Saravanan666/pypi-org huggingface dataset:
Package: esdrt.theme
IntroductionProduct containing the theme for the Effort Sharing Decission Review ToolChangelog1.35 (2019-11-26)[refs #108182] Increasing content width [batradav]1.34 (2017-02-03)[refs #81676] Remove hard coded email addresses. [nituacor]1.33 (2017-01-25)[refs #81024] Use portal title instead of hardcoded string in logo.pt.
[batradav]1.32 (2016-06-23)observationRefCell display block1.31 (2016-05-30)Nothing changed yet.1.30 (2016-05-26)refs #72740, #72748 fix js on observation and conclusion
[lucas]1.29 (2016-04-20)refs #71532 small js fix
[lucas]1.28 (2016-04-01)Fix a small js bug.
[lucas]1.27 (2016-03-16)Refs #70377 message improvements on login page [nituacor]1.26 (2016-02-03)Nothing changed yet.1.25 (2015-10-21)Nothing changed yet.1.24 (2015-10-20)Task #29850: Links to the observation are restricted1.23.1 (2015-09-21)Nothing changed yet.1.23 (2015-09-21)Nothing changed yet.1.22 (2015-09-21)Remove version check [MikelS]1.21 (2015-08-27)Update application title [MikelS]1.20 (2015-08-26)Additional collapsible headings in ‘My view’ [MikelS]1.19 (2015-07-07)Add missing functions1.18 (2015-07-06)Ticket #20628: Show logged in role.  [MikelS]1.17 (2015-06-16)Ticket #22167: Split key flags in two lines [MikelS]Ticket #26252: Tooltip icon info [MikelS]1.16 (2015-06-05)add items suggested by check-manifest [erral]remove ldap-related dependencies from setup.py. Refs #25802 [erral]1.15.5 (2015-05-07)Ticket #24552: Fix observation history1.15.4 (2015-03-10)Add dependencies previously handled by buildout [erral]1.15.3 (2015-01-19)show only workflow relevant roles. Refs #20628 [Mikel Larreategi <mlarreategi@codesyntax.com>]show group name as it comes from LDAP server. Refs #20628 [Mikel Larreategi <mlarreategi@codesyntax.com>]1.15.2 (2014-12-19)Nothing changed. Brown bag release [erral]1.15.1 (2014-12-19)Fix personal viewlet for anonymous users [erral]1.15 (2014-12-19)Show user rolres [erral]Show comments bigger [erral]1.14 (2014-12-18)Adjust markup of the chat-room tab [erral]1.13 (2014-12-10)DateTime showing [MikelS]1.12 (2014-11-25)Style changes [MikelS]1.11 (2014-10-30)Update default font [MikelS]1.10 (2014-10-15)Install dependencies [erral]Remove confidentiality checks [erral]1.9 (2014-10-14)Visualization changes [msantamaria]1.8 (2014-09-02)Javascript fixes [erral]1.7 (2014-08-25)Show attachment links in comments [erral]1.6 (2014-08-20)Update favicon [MikelS]1.5 (2014-08-08)Add collapsible panels to observation [msantamaria]1.4 (2014-07-02)Visualization improvements [msantamaria]1.3 (2014-05-27)Hide the document byline [erral]1.2 (2014-05-08)Add a viewlet with the version number [erral]1.1 (2014-04-28)Style changes
[msantamaria]Register new javascript file
[msantamaria]1.0.1 (2014-04-07)Add missing eea.icons dependency
[erral]1.0 (2014-04-04)Initial release
[erral]
Package: esds
ESDS: An Extensible Simulator For Distributed Systems and Cyber-Physical SystemsWhat is ESDS ?It is a short learning curve and coarse-grain simulator for distributed systems. ESDS contains the building blocks for the simulation ofDistributed Systems,Cyber-Physical Systems(CPS),Wireless Sensors Networks(WSN) etc. It is designed to improve the flexibility/faithfulness of nodes implementations and mitigate the learning curve compare to existing simulators.ESDS is part of a research project. Studies and validation experiments are available online. For more details please visithttp://todo.com.Installation> pip install esdsTo ensure that esds is properly working and installed on your system, you can run the simulation tests with one of the following scripts:tests/run.shtests/run.pyFeaturesEasy to useSmall APIAgent-based: node implementations are located in dedicated python filesWireless interferences detection (i.e not modelization)Node mobility (by updating the network matrices)Nodes plugins (e.g: energy consumption)What ESDS does not implements ?Network protocols (e.g IP/TCP/UDP)Wireless physical layer models (e.g: Friis and Log-Distance models, modulation, RSSI)Routing algorithms (e.g: Shortest path)And much more!Simulation APIapi.argsapi.send(<int>,<data>,<size>,<dst>)api.sendt(<int>,<data>,<size>,<dst>,<t>)api.receive(<int>)api.receivet(<int>,<t>)api.wait(<t>)api.wait_end()api.log(<message>)api.read(<register>)api.turn_on()api.turn_off()More details on the API inexample/sender.pyDocumentationThe documentation of ESDS is available in theuser manual.
Package: esel3d
esel3dEsel3Dis an interactive playground to visualize math in three spatial dimensions.
It is a 3D plotter for curves, surfaces, shapes and more. The redering usesBabylonJSgame engine to produce vibrant visuals and provides intuitive controls right inside a
Google Colab cell.Install#in the first Colab cell
!pip install esel3d

*esel3d will also work with a local Jupyter notebook but with extra configuration. However, it's a bit hacky. Once I find a permanent solution, I'll update.Example Usagefromesel3dimportplot3dimportnumpyasnp#define boundary condition and plot resolutionx_initial=-2x_final=2y_initial=-1y_final=1resolution=0.1#define functiondeffunc(x,y):return-np.power(x,2)#create instance and plot.#instance creation and plot must be in the same cell.plt3d=plot3d()plt3d.surface(func,[xinitial,xfinal],[yinitial,yfinal],resolution)plt3d.plot()NavigateExplore and view the plot from all angles in 3D space.ConfigurableAdd custom labels. Change the color scheme of the axis. Chose the background color which is the most appealing. Plot up to 3 sufaces.InspectWith the point explorer gizmo, users can view point location.Tutorials and ExamplesTuts and examples will be provided in the form of YouTube videos with accompanying example notebooks.Demo-NotebookTODOAdd configs for Jupyter Notebook compatibilityAdd animation optionMake 3D cylindrical gridMake 3D polar gridMake vector field plot
Package: esenin
Esenin pythonPython wrapper for json requests toesenin-server.InstallationpipinstalleseninUsagefromeseninimportClientnlp=Client(ip="127.0.0.1",port="9000")print(nlp.get_pos("Мама мыла раму."))id=nlp.fit_topics([["Мама","мыла","раму"],["Мама","мыла","окно"],["Мама","мыла","пол"]])print(nlp.get_topics(id["id"],"Мама"))Functions.get_pos(string)Takes arbitraryrussiantext and returns Part Of Speech tags.Seeesenin-serverfor example of request and response..fit_topics(list of lists of string, int)Takes list of documents, where document is a list of terms, and number of topics.
Trains topic modeling algorithm with given terms and number of topics.Returns the id of trained model, it's used inget_topicsfunction.Seeesenin-serverfor example of request and response..get_topics(id, string)Takes id of trained topic model and a term.
Returns probabilities of a term to be in each topic.Seeesenin-serverfor example of request and response.
Package: eseq
#eSeq: process sequenceintroductionThe tool known as eSeq can offer methods for process various sequences. Those sequence could be DNA, RNA, amino acid sequence, etc.Detect specific sequence, namely palindromic, similarity etc.Iterator of sequenceTrim adapter from sequence.Detect motifquick installationpip install eseqdevlopmentLaunch virtual environment.git clone git@github.com:Tiezhengyuan/bio_sequence.git
cd bio_sequence
source venv/bin/activateRun unit testspytest tests/unittestsQuick tourimport eseq
Package: eseries
The E-series is a system of preferred numbers used with electronic
components such as resistors and capacitors. For example, the E6
series contains six values (10, 15, 22, 33, 47, 68) which cover a
one-order of magnitude range of values (one decade) from 10 to 99.
These base values repeat again to cover the next decade from 100
to 999, as 100, 220, 330, 470, and 680.Thiseserieslibrary is useful for selecting values from the
standard E3, E6, E12, E24, E48, E96 and E192 decades.StatusInstallationTheeseriespackage is available on the Python Package Index (PyPI):The package supports Python 3 only. To install:$ pip install eseriesPython InterfaceFor full help:>>> import eseries
>>> help(eseries)In the meantime, here are some highlights.To find the nearest E24 value to 319 use:>>> from eseries import find_nearest, E24
>>> find_nearest(E24, 319)
330To find the next value greater-than or equal-to 184 in the E96 series
use:>>> from eseries import find_greater_than_or_equal, E96
>>> find_greater_than_or_equal(E96, 184)
187To find a few values around the specified value, use:>>> from eseries import find_nearest_few, E24
>>> find_nearest_few(E24, 5000)
(4700, 5100, 5600)Command-Line InterfaceThere’s also a handy command-line interface. Runeseries--helpto see a list of commands:$ eseries --help
 eseries

 Usage: eseries [options] <command> [<args> ...]

 Options:
   -h --help     Show this screen.
   -v --verbose  Use verbose logging

Available commands:
   ge
   gt
   help
   le
   lower-tolerance-limit
   lt
   nearby
   nearest
   range
   series
   tolerance
   tolerance-limits
   upper-tolerance-limit

 See 'eseries help <command>' for help on specific commands.To find a nearby value, use:$ eseries nearest E24 37726
39e3If you prefer an SI exponent symbol, supply--symbolor-s:$ eseries nearest E24 37726 -s
39 kTo show values around the given value, use thenearbycommand:$ eseries nearby E48 52e6 -s
48.7 M
51.1 M
53.6 MTo show the smallest value greater than or equal to the given value, use thegecommand:$ eseries ge E48 52e3 -s
53.6 kTo show the upper and lower tolerance limits of a nominal value, use thetolerance-limitscommand:$ eseries tolerance-limits E48 35
34.3
35.7To show all values in an inclusive range, use therangecommand:$ eseries range E6 74e-9 34e-6 --symbol
100 n
150 n
220 n
330 n
470 n
680 n
1 µ
1.5 µ
2.2 µ
3.3 µ
4.7 µ
6.8 µ
10 µ
15 µ
22 µ
33 µTestingTesting is performed for all supported versions usingtox. You’ll need to ensure that the required
Python versions are available in your environment. For example, if you’re usingpyenv, do:$ pyenv local 2.7.18 3.6.12 3.7.9 3.8.1 3.9.1before runningtox:$ toxDeployment to PyPI$ pip install -e ".[dev]"
$ bumpversion patch
$ python setup.py sdist bdist_wheel
$ twine upload --config-file <path>/credentials.pypirc dist/*
$ git push origin
Package: esetconnect
ESET ConnectAPI Docshttp://epcpublicapi-test.westeurope.cloudapp.azure.com/swagger/QuickstartInstallpipinstallesetconnectGet detectionsfromesetconnectimportEsetConnectUSERNAME="username"PASSWORD="password"withEsetConnect(USERNAME,PASSWORD)asec:fordetectioninec.get_detections().detections:print(detection.json()){"category":"DETECTION_CATEGORY_UNSPECIFIED","context":{"circumstances":"Event occurred on a modified file.","device_uuid":"83c7522f-f4a7-4a80-a055-8e1329201129","process":{"path":"C:\\Program Files\\Google\\Chrome\\Application\\chrome.exe"},"user_name":"DESKTOP-7DAP322\\username"},"display_name":"Eicar","network_communication":null,"object_hash_sha1":"3395856CE81F2B7382DEE72602F798B642F14140","object_name":"file:///C:/Users/username/AppData/Local/Temp/2f7545df-d894-4768-8741-944d7ef059f6.tmp","object_type_name":"File","object_url":"","occur_time":"2022-12-07T09:26:13","responses":{"description":null,"device_restart_required":null,"display_name":null,"protection_name":null},"severity_level":"SEVERITY_LEVEL_MEDIUM","type_name":"nil","uuid":"ec879237-d1c3-3742-ecab-05fed9ea9a58"}
Package: esets
esetExtended sets - support for set complementOverviewAnesetworks like a normal pythonsetexcept that you can invert it to generate its
complement.  For example, let's say you have the following:>>> from esets import eset
>>> s = eset(['hello', 'there'])
>>> s_invert = ~s
>>> s_invert
~eset(['hello', 'there'])In this example,s_invertcontains everythingexcept'hello'and'there'.LogicAll the logic operations you'd expect from sets are available in esets, including
intersection, union, difference, and symmetric difference.  Use the&,|,-, and^operators respectively.Similarly, conditional expressions are available to determine subset relationships.
IfA <= B, that A is a subset of B.InfinitiesYou'll note there's aninf.pymodule that implementא‎0andא‎1infinities, that
is, countable and uncountable infinities. This was implemented so that cardinality
calculations would be correct for complement sets: discussed below.>>> from esets import inf
>>> inf.countable, inf.uncountable
(א‎0, א‎1)
>>> inf.countable < inf.uncountable
TrueCardinalityTheabsexpression (alsocardinalitymethod) will return a countably infinite scalar
if in complement mode; otherwise it returns the count of items in set.>>> from esets import eset, inf
>>> s = eset(['hello', 'there'])
>>> abs(s)
2
>>> abs(~s)
א‎0
Package: esfdw
esfdw is a PostgreSQLforeign data
wrapperfor Elasticsearch.Elasticsearch is widely used for document and log data storage, in
particular as part of theELK
stack. esfdw
allows PostgreSQL to be used as a query engine for data stored in
Elasticsearch. Use cases include:Writing SQL JOIN queries against Elasticsearch documents and letting the PostgreSQL engine do the heavy liftingRunning window functions on data stored in ElasticsearchApplying PostgreSQL aggregations that do not currently have a native Elasticsearch equivalentesfdw depends onMulticorn, a PostgreSQL
extension for writing foreign data wrappers in Python.
Package: esFrontLine
# esFrontLineLimit restful requests to backend ElasticSearch cluster:  Queries for the public,## Requirements* Python 2.7 or 3.6.2+* An ElasticSearch cluster to forward queries to## InstallI will assume you have Python installed (if not, here are [Windows7 instructions](https://github.com/klahnakoski/pyLibrary#windows-7-install-instructions-))pip install esFrontLine## SetupYou must write your own setting.json file with the following properties set:* **elasticsearch** - (Array of) ElasticSearch nodes* **elasticsearch.host** - URL of the ElasticSearch node that will accept query requests* **elasticsearch.port** - port for ES (default = 9200)* **flask** - flask.run() parameters (default port = 5000)* **whitelist** - list of indexes that are allowed* **users** - list of allowed HAWK users, with their linked resources* **users.resources** - list of indexes the user is allowed* **users.hawk** - object of [Hawk credentials](https://github.com/hueniverse/hawk/blob/master/README.md)* **users.hawk.id** - any human readable name to identify the user or application* **users.hawk.key** - the secret value held by both endpoints and not shared with anyone* **users.hawk.algorithm**: always "sha256" for now* **debug** - for debuggingHere is an example of my ```settings.json``` file{"elasticsearch":[{"host":"http://elasticsearch4.metrics.scl3.mozilla.com","port":9200},{"host":"http://elasticsearch5.metrics.scl3.mozilla.com","port":9200},{"host":"http://elasticsearch7.metrics.scl3.mozilla.com","port":9200},{"host":"http://elasticsearch8.metrics.scl3.mozilla.com","port":9200}],"flask":{"host":"0.0.0.0","port":9292,"debug":false,"threaded":true,"processes":1},"users": [{"hawk": {"id": "kyle@example.com","key": "secret","algorithm": "sha256"},"resources": ["testing"]}],"whitelist":["bugs", "org_chart", "bug_summary", "reviews"],"debug":{"log":[{"filename": "./tests/results/logs/app.log","maxBytes": 10000000,"backupCount": 200,"encoding": "utf8"},{"stream":"sys.stdout"}]}}## Executionpython app.py --settings-file <path_to_file_with_JSON_settings>## Code Source[https://github.com/klahnakoski/esFrontLine](https://github.com/klahnakoski/esFrontLine)
Package: esg-coverage
The Analyst Pathway in ESGThe Role of Analyst Coverage in Translating ESG Ratings into Stock PerformanceDocumentation:https://esg-coverage.entelecheia.aiGitHub:https://github.com/entelecheia/esg-coveragePyPI:https://pypi.org/project/esg-coverageThis study investigates the role of financial analysts in disseminating Environmental, Social, and Governance (ESG) information to the market and its subsequent impact on stock performance. While the direct causal relationship between ESG ratings and analyst coverage remains unclear, we propose that analysts serve as an informational pathway, translating ESG ratings into stock performance. Using panel data analysis, we examine the relationship between ESG ratings, analyst coverage, and stock performance. Our findings suggest that companies with higher ESG ratings do not necessarily attract more analyst coverage, but the coverage they receive plays a crucial role in shaping investor perceptions and influencing stock performance. This research highlights the importance of financial analysts in the ESG ecosystem and underscores the need for more comprehensive and accurate coverage of ESG topics. Our findings have implications for companies, investors, and policymakers interested in sustainable investing and corporate social responsibility.ChangelogSee theCHANGELOGfor more information.ContributingContributions are welcome! Please see thecontributing guidelinesfor more information.LicenseThis project is released under theMIT License.
Package: esgfpid
# esgf-pidesgf-pid is a python library that is part of the ESGF data publication. It is responsible for triggering PID creation. To do this, it sends JSON messages to a RabbitMQ Messaging Queue.The library is tested using python 2.7 and recently using python 3.7.Up to version 0.7.17, pika==0.11.2 should be used. After that, pika>=1.0.1 should be used.Access to a RabbitMQ server with the necessary queues is required. The server data has to be provided to the library.## CollaboratorsMerret Buurman (DKRZ)Katharina Berger (DKRZ)mauzey1
Package: esgf-scraper
Repository healthESGF Scraper is a tool for downloading and processing climate data from the Earth System Grid Federation (ESGF). ESGF is an international
collaborative network specifically designed for dissemination of climate model output for large scale projects such as CMIP6.DocumentationSee thedocumentationfor examples of usingesgf-scraperInstallationesgf-scraper is not currentlypipinstallable as the code is not publically accessible. To install, you must checkout the
code fromGitLab.git clone git@gitlab.com:magicc/esgf-scraper.git
cd esgf-scraper
make virtual-environment
cp esgf_scraper.conf.sample ~/esgf_scraper.confAfter this has been completed, the configuration file will need to be updated with new configuration. See TODO for more information
about the contents of the configuration file.ContributingIf you’d like to contribute, please make a pull request!
The pull request templates should ensure that you provide all the necessary information.LicenseThis package is licensed under aMIT license, unless noted otherwise for specific parts.
Package: esgf-test-suite
Python nosetests scripts for ESGF integration test and validation## Purpose and limits of this tool:ESGF Test Suite is a full python application. It is designed to perform integration tests on ESGF nodes. At this point of time, the scope is to test a single data node and its three peer services (idp services, index services and compute services).ESGF Test Suite offers to run high level tests from a desktop so the tested node can be validated from the end user perspective.Current developments will also let admins to test and validate the stack by running tests on the node itself.## Requirements:Shell environmentPython 2.6 or higherFirefoxglobus-url-copyCommand for Red Hat / CentOS / Scientifix Linux:yum install python-devel openssl-devel libxml2-devel libxslt-devel globus-gass-copy-progs firefox## Installation:pip install esgf-test-suite## Configuration:vi [installation_dir]/esgf-test-suite/configuration.iniModify the nodes section. If several nodes are specified, they all should be in the same federation. Account section do not need to be modified.## Usage:[installation_dir]/esgf-test-suite/runtests.sh
Package: esg-matching
esg-matchingThe esg-matching is a library that is part of the Entity-Matching project developed by OS-Climate Foundation.
Its main purpose is to provide methods to match entities from different data sources.Currently, the library provides three main components:a database engine which can connect to a local Sql-lite database or an Oracle database elsewherea file reader which can read data sources in csv format and load its content to a databasea database-driven matcher which can perform exact matching based on database queries. Three types of matchings are provided: direct, residual and indirect matching.Install from PyPipip install esg-matching
Package: esg-riskmon-climaterisk
CLIMATE RISK ANALYTICS PACKAGEThis package provides the following analytics which can be run end-to-end (retrieval to analytics output):carbon footprint (weighted average carbon intensity - WACI; financed emissions - TCE; owned emissions - OE) for the equity portfolio and benchmark and ftse all cap.share of equity nav which falls in each MSCI low carbon transition scoreimplied temperature rise breakdown by sector and portfolio/benchmark totalattribution of WACI for portfolio t vs portfolio t-1attribution of WACI for portfolio vs benchmarkquality scorecountry-sector change in data qualitycountry-sector concentrationsPLEASE NOTE: if something looks like a bug, walks like a bug and calls(?) like a bug, it's probably a bug. Please contact Ginni to fix/discussStructure, how to install, how to run:This package providesstorages (getting data and validating)services (making calculations / analytics)How to install: pip install git+https://github.com/NBIM/climate_risk.gitThis will pick up every time a version is incremented.How to run: All of these are united in the views folder. In order to run the analytics the user needs to run the scripts in the views, not the other files.You can also use the services autonomously.Important notes on how to use this package:to run most analytics the user will need access to the underlying schemas and tables used.make sure to change the credentials to your snowflake account (email needed)scripts will run for specific portfolios (ftse all cap/eq portfolio/eq benchmark); specific fk_dim_date and specific ICB level. Make sure to change the variables at the beginning of the script to change any of these parameters.Future improvements:tests!remove storage from this package, keep only services. Or at least separate the two.generalise settings/configurations for constants and authorisationsimprove validations upon data retrievalpublished followinghttps://python-packaging.readthedocs.io/en/latest/minimal.html
Package: esguard
esguardesguard provides a Python decorator that waits for processing while monitoring the load of Elasticsearch.Quick StartYou need to launch elasticsearch before quick start.fromesguardimportESGuard@ESGuard(os_cpu_percent=95).decoratordefmock_func(x):returnxself.assertEqual(mock_func(1),1)TestYou need to launch elasticsearch before testing.$dockercomposeup-d--build
$poetryrunpytest
Package: eshop-invoice
eshop-invoice generatorGenerate invoices using python, weasyprint and yaml.Sample InvoiceUsageusage: eshop-invoice [-h] [--template TEMPLATE] [--defaults-file DEFAULTS_FILE]
                   --invoice-data-file INVOICE_DATA_FILE --out-file OUT_FILE
                   [--locale LOCALE]

Create PDF invoices from YAML files../eshop-invoice--invoice-data-fileinvoice.yml--templatedocuments/invoice/template--out-fileout.pdf~/.default-invoice-data.ymlinvoice.ymlcurrency: â‚¬
from:
  name: Kids Eshop
  address:
    street: Dorse Point
    postal_code: "01337"
    city: Dublinto:
  name: Customer Ec2
  customer_id: 10004
  address:
    street: Dorse Point 4
    postal_code: "042424"
    city: Dublin
tax_rate: 19
invoice:
  number: R10006
  date: 16.12.2021
  pay_until_date: 22.12.2021
positions:
  - net_price: 215
    amount: 1
    text: |
      Server
      Windows Server
      Monat 08.2021
  - net_price: 215
    amount: 2
    text: |
      Have a Nice Day