Saravanan666/pypi-org huggingface dataset:
Package: pyopsview
PyOpsview :snake:Python REST API Client for Opsview!InstallationInstalling Dependencies# Using `pip`pipinstall-rhttps://raw.githubusercontent.com/jpgxs/pyopsview/master/requirements.txt# Using `apt`aptupdate&&aptinstallpython-requestspython-six# Using `apt-get`apt-getupdate&&apt-getinstallpython-requestspython-six# Using `yum`yuminstallpython-requestspython-six# Using `dnf`dnfinstallpython-requestspython-six# Using `urpmi`urpmipython-requestspython-sixInstalling# From `pypi`pipinstallpyopsview# From sourcegitclonehttps://github.com/jpgxs/pyopsviewcdpyopsview
pythonsetup.pyinstallUsageBasic Usagefrom__future__importprint_functionfrompyopsviewimportOpsviewClientfrompyopsview.exceptionsimportOpsviewClientException# Authenticate the client and load the appropriate API schema for the# specific Opsview versionclient=OpsviewClient(username='admin',password='initial',endpoint='https://opsview.example.com')# Create a new client using the token; this is useful for things like ansible# where the login operation would otherwise have to be done for every operationauth_token=client.tokenclient=OpsviewClient(username='admin',token=auth_token,endpoint='https://opsview.example.com')# Get a list of the configured hosts. This returns a generator.all_hosts=client.config.hosts.list()forhostinall_hosts:print(host['check_interval'])# Find all hosts monitored by the master monitoring server.monitored_by_master=client.config.hosts.find(monitored_by='Master Monitoring Server')# Update the address of one hostupdate_host=all_hosts[-1]update_host['address']='127.128.129.130'update_host=client.config.hosts.update(update_host['id'],**update_host)# Find the Master opsview serveropsview_master=client.config.hosts.find_one(name='Opsview')ifopsview_masterisNone:raiseRuntimeError('Failed to find the Opsview master server!')# Get a list of all Service Checks on the Opsview mastermaster_service_checks=[]master_service_checks+=opsview_master['service_checks']fortemplateinopsview_master['host_templates']:template_detail=client.hosttemplates.find_one(template['name'])master_service_checks+=template_detail['service_checks']TODOAdd schemas for other versions of OpsviewAdd API operations for:DowntimeService/Host StatusLive Object Information (Runtime)AcknowledgementsScheduling RechecksGraphing DataEvent/History DataNotesSNMP queriesContributingFork it!Create your feature branch:git checkout -b my-new-featureCommit your changes:git commit -am 'Add some feature'Push to the branch:git push origin my-new-featureSubmit a pull request
Package: pyopt
pyopt version 0.84A module for command-line options with a pythonic, decorator-centric syntax.The following example auto-generates help with docstrings, type casting forarguments and enforcing argument count:import pyoptexpose = pyopt.Exposer()@expose.argsdef regular_function(arg1:str, arg2:int):'''Your help - the docstring'''# bla, etc, foobar spam...print(repr(arg1), repr(arg2))if __name__ == "__main__":expose.run()There are 3 modes of operation:1. expose.args - A decorator for positional arguments.2. expose.kwargs - A decorator for keyword arguments.3. expose.mixed - A decorator for keyword and positional arguments.Currently known compromises that are open to discussion, e-mail me:1. This module was specifically designed with python 3 in mind, certain featurescan be converted to python 2.x, but the awesome ones can't.2. Keyword command-line functions require every argument to start with adifferent letter to avoid collisions.3. Annotations aren't mandatory, I don't know if this is the right way to go,it's an explicity vs convenience issue.4. Booleans can't default to True. I couldn't think of a use case for thisso tell me if you did.License: whatever, I don't mind. Google Code made me choose so I went withthe "New BSD". If somebody has a better idea, e-mail, comment or whatnot.Hearing from whoever uses this code would be nice, but you really shouldn'tfeel obliged.Contact me at: ubershmekel at gmail
Package: pyoptflow
Optical Flow: Horn-SchunckPython implementation of optical flow estimation using only the Scipy stack for:Horn SchunckLucas-Kanade is also possible in the future, let us know if you're interested in Lucas Kanade.Installpython-mpipinstall-e.optionally, to run self-tests:python-mpipinstall-e.[tests]pytest-vExamplesThe program scripts expectdirectoryglob patternimageioloads a wide varity of imagesandvideo.Box:pythonHornSchunck.pysrc/pyoptflow/data/tests/boxbox*.bmpOffice: all time steps:pythonHornSchunck.pysrc/pyoptflow/data/tests/officeoffice*.bmpor just the first 2 time steps:pythonHornSchunck.pysrc/pyoptflow/data/tests/officeoffice.[0-2].bmpRubic:pythonHornSchunck.pysrc/pyoptflow/data/tests/rubicrubic*.bmpSpherepythonHornSchunck.pysrc/pyoptflow/data/tests/spheresphere*.bmpCompare: Matlab Computer Vision toolbox: inmatlab,
similar method in Octave and a comparison plot using Matlab Computer Vision toolbox.Reference:Inspiration
Package: pyoptgra
OptgraThis repository providespyoptgra, a python package wrapping (and including) OPTGRA.
OPTGRA is an optimization algorithm developed and implemented by Johannes Schoenmaekers, it is specifically designed for near-linear constrained problems, which commonly occur in trajectory optimization.The full documentation can be foundhere.InstallationWith PipPyoptgra is available on PyPi and can be installed with pip:pip install pyoptgraCompile from SourceFirst install a C++ compiler, a fortran compiler, cmake, python and python build, then clone the repository and build withpython-mbuildUsagePyoptgra is designed as apygmouser-defined algorithm: First create an instance of theoptgraclass with all relevant parameters, then pass a pygmo.population containing your problem to the instance’sevolvemethod:>>> import pygmo
>>> import pyoptgra
>>> prob = pygmo.problem(pygmo.schwefel(30)) # using the schwefel test problem from pygmo, with 30 dimensions
>>> pop = pygmo.population(prob, 1)
>>> algo = pygmo.algorithm(pyoptgra.optgra())
>>> pop = algo.evolve(pop) # the actual call to OPTGRALicenseCopyright 2008, 2021 European Space AgencyPyoptgra/Optgra is available under two different licenses. You may choose to license and use it under version 3 of the GNU General Public License or under the ESA Software Community Licence (ESCL) 2.4 Weak Copyleft. We explicitly reserve the right to release future versions of Pyoptgra and Optgra under different licenses.Copies of GPL3 and ESCL 2.4 can be found in the root directory of this package,
you can also obtain them athttps://www.gnu.org/licenses/gpl-3.0.txtandhttps://essr.esa.int/license/european-space-agency-community-license-v2-4-weak-copyleft
Package: pyoptica
PyOpticaPyOptica is a package for simulation of wave optics in Python. It is developed to deal with optics simulations in apythonicway; it is one of the most important presupposition of the whole project to follow the Zen of Python and create a structure that is known to users from the most popular scientific packages: NumPy or SciPy.BlogPlease visit our blog on:https://pyoptica.gitlab.io/pyoptica-blog/The GoalThe package is meant to provide functionality to propagate a wavefront through space, interact with optical elements (e.g. lenses or apertures), build optical systems, and to implement basic algorithms for holography. As I am writing these words not everything listed above has already been developed, however, we are doing are best to develop everything. Of course we would also like to inviteYOUto the development of the package. Everybody is welcome –  both experienced developers or optical engineers and students who have just embarked on their adventure with optics.Not only are we developing a software package but also we are committed to work on a series of Notebooks that would solve problems described in the most popular books on Optics. There is also a room for potential expansion of what is developed: you use the package to verify that your solution of a given problem is correct? Make a notebook out of it!DocumentationDocumentation can be foundhere.About UsOur names are Maciej Grochowicz and Michał Miler. I guess our names may sound unfamiliar to you – we are from Poland where we also studied Applied Physics at Warsaw University of Technology. Right now we are professional engineers. In case you would like to learn more about us, please visit our Linkedin profiles:Maciej GrochowiczMichał MilerLiteratureAlthough we would love to say that we relied solely on our knowledge and experience, we are obliged to list all of the books we used during the development:Joseph W. Goodman (2004)Introduction to Fourier Optics, W. H. Freeman",Kedar Khare (2016)Fourier Optics and Computational Imaging, Wiley&Sons Ltd.",David Voelz (2011)Computational Fourier Optics Matlab Tutorial, Spie Press"
Package: pyopticon
pyopticonPyOpticon is a software tool for lab controls and automation. It was developed by Richard Randall at Stanford University.
It provides a GUI interface with data logging and automation capability for devices connected to a computer's serial ports.
One uses Python to write the 'widgets' that represent each device and to configure multiple instances of widgets into 'dashboards'.
The detailed documentation should be at pyopticon.readthedocs.io .
Package: pyOptimalEstimation
"pyOptimalEstimation" PackagePython package to solve an inverse problem using Optimal Estimation
and an arbitrary Forward model following Rodgers, 2000.Tested with Python >=3.6. The last version supporting Python 2.7 was 1.1InstallationChange to the folder containing the project and dopython setup.py installin the terminal. If you do not have root privileges, you can also do:python setup.py --user installwhich will install pyOptimalEstimation inuserbase/lib/pythonX.Y/site-packagesorpython setup.py install --home=~which will install pyOptimalEstimation in~/lib/python.ReferencePlease reference to our paper if you use the pyOptimalEstimation packageMaahn, M., D. D. Turner, U. Löhnert, D. J. Posselt, K. Ebell, G. G. Mace, and J. M. Comstock, 2020: Optimal Estimation Retrievals and Their Uncertainties: What Every Atmospheric Scientist Should Know. Bull. Amer. Meteor. Soc., doi:https://doi.org/10.1175/BAMS-D-19-0027.1ExamplesA minimal working example can be found athttps://github.com/maahn/pyOptimalEstimation/blob/master/pyOptimalEstimation/examples/dsd_radar.pyTwo fullly annotated examples (microwave temperature/humidity retrieval & radar drops size distribution retrieval) are available athttps://github.com/maahn/pyOptimalEstimation_examples. They can be run online using binder.A retrieval for retrieving surface winds from satellites using RTTOV is available athttps://github.com/deweatherman/RadEstAPI documentationSeehttps://pyoptimalestimation.readthedocs.io/en/latest/for documentation.
Package: pyoptiML
# pyoptiML
This is a package that is developed to automate all the process of Machine Learning to prepare a optimal model on its own# What is it?
automate_ml is a Python package that provides fast model generation for any dataset that contains numerical values in them. It aims to help everyone to create model on their own without most knowledge about Machine Learning and how it works. This package runs on its own and suggest the best algorithm to choose to fit the data that has been processed.# Main Features
Things that automate_ml does:
1. Importing the data - is way easier now, you can just select the file from the path not to copy the path and paste them to make them run.
2. Preprocessing - It fills the nan values / NA values /  empty values on its own not with mean median or mode alternatively it uses prediction algorithm to predict the missing values.
Reasons :
Rather than filling with mean, median or mode values this could help to fill the empty values more accurately.
Model with the best accuracy will be used to fill the nan values and so the accuracy increases in filling the missing values
3. Saving - Saving the files is the most important feature that has been processed. This package asks for yes or no kinda questions to save train data, test data, standardized data and even the model everything will be saved in a new folder that is created from where the data has been selected.
4. Handled outliers with the help of IQR (Interquaratile Range)
5. Numerical data are stored in csv files
6. Models are stored in the same folder so we can just load them to test the accuraccy and can be shared
7. Testing - After the model has been saved it also asks for testing to the user. So here user can just enter the values and the model predicts the output ie the target feature the model is trained with
8. It shows the total time taken to execute the whole process
9. This package intracts with the user so it can be more comfortable than coding and creating a model out of it# Where to get itThe source code is currently hosted on Github at :https://github.com/alex-christopher/pyoptiMLpip install# Dependencies
1. Numpy
2. Pandas
3. Scikit-learnThese are the very basic libraries that are needed for machine learning and this package is built with all these to reduce the work that is done by the users# License# Documentation
The official documentation is hosted an PyData.org :# Contributing to automate_mlAll contributions, bug reports, bug fixes, documentation improvements, enhancements and ideas are welcomed.Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’…you can do something about it!# Features to improve
1. Used only 4-5 libraries without hyperparameter tuning for the final model generation.
2. Works only for regression problems
3. Works under progress for classification problems
4. Auto code generation for all the steps or all the progess that the user has done has to be generated on its own
Package: pyOptional
DescriptionLibrary provided implementation Optional object similar toJava
optional.
Using this object, You will never checkif x is None.Installpip install pyOptionalUsageExamplesfrompyOptionalimportOptionaloptional_with_value=Optional('ABC')optional_empty=Optional(None)print(optional_with_value)print(optional_empty)output:Optional of: ABC
Optional emptyMethods:get()Returns value or throwNoneValueErrorexception on empty optionalprint(optional_with_value.get())print(optional_empty.get())output:ABC
Traceback (most recent call last):
...
pyOptional.exceptions.NoneValueError: Called get on empty optionalget_or_else(default_value)Returns value if exists or default_value when emptyprint(optional_with_value.get_or_else('XYZ'))print(optional_empty.get_or_else('XYZ'))output:ABC
XYZget_or_else_get(callable_for_generate_default_value)Returns value if exists, otherwise result ofcallable_for_generate_default_valuedefgen_value():return'QWERTY'print(optional_with_value.get_or_else_get(gen_value))print(optional_empty.get_or_else_get(gen_value))print(optional_empty.get_or_else_get(lambda:'From lambda'))output:ABC
QWERTY
From lambdaget_or_raise(exception_class, *args, **kwargs) Returns value if exists or raise provided exceptionprint(optional_with_value.get_or_raise(FileNotFoundError,'Some message'))print(optional_empty.get_or_raise(FileNotFoundError,'Some message'))output:ABC
Traceback (most recent call last):
...
FileNotFoundError: Some messagemap(callable_to_transform_value)Returns optional of other value (result returned bycallable_to_transform_value) or Optional empty if source Optional
was emptyprint(optional_with_value.map(lambdaval:val*2))print(optional_empty.map(lambdaval:val*2))output:Optional of: ABCABC
Optional emptyflat_map(callable_to_transform_value)Similar to map, but if source Optional contains another Optionals,
result will contain single Optionalnested_val_optional=Optional(Optional(Optional(8)))nested_empty_optional=Optional(Optional(Optional(None)))print(nested_val_optional.map(lambdaval:val*3))print('---------------------')print(nested_empty_optional.map(lambdaval:val*3))print('---------------------')print(nested_val_optional.flat_map(lambdaval:val*3))print('---------------------')print(nested_empty_optional.flat_map(lambdaval:val*3))output:Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for *: 'Optional' and 'int'
---------------------
Traceback (most recent call last):
...
TypeError: unsupported operand type(s) for *: 'Optional' and 'int'
---------------------
Optional of: 24
---------------------
Optional emptyif_present(func)Call func with optional value if exists. If optional is empty, do
nothing.optional_with_value.if_present(lambdaval:print('found value '+val))optional_empty.if_present(lambdaval:print('found value '+val))output:found value ABCis_present()return True if Optional not empty, otherwise Falseprint(optional_with_value.is_present())print(optional_empty.is_present())output:True
FalseStatic Methods:empty()return an empty Optionalprint(Optional.empty())output:Optional empty
Package: py-optional
py-optionalOptional value implementation for python.Getting startedSeeDocumentationfor details.FeaturesPoetry (virtual environment and publish to PyPi, all with one tool)black (linting/formatter)autoflake (removing unused packages)isort (dependency organization)mypy (static type checking)pytest (including test coverage)pre-commit(hooks on commit)GitHub Actions for CI/CDmkdocs for documentation (with material theme)Installing py-optionalInstall the latest release:pipinstallpy-optionalOr you can clonepy-optionaland get started locally# ensure you have Poetry installedpipinstall--userpoetry# install all dependencies (including dev)poetryinstall# develop!Example Usageimportpy-optional# do stuffOnlyPython 3.8+is supported as required by the black, pydantic packagesPublishing to PypiPoetry's documentationNote that it is recommended to useAPI tokenswhen uploading packages to PyPI.Once you have created a new token, you can tell Poetry to use it:https://python-poetry.org/docs/repositories/#configuring-credentialsWe do this using GitHub Actions' Workflows and Repository Secrets!Repo SecretsGo to your repo settings and add aPYPI_TOKENenvironment variable:Inspect the GitHub Actions Publish Workflowsname: Publish

on:
  release:
    types:
      - created

jobs:
  publish:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v2
      ...
      ...
      ...
      - name: Publish
        env:
          PYPI_TOKEN: ${{ secrets.PYPI_TOKEN }}
        run: |
          poetry config pypi-token.pypi $PYPI_TOKEN
          bash scripts/publish.shThat's it!When you make a release on GitHub, the publish workflow will run and deploy to PyPi! 🚀🎉😎Contributing GuideWelcome! 😊👋Please see theContributing Guide.
Package: pyoptmat
pyoptmat: statistical inference for material modelspyoptmat is a package for calibrating statistical material
models to data.  The package is based onpytorchandpyroand provides a framework for using machine-learning
techniques to calibrate deterministic and statistical models against
experimental data.A “material model” is mathematically a parameterized system of ordinary
differential equations which, integrated through the experimental conditions,
returns some simulated output that can be compared to the test measurements.
pyoptmat uses Bayesian inference with the pyro package to find statistical
distributions of the model  parameters to explain the variation in the
experimental data.As an example, consider a collection of tension test data on several samples
of a material. The test measurements have some variation caused by
manufacturing variability and uncertainty in the experimental controls and
measurements.pyoptmat aims to make training a statistical model to capture these
variations easy. The image shows the results of training a simple material
model to the test data. The trained statistical model captures the
variability in the experimental data and can then be used to translate
this uncertainty to models of engineering components. Transferring
uncertainty quantified in experimental measurements to predictions of
uncertainty in engineering applications is the main reason pyoptmat was
developed.The software is provided under anMIT license.  Full
documentation is availablehere.
Package: pyoptools
pyOpToolspyOpTools is a set of packages that allow the simulation of optical systems by raytracing as well as some calculations involving wavefronts, currently under development. It is written in Python and Cython, and is being developed by the technological development group ofCombustión Ingenieros S.A.S, and the applied optics group of the Universidad Nacional de Colombia.At the moment the documentation is being created, you can find the work in progresshere
Package: pyOptoSigma
Python module for operating stepping moter stages manufactured by OptoSigma and Sigma Koki.RequirementsPython 3.4 or later. (Python 2.x are not supported.)pyserialSetup$ sudo pip3 pyserial pyOptoSigmaIn Ubuntu systems, add an user to dialout group.$ sudo gpasswd -a [username] dialoutHistory0.1 (2016-5-28)First release.Supports SHOT-702, SHOT-302GS, SHOT-304GS controllers.Supports SGSP, OSMS series stages.
Package: pyoptree
# pyoptreePython Optimal Tree### Install#### First install pyoptree through pip```pip3 install pyoptree```#### Then install solver (IMPORTANT!)The user needs to have **IBM Cplex** or **Gurobi** installed on their computer, and make sure that **the executable has been added to PATH environment variable** (i.e. command `cplex` or `gurobi` can be run on terminal).### Example```pythonimport pandas as pdfrom pyoptree.optree import OptimalHyperTreeModel, OptimalTreeModeldata = pd.DataFrame({"index": ['A', 'C', 'D', 'E', 'F'],"x1": [1, 2, 2, 2, 3],"x2": [1, 2, 1, 0, 1],"y": [1, 1, -1, -1, -1]})test_data = pd.DataFrame({"index": ['A', 'B', 'C', 'D', 'E', 'F', 'G'],"x1": [1, 1, 2, 2, 2, 3, 3],"x2": [1, 2, 2, 1, 0, 1, 0],"y": [1, 1, 1, -1, -1, -1, -1]})model = OptimalHyperTreeModel(["x1", "x2"], "y", tree_depth=2, N_min=1, alpha=0.1, solver_name="cplex")model.train(data)print(model.predict(test_data))```### Todos1. Use the solution from the previous depth tree as a "Warm Start" to speed up the time to solve the Mixed Integer Linear Programming (MILP); （Done √）2. Use the solution from sklearn's CART to give a good initial solution (Done √);