78c37821ac35004f78184c95964b0 | Python | 48,211 | 1,207 | ####################################################################################
#
# 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... |
f1b9848d85d2f35fe63ae6cabd9802522e27a0d84c5960ac27b6c4a9d6c4146d | Python | 48,279 | 1,156 | """
This module provides the selene-based utilities for training and using
Orca sequence models for multiscale genome interaction prediction. This
module contains code from selene.
"""
import os
from collections import namedtuple
import pkg_resources
from functools import wraps
import pandas as pd
import numpy as np... |
3fd1e0bfb64a13128f9ae807bf4e2ac74fc67e69c7f7dc9f76512c1648261c03 | Python | 48,295 | 1,158 | """
This module provides the selene-based utilities for training and using
Orca sequence models for multiscale genome interaction prediction. This
module contains code from selene.
"""
import os
from collections import namedtuple
import pkg_resources
from functools import wraps
import pandas as pd
import numpy as np... |
defb884e418202588cd08b24f29e038bf7f9207e2e6b1c6fd46bba83b25b2823 | Python | 48,323 | 1,500 | """Dataset accumulation and main analysis
"""
from collections import defaultdict
from itertools import combinations, product
from sklearn.linear_model import LinearRegression
import numpy as np
from sklearn.preprocessing import PolynomialFeatures, Normalizer
from sklearn.pipeline import Pipeline
import inspect
from ... |
1d61484d80013cca8fbe738e990722b88743367f0d6a507668517d0a4de2ba2b | Python | 48,344 | 1,173 | import numpy as numx
from scipy.signal import convolve2d
import pydeep.base.numpyextension as numxExt
import pydeep.misc.io as IO
import pydeep.misc.visualization as VIS
from pydeep.base.activationfunction import Sigmoid
#from sklearn.linear_model import LogisticRegression
# Set the same seed value for all algorithm... |
71fb9853fc1cbb26ade43d03084635daa5bb23f5a1a7015388306beaf5ec6e71 | Python | 48,523 | 1,537 | """
2023.08.30
author: femke.hurtak@epfl.ch
Helping script for working with the connectivity graphs
"""
import pandas as pd
import networkx as nx
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np
from scipy.sparse.linalg import svds
from scipy import sparse
import seaborn as sns
fr... |
e85e9f3689380d11eb059e905efdb41410e87265648e19163ad15cf65604576a | Python | 48,733 | 1,182 | import torch
from torch import nn
from torch.autograd import Variable
import torch.nn.functional as F
from torch.autograd import Function
from typing import Optional, Tuple, Any, List
from bernn.dl.models.pytorch.utils.stochastic import GaussianSample
from bernn.dl.models.pytorch.utils.distributions import log_normal_s... |
740f2f87fd98d60bb058b4e8714059ebc2472671f7cfaeade0cdcb670393d824 | Python | 48,764 | 983 | from KineticAssembly_AD.vectorized_rxn_net import VectorizedRxnNet
from KineticAssembly_AD import ReactionNetwork
import numpy as np
from torch import DoubleTensor as Tensor
from torch.nn import functional as F
import torch
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.colors as mcolors
im... |
43f81895c29d819f88a4d8a7b0e728e1603bdad4a6c34a200b10c7abe3b190b4 | Python | 48,895 | 1,167 | # 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 ... |
473edc01563c4e8c917593ba66a4e683f08ab4806853947f21507868a5920bd4 | Python | 48,917 | 984 | from KineticAssembly_AD.vectorized_rxn_net import VectorizedRxnNet
from KineticAssembly_AD import ReactionNetwork
import numpy as np
from torch import DoubleTensor as Tensor
from torch.nn import functional as F
import torch
import pandas as pd
from matplotlib import pyplot as plt
import matplotlib.colors as mcolors
im... |
38c222d79e3dd6ef4f465870b40f1fbdd6e97b6987c6c3e53c583a63ac0f47d8 | Python | 49,052 | 1,457 | import uuid
from typing import Any, get_args
import pytest
from langchain_core._api import LangChainDeprecationWarning
from langchain_core.documents import Document
from langchain_core.load import dumpd, load
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChun... |
a5243e2fe0cc82503101f6d7072a24d74bd1c552dfc299152ee2373e0db8b525 | Python | 49,106 | 1,153 | # This script is part of pymaid (http://www.github.com/navis-org/pymaid).
# Copyright (C) 2017 Philipp Schlegel
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 ... |
89104f6386c67879d6730578d2c4d6446adca76ce130ef246f22fa9fbd9ca409 | Python | 49,191 | 1,123 | """ Hebb-layers.
:Version:
2.0.0
:Date:
04.07.2018
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2018 Jan Melchior
This program is free software: you can redistribute it and/or modify
it under the terms of th... |
4a42c408f3e69cb3b2d690c68623cb37ddafb02d3d45ddc351600d77d625e70d | Python | 49,312 | 1,504 | import typing
from collections.abc import Callable, Iterable, Mapping, MutableMapping, Sequence
from typing import Annotated as ExtensionsAnnotated
from typing import (
Any,
Literal,
TypeAlias,
)
from typing import TypedDict as TypingTypedDict
import pytest
from pydantic import BaseModel as BaseModelV2Mayb... |
6f6ddaaca01952af56e5b8ec1a9001f8172f03caf149989110efb9eb474ec466 | Python | 49,331 | 1,243 | from equivariant_diffusion import utils
import numpy as np
import math
import torch
from egnn import models
from torch.nn import functional as F
from equivariant_diffusion import utils as diffusion_utils
# Defining some useful util functions.
def expm1(x: torch.Tensor) -> torch.Tensor:
return torch.expm1(x)
def... |
8aa3e67ab09c1465c50622538b542fdcf712dc760aea02523e9945aeb1b3859d | Python | 49,342 | 1,148 | import warnings
import math
import numpy as np
from pensa.features import \
correct_angle_periodicity, \
correct_spher_angle_periodicity, \
get_multivar_res_timeseries
from pensa.statesinfo import \
calculate_entropy, \
calculate_entropy_multthread, \
determine_state_limits
# -- Functions to c... |
37c890c1147ecc6700474c9ac4ac53b214f24d4a541be2869b3e1ddfcd9a721c | Python | 49,350 | 1,547 | """
================================
pipeline_timeseries.py
================================
A pipeline for analysing RNAseq timeseries gene expression data
Overview
========
A pipeline to perform time-dependent differential expression and
hierarchical clustering of timeseries RNA-seq data.
Input files are bam file... |
a49d316c5f1640740fdd7fde0441308fd802703b44642042b4c414fbbaef8cd0 | Python | 49,394 | 879 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 15 09:42:12 2024
@author: baran
"""
#Importing packages
import numpy as np
import mat73
import pandas as pd
from tqdm import tqdm
from sklearn.decomposition import PCA
from scipy import stats
from scipy import signal
from matplotlib import pyplot a... |
841f95e8d47fd1bc27f50b76f605bf6d0369308c68c7a65b199e51b00b30d8ef | Python | 49,401 | 1,190 | import math
from typing import Dict
import numpy as np
import torch
from torch import nn
import torch.nn.functional as F
from torch_scatter import scatter_add, scatter_mean
import utils
class EnVariationalDiffusion(nn.Module):
"""
The E(n) Diffusion Module.
"""
def __init__(
self,
... |
5ac441d19741d1c2b6190045f9d31ac99b8250967ac593fecee402c4e035bbf9 | Python | 49,402 | 1,438 | '''
Timeseries.py - functions associated with analysis of time series data
======================================================================
:Author: Mike Morgan
:Release: $Id$
:Date: |today|
:Tags: Python
Functions are split into::
* clustering assessment
* data transformation and normalisation
* differential... |
197bd9c159e822f52731f8e3d61a8364585197e5697331a3c51cca8aedcd995b | Python | 49,409 | 1,046 | # Implementation of the filters is borrowed from paper "Alias-Free Generative Adversarial Networks (StyleGAN3)" https://nvlabs.github.io/stylegan3/
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary righ... |
cff22a60c1be56e54ec9d01912a9c558dae5054c80dbe8c4968ce7b35b9d279e | Python | 49,440 | 991 | import simtk.openmm.app as app
import simtk.openmm as openmm
import simtk.unit as unit
from pkg_resources import resource_filename
import numpy as np
import os
try:
from urllib.request import urlopen
from io import StringIO
except:
from urllib2 import urlopen
from cStringIO import StringIO
from nose.plu... |
ba2e084f98f45313c273727c034d0a0d77c71444c74c2c952cdbdb331b7584fa | Python | 49,731 | 1,237 | from typing import Any, Optional, Tuple, List
def _check_torch_available():
"""Check if PyTorch is available and raise helpful error if not."""
if not TORCH_AVAILABLE:
raise ImportError(
"PyTorch is not installed. To use deep learning features, please install with: "
"pip instal... |
36f62a6d98f4a5047e45dd131012477d015b32564cf2e3c04cbddaba72c1bf94 | Python | 49,772 | 1,093 | #!/usr/bin/python3
# -*- coding: utf-8 -*-
"""
# FILE: scanatk.py
# AUTHOR: Matthias A.F. Gsell
# EMAIL: matthias.gsell@medunigraz.at
# DATE: 02.2023
"""
import os
import json
import numpy
import pandas
import itertools
import tkinter
import tkinter.ttk
import tkinter.messagebox
import tkinter.simpledialog
import tk... |
614d9f0f372608e1843433637e023021c4ac5b7251119a05cd115b7c73a6c272 | Python | 49,797 | 1,205 | import numpy as numx
from scipy.signal import convolve2d
import pydeep.base.numpyextension as numxExt
import pydeep.misc.io as IO
import pydeep.misc.visualization as VIS
from pydeep.base.activationfunction import Sigmoid
# Set the same seed value for all algorithms
numx.random.seed(42)
class Weight_layer(object):
... |
ff4c4cc1fc03499c38e4b7880d14e0c387288ea890d1ab90f34bee3983ec6ade | Python | 50,170 | 1,286 | """
all of this file except LBFGS class is from https://github.com/hjmshi/PyTorch-LBFGS/blob/master/functions/LBFGS.py
"""
import torch
import numpy as np
import matplotlib.pyplot as plt
from functools import reduce
from copy import deepcopy
from torch.optim import Optimizer
from optimization.EPIOptimize import EPIOpt... |
1d087bf6bdfbb6e99c27a1a340f7117e015425da1cc45506d3b2dc0292851ee7 | Python | 50,278 | 1,014 | """
Module to generate figures related to functional imaging during optogenetic stimulation.
Author: jonas.braun@epfl.ch
"""
import os
import sys
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.backends.backend_pdf import PdfPages
import seaborn as sns
fr... |
920108cf948bce9055b84e33a8ee2cc76bfd6824bd16781364e17de7c24136fa | Python | 50,306 | 1,128 | from copy import copy
import matplotlib.pyplot as plt
import networkx as nx
import numpy as np
import pandas as pd
import seaborn as sns
from pycirclize import Circos
def barplot(self, save=None, vector=True, dpi=300):
""" Barplot
Bar plot for proteins/genes identified and differentially regulated
accord... |
ba9c61b85b9296b61976d87979ad58dcd0e7ccc60795b7259d434e793f340052 | Python | 50,508 | 1,173 | # Copyright (c) 2008-2026 the MRtrix3 contributors.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Covered Software is provided under this License on an "as is"
#... |
f00e1774e10e27204851de32c6fac52963220dad0f16c2a98af818a9e0eed7ea | Python | 50,519 | 1,143 | ''' This module provides restricted Boltzmann machines (RBMs) with different
types of units on CPU. The structure is very close to the mathematical
derivations to simplify the understanding. In addition, the modularity
helps to create other kind of RBMs without adapting the training
algorithms.
... |
8f805b74686751051414218950620825fb62c7aed16c3950a95f9874ea23aa9b | Python | 50,581 | 1,162 | import tensorflow as tf
import numpy as np
from utils.ProcessData import get_maxDims, gen_smiles2graph
from sklearn.neighbors import KernelDensity
from sklearn.utils import gen_batches
from utils.TestModel import *
from sklearn.utils import gen_batches
import pandas as pd
import time
import matplotlib.pyplot as plt
imp... |
566c134409dda6a9eee701f5c0b0bf1f37c9f8dcb1c307577f40a11a61dd7eb4 | Python | 50,660 | 1,177 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 3 10:57:50 2019
@author: ngritti
"""
from PyQt5.QtCore import Qt, QUrl
from PyQt5.QtWidgets import (QApplication, QComboBox, QVBoxLayout, QDialog,
QGridLayout, QGroupBox, QLabel, QLineEdit, QPushButton,
QFileDialog, QMessageBox, QTa... |
c2b0558118868d5b4fbf25a660cb905588c9f1538ab1d731d4228a80e856ed9a | Python | 51,261 | 1,592 | from SMdRQA.utils import assert_3D_matrix_size
from SMdRQA.utils import compute_3D_matrix_size
from SMdRQA.utils import assert_matrix
from scipy.spatial import distance_matrix
from scipy.special import digamma
from scipy.spatial import distance
from sklearn.metrics import mean_squared_error
from sklearn.model_selection... |
69e0a07b75c1cb2e5b080be27c632c4b7929131b23a794d286a0b9764d7849e4 | Python | 51,345 | 1,497 | """PipelineTimeseries.py
===============================
"""
#############################################################
# Classes and functions for pipeline_timeseries_clustering.py
#############################################################
import sys
import os
import itertools
import math
import numpy as np
i... |
479404e8b7018a0c0e5c1882deb112bd90896c704c98ce557298b16a3be31870 | Python | 51,377 | 1,075 | """
Unit tests for NCMC switching engine.
"""
__author__ = 'John D. Chodera'
################################################################################
# IMPORTS
################################################################################
import copy
from simtk import openmm, unit
from simtk.openmm import ... |
36e2bf54e8f58e15816758bbdc5d56875318652d907fc7c7d52d94f969c076b1 | Python | 51,475 | 1,056 | import matplotlib
from bernn.utils.pool_metrics import log_pool_metrics
matplotlib.use('Agg')
CUDA_VISIBLE_DEVICES = ""
import uuid
import shutil
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import random
import json
import copy
import torch
from torch import nn
import os
import sklearn
# ... |
8728402ed9a1c7ed09077803f443f7acb6352c552fcdb9d177ff8bbeb741083d | Python | 51,566 | 1,426 |
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import re
import os, sys
import random
from scipy.stats import fisher_exact
from gensim.models import KeyedVectors
from mantis_ml.modules.pre_processing.data_compilation.process_generic_features import ProcessGenericFeatures... |
8c5e1907206fc04d1ff14933bced8031e5774b5b42254a852a44a6daf1982d03 | Python | 51,612 | 1,227 | """ Module for OmicScope Object Visualization
This module allows the user to extract and visualize information from OmicScope object.
Here, it is possible to evaluate data normalization (MA-Plot, Volcano Plot, Dynamic range plot),
individual protein abundance (barplot, boxplot), search for protein-protein interactions... |
13a46eb4a540a173b8d6de49d82ab5ae7523c6282ef31c3e646f23561a1dd890 | Python | 51,623 | 1,184 | """Obtain coverage pkl-objects and wiggle files of terminal exon coverage"""
__date__ = "2016-07-11"
__author__ = "Ralf Schmidt"
__email__ = "ralf.schmidt@unibas.ch"
__license__ = "GPL"
# imports
import sys
import os
import time
import HTSeq
from argparse import ArgumentParser, RawTextHelpFormatter
from collections i... |
299f1674bfb47ccd737f5b09ae8213fed7f54eeb40b367b69c7bb7bb1961df7d | Python | 51,708 | 1,326 | import tensorflow as tf
import numpy as np
from utils.ProcessData import get_maxDims, gen_smiles2graph
from sklearn.utils import gen_batches
import matplotlib.pyplot as plt
import pandas as pd
from sklearn import metrics
def perform_validation(model,validation,val_metric,batchsize,nfeat):
"""
Test the model o... |
ae55b5d46e5a00a917ae2970d080e1acd75c09f43a3c466658805e124214df2c | Python | 51,724 | 1,717 | """=================
Interval pipeline
=================
The interval pipeline takes a several set of :term:`bed` formatted
genomic intervals and annotates them. The intervals are expected to
be non-overlapping, which the pipeline checks.
It performs the following analyses:
* Peak location - where is the read densi... |
77d597f7bc67a68bc6a140b29f119ae2889d9da87092045cdb3c7db4d4412dfc | Python | 51,846 | 1,042 | import pytest
import simtk.openmm.app as app
import simtk.openmm as openmm
import simtk.unit as unit
from pkg_resources import resource_filename
import numpy as np
import os
try:
from urllib.request import urlopen
from io import StringIO
except:
from urllib2 import urlopen
from cStringIO import StringIO... |
3cdde855c387ee18713a0ad2226e55e8d091f9a7f9f2649cfc4fae9f93b68554 | Python | 51,863 | 1,495 | """Chat prompt template."""
from __future__ import annotations
from abc import ABC, abstractmethod
from collections.abc import Sequence
from pathlib import Path
from typing import (
Annotated,
Any,
TypedDict,
TypeVar,
cast,
overload,
)
from pydantic import (
Field,
PositiveInt,
Sk... |
30efa07b1a03977b92c4c3383f5ec29ef62981384037856af38080933ab17372 | Python | 52,025 | 1,019 | ''' Test module for RBM trainer.
:Version:
1.1.0
:Date:
29.03.2017
:Author:
Jan Melchior
:Contact:
JanMelchior@gmx.de
:License:
Copyright (C) 2017 Jan Melchior
This file is part of the Python library PyDeep.
PyDeep is free softw... |
7a80e3e8d9540e9535be655acd57a1b9412a1b03d5caef70c36ffce7d8f44eb5 | Python | 52,034 | 1,048 | import re
import sys
from typing import Tuple
import networkx as nx
import random
import torch
from torch import DoubleTensor as Tensor
from torch import rand
from torch import nn
LOOP_COOP_DEFAULT = 1
def _equal(n1, n2) -> bool:
"""
Test whether two structures have identical connectivity
:param n1: nx... |
b80922c0e2ad9ef56e0951edf7c62a9f1afec6889d1de0a7ec98fee371a13f6f | Python | 52,047 | 1,066 | from typing import Tuple
from KineticAssembly_AD import ReactionNetwork
from KineticAssembly_AD import reaction_network as RN
import networkx as nx
import torch
from torch import DoubleTensor as Tensor
from torch import rand
from torch import nn
class VectorizedRxnNet:
"""
Provides a lightweight class tha... |
1ec61f732a51596da29387aa7a41f64168c2d66cf6e971fc8b4d5c8db31fb789 | Python | 52,198 | 1,501 | """
Least-squares projection of voxel / probe charge density onto the atom-centered GTO basis.
Produces ``gt_coeffs`` in the **same raw tensor space as the eSCN readout before**
``predict_coeffs`` divides by ``sqrt(total_orbitals_in_graph))``:
linear: rho_labels ≈ scale * Phi @ (gt_coeffs / sqrt(N_orb_sum)) ... |
b5c94283063e6b5dcc2d9233408a63f806a52c5439c1d93e4105e3f6ff04aa6e | Python | 52,233 | 1,078 | from typing import Tuple
from KineticAssembly_AD import ReactionNetwork
from KineticAssembly_AD import reaction_network as RN
import networkx as nx
import torch
from torch import DoubleTensor as Tensor
from torch import rand
from torch import nn
class VectorizedRxnNet:
"""
Provides a lightweight class tha... |
37e4ca471d764ab8f6e50bd1353c19383c3f2669fc834d55110f4724e1b64b93 | Python | 52,566 | 1,080 | from typing import Tuple
from KineticAssembly_AD import ReactionNetwork
from KineticAssembly_AD import reaction_network as RN
import networkx as nx
import torch
from torch import DoubleTensor as Tensor
from torch import rand
from torch import nn
class VectorizedRxnNet:
"""
Provides a lightweight class tha... |
fe2b2de49e00789a98f1a0914bde0665f381183c623b8000c57c048a26311745 | Python | 52,713 | 1,610 | # 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... |
4c0c01524d54f6a46172985dd3834a9d6411febc6ffd7a0fd1130315c74ede08 | Python | 52,726 | 1,631 |
"""
====================
RNASeqQC pipeline
====================
Overview
========
This pipeline should be run as the first step in your RNA seq analysis
work flow. It will help detect error and biases within your raw
data. The output of the pipeline can be used to filter out problematic
cells in a standard RNA seq... |
6f389ea7019db59b7ffe8d64099e20cf74f7ce1fa5c7b5e123b79f9689c01ab5 | Python | 53,023 | 1,224 | # -*- coding: utf-8 -*-
"""
################################################
MIT License
Copyright (c) 2021 L. C. Lee
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including ... |
72c4ce97c428273db78f960e8c9dc9348bcd249dd7d2d858099c1deaa0702d75 | Python | 53,155 | 1,058 | import re
import sys
from typing import Tuple
import networkx as nx
import random
import torch
from torch import DoubleTensor as Tensor
from torch import rand
from torch import nn
LOOP_COOP_DEFAULT = 1
def _equal(n1, n2) -> bool:
"""
Test whether two structures have identical connectivity
:param n1: nx... |
48058cd5d341214943c1b455444367cf787eff2ef56106a84d1066822a3a4fca | Python | 53,373 | 1,203 | #!/usr/bin/env python3
# coding: utf-8
"""
Common source for utility functions used by ABCD-BIDS task-fmri-pipeline
Greg Conan: gconan@umn.edu
Created: 2021-01-15
Updated: 2024-01-19 (as_cli_attr); 2022-02-07 (the rest)
"""
# Import standard libraries
import argparse
from datetime import datetime # for seeing how lo... |
ba5942418b2e879618b2af4fc2d6e2baeaf4c933aabe9e7f7b94365b6b898ca2 | Python | 53,532 | 1,637 | # 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... |
4627af066aef8fdcc45b1ccc0344a50d9033f776edda85ea9e221a5e490884ba | Python | 53,582 | 1,395 | import os
from os.path import join as opj
import numpy as np
import matplotlib.pylab as plt
from PIL import Image
from pandas import DataFrame, read_csv
import torch.multiprocessing as mp
import torch
from glob import glob
import shutil
import logging
import pyvips
import time
# histolab
from histolab.slide import Sli... |
4af3258acf820ee0a4cce86ea3d6de33ef2793dce8391493609ec34b0152415d | Python | 53,595 | 1,197 | ####################################################################################
#
# 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... |
e7794cc822b74627e65548214c6121436697f150681580a2c8adf5ea50536588 | Python | 53,715 | 1,197 | ####################################################################################
#
# 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... |
f48892db909049e18ebc8a89db460bf36fc920d31d3dbeaf939b9d72a40faea1 | Python | 53,745 | 1,053 | # -*- coding: utf-8 -*-
ModulePy = str('NMF_demixing__ProofOfPrinciple_v23_20240617.py')
'''
Description: NMF analysis demixing spatio-temporal ensemble signals from Fiber Photometry ensemble video data (proof-of-principle analysis).
- Input files:
(i) raw_ensemble_video_data.mat [data_ddm... |
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