Saravanan666/pypi-org huggingface dataset:
Package: xtab
xtab.pyis a Python module and command-line program that
rearranges data from a normalized format to a crosstabulated format. It takes data
in this form:StationDateValueWQ-012006-05-234.5WQ-022006-05-233.7WQ-032006-05-236.8WQ-012006-06-159.7WQ-022006-05-155.1WQ-032006-06-157.2WQ-012006-07-1910WQ-022006-07-196.1WQ-032006-07-198.8and rearranges it into this form:Station2006-05-232006-06-152006-07-19WQ-014.53.76.8WQ-029.75.17.2WQ-03106.18.8Input and output are both text (CSV) files.A summary of its capability and usage is shown below.  Full documentation
is available athttp://xtab.osdn.io/.CapabilitiesYou can use the xtab program to:Rearrange data exported from a database to better suit its
subsequent usage in statistical, modeling, graphics, or other
software, or for easier visual review and table preparation.Convert a single file (table) of data to a SQLite database.Check for multiple rows of data in a text file with the same
key values.NotesMultiple data values can be crosstabbed, in which case the output
will contain multiple sets of similar columns.Either one or two rows of headers can be produced in the output file.
One row is the default, and is most suitable when the output file will
be further processed by other software.  Two rows facilitate readability
when the output contains multiple sets of similar columns.The xtab program does not carry out any summarization or
calculation on the data values, and therefore there should be
no more than one data value to be placed in each cell of the output
table. More than one value per cell is regarded as an error, and in
such cases only one of the multiple values will be put in the cell.Error messages can be logged to either the console or a file.  If no
error logging option is specified, then if there are multiple values
to be put in a cell (the most likely data error), a single message
will be printed on the console indicating that at least one error of
this type has occurred. If an error logging option is specified,
then the SQL for all individual cases where there are multiple values
per cell will be logged.The SQL commands used to extract data from the input file for each
output table cell can be logged to a file.As an intermediate step in the crostabbing process, data are converted
to a SQLite table. By default, this table is created in memory.
However, it can optionally be created on disk, and preserved so that
it is available after the crosstabulation is completed.There are no inherent limits to the number of rows or columns in the
input or output files. (So the output may exceed the limits of some
other software.)Input and output file names, and column names in the input file that
are to be used for row headings, column headings, and cell values are
all required as command-line arguments.  If any required arguments are
missing, an exception will be raised, whatever the error logging option.Data rows are sorted alphanumerically by the row headers and column
headers are sorted alphanumerically in the output.
Package: xtal-cartographer
Install Models with:cartographer-install -m phos -o site_packagesExample Usage:cartographer -i PDB.mtz -o PDB.map -intensity FWT -phase PHWTusage: cartographer [-h] [-m M] -i I -o O [-r [R]] [-intensity [INTENSITY]] [-phase [PHASE]]

options:
  -h, --help              show this help message and exit
  -m M, -model_path M     Path to model
  -i I, -input I          Input mtz path
  -o O, -output O         Output map path 
  -r [R], -resolution [R] Resolution cutoff to apply to mtz
  -intensity [INTENSITY]  Name of intensity column in MTZ
  -phase [PHASE]          Name of phase column in MTZusage: cartographer-install [-h] -m {phos,sugar,base} [-o {site_packages,ccp4}] [--all] [--reinstall]

Cartographer Install

options:
  -h, --help            show this help message and exit
  -m {phos,sugar,base}, --model {phos,sugar,base}
  -o {site_packages,ccp4}, --output {site_packages,ccp4}
  --all
  --reinstall
Package: xtarfile
OverviewWrapper around tarfile to add support for more compression formats.UsageFirst, install the library with the tarfile compression formats you wish to support.
The example below shows an install for zstandard tarfile support.pipinstallxtarfile[zstd]You can now use the xtarfile module in the same way as the standard library tarfile module:importxtarfileastarfilewithtarfile.open('some-archive','w:zstd')asarchive:archive.add('a-file.txt')withtarfile.open('some-archive','r:zstd')asarchive:archive.extractall()Alternatively, detecting the correct compression module based on the file extensions is also supported:importxtarfileastarfilewithtarfile.open('some-archive.tar.zstd','w')asarchive:archive.add('a-file.txt')withtarfile.open('some-archive.tar.zstd','r')asarchive:archive.extractall()DevelopmentInstall the project’s dependencies withpip install .[zstd].Run the tests viapython3 setup.py test.
Package: xtas
xtasDistributed text analysis suite based on Celery.Copyright University of Amsterdam, Netherlands eScience Center and
contributors, distributed under the Apache License (seeAUTHORS.txt,LICENSE.txt). Parts of xtas use GPL-licensed software, such as the
Stanford NLP tools, and datasets that may incur additional restrictions.
Check the documentation for individual functions.QuickstartInstall:pip install xtasStart worker:python -m xtas.worker --loglevel=infoStart web frontend:python -m xtas.webserverFor full documentation, please visithttp://nlesc.github.io/xtas/.
Package: xtb
This repository hosts the Python API for the extended tight binding (xtb) program.The idea of this project is to provide thextbAPI for Pythonwithoutrequiring an additionalxtbinstallation.InstallationDepending on what you plan to do withxtb-pythonthere are two recommended
ways to install.If you plan to use this project in your workflows, follow the
Conda Installation section.If you plan to develop on this project, proceed
with the Build from Source section.For more details visit thedocumentation.Conda InstallationInstallingxtb-pythonfrom theconda-forgechannel can be achieved by addingconda-forgeto your channels with:conda config --add channels conda-forgeOnce theconda-forgechannel has been enabled,xtb-pythoncan be installed with:conda install xtb-pythonIt is possible to list all of the versions ofxtb-pythonavailable on your platform with:conda search xtb-python --channel conda-forgeBuild from SourceThe project is build with meson, the exact dependencies are defined by thextbproject, in summary it requires a Fortran and a C compiler as well as a
linear algebra backend. Make yourself familiar with buildingxtbfirst!Additionally this project requires a development version of Python installed.
Also ensure that you have thenumpyandcffipackages installed,
configure the build of the extension with.All steps to build the project are automated usingpip install .To pass options to the meson build of xtb use--config-settingsetup-args="-Dxtb-6.5.1:la_backend=openblas"to set for example the linear algebra backend to OpenBLAS.ContributingContributions to this open source project are very welcome. Before starting,
review ourcontributing guidelinesfirst, please.Licensextb-pythonis free software: you can redistribute it and/or modify it under
the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.xtb-pythonis distributed in the hope that it will be useful,
but without any warranty; without even the implied warranty of
merchantability or fitness for a particular purpose.  See the
GNU Lesser General Public License for more details.
Package: xtcalc
Simple calculatorUsage in codefrom xtcalc import tool

# Add 5 + 34
a1 = xt.add(5, 34)

# Subtract 12 - 5
a2 = xt.subtract(12, 5)

# Divide 15 / 5
a3 = xt.divide(15, 5)

# Multiply 45 * 23
a4 = xt.multiply(45, 23)

# A random number, for example from 1 to 50
a5 = xt.rnd(1,50)

print(a1, a2, a3, a4, a5)Syntax and descriptionSyntaxDescriptionExampleaddAdd 5 + 34xt.add(5, 34)subtractSubtract 12 - 5xt.subtract(12, 5)divideDivide 15 / 5xt.divide(15, 5)multiplyMultiply 45 * 23xt.multiply(45, 23)rndRandom from 1 to 50xt.rnd(1, 50)
Package: xt-cvdata
xt-cvdataDescriptionThis repo contains utilities for building and working with computer vision datasets, developed byXtract AI.So far, APIs for the following open-source datasets are included:COCO 2017 (detection and segmentation):xt_cvdata.apis.COCOOpen Images V5 (detection and segmentation):xt_cvdata.apis.OpenImagesVisual Object Tagging Tool (VoTT) CSV output (detection):xt_cvdata.apis.VoTTCSVMore to come.InstallationFrom PyPI:pipinstallxt-cvdataFrom source:gitclonehttps://github.com/XtractTech/xt-cvdata.git
pipinstall./xt-cvdataUsageSee specific help on a dataset class usinghelp. E.g.,help(xt_cvdata.apis.COCO).Building a datasetfromxt_cvdata.apisimportCOCO,OpenImages# Build an object populated with the COCO image list, categories, and annotationscoco=COCO('/nasty/data/common/COCO_2017')print(coco)print(coco.class_distribution)# Same for Open Imagesoi=OpenImages('/nasty/data/common/open_images_v5')print(oi)print(coco.class_distribution)# Get just the person classescoco.subset(['person'])oi.subset(['Person']).rename({'Person':'person'})# Merge and buildmerged=coco.merge(oi)merged.build('./data/new_dataset_dir')This package follows pytorch chaining rules, meaning that methods operating on an object modify it in-place, but also return the modified object. The exception is themerge()method which does not modify in-place and returns a new merged object. Hence, the above operations can also be completed using:fromxt_cvdata.apisimportCOCO,OpenImagesmerged=(COCO('/nasty/data/common/COCO_2017').subset(['person']).merge(OpenImages('/nasty/data/common/COCO_2017').subset(['Person']).rename({'Person':'person'})))merged.build('./data/new_dataset_dir')In practice, somewhere between the two approaches will probably be most readable.The current set of dataset operations are:analyze: recalculate dataset statistics (e.g., class distributions, train/val split)verify_schema: check if class attributes follow required schemasubset: remove all but a subset of classes from the datasetrename: rename/combine dataset classessample: sample a specified number of images from the train and validation setssplit: define the proportion of data in the validation setmerge: merge two datasets together, returning merged datasetbuild: create the currently defined dataset using either symlinks or by copying imagesImplementing a new dataset typeNew dataset types should inherit from the basext_cvdata.Builderclass. See theBuilder,COCOandOpenImagesclasses as a guide. Specifically, the class initializer should defineinfo,licenses,categories,annotations, andimagesattributes such thatself.verify_schema()runs without error. This ensures that all of the methods defined in theBuilderclass will operate correctly on the inheriting class.Data Sources[descriptions and links to data]Dependencies/Licensing[list of dependencies and their licenses, including data]References[list of references]
Package: xtea
This is an XTEA-Cipher implementation in Python (eXtended Tiny Encryption Algorithm).XTEA is a blockcipher with 8 bytes blocksize and 16 bytes Keysize (128-Bit).
The algorithm is secure at 2014 with the recommend 64 rounds (32 cycles). This
implementation supports following modes of operation:
ECB, CBC, CFB, OFB, CTRExample:>>> from xtea import *
>>> key = " "*16  # Never use this
>>> text = "This is a text. "*8
>>> x = new(key, mode=MODE_OFB, IV="12345678")
>>> c = x.encrypt(text)
>>> text == x.decrypt(c)
TrueResourcesPyPi:https://pypi.org/project/xteaDocs:http://xtea.readthedocs.io/Source code:https://github.com/varbin/xteaIssue tracker:https://github.com/varbin/xtea/issuesChangelogVersion 0.7.1, former 0.6.1 / 0.7.0; Jun 16, 2018Improved testsPEP8-style formattingUnittests: Counter, modes (but not results of them!), test vectors[BREAKING CHANGE] Counter class is now in xtea.counterPython 3.3 is not tested anymore on Travis CI[BREAKING CHANGE] CFB mode is now correctly implemented.
By settingpartition_sizeit is possible to set the
internal partition size (in bits) as per PEP-272.Python 3: An optional C extension improves speed upto a factor of 10.[BREAKING CHANGE]block_sizeandkey_sizeis now in bytes.Skipped 0.7.0 with(unreleased) Version 0.6.0; Oct 16, 2016Python 3 does work now[BREAKING CHANGE] counters cannot return numbers any more, they must return bytestrings now[BREAKING CHANGE] Cipher objects remember state, so two consecutive calls to XTEACipher.encrypt should not return the sameimproved documentation(unreleased) Version 0.5.0; Oct 15, 2016Removed CBCMACVersion 0.4.1; Jul 30, 2015Fixed installerVersion 0.4.0; Jul 12, 2014Buggless & PEP compliant CTRCTR mode works with strings nowraises DeprecatedWarning if a number is returnedCBCMAC class added (use static method CBCMAC.new(args) to create)Version 0.3.2; Jul 11, 2014Minor FixesVersion 0.3.1; Jul 11, 2014Minor FixesFixed that the length of data will not be checkedVersion 0.3.0; Jul 11, 2014Added CFB modeFully working with PEP 272Raising NotImplementedError only on PGP-CFB (OpenPGP) modeWheel support and changelog (0.2.1)(unreleased) Version 0.2.1 - dev; Jul 10, 2014Added better wheel support for uploading (just for me) with a setup.cfgAdded this file (auto uploading on pypi/warehouse and github)(upload.py for github)Version 0.2.0; Jul 9, 2014Added a test feature; warning in CTRRaises warning on CTR, added a handler that CTR will not crash anymore ;)Version 0.1.1; Jul 9, 2014Module raises a NotImplementedError on CFBMinor changesVersion 0.1; Jun 22, 2014Initial releaseSupports all mode except CFBBuggy CTR ( “ÃŸ” = “\xc3\x9f” )Working with PEP 272, default mode is ECB
Package: xtea4
This is an XTEA-Cipher implementation in Python (eXtended Tiny Encryption Algorithm).XTEA is a blockcipher with 8 bytes blocksize and 16 bytes Keysize (128-Bit).
The algorithm is secure at 2014 with the recommend 64 rounds (32 cycles). This
implementation supports following modes of operation:
ECB, CBC, CFB, OFB, CTRExample:>>> from xtea4 import *
>>> key = " "*16  # Never use this
>>> text = "This is a text. "*8
>>> x = new(key, mode=MODE_OFB, IV="12345678")
>>> c = x.encrypt(text)
>>> text == x.decrypt(c)
TrueNoteI do NOT guarantee that this implementation (or the base cipher) is secure. If you find bugs, please report them athttps://github.com/tgates42/xtea/issues.Changelog(dev) Version 0.6.1; …Improved testsPEP8-style formattingUnittests: Counter, modes (but not results of them!), test vectors[BREAKING CHANGE] Counter class is now in xtea4.counter(unreleased) Version 0.6.0; Oct 16, 2016Python 3 does work now[BREAKING CHANGE] counters cannot return numbers any more, they must return bytestrings now[BREAKING CHANGE] Cipher objects remember state, so two consecutive calls to XTEACipher.encrypt should not return the sameimproved documentation(unreleased) Version 0.5.0; Oct 15, 2016Removed CBCMACVersion 0.4.1; Jul 30, 2015Fixed installerVersion 0.4.0; Jul 12, 2014Buggless & PEP compliant CTRCTR mode works with strings nowraises DeprecatedWarning if a number is returnedCBCMAC class added (use static method CBCMAC.new(args) to create)Version 0.3.2; Jul 11, 2014Minor FixesVersion 0.3.1; Jul 11, 2014Minor FixesFixed that the length of data will not be checkedVersion 0.3.0; Jul 11, 2014Added CFB modeFully working with PEP 272Raising NotImplementedError only on PGP-CFB (OpenPGP) modeWheel support and changelog (0.2.1)Version 0.2.1 - dev; Jul 10, 2014Never released…Added better wheel support for uploading (just for me) with a setup.cfgAdded this file (auto uploading on pypi/warehouse and github)(upload.py for github)Version 0.2.0; Jul 9, 2014Added a test feature; warning in CTRAdded a test featureRaises warning on CTR, added a handler that CTR will not crash anymore ;)Version 0.1.1; Jul 9, 2014[0.1.1] NotImplementedError on CFBModule raises a NotImplementedError on CFBMinor changesVersion 0.1; Jun 22, 2014[0.1] Initial releaseSupports all mode except CFBBuggy CTR ( “ß” = “\xc3\x9f” )Working with PEP 272, default mode is ECB
Package: xtelligent-serial
Serialization + JSONSynopsisThis library is intended to serialize objects to and from Python primitives. That is,
objects will be represented as dict, list, int, float, bool, str, and None. The resulting
primitives may be easily serialized to JSON usingpython.jsonor this library.Because the library focuses on representation with native primitives, it could be useful
for serialization to other formats.UsageThe library usesdecoratorsto mark methods in charge of serialization. There's aserializerdecorator that associates a function with serializing a specific type, and
there's a correspondingdeserializer:@serializer(datetime)defdthandler(dt:datetime):returndt.isoformat()@deserializer(datetime)defstr2dt(datestr:str):returndatetime.fromisoformat(datestr)Finally, the library implements aserializationdecorator to make a class in charge of
serializing itself. Please see theexampleto illustrate.Thextelligent_serial.jsonnamespace includes two convenience methods for reading and writing to
and from JSON. Thefrom_jsonandto_jsonfunctions are documentedhere. The
functions serialize types that your code supports with the serialization decorators.AutomaticdataclasssupportPython'sdataclasswith thefrozenoption creates the rough equivalent of aNamedTuple, but you
may still add methods and properties. Immutability is a great practice for creating testable,
readable code. A frozendataclassdoes not acquire the "infinite state machine" behaviors common
to object-oriented class design. It is beyond the scope of this document to create full justification
of immutability, and there is an abundance of material on this subject. It is obvious that a frozendataclassmaps very well to JSON documents. The consistent structure ofdataclasstypes make it
straightforward for this library to support these classes automatically, without decorators. The only
caveat is that all member attribute types must be supported by decorators, or they must also be
primitive ordataclasstypes. Again, please see theexampleto illustrate.DocumentationAPI ReferenceExampleGeneral example:Source CodeExample of use with attrs third party library:Source CodeRoadmapIntegration withjson.JSONDecoderandjson.JSONEncoder. For now, this module is an alternative
to thejsonmodule.Serialization convenience methods on the decorators.Support for automatic deserialization. Right now, it is required to pass a parameter indicating
the type to deserialize to.Research automatic support for NamedTuple.
Package: xtempmail
Temporary MailTempmail client fortempmail.plusInstallation$pipinstallgit+https://github.com/krypton-byte/xtempmailFeatureCustom Name/MailReply/send Message(support attachment file)Read Message (support Download attachment file)Delete messageDestroy InboxLock InboxUnlock InboxGenerate Secret InboxAsynchronousSynchronousExampleexample/main.pyUsage SyncfromxtempmailimportEmail,extensionimportloggingfromxtempmail.mailimportEmailMessage,EMAILlog=logging.getLogger('xtempmail')log.setLevel(logging.INFO)app=Email(name='krypton',ext=ext=EMAIL.MAILTO_PLUS))@app.on.message()defbaca(data:EmailMessage):print(f"\tFrom:{data.from_mail}\n\tSubject:{data.subject}\n\tBody:{data.text}\n\tReply -> Delete")ok=[]foriindata.attachments:# -> Forward attachmentok.append((i.name,i.download()))ifdata.from_is_local:data.from_mail.send_message(data.subject,data.text,multiply_file=ok)# -> Forward messagedata.delete()#delete message@app.on.message(lambdamsg:msg.attachments)defget_message_media(data:EmailMessage):print(f'Attachment:{[i.nameforiindata.attachments]}')@app.on.message(lambdax:x.from_mail.__str__().endswith('@gmail.com'))defgetGmailMessage(data:EmailMessage):print(f'Gmail:{data.from_mail}')if__name__=='__main__':try:app.listen_new_message(1)exceptKeyboardInterrupt:app.destroy()#destroy inboxUsage Asyncimportasyncioimportloggingfromxtempmail.aiomailimportEMAIL,EmailMessage,Emaillog=logging.getLogger('xtempmail')log.setLevel(logging.INFO)app=Email(name='krypton',ext=EMAIL.MAILTO_PLUS)@app.on.message()asyncdefbaca(data:EmailMessage):print(f"\tFrom:{data.from_mail}\n\tSubject:{data.subject}\n\tBody:{data.text}\n\tReply -> Delete")ok=[]foriindata.attachments:# -> Forward attachmenok.append((i.name,awaiti.download()))ifdata.from_is_local:awaitdata.from_mail.send_message(data.subject,data.text,multiply_file=ok)# -> Forward messageawaitdata.delete()#delete message@app.on.message(lambdamsg:msg.attachments)asyncdefget_message_media(data:EmailMessage):print(f'Attachment:{[i.nameforiindata.attachments]}')@app.on.message(lambdax:x.from_mail.__str__().endswith('@gmail.com'))asyncdefgetGmailMessage(data:EmailMessage):print(f'Gmail:{data.from_mail}')if__name__=='__main__':try:loop=asyncio.new_event_loop()loop.run_until_complete(app.listen())exceptException:asyncio.run(app.destroy())Demo
Package: xtesting
Xtesting have leveraged on Functest efforts to provide a reference testing
framework:Requirements Management:https://wiki.opnfv.org/display/functest/Requirements+managementDocker Slicing:http://testresults.opnfv.org/functest/dockerslicing/Functest Framework:http://testresults.opnfv.org/functest/framework/Xtesting aims at allowing a smooth integration of new Functest Kubernetes
testcases.But, more generally, it eases building any CI/CD toolchain for other
domains than testing Virtualized Infrastructure Managers (VIM) such asOpenStack.To learn more about Xtesting:Documentation:http://xtesting.readthedocs.io/en/latest/Gerrit:https://gerrit.opnfv.org/gerrit/#/q/project:functest-xtestingGet in touch viaemail.
Package: xtesting-db-populate
xtesting-db-populateScript to populate xtesting-db with project, tests cases and pods.This application read local xtesting files and variables to populate
test databases.Installpip install xtesting-db-populateRequirementsTo create projects and populate tests cases,testcases.yamlfile
isMandatoryTo get the testapi url, theMandatoryvariableTEST_DB_URLmust
be set with the value of the test api url
(https://testapi.test/api/v1/)If you want to set pods,One ofthe two may be set:an environment variableNODE_NAMEmust be set to the pod value
(pod1)a filepods.yamlthat should be like:---pods:-pod1-pod2Usage!~/D/v/a/v/xtesting_projecttesting-db-populate
🎯gettestapiurl[success]📤readpods.yaml[success]🤖populatepod"pod1"[skipped]🤖populatepod"pod2"[skipped]📤readtestcases.yaml[success]📦populateproject"project1"[skipped]📋populatecase"test 1"[skipped]📋populatecase"test 2"[skipped]📋populatecase"test 3"[skipped]📋populatecase"test 4"[skipped]To specify the folder where is store testcases.yaml et pods.yaml add
the folder path as argument. As an example if they are ontestsfolder:!~/D/v/a/v/xtesting_projecttesting-db-populatetests
🎯gettestapiurl[success]📤readpods.yaml[success]🤖populatepod"pod1"[skipped]🤖populatepod"pod2"[skipped]📤readtestcases.yaml[success]📦populateproject"project1"[skipped]📋populatecase"test 1"[skipped]📋populatecase"test 2"[skipped]📋populatecase"test 3"[skipped]📋populatecase"test 4"[skipped]
Package: xtestrunner
Modern style test report based on unittest framework.基于unittest框架现代风格测试报告。特点简洁、美观具有现代风格的测试报告。支持HTML/XML不同格式。支持单元、Web UI、API各种类型的测试。集成邮件/钉钉/企微/飞书发送消息。支持用例错误/失败重跑。支持标签黑、白名单。针对Selenium运行失败/错误自动截图（HTML格式）。支持多语言en、zh-CN（HTML格式）。ReportInstall>pipinstallXTestRunnerIf you want to keep up with the latest version, you can install with github repository url:>pipinstall-Ugit+https://github.com/SeldomQA/XTestRunner.git@masterdemo查看更多使用例子。unittest测试importunittestfromXTestRunnerimportHTMLTestRunnerclassTestDemo(unittest.TestCase):"""测试用例说明"""deftest_success(self):"""执行成功"""self.assertEqual(2+3,5)@unittest.skip("skip case")deftest_skip(self):"""跳过用例"""passdeftest_fail(self):"""失败用例"""self.assertEqual(5,6)deftest_error(self):"""错误用例"""self.assertEqual(a,6)if__name__=='__main__':suit=unittest.TestSuite()suit.addTests([TestDemo("test_success"),TestDemo("test_skip"),TestDemo("test_fail"),TestDemo("test_error")])with(open('./result.html','wb'))asfp:runner=HTMLTestRunner(stream=fp,title='<project name>test report',description='describe: ... ',language='en',rerun=3)runner.run(suit)Document更多例子，请阅读中文文档感谢感谢从以下项目中得到思路和帮助。HTMLTestRunnerHTMLTestRunner_cnTheme style