Saravanan666/pypi-org huggingface dataset:
Package: wmicq
Thewmicqmodule is a simple wrapper for wmic Windows command allowing to query Windows Management Instrumentation (WMI).ContentsDependenciesDownloading and installationSample usageLicenseDependenciesModuleenum34for installations targeted to Python version below 3.4,There are no dependencies for installations targeted to Python 3.4 and above.Downloading and installationThe recommended way of the module downloading and installation is to use pip:$pipinstallwmicqSample usageBelow you can find sample usage of the module:importsysimportwmicqtry:attributes=['Description','IPAddress','MACAddress']header,queryData=wmicq.query(wmicq.Category.NICCONFIG,attributes=attributes,where="IpEnabled=True")maxLen=max(len(x)forxinheader)forqinqueryData:print("{}: MAC:{}, IP:{}".format(q["Description"],q["MACAddress"],q["IPAddress"]))sys.exit(0)exceptwmicq.QueryErrorase:print("Query error:{}".format(e))sys.exit(1)LicenseThe wmicq module may be downloaded, installed, run and used in accordance with
the terms of the MIT license:Copyright (c) 2016 BaseITPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
DEALINGS IN THE SOFTWARE.
Package: wmidevicemanager
WMI Device ManagerThis is a library to get information in device manager on Windows10 based on WMI.You can get almost all information of device manager via this library.For example, "BIOS Device Name", "Driver INF Path" and so on.How to useUse files inwmidevicemanagerdirectory.You need to installcomtypeslibrary.importwmidevicemanagerwmi=wmidevicemanager.WmiDeviceManager()forxinwmi:print(x.DeviceID)pci_root=next(xforxinwmiifx.BiosDeviceName==r"\_SB.PCI0")forchildinpci_root.children:print(child.DeviceID)fordeviceinwmi:ifdevice.BiosDeviceName==r"\_SB.PCI0.RP01.PXSX"ordevice.BiosDeviceName==r"\_SB.PCI0.RP02.PXSX":device.Disable()Library for RubySeewmi_device_manager_ruby.LicensePlease use this library under MIT LicenseSee LICENSE file.
Package: wmill
wmillThe core client for theWindmillplatform.UsageBasic UsageThewmillpackage has several methods at the top-level for the most frequent operations you will need.The following are some common examples:importtimeimportwmilldefmain():# Get the value of a variablewmill.get_variable("u/user/variable_path")# Run a script synchronously and get the resultwmill.run_script("f/pathto/script",args={"arg1":"value1"})# Get the value of a resourcewmill.get_resource("u/user/resource_path")# Set the script's statewmill.set_state({"ts":time.time()})# Get the script's statewmill.get_state()Advanced UsageThewmillpackage also exposes theWindmillclass, which is the core client for the Windmill platform.importtimefromwmillimportWindmilldefmain():client=Windmill(# token=...  <- this is optional. otherwise the client will look for the WM_TOKEN env var)# Get the current version of the clientclient.version# Get the current userclient.user# Convenience get and post methods exist for https://app.windmill.dev/openapi.html#/# these are thin wrappers around the httpx library's get and post methods# list worker groupsclient.get("/configs/list_worker_groups")# create a groupclient.post(f"/w/{client.workspace}/groups/create",json={"name":"my-group","summary":"my group summary",})# Get and set the state of the scriptnow=time.time()client.state={"ts":now}assertclient.state=={"ts":now}# Run a job asynchronouslyjob_id=client.run_script_async(path="path/to/script")# Get its statusclient.get_job_status(job_id)# Get its resultclient.get_result(job_id)
Package: wmipa
SA-WMI-PA-SKPython 3 implementation of the methods presented in:Efficient WMI via SMT-Based Predicate AbstractionPaolo Morettin, Andrea Passerini, Roberto Sebastiani,in Proceedings of IJCAI 2017Advanced smt techniques for Weighted Model IntegrationPaolo Morettin, Andrea Passerini, Roberto Sebastiani,in Artificial Intelligence, Volume 275, 2019SMT-based Weighted Model Integration with Structure AwarenessGiuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani,in UAI Conference 2022SMT-based Weighted Model Integration with Structure Awareness and Multiple Integration ApproachesGiuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto SebastianipywmiWMI-PA is now part ofpywmi, a general framework for Weighted Model Integration that offers a number of different solvers, a command-line interface, etc.InstallationBase version:pip install wmipaVersion with support for NRA:pip install "wmipa[nra]"Additional requirementsAt least one following integration backend is needed:LattE integrale- Exact integration (recommended):sudo apt install -y libntl-dev libcdd-dev libcdd-tools
wget https://github.com/latte-int/latte/releases/download/version_1_7_6/latte-int-1.7.6.tar.gz
tar -xzf latte-int-1.7.6.tar.gz
cd latte-int-1.7.6
./configure GXX="g++ -std=c++11" CXX="g++ -std=c++11" --prefix=$HOME/latte && make && make install
cd ..Add$HOME/latte/binto the PATH environment variable:export PATH=$HOME/latte/bin:$PATHVolEsti- Approximated integration:```[bash]
git clone https://github.com/masinag/approximate-integration
cd approximate-integration
makeAddbinto the PATH environment variable:export PATH=$PWD/bin:$PATHMathSAT5pysmt-install --msatFor the SA-WMI-PA-SK mode, a slightly customized version of MathSAT5 is needed.
In order to install it, you need to substitute the file<venv>/lib/python3.8/site-packages/_mathsat.cpython-38-x86_64-linux-gnu.sowith the one provided in thebin/folder of this repository
(Python3.8 under Linux x86_64 is required).Support for other OS and Python versions will be added in the near future.ExamplesWe provide some examples that show how to write a model and evaluate weighted model integrals on it.
To run the code inexamples/, type: python exampleX.py
Package: wmipa-test
SA-WMI-PA-SKPython 3 implementation of the methods presented in:Efficient WMI via SMT-Based Predicate AbstractionPaolo Morettin, Andrea Passerini, Roberto Sebastiani,in Proceedings of IJCAI 2017Advanced smt techniques for Weighted Model IntegrationPaolo Morettin, Andrea Passerini, Roberto Sebastiani,in Artificial Intelligence, Volume 275, 2019SMT-based Weighted Model Integration with Structure AwarenessGiuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto Sebastiani,in UAI Conference 2022SMT-based Weighted Model Integration with Structure Awareness and Multiple Integration ApproachesGiuseppe Spallitta, Gabriele Masina, Paolo Morettin, Andrea Passerini, Roberto SebastianipywmiWMI-PA is now part ofpywmi, a general framework for Weighted Model Integration that offers a number of different solvers, a command-line interface, etc.Installationpip install wmipaAdditional requirementsAt least one following integration backend is needed:LattE integrale- Exact integration (recommended):wget https://github.com/latte-int/latte/releases/download/version_1_7_6/latte-int-1.7.6.tar.gz
tar -xzf latte-int-1.7.6.tar.gz
cd latte-int-1.7.6
./configure GXX="g++ -std=c++11" --prefix=$HOME/latte && make && make installAdd$HOME/latte/binto the PATH environment variable:export PATH=$HOME/latte/bin:$PATHVolEsti- Approximated integration:```[bash]
git clone https://github.com/masinag/approximate-integration
cd approximate-integration
makeAddbinto the PATH environment variable:export PATH=$PWD/bin:$PATHMathSAT5pysmt-install --msatFor the SA-WMI-PA-SK mode, a slightly customized version of MathSAT5 is needed.
In order to install it, you need to substitute the file<venv>/lib/python3.8/site-packages/_mathsat.cpython-38-x86_64-linux-gnu.sowith the one provided in thebin/folder of this repository
(Python3.8 under Linux x86_64 is required).Support for other OS and Python versions will be added in the near future.ExamplesWe provide some examples that show how to write a model and evaluate weighted model integrals on it.
To run the code inexamples/, type: python exampleX.py
Package: wml-ai-model-managers
WML AI Model ManagersA package with several AI classes making it easy to train data based on PyTorch.InitializationTo initialize:# Make sure the test and train data come from the same datasetmyai=WMLTextModelManagerOne(model_file_name="AmazonReviewFull.pkl",dataloader_info={"datapipe_fn":datasets.AmazonReviewFull,"vocab_folder_path":"data/AmazonReviewFull","get_dataset":True})TrainingTo train:myai.download_train_and_test_data()myai.load_model_from_scratch()myai.train()myai.save_model_to_pickle()ChatTo chat:myai=WMLTextModelManagerOne(model_file_name="AmazonReviewFull.pkl",dataloader_info={"datapipe_fn":datasets.AmazonReviewFull,"vocab_folder_path":"data/AmazonReviewFull","get_dataset":False},)myai.download_train_and_test_data()myai.load_model_from_file()# the default is 150 but we increased to 500 the limit should be myai.batch_size * myai.block_size but see if you can get larger than thismyai.chat_with_model(500)Class Initialization Propertiesdevice: Specifies whether to use CUDA, CPU, or lets it be determined by available computer hardware.dataloader_info: Important information about the pytorch dataset the model manager needs to retrieve the datasetdatapipe_fn: One of the various pytorch datapipes which can be found herehttps://pytorch.org/text/stable/datasets.html. You can implement your own custom datapipe fn as the model is looking for an IterDataPipe, make sure your custom fn have train and test splits.vocab_folder_path: Where the vocab file for the dataset will be stored. it will store test-vocab.txt and train-vocab.txt.get_dataset: Set to false if communicating with chatbot set to true if doing a training session.max_iters: Number of iterations for the entire training run.n_embd: Number of embeddings.n_head: Number of heads for multihead attention.n_layer: Number of layers (linear, activation, output functions).dropout: Number of values to turn to zero to prevent the model from memorization.model_file_name: The name of the file to save the model to (currently supports only pickle files, so please save as .pkl).reporting_loss.learning_rate.block_size: Amount of characters in a section of text that represents 1 batch.batch_size: Amount of batches the model gets to learn in 1 iteration during the training session. A training session is like a child going through pre-K through college, and each grade is 1 iteration. At the end, the model should be able to generalize (converge) well, like an adult who can meaningfully contribute to society.Changelogv0.0.2:Corrected issues with the dataloader where the model_manager would not receive the training and test dataloaders.v0.0.3:Changed the default block and batch sizes so beginners can feel more tangible results.Refactoredv1.0.0:instead of providing a dataloader argument to the modelManger provide dataloader_info according to the example abovev1.0.1abstracted more fns intov1.1.0changed from load_model_from_file to load_model_from_pickle_filechanged from save_model_to_pickle to save_model_to_pickle_filev1.1.1fixed a bug with v1.1.0
Package: wmm2015
WMM2015WMM2015 World Magnetic Model...in simple, object-oriented Python.WMM2020is also available.
Tested on Linux, Mac and Windows.
Most C compilers work.
At this time Visual Studio is not supported since MSVC doesn't export function symbols without additional headers,
which is typically done with something like SWIG.Installfor the latest release from PyPi:python-mpipinstallwmm2015Optionally, to get the cutting-edge development version:gitclonehttps://github.com/space-physics/wmm2015

python-mpipinstall-ewmm2015This Python wrapper of WMM2015 uses our build-on-run technique.
The first time you use WMM2015, you will see messages from the build system and C compiler.Usagean example scriptpythonRunWMM2015.pyor as a Python module:importwmm2015mag=wmm2015.wmm(glat,glon,alt_km,yeardec)ReferenceWMM2015inclination mapWMM2015declination map
Package: wmm2020
WMM2020WMM2020 World Magnetic Model...in simple, object-oriented Python.WMM2015is also available.
Tested on Linux, Mac and Windows.
Most C compilers work.
At this time Visual Studio is not supported since MSVC doesn't export function symbols without additional headers,
which is typically done with something like SWIG.Installfor the latest release from PyPi:python-mpipinstallwmm2020Optionally, to get the cutting-edge development version:gitclonehttps://github.com/space-physics/wmm2020

python-mpipinstall-ewmm2020This Python wrapper of WMM2020 uses our build-on-run technique.
The first time you use WMM2020, you will see messages from the Meson build system and your C compiler.Usagean example scriptpythonRunWMM2020.pyor as a Python module:importwmm2020mag=wmm2020.wmm(glat,glon,alt_km,yeardec)ReferenceWMM2020inclination mapWMM2020declination map
Package: wmm2020-cext
WMM2020_CEXTThis library was modified to make a Python C Extension. This makes the library easy to install if you have the
proper compilers installed. Additionally, I published Windows .whl binaries to pypi.org.Installwmm2020_cext has prebuilt Windows binaries (.whl files), so the library can easily be installed with pip.python-mpipinstallwmm2020_cextUse withwmm2020importwmm2020importnumpyasnpwmm2020.wmm(np.array([0]),np.array([0]),0,0)Manually InstallInstall Visual Studio C Compilershttps://wiki.python.org/moin/WindowsCompilersto compile the C extension.
If this is done correctly you can simplypip install ./path/to/libraryor build your binariespython setup.py bdist_wheel(orpip wheel --no-deps ./path/to/library). The wheel library is required.WMM2020WMM2020 World Magnetic Model...in simple, object-oriented Python.WMM2015is also available.
Tested on Linux, Mac and Windows.
Most C compilers work.
At this time Visual Studio is not supported since MSVC doesn't export function symbols without additional headers,
which is typically done with something like SWIG.Installfor the latest release from PyPi:python-mpipinstallwmm2020Optionally, to get the cutting-edge development version:gitclonehttps://github.com/space-physics/wmm2020

python-mpipinstall-ewmm2020This Python wrapper of WMM2020 uses our build-on-run technique.
The first time you use WMM2020, you will see messages from the Meson build system and your C compiler.Usagean example scriptpythonRunWMM2020.pyor as a Python module:importwmm2020mag=wmm2020.wmm(glat,glon,alt_km,yeardec)ReferenceWMM2020inclination mapWMM2020declination map
Package: wmonitor
wmonitorwmonitorwmonitor会从环境变量中获取"BUILD_TYPE"与"CONFIG_DIR"demo.pyfromwmonitor.metricimportMetricMetric.counter("demo_counter").send()Metric.counter("demo_counter").tags("business=123").send()Metric.counter("demo_counter",2).send()Metric.gauge("demo_gauge").send()Metric.state("demo_state").send()configBUILD_TYPE文件名称BETAwmonitor_beta.cfgDEVwmonitor_dev.cfgPRODwmonitor_prod.cfgDEFAULT or ''wmonitor.cfgwmonitor.cfg[web]
domain = http://b1.wmonitor-adaptor.beta.wormpex.com
[app]
app_code = ai_vision_vision_dynamic_cabinet_service
Package: wmo-sphinx-theme
The WMO Sphinx ThemeASphinxtheme for documenting WMO projects.Installation and usageThe theme is available onPyPI, and can
be installed as follows:pip3installwmo-sphinx-themeEnavbling the theme from within your project'sconf.py:importwmo_sphinx_themehtml_theme='wmo_sphinx_theme'html_theme_path=wmo_sphinx_theme.get_html_theme_path()html_css_files=['wmo.css']Releasingrm-frbuilddist*.egg-info
python3setup.pysdistbdist_wheel--universal
twineuploaddist/*Code ConventionsPEP8Bugs and IssuesAll bugs, enhancements and issues are managed onGitHub.ContactMaaike Limper
Package: wmowonen.theme
IntroductionThis theme uses grunt to generate CSS form less, minify JS, and create
template files.Requirements and installation:
* Install nodejs and npm
* Go to dev folder in this product
* Runnpm installRunninggruntwill generate the files for you.More on installing grunt via npm:http://gruntjs.com/getting-startedInstall the following packages using npm install:font-awesome
grunt-contrib-concat
grunt-contrib-less
grunt-contrib-watch
jquery-placeholder
grunt-contrib-jshint
grunt-contrib-uglify
grunt-remove-logging
twitter-bootstrap-3.0.0
lessChangelog0.12 (2014-07-22)Nothing changed yet.0.11 (2014-07-22)Nothing changed yet.0.10 (2014-07-22)Nothing changed yet.0.9 (2014-07-22)Nothing changed yet.0.8 (2014-07-03)Nothing changed yet.0.7 (2014-06-24)Translate Prev/Next button #17340Theme tags in tagnavigator_view color #17342Show orange box with date on collections #17384Problems with Mailchimp #17345Picture should take entire width #17348Remove share buttons code from print version #17351Viewing the website on a iPhone #17352Previous / next buttons: last batch on next row #17353Regio overview page: grey text -> black #17354Suggesties portlet: Remove date+time on news item #173550.6 (2014-06-03)Fixed the search template in order to be able to use the Plone AJAX calls. [davidjonas]Added hardcoded filters for the Thema’s sections in the search template. [davidjonas]LESS for the new filters. [davidjonas]Fixed image size and alignment on homepage. [davidjonas]Various minor CSS fixes. [davidjonas]0.4 (2014-05-12)Nothing changed yet.0.3 (2014-05-07)Nothing changed yet.0.2 (2014-05-05)Nothing changed yet.0.1 (2014-04-25)Initial release
Package: wmp
wmp-facewmp-face is a Python module for face detection & recognition built on
top of dlib's facial recognizer.This codebase was developed under Wesleyan Media Project - a research
group that tracks all election-related advertising to give journalists
and political scientists the data and the tools to hold governments and
special interests accountable. Mostly notably, wmp-face was instrumental
in pre-processing the data used for "Do Women Candidates 'Run as Women'
Online?" (Yao, et al.).
Package: wmr-cba
wmr_cbawmr_cba is a Python library for controlling aWest Mountain Radio CBAIV.libusb is used, tested in both Linux and Windows.  A simple Linux udev rules file is provided, as well as a Windows libusb WinUSB driver.  See the /drivers/ folders of theGitHub repofor these files.  Windows users can also use the Windows drivers provided by West Mountain Radio, achieved by ctyping the West Mountain Radio driver DLLs.This was developed by following theSDKof the CBAIV provided by West Mountain Radio.How to useThe package can be installed using pip:pip install wmr_cbaSome example code:fromwmr_cbaimportwmr_cbacba=wmr_cba.CBA4()cba.do_start(1.0)time.sleep(5)print("Measured voltage under 1 Amp load: "+str(cba.get_voltage()))cba.do_stop()cba.close()Licensewmr_cba is released under the MIT License. See LICENSE for more information.
Package: wmsd
TOPSIS ranking and interpretation using WMSD-spaceThis repository hosts the code forMSDTransformer- a scikit-learn compatible python library, that lets you create TOPSIS rankings of alternatives, visualize rankings in WMSD space, and get suggestions for improvement actions.RequirementsMSDTransformer uses PySCIPOpt to determine exact WMSD boundaries quickly. If you use conda, you should be fine performing the follwoing command (easier option):conda install --channel conda-forge pyscipoptIf you use PyPI, then you will need a working installation of theSCIP Optimization
Suite(harder option, but might run faster). Please, make sure that your SCIP installation works, before installing MSDTransformer. For more details on how to install SCIP, please refer to thePySCIPOpt documentation.After installing PySCIPOpt (or if you already have SCIP installed), you can install the other dependencies by running:pip install -r requirements.txtInstallationOnce you have all the dependencies, you can install MSDTransformer from source. To do so, clone the repository and run the following command in the root directory of the repository:pip install .QuickstartimportpandasaspdfromMSDTransformerimportMSDTransformerdf=pd.read_csv("../data/students.csv",sep=';',index_col=0)students_transformer=MSDTransformer("R")# Relative distancestudents_transformer.fit(df,expert_range=[[0,100],[1,6],[1,6]])students_transformer.transform(df)students_transformer.show_ranking()students_transformer.plot()CitationIf you use this project in your research, please consider citing the following papers:Susmaga, R.et al.(2023) MSD-space: Visualizing the inner-workings of TOPSIS aggregations.European Journal of Operational Research, 308(1), 229-242.Susmaga, R.et al.(2023) Towards Explainable TOPSIS: Visual Insights into the Effects of Weights and Aggregations on Rankings. Arxiv preprint.
Package: wm.showhidecolumns
ContentsWhat does it do?How does it work?How do I use it?How can I help?ContributorsChangelog0.1 (2013-09-05)0.1rc1 (2013-08-19)What does it do?This Plone addon allows editors to control the visibility of portlet columns -
no matter if they are empty or contain portlets.Editors can choose between:showing a column (even if it does not contain any portlets)hiding a column (even if it contains portlets)only show non-empty columns (standard Plone behaviour)How does it work?wm.showhidecolumnsuses infrastructure available in Plone 4:the request variablesdisable_plone.rightcolumnanddisable_plone.leftcolumnare used to control the column visibilty. (no
portletmanagers get overwritten or monkey-patched).archetypes.schemaextenderis used to make the settings available on all types
implementingwm.showhidecolumns.interfaces.IControlColumnsplone.browserlayeris used to make sure the extra fields and the viewlet that
adds the request variables only show up if the product is installed.How do I use it?To allow control column visibilty on custom content types just make them
implementwm.showhidecolumns.interfaces.IControlColumns.How can I help?Add translations for your language (currently English and German)Add support for Dexterity content typesAdd testsreport bugs or request features in theissue trackerContributorsHarald Friessnegger, AuthorChangelog0.1 (2013-09-05)make settings language-dependent so editors can decide to show/hide
columns on single translated articles. [fRiSi]0.1rc1 (2013-08-19)initial release [fRiSi]
Package: wmsigner
WebMoney SignerОписаниеМодуль для создания подписей с использованием файла ключей
WebMoney Keeper ClassicВерсия0.1.1Предназначен для взаимодействия с программнымиX-интерфейсамиWebMoney,XML-интерфейсамиWebMoney Exchanger и др.Установка# через pip[sudo]pip3installwmsigner# из репозиторияgitclonehttps://github.com/egorsmkv/wmsignercdwmsigner[sudo]pythonsetup.pyinstallИспользованиеfromwmsignerimportSigner# Файл ключей должен быть резервным, а не основнымsigner=Signer(wmid='000000000000',keys='./data/000000000000.kwm',password='************')signature=signer.sign('Данные, которые будут подписаны.')ТестыДля запуска тестов необходимо изменить данные в файлеtests/test_signer.py.
Package: wmss
WMSS: A Web Map Service SamplerDescriptionThewmssmodule allows extracting multiple image samples from Web Map Services (WMS), one of the mapping services described in the Open Geospatial Consortium (OGC) standard collection (OGC, 2006).InstallationInstall the package usingpip>>>pipinstallwmssDependenciesGUI programming in Python:easyguiProgramming with Open Geospatial Consortium (OGC) web service:OWSLibPython interface to PROJ (cartographic projections and coordinate transformations library):PyprojPure Python read/write support for ESRI Shapefile format:pyshp