Saravanan666/pypi-org huggingface dataset:
Package: factiva-core
Python package with root definitions and dictionaries, to support other functional packages. Get more details in theofficial documentationUserKey: Represents an API user defined as a user key only (no O-Auth). This class is used in the Snapshots & Streams servicesDicts: Module that contains mulitple dictionaries for data combination or better human-reading.InstallationTo install this library, run the following commands. However, this library will be installed as a dependency of other Factiva or Dow Jones packages.$ pip install --upgrade factiva-coreUsing Library servicesQuick examples that show how to use the included services.Creating a User Instance and Getting its statisticsCreateUserKeyinstance and retrieve a summary of the account statistics.fromfactiva.coreimportUserKeyu=UserKey(key='abcd1234abcd1234abcd1234abcd1234',stats=True)print(u)<class 'factiva.core.userkey.UserKey'>
|-key = ****************************1234
|-cloud_token = **Not Fetched**
|-account_name = AccName1234
|-account_type = account_with_contract_limits
|-active_products = DNA
|-max_allowed_concurrent_extractions = 5
|-max_allowed_extracted_documents = 200,000
|-max_allowed_extractions = 3
|-currently_running_extractions = 0
|-total_downloaded_bytes = 7,253,890
|-total_extracted_documents = 2,515
|-total_extractions = 1
|-total_stream_instances = 4
|-total_stream_subscriptions = 1
|-enabled_company_identifiers = [{'id': 4, 'name': 'isin'}, {'id': 3, 'name': 'cusip'}, {'id': 1, 'name': 'sedol'}, {'id': 5, 'name': 'ticker_exchange'}]
|-remaining_documents = 197,485
|-remaining_extractions = 2Loading Factiva Industry HierarchyLoads the Industry hierarchy dataset which, among others, contain the Industry Factiva Code.fromfactiva.coreimportdictsih=dicts.industries_hierarchy()ih.head()ind_fcode            name   parent
0   indroot  *DJ Industries
1        i0     Agriculture  indroot
2    i01001         Farming       i0
3    i03001     Aquaculture   i01001
4  i0100144   Cocoa Growing   i01001Using Proxy on requestsTo enable the proxy options on each request call, you need to set this environment variables$ export HTTP_PROXY="http://10.10.1.10:3128"
$ export HTTPS_PROXY="http://10.10.1.10:1080"To use HTTP basic auth with your proxy, use thehttp://user:password@host/syntax in any of the above configuration entries$ export HTTPS_PROXY="http://user:pass@10.10.1.10:1080"
Package: factiva-country-code-mapping
Factiva Country Code MappingThis utility simplifies mapping between country names, DJII region codes, and ISO Alpha 2 country codes. There were two approaches explored in this implementation: the first was to create a list containing lists for each mapping of country name to DJII RC to ISO Alpha 2 code, and the second was to create three separate dictionaries with each of the various data type as keys. This was both a simple exercise to compare O(n) between searching the underlying data using lists vs dictionaries, as well as to actually transform data files being used with the Factiva search engine.InstallationTo install this library, run the following commands.$ pip install -i factiva-country-code-mappingUsageThe utility can be run by adding csv files to the 'process' folder; the input files should contain a newline ('\n') delimited list of country codes, DJII region codes, or ISO Alpha 2 country codes that are to be mapped to either of the other two data types. I've included some sample inputs and outputs in the 'process' folder - both inputs should be uploaded there and outputs will be displayed there. This utlity will output the other two data types by running the following command.python -m country_code_mapping <input file> [method-options] [output-options]<input file>- filename of input file located in the 'process/input' folder[method-options]- method of parsing with the below valuesl = list approach (slower)
        d = dictionaries approach (faster)[output-options]- preferred output format with the below values0 = standard country names
        1 = DJII region codes
        2 = ISO Alpha 2 country codes
Package: factiva-news
This library simplifies the integration to Factiva API services for news-related services.The following services are currently implemented.Snapshots: Allows to run each snapshot creation, monitoring, download and local exploration, in an individual manner. Also allows to run the whole process within a single method.Streams: In addition to creating and getting stream details, contains the methods to easily implement a stream listener and push the content to other locations appropriate for high-available setups.The previous components rely on the API-Key authentication method, which is a prerequisite when using either of those services.InstallationTo install this library, run the following commands.$ pip install --upgrade factiva-newsUsing Library servicesBoth services, Snapshots and Streams are implemented in this library.Enviroment varsTo be able to use Stream Listener options, add the following environment vars depending on your selected listener toolTo use BigQuery Stream Listener
.. code-block:$ export GOOGLE_APPLICATION_CREDENTIALS="/Users/Files/credentials.json"
$ export STREAMLOG_BQ_TABLENAME=project.dataset.tableTo use MongoDB Stream Listener
.. code-block:$ export MONGODB_CONNECTION_STRING=mongodb://localhost:27017
$ export MONGODB_DATABASE_NAME=factiva-news
$ export MONGODB_COLLECTION_NAME=stream-listenerTo define custom directories. If they are not set, the project root path will be used
.. code-block:$ export DOWNLOAD_FILES_DIR=/users/dowloads
$ export STREAM_FILES_DIR=/users/listeners
$ export LOG_FILES_DIR=/users/logsSnapshotsCreate a new snapshot and download to a local repository just require a few lines of code.fromfactiva.news.snapshotimportSnapshotmy_query="publication_datetime >= '2020-01-01 00:00:00' AND LOWER(language_code) = 'en'"my_snapshot=Snapshot(user_key='abcd1234abcd1234abcd1234abcd1234',# Can be ommited if exist as env variablequery=my_query)my_snapshot.process_extract()# This operation can take several minutes to completeAfter the process completes, the output files are stored in a subfolder named as the Extraction Job ID.In the previous code a new snapshot is created using my_query as selection criteria and user_key for user authentication. After the job is being validated internally, a Snapshot Id is obtained along with the list of files to download. Files are automatically downloaded to a folder named equal to the snapshot ID, and contents are loaded as a Pandas DataFrame to the variable news_articles. This process may take several minutes, but automates the extraction process significantly.StreamsCreate a stream instance and get the details to configure the stream client and listen the content as it is delivered.fromfactiva.news.streamimportStreamstream_query=Stream(user_key='abcd1234abcd1234abcd1234abcd1234',# Can be ommited if exist as env variableuser_key_stats=True,query="publication_datetime >= '2021-04-01 00:00:00' AND LOWER(language_code)='en' AND UPPER(source_code) = 'DJDN'",)print(stream_query.create())
Package: factiva-pipelines
Tools for processing and analysing data from Snapshots and Streams.InstallationTo install this library, run the following commands.$ pip install --upgrade factiva-pipelinesUsing Library servicesCreate a new snapshot and download to a local repository just require a few lines of code.fromfactiva.pipelinesimportsnapshot_filesassffromfactiva.pipelinesimportmetadataasfmall_articles=sf.read_folder('./nag6oqitd2',only_stats=True)all_articles=fm.expand_country_codes(covid)all_articles=fm.expand_industry_codes(covid)In the previous code a folder from a Snapshot download is read fully into a Pandas Dataframe. Then, some metadata codes are expanded into new columns with their human-readable texts.
Package: factli
DBoeS-statsCollectors of reputation metrics of public speakers in social media platformsAboutThe Facebook directory-extracts posts of accounts and creates individual folders containing JSON response for every account (get_posts.py).extracts content from CrowdTangle and creates CSV files containing the statistics of facebook pages (get_list.py).Storing API Access TokenStore your API access token in a python file, name it Access_Token.py and save it in the DBoeS-stats directory.
In the file store the access token as:access_token = "API access token generated from your crowd tangle account"InstallationInstallpoetryClone repositoryIn the directory runpoetry installRunpoetry shellto start development virtualenvRunfactli.Cloning this RepoTo clone this repository type:git clone https://github.com/Leibniz-HBI/DBoeS-stats.gitUsageusage: factli [OPTIONS]

Options:
  --list_id TEXT       Saved List ID
  --count INTEGER      Number of posts returned per call, maximum 100,
                       defaults to 10
  --access_token TEXT  Your unique access token
  --start_date TEXT    Start Date (older), Format=YYYY-MM-DD, if not given
                       defaults to NULL
  --end_date TEXT      End Date(newer), Format=YYYY-MM-DD, if not given
                       defaults to current date
  --time_frame TEXT    The interval of time to consider from the endDate. Any
                       valid SQL interval, eg: "1 HOUR" or "30 MINUTE"
  --log_level TEXT     Level of output detail (DEBUG, INFO, WARNING, ERROR).
                       Warnings and Errors are               always logged in
                       respective log-files `errors.log` and `warnings.log`.
                       Default: ERROR
  --log_file TEXT      Path to logfile. Defaults to standard output.
  --sched TEXT         If given, waits "sched" hour(s) and then repeats.
  --notify TEXT        If given, notify email address in case of unexpected
                       errors. Needs further setup. See README.
  --path TEXT          If given, stores the output at the desired location
                       (Absolute Path needed)
  --help               Show this message and exit.Email notifications with the-nargument useyagmail.OutputOutput of get_posts.py stores the raw JSON response in the following folder structure:Facebook/results/list_id/account_id/start-date_end-date.jsonAn example of the JSON data can be viewedhere.Ensure that factli is continuously running, even after restartIf your system can run cronjobs, stop twacapic, runcrontab -eand add the following to your crontab:30 6 * * *    sh -c "cd PATH/TO/YOUR/DBoeS-stats/WORKING/DIRECTORY && PATH/TO/Poetry-env run factli [YOUR ARGUMENTS HERE]" >> out.txt 2>&1This will start collection at 0630Hr (GMT) everyday.
Package: factlog
Links:Repository(at GitHub)Issue tracker(at GitHub)PyPITravis CIFactlog logs your activity on files and uses it for searching.Rich command line interface.  Useful for unix style searching:factlog list | xargs grep 'def record'“I want to see some Python files I edited recently”:factlog list --include-glob '*.py'“I want to list files in this particular project I touched.  I don’t
care about in which branch I opened the file.”:factlog list --under BRANCH-A --under BRANCH-B --relative“I want to see last 50 notes I took with title”:factlog list --under MY-NOTE-DIRECTORY --relative --title --limit 50“The files I touched are huge.  I want to search only the locations
I touched.”:factlog list -C 50 | grep 'def record'Editor pluginFactlog currently only have Emacs integration.  If you make a plugin
for other editors, please let me know.  Seeinterfaces for pluginfor more information.EmacsFactlog has Emacs plugin.
You can getfactlog.elfrom factlogrepository.Command line programsFactlog is easy to integrate with command line programs such aslessandvim.  Seeshell/config.shfor sample setup.
You can useshell/config.shlike this:source PATH/TO/factlog/shell/config.sh
alias less="factlog-record-wrapper \less"
alias vim="factlog-record-wrapper \vim"Interfaces for pluginCommand line interfaceIf your editor can run a command line program, it is possible to
write a factlog plugin!  Seefactlog record--help.RPC interfaceWork in progress…Python interfaceWork in progress…More to come / ideasRanking based on many data points: how many times you
write to the file, how recent you visited the file, etc.Understand “project” (VCS repository).Concurrent grep.Extract URLs in the documents and use them as URL bookmark.LicenseFactlog is licensed under LGPL v3.
See COPYING for details.
Package: factocli
UNDER DEVELOPMENTFacto-CliIs a colorfull interactive Cli for managing a Factorio Headless Server written in Python 3.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.InstallingA step by step series of examples that tell you how to get a development env runningSay what the step will beGive the exampleAnd repeatuntil finishedEnd with an example of getting some data out of the system or using it for a little demoRunning the CliExplainingAuthorEdris KeyamLicenseThis project is licensed under the MIT License
Package: factom-harmony-connect
GETTING STARTEDThis section contains a summary of the steps required to get started
with Python Connect SDK installation.System RequirementsIn order to use this Python SDK, you will need the following tool:Python version >= 3.5InstallationPublished packagepip install factom-harmony-connectOpen-source packageClone the repoSetup a virtual environment (optional)Install dependenciespip install -r requirements.txtTo use the SDK, you have to import:from factom_sdk import FactomClientFor robust documentation, see the github repositoryHERE
Package: factoradic
Thefactorial number system(also known asfactoradic) is a way of
representing an integer as the sum of multiples of factorials. All
integers have a unique representation in the factoradic number system.
For example, the number 1337 can be represented as:1*6! + 5*5! + 0*4! + 2*3! + 2*2! + 1*1! + 0*0!with coefficients 1 5 0 2 2 1 0. This is the unique factoradic
representation of decimal 1337.Factoradic numbers have uses in combinatorics, particularly in the
numbering of permutations. Thisfactoradiclibrary is useful for
converting to and from factoradic number representations both in
Python and from the command-line.StatusInstallationThefactoradicpackage is available on the Python Package Index (PyPI):The package supports Python 3 only. To install:$ pip install factoradicPython InterfaceFor full help:>>> import factoradic
>>> help(factoradic)In the meantime, here are some highlights.To convert from an integer to factoradic useto_factoradic():>>> factoradic.to_factoradic(1337)
[0, 1, 2, 2, 0, 5, 1]The result is the list of coefficients where the factorial of each
zero-based index gives a place value, and the item at that index is
the coefficient by with the place value is to be multiplied. The
elements are from least-significant to most-significant. Since the
coefficient at any index must be less that or equal to the index,
the coefficient at index 0 is always 0.To convert from factoradic usefrom_factoradic():>>> factoradic.from_factoradic([0, 1, 2, 2, 0, 5, 1])
1337Command-Line InterfaceThere’s also a handy command-line interface. Runfactoradic--helpto see a list of commands:$ factoradic --help
Factoradic.

Convert to and from the factorial number system.

Usage:
  factoradic from-integer <integer> [--expression]
  factoradic to-integer <coefficient-0> [<coefficient-1> [<coefficient-n>...]]

Options:
  -e --expression  Show as a mathematical expression.To convert from an integer to factoradic, use thefrom-integersubcommand:$ factoradic from-integer 1729
0 1 0 0 2 2 2The coefficients are reported from least-significant to most-significant.
The see the results as a math expression, specify the--expressionflag:$ factoradic from-integer 1729 --expression
2*6! + 2*5! + 2*4! + 0*3! + 0*2! + 1*1! + 0*0!To convert from factoradic representation use theto-integersubcommand,
specifying the coefficients from least-significant to most-significant:$ factoradic to-integer 0 1 0 0 2 2 2
1729
Package: factor-analysis
Factor AnalysisYour data is factorized into latent variables and noise parameters all within the same sample.mdenotes sample length,ndenotes number of features for the data samplekdenotes number of latent features to be represented for the data sampleλdenotes the factorzdenotes the latent variable of sizemxkϵdenotes the noise parameters of sizemx ``nψ` denotes the covariance of `ϵ`Factor Analysis equationx = μ + λz + ϵWe determineλandψusing posterior distribution ( z | x ) by expectation maximisation. The method is useful to predict the factor variables from a posterior distribution known to the user provided the data you are processing can be fit into the equation.importtensorflowastff=factor_analysis.factors.Factor(data,factor_analysis.posterior.Posterior(covariance_prior,means))noise=factor_analysis.noise.Noise(f,f.posterior)withtf.Session()assess:print(f.create_factor().eval())print(noise.create_noise(f.create_factor()).eval())
Package: factorcalc
factor.f(integer)factor.f(int) -> list of int – provides a list of all the factors of the first argumentfactor.gcf(num1, num2)factor.gcf(translatable to int, translatable to int) -> int – provides the greatest common factor of the integers in the first argumentfactor.prime(integer)factor.prime(int) -> bool – checks if the first argument is primefactor.primes(minimum, maximum)factor.primes(int, int) -> list of int – returns all prime numbers within the range of the first and second arguments including the arguments themselvesmult(number, quantity)factorcalc.mult(int, int > 0) -> list of int – returns a list containing, in order, multiples of the first argument with the second argument being its length
This module does not relate to factor calculation but to multiple calculation. It has been added beause it might be of use.
Package: factordb-pycli
factordb-pythonTheFactorDBis the database to store known factorizations for any number.
This tool can use on your command line.
And also you can use this tool with python 2 & 3 scripts.InstallationThe easiest way to install factordb-pycli is to usepip:$pipinstallfactordb-pyclior, if you are not installing in avirtualenv:$sudopipinstallfactordb-pycliIf you have the factordb-pycli installed and want to upgrade to the latest version you can run:$pipinstall--upgradefactordb-pycliGetting StartedCLIIf you want to know the result of factorization of 16, you should type like this:$factordb16Then, you can get the answer from factordb.com.$factordb162222If you want to know more detail of result, you can get an answer of JSON format.$factordb--json16{"id":"http://factordb.com/?id=2","status":"FF","factors":[2,2,2,2]}Library usageIf you want to use this script with Python, you should typeimportstatement on your code like this:from factordb.factordb import FactorDBThen, you can get the answer with Python lists.In [1]: from factordb.factordb import FactorDB

In [2]: f = FactorDB(16)

In [3]: f.get_factor_list()
Out[3]: []

In [4]: f.connect()
Out[4]: <Response [200]>

In [5]: f.get_factor_list()
Out[5]: [2, 2, 2, 2]

In [6]: f.get_factor_from_api()
Out[6]: [['2', 4]]

In [7]: f.get_status()
Out[7]: 'FF'LicenseMIT
Package: factordb-python
factordb-pythonTheFactorDBis the database to store known factorizations for any number.
This tool can use on your command line.
And also you can use this tool with python 2 & 3 scripts.InstallationThe easiest way to install factordb-pycli is to usepip:$pipinstallfactordb-pyclior, if you are not installing in avirtualenv:$sudopipinstallfactordb-pycliIf you have the factordb-pycli installed and want to upgrade to the latest version you can run:$pipinstall--upgradefactordb-pycliGetting StartedCLIIf you want to know the result of factorization of 16, you should type like this:$factordb16Then, you can get the answer from factordb.com.$factordb162222If you want to know more detail of result, you can get an answer of JSON format.$factordb--json16{"id":"http://factordb.com/?id=2","status":"FF","factors":[2,2,2,2]}Library usageIf you want to use this script with Python, you should typeimportstatement on your code like this:from factordb.factordb import FactorDBThen, you can get the answer with Python lists.In [1]: from factordb.factordb import FactorDB

In [2]: f = FactorDB(16)

In [3]: f.get_factor_list()
Out[3]: []

In [4]: f.connect()
Out[4]: <Response [200]>

In [5]: f.get_factor_list()
Out[5]: [2, 2, 2, 2]

In [6]: f.get_factor_from_api()
Out[6]: [['2', 4]]

In [7]: f.get_status()
Out[7]: 'FF'LicenseMIT
Package: factored
IntroductionFactored is a comprehensive 2-factor authentication system that works with any web technology. It intercepts the web request and makes sure the user does not see anything unless they first authenticate, seamless to any webapplication. It is compatible with NGINX, Apache Traffic Server and any app that uses LUA or WSGI.Read the docsCreditFor implementation, customizations or the factored manager product that centralizes factored configuration, please contact us athttps://wildcardcorp.cominfo@wildcardcorp.com715.869.3440Notes on RequirementsIf you wish to use the LDAP auto user finder, then you need:python-ldappython librarylibldap2-devsystem librarylibsasl2-devsystem libraryIf you wish to use sqlite (IE to get the default dev settings to work), then
you need:libsqlite3-devsystem libraryChangelog4.1.0 (2021-10-11)bump boostrap dependencyremove jquery dependencytweak auth list style to better present buttons4.0.10 (2019-01-07)bump requests version4.0.9 (2018-08-02)fix redirect location generation4.0.8 (2017-07-27)bug fixes4.0.7 (unreleased)fix ats plugin to be compatible with ats 7.2.0add sms plugin4.0.6 (2017-04-17)add LDAPAutoUserFinder4.0.4 (2016-01-27)be able to set valid message ids4.0.3 (2015-06-08)use waitress in examples instead4.0.2 (2015-05-18)use form text overrides properly4.0.1 (2015-04-23)pyramid 1.5 compatibility4.0a3 (2014-12-11)fix template customization registrationadd factored headerupgrade bootstrapbe able to plug in different db backends3.0.5 (2014-09-10)fix bad packaging3.0.4 (2014-09-10)do not require specific version of sqlalchemy3.0.3 (2014-04-18)url decode for web server plugins to properly check auth tkt3.0.2 (2014-04-18)better error handling for web server plugins3.0.1 (2014-04-17)distribution fixes3.0 (2014-04-17)better docschange script namesats and nginx plugins2.2 (2014-04-16)move all form logic to plugin so everything can be overridden2.1 (2013-06-04)script and template fixes2.0rc1 (2013-01-31)more robust multi-use environment with database connectionssupport more algorythms for auth ticketrefactor so it’s more modularbe able to easily customize all templatesbe able to customize textpull out auth_tkt module of paste so we can customize a bit1.1a2 (2012-03-26)specify appname to customize google auth code entry.redirect to original url if possiblebe able to provide “remember me” functionality1.1a1 (2012-03-26)add auto user finder supportadd ability to send google code reminders. This
can work well with the autouserfinder1.0a1 (2012-03-23)Initial release
Package: factoree-ai-pipeline
factoree_ai_pipelinefactoree_ai_pipelineis a Python library to connect to factoree.ai ETL.InstallationUse the package managerpipto installfactoree_ai_pipeline.pipinstallfactoree_ai_pipelineUsagefactoree_ai_pipeline.bronze_to_silver_runnerimportBronzeToSilverRunner,TransformUtility,TransformTupleTransformUtility classfactoree_ai_pipeline.bronze_to_silver_runner.TransformUtilityis an abstract class which implements raw data file transformation from a client-defined format to the standardized format defined by factoree.ai.The implementation of the class is client- and site-specific and should contain.transform()method which returns an object of theTransformTupletype.BronzeToSilverRunner classfactoree_ai_pipeline.bronze_to_silver_runner.BronzeToSilverRunneris an abstract class which reads raw data files from the bucket they were uploaded to, performs transformation, and writes standardized output to a next level bucket.The implementation of the class is client- and site-specific and receives an object of the implementedTransformUtilityclass as an input.LicenseFor licensing seeLICENSE.txt.