Saravanan666/pypi-org huggingface dataset:
Package: opcsim
opcsimopcsim is a Python library for simulating low-cost Optical Particle Sensors (both Optical Particle Counters and Nephelometers) and
their response to various aerosol distributions.CitationThe paper for this library can be found on the AMT websitehere. It should be cited as:Hagan, D.H. and Kroll, J.H.: Assessing the accuracy of low-cost optical particle sensors using a physics-based approach,Atmos. Meas. Tech., 13, 6343-6355,https://doi.org/10.5194/amt-13-6343-2020, 2020.DocumentationFull online documentation can be foundhere.The docs include atutorial, anexample gallery, and anAPI Reference.In addition, documentation can be built locally for development purposes. To do so, please check out the complete details in thecontributing to opcsimsection of the documentation.DockerIf you are familiar with Docker, there is a Docker image available to get up and running with OPCSIM with ease. To get started
with an ephemeral container with a jupyter lab interface, navigate to your preferred working directory and execute:$dockerrun--rm-p8888:8888-eJUPYTER_ENABLE_LAB=yes-v"$PWD":/home/joyvan/workdhhagan/opcsim:latestOnce executed, you should see the url with token in your terminal that will allow you to bring up the jupyter lab instance.DependenciesOpcsim is supported for python3.6.1+.Installation requiresscipy,numpy,pandas,matplotlib,
andseaborn.InstallationTo install (or upgrade to) the latest stable release:$pipinstallopcsim[--upgrade]To install the development version directly from GitHub using pip:$pipinstallgit+https://github.com/dhhagan/opcsim.gitIn addition, you can either clone the repository and install from source or download/unzip the zip file and install from source using poetry:$gitclonehttps://github.com/dhhagan/opcsim.git
$cd/opcsim
$poetryinstallTestingAll tests are automagically run via GitHub actions and Travis.ci. For results of these tests, please click on the link in the above travis badge. In addition, you can run tests locally using poetry.To run tests locally:$poetryrunpytesttestsDevelopmentopcsimdevelopment takes place on GitHub. Issues and bugs can be submitted and tracked via theGitHub Issue Trackerfor this repository. As ofv0.5.0,opcsimusespoetryfor versioning and managing dependencies and releases.
Package: opcua-tools
opcua-toolsPython/pandas-based tools for OPC UA information models.
OPC UA information models are represented and manipulated in Pandas Dataframes.
There is a parser from NodeSet2 XML files, functions for manipulating/extracting information from information models and a NodeSet2 generator.
There are some general tests here, but most of the testing is indirect through its use inquarryand other internal tools.InstallationTo install, run this command:pip install git+https://github.com/PrediktorAS/opcua-tools.gitUsageForthcoming... see the test cases.LoggingThroughout the package the pythonloggingmodule is used for logging. The
logging module follows a "bubble up" strategy which means the logging message
will be available to the application using the package. In each file a
NullHandler is added as the Handler for logging. As this is a library (or package)
this is recommended from the authors of the logging package (see.here). This means there is
no configuration for logging. If logging to console is desired this must be
setup in the application which uses this package.There is no configuration needed to connect to this package. Simply setting up
logging within the application which uses this library, will cause the logs
within this package to appear. A basic logging configuration can be found in thedocumentationfor the logging
module.LicenseThe code in this repository is copyrighted toPrediktor AS, and is licensed under the Apache 2.0 license.Exceptions apply to some of the test data and static files for parsing/generation (see document headers for license information).Author:Magnus Bakken
Package: opcua-webclient
python-opcua-webclientA OPCUA web client, implemented by Python.Installpipinstallopcua-webclientUserun command:opcua-webclienton Windows use:opcua-webclientif can't find opcua-webclient command, try:python -m opcuawebclientthen use web browser(recommend Chrome or Firefox) openhttp://127.0.0.1:8000, enjoy it.use youself bind address:opcua-webclient 127.0.0.1:8080CHANGES0.0.1first version0.0.2fix bugs0.0.3fix disconnect bug0.0.6fix websocket bug1.0.0support Python3
Package: opd
NAMEOPD - Original Programmer DaemonSYNOPSISopd
opd <cmd> [key=val] [key==val]
opd [-a] [-c] [-v]DESCRIPTIONOPD is a python3 library implementing the 'opd' package. It
provides all the tools to program a bot, such as disk perisistence
for configuration files, event handler to handle the client/server
connection, code to introspect modules for commands, deferred
exception handling to not crash on an error, a parser to parse
commandline options and values, etc.

OPD provides a demo bot, it can connect to IRC, fetch and
display RSS feeds, take todo notes, keep a shopping list
and log text. You can also copy/paste the service file and run
it under systemd for 24/7 presence in a IRC channel.

OPD is Public Domain.INSTALL$ pipx install opd
$ pipx ensurepathUSAGEwithout any argument the program starts itself as a daemon

$ opd
$

if there is already a daemon running the program won't start

$ bin/opd
daemon is already running.

provding a command it will run it in the cli

$ opd cmd
cmd,err,mod,req,thr,ver
$

the -c option starts a console

$ opd -c
>

the -v option turns on verbose

$ opd -cv
OPD CV started Sat Feb 10 13:50:56 2024
>

use mod= to load additional modules

$ opd mod=irc,rss
$

the ``mod`` command shows a list of modules

$ opd mod
cmd,err,fnd,irc,log,mod,req,rss,tdo,thr
$

the -a option will load all available modulesCONFIGURATIONirc

$ opd cfg server=<server>
$ opd cfg channel=<channel>
$ opd cfg nick=<nick>

sasl

$ opd pwd <nsvnick> <nspass>
$ opd cfg password=<frompwd>

rss

$ opd rss <url>
$ opd dpl <url> <item1,item2>
$ opd rem <url>
$ opd nme <url> <name>COMMANDScmd - commands
cfg - irc configuration
dlt - remove a user
dpl - sets display items
fnd - find objects
log - log some text
met - add a user
mre - displays cached output
pwd - sasl nickserv name/pass
rem - removes a rss feed
req - reconsider
rss - add a feed
thr - show the running threadsSYSTEMDsave the following in /etc/systemd/system/opd.service and
replace "<user>" with the user running pipx

[Unit]
Description=Original Programmer Daemon
Requires=network.target
After=network.target

[Service]
Type=simple
User=<user>
Group=<user>
WorkingDirectory=/home/<user>/.opd
ExecStart=/home/<user>/.local/pipx/venvs/opd/bin/opd
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target

then run this

$ mkdir ~/.opd
$ sudo systemctl enable opd --now

default channel/server is #opd on localhostFILES~/.opd
~/.local/bin/opd
~/.local/pipx/venvs/opd/
~/.local/pipx/venvs/opd/share/doc/opd/README.rstAUTHORBart Thate <bthate@dds.nl>COPYRIGHTOPD is Public Domain.
Package: opdb
opdbopcode level debugger for pythoninstallpip install opdbdebugfrom opdb.debugger import debug
debug('sample.cpython-38.pyc')OR$ python debug.py sample.cpython-38.pyc

> /home/ctf/notebook/sample.py(1)<module>()
-> #!/usr/bin/env python
(opdb) s
[INFO] [do_step] [lasti] 0
[INFO] [do_stack] [stack] []
[INFO] [disassemble_string] 0 LOAD_CONST 0 (<code object f at 0x7f77015ec190, file "sample.py", line 1>) 
> /home/ctf/notebook/sample.py(3)<module>()
-> import sys
(opdb) 
[INFO] [do_step] [lasti] 2
[INFO] [do_stack] [stack] [<code object f at 0x7f77015ec190, file "sample.py", line 1>]
[INFO] [disassemble_string] 2 LOAD_CONST 1 ('f') 
> /home/ctf/notebook/sample.py(5)<module>()
-> 
(opdb) 
[INFO] [do_step] [lasti] 4
[INFO] [do_stack] [stack] ['f', <code object f at 0x7f77015ec190, file "sample.py", line 1>]
[INFO] [disassemble_string] 4 MAKE_FUNCTION 0
> /home/ctf/notebook/sample.py(7)<module>()
(opdb) 
[INFO] [do_step] [lasti] 6
[INFO] [do_stack] [stack] [<function f at 0x7f7701bba1f0>]
[INFO] [disassemble_string] 6 STORE_NAME 0(f)
> /home/ctf/notebook/sample.py(9)<module>()
(opdb) 
[INFO] [do_step] [lasti] 8
[INFO] [do_stack] [stack] []
[INFO] [disassemble_string] 8 LOAD_CONST 2 (<code object main at 0x7f77015ec240, file "sample.py", line 1>) 
> /home/ctf/notebook/sample.py(11)<module>()
(opdb) 
[INFO] [do_step] [lasti] 10
[INFO] [do_stack] [stack] [<code object main at 0x7f77015ec240, file "sample.py", line 1>]
[INFO] [disassemble_string] 10 LOAD_CONST 3 ('main') 
> /home/ctf/notebook/sample.py(13)<module>()
...
(opdb)tracefrom opdb.tracer import trace
trace('sample.cpython-38.pyc')OR$ python trace.py sample.cpython-38.pyc 
[INFO] [disassemble_string] 0 LOAD_CONST 0 (<code object f at 0x7f36cca59ea0, file "sample.py", line 1>) 
[INFO] [disassemble_string] 2 LOAD_CONST 1 ('f') 
[INFO] [disassemble_string] 4 MAKE_FUNCTION 0
[INFO] [disassemble_string] 6 STORE_NAME 0(f)
[INFO] [disassemble_string] 8 LOAD_CONST 2 (<code object main at 0x7f36cca60500, file "sample.py", line 1>) 
[INFO] [disassemble_string] 10 LOAD_CONST 3 ('main') 
[INFO] [disassemble_string] 12 MAKE_FUNCTION 0
[INFO] [disassemble_string] 14 STORE_NAME 1(main)
[INFO] [disassemble_string] 16 LOAD_NAME 2(__name__)
[INFO] [disassemble_string] 18 LOAD_CONST 4 ('__main__') 
[INFO] [disassemble_string] 20 COMPARE_OP 2(==)
...patchPatch unreachable code to 'NOP' automatically.from opdb.patcher import patch
patched = patch('sample.cpython-38.pyc')
decompile(patched)OR$ python patch.py sample.cpython-38.pyc 
1
patch: <code object <module> at 0x7fa6960c4df0, file "sample.py", line 1>
patch: <code object f at 0x7fa6964e9f50, file "sample.py", line 1>
patch: <code object main at 0x7fa6963ed920, file "sample.py", line 7>
patched_file: sample.cpython-38.pyc_patched.pyc
Package: opdm-api
OPDMPython implementation of OPDM SOAP API. OPDM is used to exchange Electrical Grid Models between ENTSO-E TSO-s and RSC-sOther relevant API-s:ECPEDXInstallationpip install opdm-apiorpip install --user opdm-apiorpython -m pip install --user opdm-apiUsageInitialiseimport OPDM

service = OPDM.create_client("https://opdm.elering.sise:8443", username="user", password="pass")Upload FileUpload a fileresponse = service.publication_request(file_path_or_objet)Upload all files in a directoryimport glob
imort os

for file_name in glob.glob1(directory_path, "*.zip"):
    service.publication_request(os.path.join(directory_path, file_name))Get File Upload/Publication Reportpublication_report = service.get_profile_publication_report(model_ID)orpublication_report = service.get_profile_publication_report(filename="uploaded_file_name.zip")Subscribe for Model publicationsGet available Publicationsavailable_publications = service.publication_list()Subscribe for BDSavailable publications: BDS, IGM, CGMresponse = service.publication_subscribe("BDS")Subscribe for all IGM-s except RTtime_horizons = [f"{item:02d}" for item in list(range(1,32))] + ["ID", "1D", "2D", "YR"]
for time_horizon in time_horizons:
    print(f"Adding subscription for {time_horizon}")
    response = service.publication_subscribe("IGM", subscription_id=f"IGM-{time_horizon}", metadata_dict={'pmd:timeHorizon': time_horizon})
    print(response)Cancel Subscriptionresponse = service.publication_cancel_subscription(subscription_id)Query DataModelModel consists of multiple filesquery_id, response = service.query_object(object_type = "IGM", metadata_dict = {'pmd:scenarioDate': '2019-07-28T00:30:00', 'pmd:timeHorizon': '1D'})Filequery_id, response = service.query_profile('pmd:timeHorizon': '1D', 'pmd:cgmesProfile': 'SV'})Create nice table of returned Query responsesimport pandas

pandas.set_option("display.max_rows", 12)
pandas.set_option("display.max_columns", 10)
pandas.set_option("display.width", 1500)
pandas.set_option('display.max_colwidth', -1)

print(pandas.DataFrame(response['sm:QueryResult']['sm:part'][1:]))Download a FileDownload to OPDM Client and return local path to the fileresponse = service.get_content(file_UUID)
print(response['sm:GetContentResult']['sm:part'][1]['opdm:Profile']['opde:Content'])Download and Save fileimport base64
response = service.get_content(file_UUID, return_payload=True)
with open(f"{file_UUID}.zip", 'wb') as cgmes_file:
    report_file.write(base64.b64decode(response['sm:GetContentResult']['sm:part'][1]['opdm:Profile']['opde:Content'].encode()))Manage RulesetsList available Rulesetservice.list_available_rulesets()Install Rulesetservice.install_rulesets(version="2.0.122")Get installed Ruleset versionservice.get_installed_ruleset_version()Reset Rulesetservice.reset_ruleset()ExamplesDownload latest BoundaryDownload all BoundariesInstall latest Rule SetUpload IGM
Package: ope
opeAbstracts away runninggnu-parallelon tools with FASTA input and provides parsers for some common
formats produced by said tools.Example usage:ope parallel -j 4 [query.pep.fa] hmmscan --domtblout [results.tbl] -E 1e-05 -o /dev/null /store/biodb/Pfam-A.hmm /dev/stdinHence, the onlygnu-parallelflag one needs to worry about is-jfor the number of cores to use.
Note that you also need to set the input in the actual tool invocation as/dev/stdin.Free software: MIT licenseDocumentation:https://ope.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-12-11)First release on PyPI.
Package: opedia
OpediaOpedia is an open source database service to integrate, visualize, and analyze ocean datasets such as satellite data, in-situ observations, and model outputs. The project is supported bySimons Foundation.Documentationhttps://cmap.readthedocs.io/en/latest/System RequirementsAnaconda Distribution Pyhton 3.7 -- 2.7Make sure python is in system path.InstallationSet up database drivers (only required on macOS).installhomebrew; a package manager for macOS.ODBC driver provides the connection to the Opedia database.$ /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"
$ brew install unixodbc freetds ffmpegffmpeg package is used to generate video contents.InstallPython Anaconda Distribution.Make sure python is added to the system path.  Anaconda distribution covers the majority of python modules used by Opedia. Finally pip-install the opedia package itself.$ pip install opediaAuthorMohammad Dehghani Ashkezarimdehghan@uw.edu
Package: opedia-dataset-validator
opedia_dataset_validatorA tool to detect errors in anOpedia dataset Excel file.InstallFrom sourcepythonsetup.pyinstallFrom PyPi withpip# Probably should put this in a virtual environmentpipinstallopedia_dataset_validatorUsageUsage: opedia_dataset_validator [OPTIONS] INPUT OUTPUT

  A tool to validate Opedia dataset files.

  INPUT should be an Opedia dataset Excel file conforming to the
  sepcification at
  https://github.com/mdashkezari/opedia/tree/master/template.

  OUTPUT should be an output file path or - for STDOUT. Output will be in
  the form of a tab-delimited text file encoded in UTF-8.

Options:
  -a, --all-errors  Print all errors. [default: only the first error of each
                    kind]
  -v, --value       Print cell values in error report. [default: False]
  --version         Show the version and exit.
  -h, --help        Show this message and exit.Run testsInstallpytestand this package in a virtual environment, then runpytestfrom the root of the source directory. Or, to test against Python 2 and 3, installtoxand then runtoxfrom the root of the source directory.
Package: open311
A Python API wrapper for the Open311 API v2.UsageThe Python wrapper follows closely with the structure of the [Ruby
Open311 API wrapper](https://github.com/codeforamerica/open311).>>> from open311 import Open311
>>> o = Open311()>>> # If you forgot to configure your instance.
... o.configure(endpoint='http://open311.endpoint.com',
...             api_key='my_api_key', jurisdiction='endpoint.com')>>> # You can also reset your instance to its original state.
... o.reset()>>> # Receive a list of services available -- in dictionary form.
... o.service_list()>>> # Specific service definition.
... o.service_definition('033')>>> # Service requests.
... o.service_requests()>>> # Get a specific service request.
... o.get_service_request('638344')>>> # Post a service request.
... o.post_service_request(
...     service_code='001', address_string='123 Any Street',
...     first_name='John', last_name='Smith',
...     phone='111-111-1111', email='me@email.com',
...     description='A large sinkhole is destroying the street',
...     media_url='http://imgur.com/123_street_sinkhole.png')>>> # Get a request id from a token.
... o.request_id_from_token('123456')CopyrightCopyright (c) 2011 Code for America Laboratories.See LICENSE for details.
Package: open3d
Open3D is an open-source library that supports rapid development of software
that deals with 3D data. The Open3D frontend exposes a set of carefully selected
data structures and algorithms in both C++ and Python. The backend is highly
optimized and is set up for parallelization. We welcome contributions from the
open-source community.If you use Open3D in an academic project, please cite our paper:@article{Zhou2018,
    author    = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
    title     = {{Open3D}: {A} Modern Library for {3D} Data Processing},
    journal   = {arXiv:1801.09847},
    year      = {2018},
}Core features3D data structures3D data processing algorithmsScene reconstructionSurface alignment3D visualizationPhysically based rendering (PBR)3D machine learning support with PyTorch and TensorFlowGPU acceleration for core 3D operationsAvailable in C++ and PythonSupported platformsThe package has been tested on:Ubuntu 18.04 and 20.04Windows 10 64-bitmacOS High Sierra and aboveWith Python versions:3.83.93.103.11ResourcesWebsite:www.open3d.orgCode:github.com/isl-org/Open3DDocumentation:www.open3d.org/docsLicense:The MIT license
Package: open3d-cpu
Open3D is an open-source library that supports rapid development of software
that deals with 3D data. The Open3D frontend exposes a set of carefully selected
data structures and algorithms in both C++ and Python. The backend is highly
optimized and is set up for parallelization. We welcome contributions from the
open-source community.If you use Open3D in an academic project, please cite our paper:@article{Zhou2018,
    author    = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
    title     = {{Open3D}: {A} Modern Library for {3D} Data Processing},
    journal   = {arXiv:1801.09847},
    year      = {2018},
}Core features3D data structures3D data processing algorithmsScene reconstructionSurface alignment3D visualizationPhysically based rendering (PBR)3D machine learning support with PyTorch and TensorFlowGPU acceleration for core 3D operationsAvailable in C++ and PythonSupported platformsThe package has been tested on:Ubuntu 18.04 and 20.04Windows 10 64-bitmacOS High Sierra and aboveWith Python versions:3.83.93.103.11ResourcesWebsite:www.open3d.orgCode:github.com/isl-org/Open3DDocumentation:www.open3d.org/docsLicense:The MIT licenseThis wheel only contains CPU functionality. Use the open3d wheel for full functionality.
Package: open3d-python
Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community.If you use Open3D in an academic project, please cite our paper:@article{Zhou2018,
    author    = {Qian-Yi Zhou and Jaesik Park and Vladlen Koltun},
    title     = {{Open3D}: {A} Modern Library for {3D} Data Processing},
    journal   = {arXiv:1801.09847},
    year      = {2018},
}Core featuresBasic 3D data structuresBasic 3D data processing algorithmsScene reconstructionSurface alignment3D visualizationPython bindingSupported platformsThe package has been tested on:Ubuntu 16.04 with Python 2.7 and 3.5Windows 10 with Python 2.7 and 3.5MacOS High Sierra 10.13.4 with Python 2.7ResourcesWebsite:www.open3d.orgCode:github.com/intel-isl/Open3DDocument:www.open3d.org/docsLicense:The MIT license
Package: open3SPN2
Open-3SPN2A Implementation of the 3SPN.2 and 3SPN.2C coarse-grained molecular model of DNA in OpenMM.3SPN.2 and 3SPN.2C are DNA coarse-grained forcefields developed by thede Pablo group. Each DNA nucleotide is modelled by 3 beads: one bead for the phosphate, a second one for the sugar and a third one nucleobase. These forcefields were adapted by theWolynes groupto model protein-DNA interactions as a complement for theAWSEMcoarse-grained protein forcefield.InstallationInstallation of the open3SPN2 repository is available through anaconda. Some of the dependencies  are contained in the omnia channel (openmm, pdbfixer) or in the conda-forge channel (mdtraj).conda config --append channels conda-forgeconda install -c wolynes-lab open3spn2For protein-DNA simulations you will also need to installopenAWSEMand add the openAWSEM path to the$PYTHONPATHenvironment variable. In linux you can set the path variables on~/.bashrc.export PYTHONPATH=/path/to/openAWSEM:$PYTHONPATHDocumentationFurther documentation and tutorials are hosted inreadthedocs.CitationsIf you publish any work using the open3SPN2 package, please include the following references:Open3SPN2
Lu, W., Bueno, C., Schafer, N. P., Moller, J., Jin, S., Chen, X., ... & Wolynes, P. G. (2021). OpenAWSEM with Open3SPN2: A fast, flexible, and accessible framework for large-scale coarse-grained biomolecular simulations. PLoS computational biology, 17(2), e1008308.https://doi.org/10.1371/journal.pcbi.10083083SPN.2
Hinckley, D. M., Freeman, G. S., Whitmer, J. K., & De Pablo, J. J. (2013). An experimentally-informed coarse-grained 3-site-per-nucleotide model of DNA: Structure, thermodynamics, and dynamics of hybridization. Journal of Chemical Physics, 139(14).https://doi.org/10.1063/1.48220423SPN.2C
Freeman, G. S., Hinckley, D. M., Lequieu, J. P., Whitmer, J. K., & De Pablo, J. J. (2014). Coarse-grained modeling of DNA curvature. Journal of Chemical Physics, 141(16).https://doi.org/10.1063/1.4897649