Saravanan666/pypi-org huggingface dataset:
Package: omaslib
omaslibBasic library for OMAS Python-based backend. OMAS relies as much as possible on
standard ontologies. Currently, the following ontologies are being used:xs:<http://www.w3.org/2001/XMLSchema#>rdf:<http://www.w3.org/1999/02/22-rdf-syntax-ns#>rdfs:<http://www.w3.org/2000/01/rdf-schema#>owl:<http://www.w3.org/2002/07/owl#>skos:<http://www.w3.org/2004/02/skos/core#>sh:<http://www.w3.org/ns/shacl#>Setup of IRI's and namespacesThe generic base IRI that is to be used has the form ofhttp://omas.org/<location><location>is aunique nameidentifying the OMAS installation of the
typeNCName.System namespaceSystem ontologies and SHACL filesAll system relevant definitions like system-wide ontologies or shacl definitions
are in a graph namedhttp://omas.org/base#System-wide dataData such as project information, user data etc. is in a graph namedhttp://omas.org/<location>/data#Project specific graphsEach project has two namespaces (aka "named graphs")Project specific dataAll project specific data resides in a graph namedhttp://omas.org/<location>/<project>/data#The project name must be unique to the location and is of
typeNCName.
Package: omataxonomy
OMA Taxonomyomataxonomy is a library based onete's ncbi_taxonomy modulethat is used internally for theOMA project, but that can also
be used in different contexts.
Essentially, it combines the NCBI Taxonomy and the GTDB taxonomy (including all their genome as subspecies).
For GTDB we generate stable taxon ids by hashing the scientific names.omataxonomy stores the data in a sqlite database under${HOME}/.config/omataxonomy/and therefor uses little resources when being used as not all the data will be loaded into memory.InstallOMA Taxonomy can be installed directly from pip:pip install omataxonomyUsagefrom omataxonomy import Taxonomy
tax = Taxonomy()
print(tax.get_name_lineage(['RS_GCF_006228565.1', 'GB_GCA_001515945.1', "f__Leptotrichiaceae", "Homo sapiens", "Gallus"])){'f__Leptotrichiaceae': ['root', 'd__Bacteria', 'p__Fusobacteriota', 'o__Fusobacteriales', 'f__Leptotrichiaceae'], 'Gallus': ['root', 'cellular organisms', 'Eukaryota', 'Opisthokonta', 'Metazoa', 'Eumetazoa', 'Bilateria', 'Deuterostomia', 'Chordata', 'Craniata', 'Vertebrata', 'Gnathostomata', 'Teleostomi', 'Euteleostomi', 'Sarcopterygii', 'Dipnotetrapodomorpha', 'Tetrapoda', 'Amniota', 'Sauropsida', 'Sauria', 'Archelosauria', 'Archosauria', 'Dinosauria', 'Saurischia', 'Theropoda', 'Coelurosauria', 'Aves', 'Neognathae', 'Galloanserae', 'Galliformes', 'Phasianidae', 'Phasianinae', 'Gallus'], 'GB_GCA_001515945.1': ['root', 'd__Bacteria', 'p__Firmicutes_B', 'c__Moorellia', 'o__Desulfitibacterales', 's__Desulfitibacter sp001515945', 'GB_GCA_001515945.1'], 'Homo sapiens': ['root', 'cellular organisms', 'Eukaryota', 'Opisthokonta', 'Metazoa', 'Eumetazoa', 'Bilateria', 'Deuterostomia', 'Chordata', 'Craniata', 'Vertebrata', 'Gnathostomata', 'Teleostomi', 'Euteleostomi', 'Sarcopterygii', 'Dipnotetrapodomorpha', 'Tetrapoda', 'Amniota', 'Mammalia', 'Theria', 'Eutheria', 'Boreoeutheria', 'Euarchontoglires', 'Primates', 'Haplorrhini', 'Simiiformes', 'Catarrhini', 'Hominoidea', 'Hominidae', 'Homininae', 'Homo', 'Homo sapiens'], 'RS_GCF_006228565.1': ['root', 'd__Bacteria', 'p__Firmicutes_B', 'c__Moorellia', 'o__Moorellales', 'f__Moorellaceae', 'g__Moorella', 's__Moorella thermoacetica', 'RS_GCF_006228565.1']}
Package: ombot-utils
ombot_utilsOMBot项目的通用工具包，提供项目通用的处理模块，包括数据结构（schemas）日志处理（log_handler）回调处理（callback_handler）错误处理（error_handler）数据处理（data_handler）1. 数据结构（schemas）数据结构模块定义了程序运行中所有使用的数据交互结构，保证各模块之间的接入接口规范。1.1. 介绍常用的数据结构包括：ChatRecords：聊天记录，包括一次聊天交互过程中的所有上下文记录。包含get_recent方法，返回最近的聊天记录。Message：聊天信息。DetObject：识别目标，图片类信息经过处理后，有可能返回的识别结果。Reply：回复消息的结构。1.2. 可选参数（OPT）可选参数用于定义数据结构中的字段可选参数值，保证字段的有效性。OPT 类可以用于参数取值，避免对可选值的记忆。from ombot_utils import schemas

reply = schemas.Reply(
    code=200,
    took=10,
    bot_id=records.bot_id,
    session_id=records.session_id,
    dialog_id='111',
    status=schemas.OPT.CHAT_STATUS.END_ANSWER,
    message=message,
)2 日志处理（log_handler）日志处理模块用于替换Python的默认日志处理。logging 模块 import 后需要进行一次初始化，之后可以直接使用，方法与Python的logging模块相同。from ombot_utils import logging
logging.init_logger('ombot', 'ombot')

logging.info('Logging something')3 回调处理（callback_handler）回调模块包含两个方法，call方法用来回调正确识别的结果，error方法用来回调处理失败的信息。from ombot_utils import Callback
from ombot_utils.schemas import OPT

callback = Callback(chat_records)
callback.call(reply, OPT.CHAT_STATUS.END_ANSWER)
callback.error('Error information')4 错误处理（error_handler）使用错误处理类VQLError进行错误处理。
最简单的用法是使用错误代码抛出错误：from ombot_utils import VQLError
raise VQLError(500)可以使用自定义的错误信息代替内置的错误信息注意：错误信息可能会被显示在用户界面上，因此请使用简单明了的描述语言from ombot_utils import VQLError
raise VQLError(500, msg='自定义错误信息')同时，可以使用'detail'字段回传报错的详细错误信息。这部分信息会出现在系统日志中，用于错误排查。from ombot_utils import VQLError
import traceback

raise VQLError(500, detail=traceback.format_exc())调试模式（Debug mode）设置环境变量IS_DEBUG=True可以开启debug模式，在该模式下有几点和正常模式不同：回调将不进行接口调用，仅日志显示回调数据。日志将不保存文件，仅作显示。
Package: omccolors
Order of Magnitude ColorsOMC allows to generateorder of magnitude colorscolor schemes from existing
matplotlib color scales, that can be used in matplotlib visualizations.OMC color scales are usefull to encode data containing large value ranges.Details on the method and large value ranges including an application to meteorological data
can be found in the underlying paper:Braun, Daniel, et al. "Color Coding of Large Value Ranges Applied to Meteorological Data." arXiv preprint arXiv:2207.12399 (2022).Installationpip install omccolorsUsagefrom omccolors import omccolors

##choose matplotlib colormap you want to use:
# Viridis: "viridis"
# Plasma: "plasma"
# ...
cmap = "viridis"

#data_min, data_max: minimum and maximum values of your data

(colormap, min_exp, max_exp) = omccolors.generate_omc(data_min, data_max, cmap)

[ Create a matplotlib figure with 'colormap']FunctionsThe following funtions are included in the OMC package:Generation of OMC matplotlib scale:generate_omc(min_value, max_value, colorscale)

#input: minimum value, maximum value, matplotlib colorscale (default: "viridis")
#output: (omc colormap, minimum exponent, maximum exponent)Show the colormap with its greyscale:view_colormap(colorscale)

#input: matplotlib colormapShow the RGB and HSV gradients of a matplotlib colormap:show_gradients(colorscale)

#input: matplotlib colormapGet the corresponding RGB color values for a specific data point:get_rgb(colorscale, value, max_exp, min_exp)

#input: matplotlib colormap, value of a data point, maximum exponent, minimum exponent
#output: RGB-values from the respective point in the colormapGet the corresponding HSV color values for a specific data point:get_hsv(colorscale, value, max_exp, min_exp)

#input: matplotlib colormap, value of a data point, maximum exponent, minimum exponent
#output: HSV-values from the respective point in the colormapShow the perceived color differences between adjacent colorscol_diff_adj(colorscale)

#input: matplotlib colormapShow the perceived color differences from the colormap to the backgroundcol_diff_back(colorscale)

#input: matplotlib colormapShow the perceived color differences between the colors of each exponentcol_diff_exp(min_exp, max_exp, colorscale)

#input: minimum exponent, maximum exponent, matplotlib colormapShow the perceived color differences between the atrt end end colors of each exponentcol_diff_se(min_exp, max_exp, colorscale)

#input: minimum exponent, maximum exponent, matplotlib colormapExamplesThe code for the examples can be found in 'examples/'ScatterplotChoroplethCreditsBased on the work by Braun et al.:Braun, Daniel, et al. "Color Coding of Large Value Ranges Applied to Meteorological Data." arXiv preprint arXiv:2207.12399 (2022).LicenseThis code is released under the MIT. See LICENSE for details.
Package: omdata
It aims data standardization and interoperability between oinsight
modules and its third party clients.oinsightis a set of data
mining and machine learning functionalities developed under the
OpenMaker Project:http://openmaker.eu/Current functionalitiesCurrent version provides a set of command-line tools as well as a python
module * to be able to validate a JSON file against its predesigned
schema * to be able to inspect and query the design of a JSON schemaInstallVia Python’s standard distribution channel PyPIpipinstallomdataVia its GitHub sourcegitclonehttps://github.com/bulentozel/omdata.gitcdomdatapipinstall.UsageCommand line toolsValidating a json file against its schema:om-jsonvalidate-q<jsonfile>-s<schemafile>Inspecting top level major fields/dictionary entries of the data file:om-schemainspectfields-top-s<schemafile>Listing all -including the nested ones- fields of an expected data file:om-schemainspectfields-all-s<schemafile>Listing all required fields of a json file to be generated:om-schemainspectfields-required-s<schemafile>Querying description of each fields:om-schemainspectquestions-s<schemafile>In the case of OpenMaker survey data implementation this corresponds to
a query to see the mapping between a survey question and the
corresponding json entry.In other applications>>>importomdataFor the details see theJupyter Notebook TutorialToDoPublishing the Docstring documentation on GitHub pagesEditing functionalities on a given schema fileUpdate test suits for command-line entry points.Learn more about the OpenMaker project:http://openmaker.eu/
Package: omdclient
omdclientomdclient provides a suite of command-line tools to interact with the APIs
associated with thecheck_mk/Open Monitoring Distribution tool suite.WATO APIsWATO is used to create, remove, and modify entries within the OMD user
suite.  This is documented at:http://mathias-kettner.com/checkmk_wato_webapi.htmlomd-activateActivates changes made by the API user.omd-bulkimportTakes a list of hosts on STDIN and adds them to a specific folder in OMD.omd-host-crudCreates/Reads/Updates/Deletes entries from an existing monitoring
interface.omd-host-tagupdate/remove a given host tag in OMDomd-reinventoryReinventory a host in OMD.Multisite/Nagioshttps://mathias-kettner.de/checkmk_multisite_automation.htmlomd-nagios-ackAcknowledges host/service alerts from the command-line.omd-nagios-downtimeSchedules host/service downtimes from the command-line.omd-nagios-hostlistPrint a list of all hosts in the given nagios instance.omd-nagios-hosts-with-problemPrint a list of hosts that are currently exhibiting a specific problem.omd-nagios-reportPrints a human-readable report on current host and service alerts.Setup / How To Use/etc/omdclient/config.yamlYou'll have to populate this file on your own:server: 'xxxxxx.example'
site: 'xxxxxx'
user: 'xxxx-api'
apikey: 'xxxxxx'If you set the 'OMDCONFIG' environment variable you can point at different
configs, e.g.:OMDCONFIG=/tmp/myconfig.yaml omd-activateConfiguration of 'expanded views'The report scripts depend on 'expanded view' versions of thehostproblemsandsvcproblemsviews, which add comments.  In order to
add these, you generally have to:Edit viewhostproblems- it's a default view, so you'll go to 'clone'.Change the name fromhostproblemstohostproblems_expanded.Update the list of columns to read:HostnameHost iconsHost stateOutput of host check pluginNumber of services in state OKNumber of services in state WARNNumber of services in state UNKNOWNNumber of services in state CRITNumber of services in state PENDINGThe age of the current host stateHost comments(newer versions) set to 'public' and 'hidden'.Save.Edit the viewsvcproblemsand createdsvcproblems_expanded, same
as above but just add the columnService Comments.In newer versions of check_mk, you may also need to make these views
Public (checkVisibility/Make this view available for other users/Publish to all users).  Also, thehostproblemsbase view may have
changed: I have forhostproblems_expanded:(Thanks to Christian Bryn -https://github.com/epleterte- for the docs!)How To BuildThere is aMakefile.bakand a*.specfile that mirrors my local build
process for RPMs, if this matches your requirements; just runmake -f Makefile.bak build-nomock.Otherwise, you may want to just follow the general instructions
in*.spec.  Scripts fromusr/bin/*go into your path; create/etc/omdclient/config.yamlas described above; make man pages withpod2manif you're ambitious; and runpython setup.py installto
install the python library.Debianmake -f Makefile.deb buildThat should build a full .deb package.
Package: omdctk
OMD Curation ToolkitOmics Dataset Curation Toolkit (OMD Curation Toolkit) is a suite of programs designed for the download and curation of metadata and fastq files of public omics datasets. Centered on the European Nucleotide Archive (ENA), this workflow provides a standardized framework intended to facilitate the arduous task of curating public omics projects.Getting startedFor further details, see the following:Documentation(https://github.com/tbcgit/omdctk/wiki)Installation(https://github.com/tbcgit/omdctk/wiki/Installation)Tutorial Full Example(https://github.com/tbcgit/omdctk/wiki/Tutorial-Full-Example)AcknowledgementsThis package has been jointly developed in theTheory, Bioinformatics and Computation(https://www.uv.es/tbc) andEvolutionary Genetics(https://www.uv.es/symbiosis) research groups at theInstitute for Integrative Systems Biology (I2SysBio), University of Valencia and Consejo Superior de Investigaciones Científicas (CSIC), Valencia, Spain. SPE is supported by an FPU grant from the Spanish Ministry of Universities (Reference: FPU20/05756).LicenseThis package is licensed under the MIT License. See the LICENSE file for details.
Package: omd-emea
OMD-EMEA Code RepositoryOverviewomd-emeais a Python package that houses generic code and processes that is used across multiple projects and clients and can be used in other projects. Having and maintaining a Python package using PyPI makes it much easier for others in the company to import code and starting playing with the funcs and processes.Main FeaturesThere are three main areas (this might increase in the future of course) that houses functionality:apis- contains code utilising the APIs of Facebook and Adveritytaxonomy_field_checker- contains functionality to retrieve the latest field names from Taxonomy Checker depending on the name of the client.general- contains generic functions and classes that may come in handy in current and upcoming projects.How to install itThe source code is currently hosted on GitLab at:https://gitlab.com/omd_MarketingIntelligence/code_repository/-/tree/asdev. Note that it is currently sitting in the branchasdev.Installation is available with the latest release at thePython Package Index (PyPI):pip install omd-emeaIf you are desperate to install from GitLab, please run the following command:git clone -b asdev https://gitlab.com/omd_MarketingIntelligence/code_repository.gitExample usageBelow are some examples on some of the functionality of the package:Importing a table from BigQuery to a pandas dataframe using theGCPclass inside the general sub module:fromomd_emea.general.general_functionsimportGCPGCP.bq_table_to_dataframe(table_name='example-table',project_id='example-project').head()Sending an email to an email address using theGeneralclass inside the general sub module:fromomd_emea.general.general_functionsimportGeneralGeneral.send_email_func(receiver_email='aniruddha.sengupta@omd.com',message='Hello, this is a test',username='omddataengineering@gmail.com',password='pwd')Using theLoggerclass inside the general sub module to create a test log and upload to a GCP bucket:fromomd_emea.general.general_functionsimportLogging# Initiate the loggerbucket_name='example-bucket'logging_file=Logging(bucket_name=bucket_name).instantiate_log('Initiating the logger')Logging(bucket_name=bucket_name).read_file(file=logging_file)# Make an example logLogging(bucket_name=bucket_name).write_to_file(file=logging_file,log='This is an example log')# Seeking the logging file - comes after all logs have been writtenLogging(bucket_name=bucket_name).seek_file(file=logging_file)# Upload the logging fileLogging(bucket_name=bucket_name).upload_log(project='example-project',file=logging_file)
Package: omdenalore
OmdenaLoreOmdenaLore is a community-driven library build by certified Omdena collaborators. The codebase is a mixture of tutorials and codes structured by specific domain names. With a growing number of collaborators, the library is ever-growing and versatile to handle simple and complex data science tasks.We encourage coders around the world to collaborate in the repository and make a real-world impact. Get access to the repository and the community here.Documentation and GuidelinesFAQ and SupportIf you have a question that is not listed below, please feel free to reach out to us atkaushal@omdena.comQ. Will there be a redundancy check to make sure the code pushed in the Pull Requests is verified?Yes: The main branch is protected in the repository which means that for a Pull Request to be merged into the main branch, at least one reviewer will need to review it.Q. Will we have access to the repository?Yes: Since this repository is private, you will be given push access ON REQUEST to the repository. To submit a request, please fill this request form.Q. Can I push my changes to the main branch?No: In order to make sure the main branch consists of functional and usable code for all collaborators, we have protected the branch so that a proper Pull Request + review process can be used to merge new changes into the main branch.Q. Is it okay if I add code from the internet to the repository?Yes: The code taken from the internet is acceptable but please make sure that a similar or same code doesn’t exist in the repository.Q. How do I check if a code already exists in the repository or not?The repository is divided into several domains and all domains contain name specific modules. According to the functionality of the code you are pushing, please check the corresponding domain folder and module to see if a similar code exists or not.Q. Do I need to take care of any specification before I create a pull request?Yes: Please refer to the Guideline section of this document.Q. Should I add author name or credits to the code?Optional: If you wish to add credits or author name to the code, please do so in the doc string of your functions or end of the module.Q. What should I do if I don’t find any modules or domain folder for my code?If you don’t find an appropriate location for your code, you can create your own folder or module. If you are unsure if a similar module or domain exists, please get in touch with the repository manager or get support from the Omdena team.
Package: omdoc
## OMDOCOMDOC is a lightweight documentation builder that utilises [jinja2][0] templates as placeholders for markdown and code.Code samples are parsed with [pygments][1] and the content is parsed with markdown.You're in total control of how it looks, feels and responds to different devices.No magic, it's plain ol' html with a dash of jinja, markdown and pygments.## Features### codeAny blocks of code inside code tags are parsed; eg```<code lang="bash">pip install omdoc</code>```An attempt is made to match the lang attribute against [known pygments lexers][2]. If no match is found it defaults to python.### MarkdownAny content inside of ...```{% markdown %} stuff to mark down  {% endmarkdown%} ```... gets marked down.## Installation```pip install omdoc```## Usage1. Make folder for docs2. Create layout.html inside of docs folder2. Style as you see fit3. Create pages that inherit layout.html in docs5. cd 'docs'6. run ``omdoc``Your documentation will now be in <docs>/_build## What about static media?Proceed as you would building a a normal site. Folders inside of `docs/` get copied into the final build. Just make sure to use relative paths in your html & css.## StatusEarly alpha.## Known caveats### Nested FoldersDoesn't support nested folders for docs so `docs/about/this.html` doesn't work. This will get fixed later one[0]: http://jinja.pocoo.org/docs/[1]: http://pygments.org/[2]: http://pygments.org/docs/lexers/
Package: omega
AboutA package of symbolic algorithms using binary decision diagrams (BDDs)
for synthesizing implementations fromtemporal logic specifications.
This is useful for designing systems, especially vehicles that carry humans.Synthesisalgorithms forMooreorMealyimplementations of:generalized Streett(1)specifications (known asGR(1))generalized Rabin(1) specifications (counter-strategiesto GR(1))detection of trivial realizability in GR(1) specifications.Seeomega.games.gr1and the examplegr1_synthesis_intro.Enumeration of state machines (asnetworkxgraphs) from the synthesized
symbolic implementations. Seeomega.games.enumeration.Facilities to simulate the resulting implementations with little and
readable user code. Seeomega.stepsand the examplemoore_moore.Code generation for the synthesizedsymbolicimplementations.
This code is correct-by-construction. Seeomega.symbolic.codegen.Minimal coveringwith a symbolic algorithm to find a minimal cover, and to
enumerate all minimal covers. Used to convert BDDs tominimalformulas.
Seeomega.symbolic.coverandomega.symbolic.cover_enum, and the
exampleminimal_formula_from_bdd.First-orderlinear temporal logic(LTL) withrigid quantificationand substitution.
Seeomega.logic.lexyacc,omega.logic.ast, andomega.logic.syntax.Bitblasterof quantified integer arithmetic (integers -> bits).
Seeomega.logic.bitvector.Translation frompastto future LTL, usingtemporal testers. Seeomega.logic.past.Symbolic automata that manage first-order formulas by seamlessly usingbinary decision diagrams(BDDs) underneath. You can:declare variables and constantstranslate:formulas to BDDs andBDDs tominimalformulas via minimal coveringquantifysubstituteprime/unprime variablesget the support of predicatespick satisfying assignments (or work with iterators)define operatorsSeeomega.symbolic.temporalandomega.symbolic.folfor more details.Facilities to write symbolic fixpoint algorithms.
Seeomega.symbolic.fixpointandomega.symbolic.prime, and the examplereachability_solver.Conversion from graphs annotated with formulas to temporal logic formulas.
These graphs can help specify transition relations.
The translation is in the spirit ofpredicate-action diagrams.Seeomega.symbolic.logicizerandomega.automatafor more details, and
the examplesymbolic.Enumeration and plotting of state predicates and actions represented as BDDs.
Seeomega.symbolic.enumeration.DocumentationIndoc/doc.md.Examplesimportomega.symbolic.folas_folctx=_fol.Context()ctx.declare(x=(0,10),y=(-2,5),z='bool')u=ctx.add_expr(r'(x <= 2) /\ (y >= -1)')v=ctx.add_expr(r'(y <= 3) => (x > 7)')r=u&~vexpr=ctx.to_expr(r)print(expr)Installationpip install omegaThe package and its dependencies are pure Python.For solving demanding games, install theCythonmoduledd.cuddthat interfaces toCUDD.
Instructions are availableatdd.LicenseBSD-3, seeLICENSEfile.
Package: omega-coding
Python package implementing omega coding.How do I install this package?As usual, just download it using pip:pipinstallomega_codingTests CoverageSince some software handling coverages sometime get slightly different results, here’s three of them:Usage examplefromomega_codingimageomega_coding,decode_omega_codingomega_coding(14)# 1111100assertdecode_omega_coding(omega_coding(14))==14# True
Package: omegaconf
OmegaConfDescriptionProjectCode qualityDocs and supportOmegaConf is a hierarchical configuration system, with support for merging configurations from multiple sources (YAML config files, dataclasses/objects and CLI arguments)
providing a consistent API regardless of how the configuration was created.ReleasesStable (2.2)OmegaConf 2.2 is the current stable version.What's newDocumentationSource codeInstall withpip install --upgrade omegaconfPrevious release (2.1)OmegaConf 2.1 is the current stable version.What's newDocumentationSlidesSource codeInstall withpip install omegaconf==2.1Previous release (2.0)What's newDocumentationSlidesSource codeInstall withpip install omegaconf==2.0.6Live tutorialRun the live tutorial:
Package: omegalul
omegalulA Python library for building omegle clientsCreditsThis library is mostly based onnucular's omegle reverse engineering gistUsageimportomegalul# start a chatchat=omegalul.start_chat()print(chat)whileTrue:event=chat.fetch_event()print(event)ifevent[0]==omegalul.Event.GOTMESSAGE:chat.send_message(event[1])print('sent message back')