Saravanan666/pypi-org huggingface dataset:
Package: tms
tms: Test Match Specialtms lets you test values in test code, where you may not be able to test
against an exact, known value.Some quick examples:Test an object’s class and instance attributes:assert zoo.get('elephant') == tms.InstanceOf(Elephant, name='nelly')Match data structures:assert resultset.any() == {'id': tms.InstanceOf(int),
                           'name': tms.Contains('bob'),
                           'data': tms.Anything()}Common types have shortcuts, eg:tms.Bool()
tms.Str()
tms.Unicode()
tms.Bytes()
tms.Int()
tms.Float()Test for in / not in:assert 'business' == tms.Contains('sin')
assert 'my lunch' == tms.DoesntContain('nuts')Test attributes:assert Circle(1) == tms.InstanceOf(Circle, radius=1)

assert Circle(1) == tms.InstanceOf(Circle, radius=tms.InstanceOf(int))

assert Circle(1) == tms.InstanceOf(Circle, has_attrs=['radius'])Test for arbitrary conditions:assert 1 == tms.Int(lambda x: x > 0)
assert Circle(1) == tms.InstanceOf(Circle, lambda x: x.radius == 1)Test dictionaries or dict-like objects:# Check that it contains keys and values
assert {'x': 1} == tms.DictLike(x=1)

# Check that it contains certain keys
assert dict(x=1, y=2) == tms.Contains('x', 'y')

# Check that it doesn't contain certain keys
assert dict(x=1, y=2) == tms.DoesntContain('foo', 'bar')Combining tests:assert mylunch == tms.InstanceOf(Sandwich) & tms.DoesntContain(cheese)Unlikehamcrest, tms uses the__eq__method to evaluate comparisons. This means it works with python’s
built in assert statement. This also means it works well with other test
libraries: you can drop atms.Matcherobject into any regular equality
test and have it work.Note that this also means that it may not work for objects that override
the __eq__ method. If the matcher doesn’t seem to be firing, try putting it on
the left hand side of the comparison:>>> import tms
>>> class MySpecialObject(object):
...     def __eq__(self, other):
...         return False
...
>>> MySpecialObject() == tms.Anything()
False
>>> tms.Anything() == MySpecialObject()
TrueCHANGELOG0.1.2 (released 2015-09-01)Added shortcuts for common builtin types, so you can now writetms.Int()instead oftms.InstanceOf(int).Addedtms.Passes. This takes an arbitrary function that is expected
to return a boolean,
egassert random.randrange(1, 3) == tms.Passes(lambda x: 1 <= x < 3).
This is integrated withtms.InstanceOfandtms.Anything,
so you can write egtms.InstanceOf(MyClass, lambda x:x.name.startswith('foo')).0.1.1Initial release
Package: tmscc
=======================================Topic Models for Single Cell Clustering=======================================.. image:: https://img.shields.io/pypi/v/tmscc.svg:target: https://pypi.python.org/pypi/tmscc.. image:: https://img.shields.io/travis/tarohi24/tmscc.svg:target: https://travis-ci.org/tarohi24/tmscc.. image:: https://readthedocs.org/projects/tmscc/badge/?version=latest:target: https://tmscc.readthedocs.io/en/latest/?badge=latest:alt: Documentation StatusA package for reducing dimension of gene expression profiles and doing clustering them.Installation-------.. code-block:: console$ pip install tmsccfor more information, see https://tmscc.readthedocs.io/en/latest/installation.html.Example-------.. code-block:: pythonfrom tmscc import tmimport numpy as npimport pandas as pdfrom sklearn.cluster import KMeansprofile = pd.DataFrame(np.arange(200).reshape([5, 40]))  # gene expression profile (genes*cells matrix)profile.index = ['CHEK2', 'MSH2', 'PTEN', 'TSC1', 'HER2']lda = tm.LDA(n_topics=4,profile=profile,outdir='~/tmp',)# LDA's estimation (This takes some time.)lda.estimate()# lda's theta() can be used for clustering, such as k-meanskmeans = KMeans(n_clusters=2).fit_predict(lda.theta())* Free software: MIT license* Documentation: https://tmscc.readthedocs.io.Features--------* TODORequirements-------* Python >= 3.5* Java >= 1.8Credits-------* This package owes what this is to `Mallet`_. Thank you for the wonderful toolkit!.. _Mallet: http://mallet.cs.umass.edu/=======History=======0.2.0 (2018-03-16)------------------* Add LDA implementation.* Add some documents.0.1.0 (2018-03-14)------------------* First release on PyPI.
Package: tmscoring
tmscoringPython implementation of theTMscoreprogram to compare structures of the same protein.Usage:We provide three classes,TMscoring,Sscoring, andRMSDscoring, that only differ in their default
optimisation score.They are initialised with the file paths to two PDB files:alignment = tmscoring.TMscore('structure1.pdb', 'structure2.pdb')

# Find the optimal alignment
alignment.optimise()

# Get the TM score:
alignment.tmscore(**alignment.get_current_values())

# Get the TM local scores:
alignment.tmscore_samples(**alignment.get_current_values())

# RMSD of the protein aligned according to TM score
alignment.rmsd(**alignment.get_current_values())

# Returns the transformation matrix between both structures:
alignment.get_matrix(**alignment.get_current_values())

# Save the aligned files:
alignment.write(outputfile='aligned.pdb', append=True)The structures can be matched by index (default), or performing a global sequence alignment with Smith-Waterman
using a match score of 2, mismatch of -1, a gap penalty of -0.5 for opening and -0.1 for extending.Utility functions:get_tm(path_to_pdb1, path_to_pdb2)andget_rmsd(pdb1, pdb2)are simple wrappers that compute TM score or RMSD.What is different?tmscoring is a Python library that conveniently exposes all the necessary variables.
This removes the necessity to parse files.Also, the minimisation engine isMINUIT's Migrad, a powerful and robust derivative-free minimisation algorithm,
heavily tested by particle physicists for decades.
In our testing,tmscoringyields the same or slightly better scores than upstreamTMscore.
Package: tms-django-api-version
Django API VersionSimple api endpoint to display version from configCreate aversion.pyfile containing theVERSIONvariable besides yoursettings.pyVERSION='0.0.1'#You can also use an environment variable with the followingimportosVERSION=os.getenv('MY_VERSION','unknown')Then import the version in yoursettings.pyfrom<project>.versionimportVERSIONAnd finally add the views in your urlspath("version/",version_views.VersionView.as_view(),name="version",)Then the version will be return on the corresponding endpoint.
Package: tms-django-log
Django Log ShortcutInstall the packagepipinstalltms_django_logAdd the application in theINSTALLE_APPS:INSTALLED_APPS=[...'django.contrib.admin','django.contrib.contenttypes','tms_django_log',...]Apply migrationspythonmanage.pymigrateThen use the shortcutfromtms_django_log.modelsimportadd_logadd_log("Something happened",detail="More details here",level=3)
Package: tms-django-router
DRF Hybdrid RouterInstall the packagepipinstalltms_django_routerThe router can be instanciate this way:fromtms_django_router.routersimportHybridRouterrouter=HybridRouter()Then it can use the sameregistermethod than the original DRFDefaultRouterfromtms_django_router.routersimportHybridRouterfrommypackageimportviewsrouter=routers.HybridRouter()router.register(r"endpoint",views.MyViewSet)And provides a way to add api view to DRF browsable api without using viewsets:from django.urls import path

router.add_api_view(
    "another",
    path(
        "version/",
        views.MyView.as_view(),
        name="another",
    ),
)Then the urls can be included the same way thanDefaultRouter:fromdjango.urlsimportincludeurls=[path("api/",include(router.urls))]
Package: tm-sentry-dingding
Sentry-DingDingSentry的钉钉通知插件安装$pipinstallsentry-dingding使用在项目的所有集成页面找到DingDing插件，启用，并设置Access Token在插件上使用Test Plugin进行测试，当配置好Access Token后，在钉钉群内会得到以下警告点击href按钮，打开异常详情页面。
Package: tms-pypi
п»їMIT LicenseCopyright (c) [year] [fullname]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 without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: tmsutils
tmsutilscollection of utility functions to make task easyFunction list:csv_to_linear_tsv- function to convert given csv file into linear tsv filefile_split- function to split text or csv file into given chunksmerge_sqlite_db- function to merge sqlite database file of same schemaget_string_product- function to generate product of the given string of given lengthget_string_combinations- function to generate combination of the given string of given lengthdict_to_object- function to convert dictionary or json dump into objectClasses:S3(wrapper for S3 data storages)file_exists- check if given key exists or notget_temporary_link- get temporary download link for a given keyupload- upload your file or directory to s3 storagedownload- download given key or entire folder from s3 storageFileIO(wrapper forhttps://file.io'snapchat' of file sharing)NOTE: it requires 'curl' command line tool to be installedupload- upload file to 'https://file.io' and returns file download linkdownload- download file from the given file.io url to given locationCLI (Command line Utilities):gi(designed to help you create .gitignore files for your git repositories)It is used to generate gitignore foroperating systems,IDEs, orProgramming LanguagesThe below command will generate .gitignore for python (PL) and code (IDE) in same place.$ tms gi python,codeIn case of any such type which is not valid then$ tms gi python3,abcdx

"python3" is invalid, try from "python"
"abcdx" is invalid.mdb(merging sqlite database command)It is used to merge sqlite3 database filesOptions:
    --path = default current directory from where command runs
    --extension = default is set to sqlite, can also use 'db'

It returns db file path if which all other are merged or return No file found.sf(splitting files (eg: txt, csv, tsv etc..) into equal chunks of given size)takes file path as an argument, it splits the file by default into 10 chunksOptions:
    FILEPATH - path to the file
    --chunk, -c <int> number of chunks (default=10)
Package: tmsyscall
tmsyscallRequirementsPython 2.7 or 3.4+ (currently tested with 2.7, 3.4)InstallationpipinstalltmsyscallUsageCheck the documentation athttp://tmsyscall.readthedocs.io/en/latest/Something here.UsageSomething else here.Bugs and Feature RequestsBug reports and feature requests are happily accepted via theGitHub
Issue Tracker. Pull
requests are welcome. Issues that don’t have an accompanying pull
request will be worked on as my time and priority allows.DevelopmentTo install for development:Fork thetmsyscallrepository on GitHubCreate a new branch off of master in your fork.$virtualenvtmsyscall$cdtmsyscall&&sourcebin/activate$pipinstall-egit+git@github.com:YOURNAME/tmsyscall.git@BRANCHNAME#egg=tmsyscall$cdsrc/tmsyscallThe git clone you’re now in will probably be checked out to a specific
commit, so you may want togit checkout BRANCHNAME.Guidelinespep8 compliant with some exceptions (see pytest.ini)100% test coverage with pytest (with valid tests)TestingTesting is done viapytest, driven bytox.testing is as simple as:pip install toxtoxIf you want to pass additional arguments to pytest, add them to the
tox command line after “–”. i.e., for verbose pytext output on py27
tests:tox-epy27---vRelease ChecklistOpen an issue for the release; cut a branch off master for that
issue.Confirm that there are CHANGES.rst entries for all major changes.Ensure that Travis tests passing in all environments.Ensure that test coverage is no less than the last release (ideally,
100%).Increment the version number in tmsyscall/version and add version
and release date to CHANGES.rst, then push to GitHub.Confirm that README.rst renders correctly on GitHub.Upload package to testpypi:Make sure your ~/.pypirc file is correct (a repo calledtestforhttps://testpypi.python.org/pypi)rm-Rfdistpython setup.py register-rhttps://testpypi.python.org/pypipython setup.py sdist bdist_wheeltwine upload-rtest dist/*Check that the README renders athttps://testpypi.python.org/pypi/tmsyscallCreate a pull request for the release to be merged into master. Upon
successful Travis build, merge it.Tag the release in Git, push tag to GitHub:tag the release. for now the message is quite simple:git tag-s-aX.Y.Z-m'X.Y.Z releasedYYYY-MM-DD'push the tag to GitHub:git push origin X.Y.ZUpload package to live pypi:twine upload dist/*make sure any GH issues fixed in the release were closed.
Package: tmt
Thetmttool provides a user-friendly way to work with tests.
You can comfortably create new tests, safely and easily run tests
across different environments, review test results, debug test
code and enable tests in the CI using a consistent and concise
config.The python module and command-line tool implement the Metadata
Specification which allows storing all needed test execution data
directly within a git repository. Together with possibility to
reference remote repositories it makes it easy to share test
coverage across projects and distros.The Flexible Metadata Formatfmfis used to store data in both
human and machine readable way close to the source code. Thanks to
inheritance and elasticity metadata are organized in the structure
efficiently, preventing unnecessary duplication.See thehttps://tmt.readthedocs.ioweb for detailed documentation.
Package: tmtk
Master:Develop:Anaconda Cloud latest package:A toolkit for ETL curation for the tranSMART data warehouse. The
TranSMART curation toolkit (tmtk) can be used to edit and validate
studies prior to loading them withtransmart-batch.For general documentation visitreadthedocs.InstallationInstalling via Anaconda Cloud or Pip package managersAnaconda:condainstall-cconda-forgetmtkPip:pipinstalltmtkInstalling manuallyInitialize a virtualenvpython3-mvenvenvsourceenv/bin/activateInstallation from sourceTo installtmtkand all dependencies into your Python environment,
and enable the Arborist Jupyter notebook extension, run:pipinstall-rrequirements.txtpythonsetup.pyinstallor if you want to run the tool from code in development mode:pipinstall-rrequirements.txtpythonsetup.pydevelopjupyter-nbextensioninstall--pytmtk.arboristjupyter-serverextensionenabletmtk.arboristRequirementsThe dependencies are inrequirements.txt,
optional dependencies are inrequirements-dev.txt.LicenceLGPL-3.0
Package: tmtoolP
tm-pip-tools工具集合上传pypipython setup.py bdist_wheel
twine upload dist/*安装(Python 版本>=3.8.0)pip install --upgrade tmtoolP使用发送邮件from tmtool.tools import Tool
tool = Tool()

tool.mail_from_user_host = '发件地址host'
tool.mail_from_user = '发件人邮箱号'
tool.mail_from_user_pwd = '发件人密码'

tool.send_mail_msg(to_user='收件人邮箱地址（这里是列表，可填写多个）', title='邮件标题', content='邮件内容')格式转换tool.json_dumps({"test": "python字典"})  # 字典转字符串
tool.json_loads('{"test": "python字典"}')  # 字符串转字典

tool.xml_to_dict('<xml><data>字符串</data></xml>')  # xml转字典
tool.dict_to_xml({"test": "python字典"})  # 字典转xml企业微信机器人from tmtool.tools import Tool
tool = Tool()

tool.qy_wechat_token = '7fdda192-cfcb-4eb5-87a5-b341574562d5'

# @ 所有人
tool.send_qy_wechat_msg("自测内容，忽略！", at_all=['@all'])
# @ 指定人
tool.send_qy_wechat_msg("自测内容，忽略！", at_all=['18600967174'])
# @ 指定多人
tool.send_qy_wechat_msg("自测内容，忽略！", at_all=['18600967174', '15210205078'])时间转换tool.time_stamp()       # 10位秒级时间戳
tool.time_stamp('ms')   # 13位毫秒时间戳

tool.get_now_time()     # 获取当前时间

tool.date_to_time_stamp('2022-03-25 00:00:00')  # 时间字符串转为时间戳
tool.time_stamp_to_date(1732312234)             # 时间戳转为时间字符串随机数据tool.random_phone()  # 随机手机号
tool.random_name()  # 随机姓名
tool.random_ssn()  # 随机身份证

tool.random_string(16)  # 随机位数的字符串
tool.random_number(8)  # 随机位数的数字http请求tool.http_client(url='', data={}, method='GET')  # get请求

tool.http_client(url='', data={}, method='POST')  # post请求 -(暂无header #TODO) 

tool.trans_data_to_url(url='接口地址', data={})  # 把参数拼接到url上MySQL数据库相关# 生成sql语句

from tmtool.TmMySQL import SqlStr

tmsql = SqlStr()

# 查询语句 -> target不传时，为全部字段，即*，where={'key':'value'}
tmsql.select_sql_str(table='user', where={'id': 1, 'nick_name': '测试用户'})

# 查询语句 -> target=[i1,i2,i3]时，为相应表字段
tmsql.select_sql_str(table='user', target=['username', 'password'], where={'id': 1, 'nick_name': '测试用户'})

# TODO
Package: tmtools
TM-ToolsPython bindings for the TM-align algorithm and codedeveloped by Zhang et
alfor protein structure comparison.InstallationYou can install the released version of the package directly from PyPI by
runningpip install tmtoolsPre-built wheels are available for Linux, macOS, and Windows, for Python 3.6
and up.To build the package from scratch, e.g. because you want to contribute to it,
clone this repository, and then from the root of the repository, runpip install -e . -vThis requires a C++ compiler to be installed with support for C++ 14.UsageThe functiontmtools.tm_aligntakes two NumPy arrays with coordinates for the
residues (with shape(N, 3)) and two sequences of peptide codes, performs the
alignment, and returns the optimal rotation matrix and translation, along with
the TM score:>>>importnumpyasnp>>>fromtmtoolsimporttm_align>>>>>>coords1=np.array(...[[1.2,3.4,1.5],...[4.0,2.8,3.7],...[1.2,4.2,4.3],...[0.0,1.0,2.0]])>>>coords2=np.array(...[[2.3,7.4,1.5],...[4.0,2.9,-1.7],...[1.2,4.2,4.3]])>>>>>>seq1="AYLP">>>seq2="ARN">>>>>>res=tm_align(coords1,coords2,seq1,seq2)>>>res.tarray([2.94676159,5.55265245,-1.75151383])>>>res.uarray([[0.40393231,0.04161396,-0.91384187],[-0.59535733,0.77040999,-0.22807475],[0.69454181,0.63618922,0.33596866]])>>>res.tm_norm_chain10.3105833326322145>>>res.tm_norm_chain20.414111110176286If you already have some PDB files, you can use the functions fromtmalign.ioto retrieve the coordinate and sequence data. These functions rely onBioPython, which is not installed by default to keep dependencies
lightweight. To use them, you have to installBioPythonfirst (pip install biopython). Then run:>>>fromtmtools.ioimportget_structure,get_residue_data>>>fromtmtools.testingimportget_pdb_path>>>s=get_structure(get_pdb_path("2gtl"))>>>s<Structureid=2gtl>>>>chain=next(s.get_chains())>>>coords,seq=get_residue_data(chain)>>>seq'DCCSYEDRREIRHIWDDVWSSSFTDRRVAIVRAVFDDLFKHYPTSKALFERVKIDEPESGEFKSHLVRVANGLKLLINLLDDTLVLQSHLGHLADQHIQRKGVTKEYFRGIGEAFARVLPQVLSCFNVDAWNRCFHRLVARIAKDLP'>>>coords.shape(147,3)CreditsThis package arose out of a personal desire to better understand both the
TM-score algorithm and thepybind11library to
interface with C++ code. At this point in time it contains no original research
code.If you use the package for research, you should cite theoriginal TM-score
papers:Y. Zhang, J. Skolnick,Scoring function for automated assessment of protein
structure template quality, Proteins, 57: 702-710 (2004).J. Xu, Y. Zhang, How significant is a protein structure similarity with
TM-score=0.5? Bioinformatics, 26, 889-895 (2010).LicenseThe original TM-align software (version 20210224, released under the MIT
license) is bundled with this repository (src/extern/TMalign.cpp). Some small
tweaks had to be made to compile the code on macOS and to embed it as a
library. This modifications are also released under the MIT license.The rest of the codebase is released under the GPL v3 license.
Package: tmu
Implements the Tsetlin MachineTsetlin MachineCoalesced Tsetlin MachineConvolutional Tsetlin MachineRegression Tsetlin MachineWeighted Tsetlin MachineFeatures and ExtensionsSupport for continuous features:https://arxiv.org/abs/1905.04199Drop clause:https://arxiv.org/abs/2105.14506Type III Feedback (to be published)Focused negative sampling:https://ieeexplore.ieee.org/document/9923859Multi-task classifier (to be published)Autoencoder:https://arxiv.org/abs/2301.00709Literal budget:https://arxiv.org/abs/2301.08190Incremental clause evaluation (to be published)Sparse computation with absorbing exclude (to be published)One-vs-one multi-class classifier (to be published)Technical DetailsTMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.
Package: tmux-control
tmux-controlconfigure and control tmuxsetupsudoapt-getinstallcmussudoapt-getinstallelinkssudoapt-getinstallhtopsudoapt-getinstallrangersudoapt-getinstalltmuxsudopipinstalltmux_controlconfigurationsanalysis modeThis mode is designed to be ergonomic for running an analysis. The
bottom terminal runs the main analysis code while ranger allows for
quick viewing of ongoing analysis output files.-----------------------
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|---------------------|
|                     |
|      terminal       |
|                     |
|                     |
-----------------------badass modeThis mode is designed to be useful but also to show off the size of
one’s metaphorical terminal configuration cock. It opens arXiv so you
can be a physics stunna, and cmus for acid dinner jazz.-----------------------
| ranger   |          |
|----------|          |
| terminal |          |
|----------|          |
| htop     |  ranger  |
|----------|          |
| arXiv    |          |
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| cmus     |          |
-----------------------detail modeThis mode is designed to be a less fun version of badass mode.-----------------------
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| ranger   |          |
|----------|          |
| terminal |          |
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| htop     |          |
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| arXiv    |          |
|          |          |
-----------------------edit mode (default)This mode is designed to be ergonomic for coding.-----------------------
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| terminal |  ranger  |
|          |          |
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-----------------------Nvidia modeThis mode is designed to display system resource usage for a system
featuring Nvidia hardware.-----------------------
|                     |
|        htop         |
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|     nvidia-smi      |
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-----------------------run modeThis mode launches all of the scripts at a specified directory in
separate windows.-----------------------
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|      scripts        |
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-----------------------work modeThis mode is designed to be a compromise between analysis, edit and
badass modes.-----------------------
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|       ranger        |
|---------------------|
|                     |
|      terminal       |
|---------------------|
|          |          |
|  ranger  |  cmus    |
|          |          |
-----------------------usagetmux-control.py--help