Saravanan666/pypi-org huggingface dataset:
Package: ore.recipe.svnlib
ore.recipe.svnlibA Recipe for downloading and compiling the python subversion
libraries, this is recipe is based on the zc.recipe.cmmi, and accepts
all the same recipe options. it downloads and compiles a subversion
release, additionally it executes the commands to compile and install
the python subversion bindings. This recipe assumes the usage of a
python with developer headers, and a swig installation, both can be
passed in via the extra_options section, if you have installed them in
custom locations or via buildout.a note of caution regarding python subversion bindings, releases prior
to 1.5.0 have memory leaks in the bindings api.an example buildout part, and another part which uses it to construct
a python interpreter with the svn python bindings:[subversion]
recipe=ore.recipe.svnlib
url=http://subversion.tigris.org/downloads/subversion-1.5.2.tar.bz2
extra_options=--without-apxs

[python]
recipe=zc.recipe.egg
eggs =
interpreter = python
extra-paths =
  $[subversion:location}/lib/svn-pythonChanges0.1.3 ( 2008.5.8 )both swig_pydir and swig_pydir_extra need to be passed in
addition to destdir to give arbitrary location installation,
the swig helper libdir is installed into the –prefix by
default.0.1.2 ( 2008.5.7 )0.1.1 ( 2008.5.5 )0.1.0 ( 2008.5.5 )First Release
Package: ores
[![Build Status](https://travis-ci.org/wikimedia/ores.svg)](https://travis-ci.org/wikimedia/ores)
[![Test coverage](https://codecov.io/gh/wikimedia/ores/branch/master/graph/badge.svg)](https://codecov.io/gh/wikimedia/ores)ORESA webserver for hosting scoring services. For more information, see [the ORES documentation on MediaWiki](https://mediawiki.org/wiki/ORES).InstallationORES is based on Python 3. Use pip to install ORES:pip install ores(orpip3 install oresif your distribution defaults to Python 2)If you’re running with the default Redis configuration, you’ll need to install a few more optional libraries,pip install ores[redis]Then you can easily run a test server by:ores applications.wsgiUse the-hargument to view its usage.ores applications.wsgi-hVisit these pages to see if your installation works,http://localhost:8080/http://localhost:8080/v2/scores/testwiki/revid/641962088?features=trueRunning ores using docker composerAs an easy way to run ores for development, download and install [docker-compose](https://docs.docker.com/compose/) and then do:docker-composebuild &&docker-composeupores will be accessible through localhost:8080Running testsFor a native installation, make sure you installed dependencies for testing:pip install-rtest-requirements.txtthen run:py.test .For docker installation, run:docker-composeexecores-workerpy.test /oresUtilitiesORES provies several utilities:
*precached: Starts a daemon that requests scores for revisions as they happen
*score_revisions: Scores a set of revisions using an ORES API
*stress_test: Scores a large set of revisions at a configurable rate
*test_api: Runs a series of tests against a live ORES APIIn order to run any of them, run it through./utilitywrapper:./utility test_api-hFor docker installations run it through one of containers:docker-composeexecores-worker/ores/utility test_api-hAuthors[Aaron Halfaker](https://github.com/halfak)[Yuvi Panda](https://github.com/yuvipanda)[Amir Sarabadani](https://github.com/Ladsgroup)[Justin Du](https://github.com/mdew192837)[Adam Wight](https://github.com/adamwight)
Package: oresapi
# ORES APIThis repo contains a python package called “oresapi”.  This package makes it
straightforward to query the ORES API in a performant way.# Examples## CLI` $ echo-e'{"rev_id":123456}\n{"rev_id":234567}' | ./utility score_revisionshttps://ores.wikimedia.org"example@email.org" enwiki damaging goodfaith2020-04-2309:58:18,547 INFO:oresapi.utilities.score_revisions--Reading input from <stdin>2020-04-2309:58:18,547 INFO:oresapi.utilities.score_revisions--Writing output to from <stdout> {"score": {"damaging": {"score": {"probability": {"false": 0.8895027179951033, "true": 0.11049728200489672}, "prediction":false}},"goodfaith": {"score": {"probability": {"false": 0.0379063620644855, "true": 0.9620936379355145}, "prediction":true}}},"rev_id": 123456} {"score": {"damaging": {"score": {"probability": {"false": 0.980325356782424, "true": 0.01967464321757594}, "prediction":false}},"goodfaith": {"score": {"probability": {"false": 0.011545340309922048, "true": 0.988454659690078}, "prediction":true}}},"rev_id": 234567} `## Python` >>> from oresapi import Session >>> >>> my_session = Session( ..."https://ores.wikimedia.org",...user_agent="Demonstratinghow to use Session - my_address@email.com") >>> >>> rev_ids = [12345, 678910] >>> results =my_session.score("enwiki",["damaging","goodfaith"],rev_ids) >>> >>> for rev_id, result in zip(rev_ids, results): ...     print(rev_id, result) ... 12345 {'goodfaith': {'score': {'prediction': True, 'probability': {'true': 0.9454305247958957, 'false':0.05456947520410427}}},'damaging': {'score': {'prediction': False, 'probability': {'true': 0.1774477630070302, 'false':0.8225522369929699}}}}678910 {'goodfaith': {'score': {'prediction': True, 'probability': {'true': 0.9971569940025945, 'false':0.002843005997405501}}},'damaging': {'score': {'prediction': False, 'probability': {'true': 0.008388045944843079, 'false':0.9916119540551569}}}}`# Author
* Aaron Halfaker -https://github.com/halfak
Package: oresat-configs
OreSat ConfigsFirmware/software configurations for OreSat missions.Includes:A centeralize "database" for all OreSat card object dictionaries (OD)Beacon definition for each OreSat missionThe C3 F-RAM data definition (object values to be saved to the F-RAM chip)Having all the OD defined in one place makes it much easier to update
OD definitions without having to go to each repo to update each cards OD.
Also, the C3 can easily import all the OD definitions to be used for SDO
transfers.How This WorksAll object dictionaries for a specific OreSat mission are defined byYAMLfiles.All the OD configs are inoresat_configs/base/All the beacon configs (beacon.yaml)are in their mission directories:oresat_configs/oresat<mission_num>/All card specific configs are are named<card_name>.yamlformat.
They contain all card specific objects and PDOs.NOTE:The cards YAML are simular to CANopen's.edsfiles; they are
for a device type, not a unique device on a CAN network (if you add an
object tosolar.yaml, all solar cards will have that object).Thesw_common.yamldefines all CANopen standard objects, common objects,
and common PDOs for all Octavo A8-based cards.Thefw_common.yamldefines all CANopen standard objects, common objects,
and common PDOs for all STM32-based cards.Astandard_object.yamlcontains some CANopen standard objects that any<card_name>.yamlor*_common.yamlcan flag to include.Thebeacon.yamlfile defines the beacon definition as all the data is
pulled strait out the the C3's OD, which is mostly build from all other ODs.Thec3.yamlfile also defines what objects have their values periodically
saved to the C3's F-RAM chip.SetupInstall project dev dependencies.$pipinstall-rrequirements.txtUpdating a ConfigAfter updating configs for card(s), run the unit tests to validate all the
configs.$python3-munittestIf there are no errors, the configs are valid.Build and install the new version of oresat-configs to build, test, and/or
import with.Once the change have been tested with firmware/software, open a Pull
Request to this repo to get all changes into the next release.Build and Install Local PackageJust run thebuild_and_install.shscript.$./build_and_install.sh
Package: oresat-dxwifi
# oresat-dxwifi-softwareSoftware for OreSat’s dxwifi board. Apart of OreSat Live mission, a real-time
video broadcast from space.## QuickstartInstall dependenies$ pip install -r requirements.txtMake a virtual CAN bus
-$ sudo ip link add dev vcan0 type vcan-$ sudo ip link set vcan0 upRun$ python -m oresat_dxwifi- Can mock the SkyTraq by adding the-mflag
- See other options with-hflag
Package: oresat-gps
OreSat GPS SoftwareSoftware for Linux version of the GPS card.Like all OreSat software projects it is built using OLAF (OreSat Linux App
Framework), which it built ontop ofCANopen for Python. See theoresat-olaf repofor more info about OLAF.QuickstartInstall dependenies$pip3install-rrequirements.txtMake a virtual CAN bus$sudoiplinkadddevvcan0typevcan
$sudoiplinksetvcan0upRun the GPS app$python3-moresat_gpsCan select the CAN bus to use (vcan0,can0, etc) with the-b BUSarg.Can mock hardware by using the-m HARDWAREflag.The-m allflag can be used to mock all hardware (CAN bus is always
required).The-m skytraqflag would only mock the skytraqSee other options with-hflag.A basicFlask-based website for development and integration can be found athttp://localhost:8000when the software is running.
Package: oresat-olaf
A pythonic CANopen application framework for all OreSat Linux boards. Built
ontop onpython-canopen. It is designed to handle all the common OreSatCANopenNode functionality including support forECSS CANBus Extended
Protocol, file transfer over CAN, and updating the Linux board.InstallingInstall using pip:$ pip install oresat-olafContributingFor guidance on setting up a development environment and how to contribution
to OLAF, see theCONTRIBUTING.rst.
Package: oresat-tpane
Terminal PaneA wrapper... to the NCurses library, providing high-level interface to "Panes" and "Windows" allowing for easier development of TUI applicationsQuick StartInstall$pip install oresat-tpaneDevelopment and ContributionInstall Locally$pip install -e .[dev](Note: the-eflag creates a symbolic-link to your local development version. Set it once, and forget it)
Package: ore.yui
you can use this either standard zope.viewlet javascript viewlets, or via
zc.resourcelibrary, the latter allows for automatic dependency resolution
and inclusion from within components.Resource LibrariesThe package comes with the following resource libraries defined.yui-core - yahoo, dom, event core librariesyui-css - reset, fonts, grids cssyui-calendar - date time/calendar widgetyui-element - Element Libraryyui-button - Button Widgetyui-menu - Menu Widgetyui-tree - Tree Widgetyui-editor - Simple Rich Text Editoryui-rte  - Full Rich Rich Text EditorChanges0.5.3 - January 5th, 2010update to YUI version 2.8.2fixed a zcml typo0.5.1 - December 23rd, 2008packaging fixes0.5.0 - December 17th, 2008add drag drop and animation lib0.4.4 - July 22nd, 2008update to 2.5.2, fix a zcml typo ( trigger in  non dev mode using tabview ).0.4.3 - July 14th, 2008fix a zcml typo (trigger in non dev mode using rte)0.4.2 - May 20th, 2008add grids/reset css0.4.1 - May 9th, 2008fix issue with load of package in non development mode.0.4.0 - May 2nd, 2008Includes YUI-2.5.1First Public Release
Package: orf
orf: ordered random forestsWelcome to the repository of thePythonpackageorffor random forest estimation
of the ordered choice models. For theRversion of theorfpackageLechner and Okasa (2020)please refer to theCRANrepository.IntroductionThePythonpackageorfis an implementation of the Ordered Forest estimator
as developed inLechner and Okasa (2019).
The Ordered Forest flexibly estimates the conditional probabilities of models with
ordered categorical outcomes (so-called ordered choice models). Additionally to
common machine learning algorithms the Ordered Forest provides functions for estimating
marginal effects and thus provides similar output as in standard econometric models
for ordered choice. The core Ordered Forest algorithm relies on the fast forest
implementation from thescikit-learn(Pedregosa et al., 2011)
library.InstallationIn order to install the latestPyPireleased version runpip install orfin the terminal.orfrequires the following dependencies:numpy (>=1.21.0)pandas (>=1.3.5)scipy (>=1.7.2)scikit-learn (>=1.0.2)joblib (>=1.0.1)plotnine (>=0.8.0)In case of an installation failure due to dependency
issues or conflicts with Anaconda distribution,
consider installing the package in a virtual
environment.The implementation relies on Python 3 and is compatible with version 3.8, 3.9 and 3.10.ExamplesThe example below demonstrates the basic functionality of the Ordered Forest.## Ordered Forestimportorf# load example datafeatures,outcome=orf.make_ordered_regression()# initiate Ordered Forest with custom settingsoforest=orf.OrderedForest(n_estimators=1000,min_samples_leaf=5,max_features=2,replace=False,sample_fraction=0.5,honesty=True,honesty_fraction=0.5,inference=False,n_jobs=-1,random_state=123)# fit Ordered Forestoforest.fit(X=features,y=outcome)# show summary of the Ordered Forest estimationoforest.summary()# evaluate the prediction performanceoforest.performance()# plot the estimated probability distributionsoforest.plot()# predict ordered probabilities in-sampleoforest.predict(X=None,prob=True)# evaluate marginal effects for the Ordered Forestoforest.margins(X=None,X_cat=None,X_eval=None,eval_point='mean',window=0.1)For more detailed examples see the package description.ReferencesLechner, M., & Okasa, G. (2019). Random Forest Estimation of the Ordered Choice Model.
arXiv preprint arXiv:1907.02436.https://arxiv.org/abs/1907.02436Lechner, M., & Okasa, G. (2020). orf: Ordered Random Forests.
R package version 0.1.3,https://CRAN.R-project.org/package=orfPedregosa et al. (2011). Scikit-learn: Machine Learning in Python. JMLR 12, pp. 2825-2830.https://jmlr.csail.mit.edu/papers/v12/pedregosa11a.htmlTheorflogo has been created via R-packagehexStickerusingTourneyfont designed by Tyler Finck, ETC.
Package: orffinder
ORFFinderORFFinder in Python. Inspired by NCBI's version:https://www.ncbi.nlm.nih.gov/orffinder/Finds the open reading frame (6-frame scan) on a given 5' to 3' nucleotide.Installation:pip3 install orffinderTerminal UsageThree command-line executable commands are available:orffinder-to-gtforffinder-to-sequenceandorffinder-to-gff3.Documentation for these commands can be retrieved by specifying<command> -h.API UsageImport the packageIMPORTANT: Your DNA/RNA strand should always be from the 5' to 3' direction when input!(Image credit: Khan Academy)fromBioimportSeqIOfromorffinderimportorffindersequence=SeqIO.read("gene.fasta","fasta")orffinder.getORFs(sequence,minimum_length=75,remove_nested=True)DocumentationgetORFs()Returns the loci of discovered ORFs in a dictionary format.

sequence: sequence in Biopython Seq or String format.
minimum_length: minimum size of ORF in nucleotides. Default: 75
start_codons: recognised 3-base-pair codons for initialisation. Default: ["ATG"]
stop_codons: recognised 3-base pair condons for termination. Default: ["TAA", "TAG", "TGA"]
remove_nested: remove all ORFs completely encased in another. Default: False
trim_trailing: remove ORFs are the edge of the sequence that do not have a defined stop codon. Default: FalsegetORFNucleotides()Returns a list of Biopython Seq objects or loci of discovered ORFs with Biopython Seq objects in a dictionary format.

sequence: sequence in Biopython Seq or String format.
return_loci: return the loci together with the nucleotide sequences. Default: False
minimum_length: minimum size of ORF in nucleotides. Default: 75
start_codons: recognised 3-base-pair codons for initialisation. Default: ["ATG"]
stop_codons: recognised 3-base pair condons for termination. Default: ["TAA", "TAG", "TGA"]
remove_nested: remove all ORFs completely encased in another. Default: False
trim_trailing: remove ORFs are the edge of the sequence that do not have a defined stop codon. Default: FalsegetORFProteins()Returns a list of Biopython Seq objects or loci of discovered ORFs with Biopython Seq objects in a dictionary format.

sequence: sequence in Biopython Seq or String format.
translation_table: translation table as per BioPython. Default: 1
return_loci: return the loci together with the protein sequences. Default: False
minimum_length: minimum size of ORF in nucleotides. Default: 75
start_codons: recognised 3-base-pair codons for initialisation. Default: ["ATG"]
stop_codons: recognised 3-base pair condons for termination. Default: ["TAA", "TAG", "TGA"]
remove_nested: remove all ORFs completely encased in another. Default: False
trim_trailing: remove ORFs are the edge of the sequence that do not have a defined stop codon. Default: FalseDependenciesBiopython (https://biopython.org/)
Package: orf-tools
orf_toolsPackage to manipulate open reading frames (ORFs).Dependent on pandas.As of today, one function available: extend_orfs()
See its docstring in the codeAuthorsJoan Pallares started the extend_orfs.
Marco Mariotti later optimized it.Installationconda install -c conda-forge -c bioconda -c mmariotti orf_toolsOr:pip install orf_tools
Package: org2ical
org2icalGenerate aniCalendar(.ics) file based on aOrgMode(.org) file.This package is especially useful if your OrgMode file is too large to be exported withOrgMode iCalendar Export.Installation:pipinstall-Uorg2icalUsage:importorg2ical# Ref: https://orgmode.org/manual/Timestamps.htmlorg_str="""* Meet Peter at the movies<2006-11-01 Wed 19:15>* Discussion on climate change<2006-11-02 Thu 20:00-22:00>"""ical_str,warnings=org2ical.loads(org_str)assertwarnings==[]print(ical_str)Results:BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//j3soon//org2ical//EN
BEGIN:VEVENT
DTSTAMP:20221114T153849Z
UID:2f8059beaf05751acc703c2a12eee7e8
DTSTART:20061101T191500Z
DTEND:20061101T191500Z
SUMMARY:Meet Peter at the movies
DESCRIPTION:  <2006-11-01 Wed 19:15>\n\nOrg Path: Meet Peter at the movies
CATEGORIES:TIMESTAMP
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20221114T153849Z
UID:72b76db7dacae0489b50bb9c9b3b3c34
DTSTART:20061102T200000Z
DTEND:20061102T220000Z
SUMMARY:Discussion on climate change
DESCRIPTION:  <2006-11-02 Thu 20:00-22:00>\n\nOrg Path: Discussion on climate change
CATEGORIES:TIMESTAMP
END:VEVENT
END:VCALENDARPlease note that theDTSTAMPhere depends on your current time.Import to Thunderbird's Lightning CalendarExport a Org (.org) file to an iCal (.ics) file using org2ical.In Thunderbird, selectEvents and Tasks > Import...and follow the steps to import the iCal file.(Optional) Set category colors:DEADLINE : rgb(255, 128, 128)
SCHEDULED: rgb(128, 192, 128)
TIMESTAMP: rgb(128, 128, 255)
CLOCK    : rgb(255, 192, 128)(Optional) Fill events with category color by followingthis Stack Overflow answer.
Package: orgafold
OrgafoldQuickly navigate through a high number of files, perhaps obtained after a disk recovery.Have you ever found yourself in a situation where there were 100,000 files in a folder, and you needed to make sense of them? Orgafold can help you determine how much space files with a specific extension, mimetype, or date take up. Alternatively, it can assist you in organizing them into folders if the existing directory structure lacks meaning. Never overwrites a file, appending a counter.SOURCE '/src', TARGET '/target'
/src/blah1/file1 -> /target/2019/file1
/src/blah1/file2 -> /target/2019/file2
/src/blah2/file1 -> /target/2019/file1 (2)
/src/blah2/foo/file3 -> /target/2019/foo/file3Seeorgafold --helpfor complete help.InstallationInstall with a single command fromPyPipip3installorgafoldExamplesAggregate by file suffixes.$ orgafold . --suffix
Analysis: suffix
351× .arw 7.7 GB
6× .indd 1.9 GB
136× .jpg 1.0 GB
4× .tif 525.5 MB
27× .pages 71.2 MB
33× .numbers 23.7 MB
10× .pdf 17.8 MB
2× .png 11.2 MB
2× .doc 1.5 MB
1× .docx 499.5 kB
4× .xlsx 122.9 kB
1× .xml 3.0 kBAggregate by the mime type.$ orgafold . --mime
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 597/597 [00:26<00:00, 22.60it/s]
Analysis: mime
493× image 9.3 GB
83× application 2.0 GB
4× text 4.5 kBAggregate by file suffixes and their modification year.$ orgafold . --suffix --year
Analysis: suffix year
276× .arw 2016 6.2 GB
1× .indd 2015 1.9 GB
54× .arw 2014 1.1 GB
34× .jpg 2015 678.7 MB
4× .tif 2015 525.5 MB
16× .arw 2012 332.6 MB
39× .jpg 2014 212.3 MB
57× .jpg 2016 136.3 MB
20× .pages 2016 67.3 MBDry-run, copying into folder structure, given by the year and suffix.$ orgafold . --suffix --year --copy --output ~/tidy --dry --recursive
Dry run only
Would copy Untitled-1.pdf → ~/tidy/2016-pdf/Untitled-1.pdf
Would copy Untitled-2.pdf → ~/tidy/2016-pdf/Untitled-2.pdf
Would copy log2.pages → ~/tidy/2016-pages/log2.pages
Would copy log3.pages → ~/tidy/2016-pages/log3.pages
Would copy memories.indd → ~/tidy/2015-indd/memories.indd
Would copy log1.pages → ~/tidy/2016-pages/log1.pages
Would copy ymca.doc → ~/tidy/2014-doc/ymca.doc
Would copy Untitled-3.pdf → ~/tidy/2016-pdf/Untitled-3.pdf
Would copy other papers/cool.xlsx → ~/tidy/2016-xlsx/cool.xlsx
Would copy other papers/workload (2).numbers → ~/tidy/2016-numbers/workload (2).numbers
Would copy other papers/jnh-lo.jpg → ~/tidy/2016-jpg/jnh-lo.jpg
Package: organage
organageA package to estimate organ-specific biological age using aging models trained on SomaScan plasma proteomics data (Oh and Rutledge et al.Nature2023)System requirementsHardware requirementsorganage package requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS RequirementsThis package is supported for macOS and Linux. The package has been tested on the following systems:maxOS 11.7.1Linux: CentOS 7.xPython dependenciespython>=3.9 and <=3.10dill>=0.3.6pandas>=1.5.3scikit-learn==1.0.2. aging models were trained using this specific version of scikit-learnInstallation$pipinstallorganageUsagesee docs/example.ipynbLicenseorganagewas created by Hamilton Oh. It is licensed under the terms of the MIT license.Creditsorganagewas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: organdesigner
Organdesigner is a graphical editor for organ description files.It is designed initially to support theGENPO(General Purpose Organ) file format.Features:Input GENPO organ (*.org) and SoundFont2 (*.sf2) files.Validated XML output to GENPO organ files.Move and copy stops within and between divisions.Organ Editor: change name and description of organ.Division Editor: add, delete, modify divisions.Couplers Editor: add, delete, modify couplers.Pistons Editor: add, delete, modify combination pistons.
Toe Pistons affect all divisions.Edit stops: change the name, MIDI program number, and MIDI velocity
of stops.
Package: organization-map
Organization Map GeneratorInstallationpipinstallorganization-mapDependenciesPIL (pip install pillow)Organization structureorgs=[[{'id':11,'name':'Main Org\nLine 2','childs':[21,22],'fill':'#384891','color':'white'}],[{'arrowtext':'50%','id':21,'name':'Leaf 21','childs':[31,32,33]},{'id':22,'name':'leaf3','childs':[34]}],[{'id':31,'name':'leaf4','childs':[40]},{'id':32,'name':'leaf5'},{'id':33,'name':'leaf6','childs':[41,42]},{'id':34,'name':'leaf7'}],[{'id':40,'name':'leaf40'},{'id':41,'name':'Bottom Leaf','childs':[51],'fill':'red','color':'white'},{'id':42,'name':'Bottom Leaf'}],[{'id':51,'name':'Bottom Leaf 2\nLine 2\nLong Line 3'}]]Code example:fromorganization_mapimportgeneratorgenerator.generate_image(orgs).show()OutputMulti Levelorgs=[[{'id':'2','name':'Father','labels':['','',''],'childs':[['22'],['22','23'],[]],'parents':[[],[],[]],'level':0}],[{'id':'22','name':'Daughter','labels':['111 %','222 %',''],'childs':[[],[],[]],'parents':[['2'],['2'],[]],'level':1},{'id':'23','name':'Son','labels':['','223 %',''],'childs':[[],['33'],[]],'parents':[[],['2'],[]],'level':1}],[{'id':'33','name':'Daughter 2','labels':['','2333 %',''],'childs':[[],[],[]],'parents':[[],['23'],[]],'level':2}]]Code example:fromorganization_mapimportgeneratorgenerator.generate_image(orgs,multi_level=True).show()Output