Saravanan666/pypi-org huggingface dataset:
Package: extras
extras is a set of extensions to the Python standard library, originally
written to make the code within testtools cleaner, but now split out for
general use outside of a testing context.Documentationpydoc extras is your friend. extras currently contains the following functions:try_importtry_importssafe_hasattrWhich do what their name suggests.LicensingThis project is distributed under the MIT license and copyright is owned by
the extras authors. See LICENSE for details.Required DependenciesPython 2.6+ or 3.0+Bug reports and patchesPlease report bugs using github issues at <https://github.com/testing-cabal/extras>.
Patches can also be submitted via github.  You can mail the authors directly
via the mailing listtesttools-dev@lists.launchpad.net. (Note that Launchpad
discards email from unknown addresses - be sure to sign up for a Launchpad
account before mailing the list, or your mail will be silently discarded).Historyextras used to be testtools.helpers, and was factored out when folk wanted to
use it separately.ThanksMartin Pool
Package: extraspaceremover
Extra Spaces RemoverThis project is an exta spaces remover that you can use to clean your text from unwanted extra spaces.InstallationRun the following to install:pipinstallextraspaceremoverUsagefromspaceremoverimportremove_extra_spacestext_with_extra_spaces='This is   an example   of text with extra spaces   'cleaned_without_extra_spaces=remove_extra_spaces(text_with_extra_spaces)print(cleaned_without_extra_spaces)# Generates 'This is an example of text with extra spaces'Devoloping Extra Spaces RemoverTo install  extraspaceremover, along with the tools you need to develop and run tests, run the following in your virtualenv:pipinstall-e.[dev]
Package: extras-require
Display a warning at the top of module documentation that it has additional requirements.DocsTestsPyPIAnacondaActivityQAOtherOverviewThis extension assumes you have a repository laid out like this:.
├── chemistry_tools
│   ├── __init__.py
│   ├── formulae
│   │   ├── __init__.py
│   │   ├── compound.py
│   │   ├── formula.py
│   │   ├── parser.py
│   │   └── requirements.txt
│   ├── constants.py
│   └── utils.py
├── doc-source
│   ├── api
│   │   ├── chemistry_tools.rst
│   │   ├── elements.rst
│   │   ├── formulae.rst
│   │   └── pubchem.rst
│   ├── conf.py
│   ├── index.rst
│   └── requirements.txt
├── LICENSE
├── README.rst
├── requirements.txt
├── setup.py
└── tox.iniThe file./chemistry_tools/formulae/requirements.txtcontains the additional requirements to run theformulaesubpackage. These would be defined insetup.pylike this:setup(extras_require={"formulae":["mathematical>=0.1.7","pandas>=1.0.1","pyparsing>=2.2.0","tabulate>=0.8.3","cawdrey>=0.1.2","quantities>=0.12.4",],})A message can be displayed in the documentation to indicate that the subpackage has these additional requirements that must be installed.For instance, this:..extras-require::formulae:file:formulae/requirements.txtwill produce this:The path given in:file:is relative to thepackage_rootvariable given inconf.py, which in turn is relative to the parent directory of the sphinx documentation.I.e, this line:package_root="chemistry_tools"points to./chemistry_tools, and therefore:file: formulae/requirements.txtpoints to./chemistry_tools/formulae/requirements.txt.Requirements can also be specified inpyproject.toml(using the option:pyproject:),setup.cfg(using the option:setup.cfg::), or by typing in the requirements manually, one per line.The:scope:option can be used to specify a different scope for additional requirements, such aspackage,module,classorfunction. Any string value can be supplied here.Installationextras_requirecan be installed from PyPI or Anaconda.To install withpip:$python-mpipinstallextras_requireTo install withconda:First add the required channels$condaconfig--addchannelshttps://conda.anaconda.org/conda-forge$condaconfig--addchannelshttps://conda.anaconda.org/domdfcodingThen install$condainstallextras_requireEnableextras_requireby adding “sphinxcontrib.extras_require” to theextensionsvariable inconf.py:extensions=[..."sphinxcontrib.extras_require",]For more information seehttps://www.sphinx-doc.org/en/master/usage/extensions/index.html#third-party-extensions.LinksSource:https://github.com/sphinx-toolbox/extras-requireBugs:https://github.com/sphinx-toolbox/extras-require/issues
Package: extra-streamlit-components
Extra-Streamlit-ComponentsAn all-in-one place, to find complex or just not available components by default on streamlit.Explained in details in my bookWeb Application Development with StreamlitComponentsFirstly, addimport extra_streamlit_components as stxRouterRoute to specific pages in Streamlit. This leverages the use of query parameters to make custom routes in your Streamlit application. For best experience, make sure to include the st.cache_resource function decorator while initializing the Router object.@st.cache_resource(hash_funcs={"_thread.RLock":lambda_:None})definit_router():returnstx.Router({"/home":home,"/landing":landing})defhome():returnst.write("This is a home page")deflanding():returnst.write("This is the landing page")router=init_router()router.show_route_view()c1,c2,c3=st.columns(3)withc1:st.header("Current route")current_route=router.get_url_route()st.write(f"{current_route}")withc2:st.header("Set route")new_route=st.text_input("route")ifst.button("Route now!"):router.route(new_route)withc3:st.header("Session state")st.write(st.session_state)Cookie ManagerA browser cookie store and manager.
Built onuniversal-cookiewith the capability of using its optionsSecurity Note:In shared domains such as share.streamlit.io, other web developers can have access to the cookies you set and the same goes for you. This is not to be treaded as security bug but a circumstance the developer need to be aware of.importdatetimest.write("# Cookie Manager")@st.cache_resourcedefget_manager():returnstx.CookieManager()cookie_manager=get_manager()st.subheader("All Cookies:")cookies=cookie_manager.get_all()st.write(cookies)c1,c2,c3=st.columns(3)withc1:st.subheader("Get Cookie:")cookie=st.text_input("Cookie",key="0")clicked=st.button("Get")ifclicked:value=cookie_manager.get(cookie=cookie)st.write(value)withc2:st.subheader("Set Cookie:")cookie=st.text_input("Cookie",key="1")val=st.text_input("Value")ifst.button("Add"):cookie_manager.set(cookie,val)# Expires in a day by defaultwithc3:st.subheader("Delete Cookie:")cookie=st.text_input("Cookie",key="2")ifst.button("Delete"):cookie_manager.delete(cookie)TabBarInspire from React'sScrollMenu, this component receives a list ofTabBarItemData, and returns theidof the
selected tabchosen_id=stx.tab_bar(data=[stx.TabBarItemData(id=1,title="ToDo",description="Tasks to take care of"),stx.TabBarItemData(id=2,title="Done",description="Tasks taken care of"),stx.TabBarItemData(id=3,title="Overdue",description="Tasks missed out"),],default=1)st.info(f"{chosen_id=}")BouncingImageProbably not the best naming but this component, renders an image by its path or url, and animates by zooming in and
out repetitively giving an illusion of a bounce.image_url="https://streamlit.io/images/brand/streamlit-logo-secondary-colormark-darktext.svg"stx.bouncing_image(image_source=image_url,animate=True,animation_time=1500,height=200,width=600)StepperBarA streamlit wrapper on MaterialUI's Stepperval=stx.stepper_bar(steps=["Ready","Get Set","Go"])st.info(f"Phase #{val}")
Package: extra-utils
MIT 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.更多方法请看模块，或者联系作者一起探讨。
Package: extrawest-ocpi
Extrawest OCPIPython implementation of Open Charge Point Interface (OCPI) protocol based on fastapi.Supported OCPI versions: 2.2.1, 2.1.1OCPI Documentation:2.2.1,2.1.1RequirementsPython >= 3.10Installationpipinstallextrawest-ocpiDocumentation, installation and quickstart instructions areHERERoadmapIssues fixingRelatedThe project was created through inspiration and adaptation of this projectPY_OCPI.LicenseThis project is licensed under the terms of theMITlicense.
Package: extr-ds
extr-dsLibrary to programmatically build labeled datasets for Named-Entity Recognition (NER) and Relation Extraction (RE) Machine Learning tasks.Installpip install extr-dsCommand LineseeInstructionson how to use the command line utility to manage your project.1. Init Projectextr-ds --init2. Split and Annotateextr-ds --split3.a Annotate Entities or Relations Again?extr-ds --annotate -ents
extr-ds --annotate -rels3.b Change Relation Extraction Labelextr-ds --relate -label NO_RELATION=5,7,93.b Remove Relation Extraction Instanceextr-ds --relate -delete 5,6,73.c Recover removed Relation Extraction Instancesextr-ds --relate -recover 5,6,74. Saveextr-ds --save -ents
extr-ds --save -rels5. Reset "Gold Standard" datasetsextr-ds --reset6. Help!?extr-ds --helpAPIExampletext='Ted Johnson is a pitcher.'1. Label Entities for Named-Entity Recognition Task (NER)fromextrimportRegEx,RegExLabelfromextr.entitiesimportEntityExtactorfromextr_ds.labelersimportIOBentity_extractor=EntityExtactor([RegExLabel('PERSON',[RegEx([r'(ted\s+johnson|ted)'],re.IGNORECASE)]),RegExLabel('POSITION',[RegEx([r'pitcher'],re.IGNORECASE)]),])sentence_tokenizer=## 3rd party tokenizer ##label=IOB(sentence_tokenizer,entity_extractor).label(text)## label == <Label tokens=..., labels=['B-PERSON', 'I-PERSON', 'O', 'O', 'B-POSITION', 'O']>2. Annotate for Relation Extraction Task (RE)fromextr.entitiesimportEntityExtractorfromextr.relationsimportRegExRelationLabelBuilder,\RelationExtractorfromextr_ds.labelersimportRelationClassificationfromextr_ds.labelers.relationimportRelationBuilder,BaseRelationLabeler,RuleBasedRelationLabelerperson_to_position_relationship=RegExRelationLabelBuilder('is_a')\.add_e1_to_e2('PERSON',[r'\s+is\s+a\s+',],'POSITION')\.build()base_relation_labeler=BaseRelationLabeler(RelationBuilder(relation_formats=[('PERSON','POSITION','NO_RELATION')]))rule_based_relation_labeler=RuleBasedRelationLabeler(RelationExtractor([person_to_position_relationship]))labeler=RelationClassification(EntityExtractor([RegExLabel('PERSON',[RegEx([r'(ted johnson|bob)'],re.IGNORECASE)]),RegExLabel('POSITION',[RegEx([r'pitcher'],re.IGNORECASE)]),]),base_relation_labeler,relation_labelers=[rule_based_relation_labeler])results=labeler.label(text)## results.relation_labels == [##    <RelationLabel sentence="<e1>Ted Johnson</e1> is a <e2>pitcher</e2>." label="is_a">## ]3. Find and define the type of difference between labelsfromextr_ds.validatorsimportcheck_for_differencesdifferences_in_labels=check_for_differences(['B-PERSON','I-PERSON','O','O','B-POSITION','O'],['B-PERSON','O','O','O','B-POSITION','O'])## differences_in_labels.has_diffs == True## differences_in_labels.diffs_between_labels == [##      <Difference index=1, diff_type=DifferenceTypes.S2_MISSING>## ]differences_in_labels=check_for_differences(['B-PERSON','I-PERSON','O','O','B-POSITION','O'],['B-PERSON','B-PERSON','O','O','B-POSITION','O'])## differences_in_labels.has_diffs == True## differences_in_labels.diffs_between_labels == [##      <Difference index=1, diff_type=DifferenceTypes.MISMATCH>## ]
Package: ext-reactions
Reaction Commands ExtensionExtension to discord.py Bot that adds reaction based commands.I'll finish this eventually anddocument it somewhere maybe.Nice, docs are here😄😄😄# install with pippython3-mpipinstallext-reactions# or windowspy-3-mpipinstallext-reactions# developement installpython3-mpipinstall-Ugit+https://github.com/z03h/ext-reactions@master# or windowspy-3-mpipinstall-Ugit+https://github.com/z03h/ext-reactions@masterExample codeimportasyncioimportdiscordfromdiscord.extimportcommands,reactioncommandsintents=discord.Intents.default()intents.members=Truebot=reactioncommands.ReactionBot(command_prefix='!',prefix_emoji='🤔',listening_emoji='👀',intents=intents)@bot.reaction_command('🏓')asyncdefping(ctx):awaitctx.send('Pong!')withopen('definitely-not-my-token','r')asf:token=f.read()bot.run(token)Why would you use this?You wouldn't, but you can use/subclassReactionBotand it should behave
like a normalext.commands.Bot.You can make Bot commands without message intent, so it has that going for it
Package: extremeflash
Extreme FlashThis tool allows flashing Enterasys / Extreme Networks access points fully automatically, using OpenWRT's initramfs image.InstallationInstall the tool:pipx install extremeflashorpip install extremeflashUsagePrerequisitesconnect a USB serial device to the local machine and to the serial port of the Enterasys APidentify the IP configured on the local machine that will be used to connect to the Enterasys APdownload an OpenWRT initramfs-kernel image, e.g.for the WS-AP3710idownload an OpenWRT-based squashfs-sysupgrade, e.g.for the WS-AP3710iNote: While it is generally recommended to use the same version for initramfs-kernel and squashfs-sysupgrade, the process
can work with different versions. This is especially important if a downstream OpenWRT firmware (e.g. Gluon) with a different
version should be installed on the router.Run the toolMake sure that the serial cable is connected to the access point, but it is not powered on yetRun the toollet it autodetect the serial port:extremeflash --local-ip 192.168.1.70/24 \
--model AP3710 \
-i ~/Downloads/openwrt-23.05.0-mpc85xx-p1020-enterasys_ws-ap3710i-initramfs-kernel.bin \
-j ~/Downloads/openwrt-22.03.3-mpc85xx-p1020-enterasys_ws-ap3710i-squashfs-sysupgrade.binor manually specify the serial port:extremeflash  --port /dev/ttyUSB0 --local-ip 192.168.1.70/24 \
--model AP3710 \
-i ~/Downloads/openwrt-23.05.0-mpc85xx-p1020-enterasys_ws-ap3710i-initramfs-kernel.bin \
-j ~/Downloads/openwrt-22.03.3-mpc85xx-p1020-enterasys_ws-ap3710i-squashfs-sysupgrade.binFor more information run:extremeflash --helpPower the access point and connect the LAN cable to power the AP.Note: for APs with two or more LAN ports, make sure the local machine is connected to port 2, not port 1.The tool will flash the access point automatically. When it finishes, the access point
can be reached via192.168.1.1(OpenWRT's default IP).ContributingInstall dependenciesIf the dependencies are not already installed, runpoetry installfollowed by apoetry shellto get an environment with all necessary dependencies.Running modified codeAfter modifying the code, run the tool by executingpython -m extremeflashinside the repository's folder. For example:python3 -m extremeflash --help
Package: extremevariantfilter
ExtremeVariantFilterExtreme Variant Filter is a set of tools developed to aid in the identification of false positive variants in Genomic Variant Call Files based on XGBoost.Functionsapply_filterUsage:
    apply_filter (--vcf STR) (--snp-model STR) (--indel-model STR) [--verbose]

Description:
    Apply models generated by train_model to a VCF.

Arguments:
    --vcf STR                     VCF to be filtered
    --snp-model STR               Model for applying to SNPs
    --indel-model INT             Model for applying to InDels

Options:
    -h, --help                      Show this help message and exit.
    -v, --version                   Show version and exit.
    --verbose                       Log output

Examples:
    apply_filter --vcf <table> --snp-model <snp.pickle.dat> --indel-model <indel.pickle.dat>train_modelUsage:
    train_model (--true-pos STR) (--false-pos STR) (--type STR) [--out STR] [--njobs INT] [--verbose]

Description:
    Train a model to be saved and used to filter VCFs.

Arguments:
    --true-pos STR          Path to true-positive VCF from VCFeval or comma-seperated list of paths
    --false-pos STR         Path to false-positive VCF from VCFeval or comma-seperated list of paths
    --type STR              SNP or INDEL

Options:
    -o, --out <STR>                 Outfile name for writing model [default: (type).filter.pickle.dat]
    -n, --njobs <INT>               Number of threads to run in parallel [default: 2]
    -h, --help                      Show this help message and exit.
    -v, --version                   Show version and exit.
    --verbose                       Log output

Examples:
    train_table --true-pos <path/to/tp/vcf(s)> --false-pos <path/to/fp/vcf(s)> --type [SNP, INDEL] --njobs 20InstallTo install and run EVF simply type:pip install extremevariantfilterAlternatively, clone this repo and build using the following commands:git clone https://github.com/stLFR/extremevariantfilter.git
cd extremevariantfilter
pip install .stLFR Paper ResultsIf you'd like to use this tool to corroborate the results from thestLFR Paper on Bioarxivpaper,
the models used for variant filtering are available within themodels/directory.
In order to get identical results, after installation, use the commandpip install -r requirements.txtfrom within this directory to ensure your
environment matches the one we used for our results.
Different versions of certain packages will result in variable results.
Package: extremitypathfinder
python package for fast geometric shortest path computation in 2D multi-polygon or grid environments based on visibility graphs.Quick Guide:Install the package with the optional Numba extra for a significant speedup:pip install extremitypathfinder[numba]fromextremitypathfinderimportPolygonEnvironmentenvironment=PolygonEnvironment()# counter clockwise vertex numbering!boundary_coordinates=[(0.0,0.0),(10.0,0.0),(9.0,5.0),(10.0,10.0),(0.0,10.0)]# clockwise numbering!list_of_holes=[[(3.0,7.0),(5.0,9.0),(4.5,7.0),(5.0,4.0),],]environment.store(boundary_coordinates,list_of_holes,validate=False)start_coordinates=(4.5,1.0)goal_coordinates=(4.0,8.5)path,length=environment.find_shortest_path(start_coordinates,goal_coordinates)For more refer to thedocumentation.Also see:GitHub,PyPI
Package: extreqs
extreqsParse python requirements.txt files into setuptools extras.Usageextreqslooks for special comments (#extra:) in your requirements files.
Note the lack of space after the#!
Anything which follows that (until the end of line, or another#) is treated as a whitespace-separated list of extras.
For example,#extra: dev test docmarks dependencies belonging to thedev,test, anddocextras.If the#extra:comment is on the same line as (following) a dependency, then just that dependency belongs to that extra.
If the#extra:comment is on a line on its own, all dependencies below it belong to that extra, until the next#extra:line.For example:# requirements.txt
dep1
dep2  #extra: extra1

#extra: extra2
dep3

#extra: extra3  # you can still have freeform comments!
dep4  #extra: extra4 extra5
dep5would be parsed intoinstall_requires=["dep1"]extras_require={"extra1":["dep2"],"extra2":["dep3"],"extra3":["dep4","dep5"],"extra4":["dep4"],"extra5":["dep4"],}Additionally, entire files can belong to a particular extra.Note that python extras are not smart enough to deal with dependencies which belong only tocombinationsof extras, ornegativeextras: a dependency which belongs to multiple extras (given by the context of the file, block, or line) just belongs to multiple extras.
This is a limitation of python packaging and cannot be addressed here.In yoursetup.py:#!/usr/bin/env python3"""setup.py"""frompathlibimportPathfromextreqsimportparse_requirements_files_dictfromsetuptoolsimportsetuphere=Path(__file__).resolve().parentreq_kwargs=parse_requirements_files_dict(# files without an extra context are in *argshere/"requirements.txt",# files with an extra context are in **kwargsoptional=here/"requirements-optional.txt",)setup(name="my_package",...**req_kwargs,...)extreqsis an install-time dependency, and so must be added to yourpyproject.toml:# pyproject.toml[build-system]requires=["setuptools","extreqs"]build-backend="setuptools.build_meta"Look out for dependency specifiers which are accepted by pip, but not by setuptools (e.g. editable install-eor references to other requirement files-r).NotesThis package should only be used in certain circumstances, and may lead to bad habits if over-used.
Requirements files are intended for specifying reproducible (i.e. hard version constraints), maximal environments for other developers, CI, and so on to be able to run all tests, features, lints etc..
Package dependencies are intended to specify the minimal dependencies with permissive version constraints for users to install the package for use.This package is, therefore, more applicable to distributing applications (CLI, web backends, etc.) than it is libraries.
Package: extrudion
Python Package to extract information about the Stress, Strain and Young Modulus from a set of.TRAfilesSet up InstructionsRun the following command in the conda/miniconda terminal to install the package:pip install extrudionImport the package in your Jupyter project or Python script:import extrudion as exAvailable commands:Analyze a whole directoryex.analyzeDirectory(folder_path, cut_off = True, sample_thickness = 10)plots all the.TRAfiles in the given directory.returns a pandas DataFrame containingArguments:folder_pathcan be left empty to analyze the current working directory (e.g.ex.analyzeDirectory())folder_pathcan be a relative path to a folder within the current working directory (e.g.ex.analyzeDirectory('data'))folder_pathcan be the absolute path (e.g.ex.analyzeDirectory('C:\Users\Desktop\extrusion_data'))cut_offis by defaultTrueand must not be specified. If it is true, cuts the data at the max value of Stresssample_thickenessis the value inmmof the size of the sample. The surface of the sample issample_thickeness * 10converted in meters squared. If not specified, a value of 10 mm is assumed.The results are saved inanalysis.csvfile in the same directory.Analyze a single fileex.analyzeFile(*file_path*, folder=[], cut_off = True, sample_thickeness = 10)analyzeFile, analyzes a single file and returns the pandas.Dataframe with the results as shown below.
-foldermust be specified as a second argument
-cut_offandsample_thicknesscan be specified as a third argument as shown above.Mathematical FormulasStress= Force['N'] /sample_thickness/ 10 * 10^3returns gives thestressinkPaStrain= ln( length['mm'] / initial length ['mm'] )Young Modulus= slope of the best line fit for the curveIntercept= the incercet of the previous fitYield Stress and Strainare the point of intersection for the Young modulus line shifted by 0.02 in the Strain and the data.Example