Saravanan666/pypi-org huggingface dataset:
Package: quality-viewer
quality-viewerA simple CLI for renderingQuality Views. Built onGraphviz.Installationpip install quality-viewerUsageTo use, you need to create a series of JSON documents in a folder. These files define the components in your quality view and the individual scores for each attribute. Scores are out of 10,quality-vieweruses the mean to generate the colours in the PDF.This is an example of the component definition:{
    "component": "my-lovely-component",
    "depends_on": ["my-other-component"],
    "attributes": [
        {
            "name": "code",
            "score": 5,
            "notes": "I wrote this in Uni"
        },
        {
            "name": "tests",
            "score": 2,
            "notes": "I wrote this in Uni"
        }
    ]
}Then you can run the CLI against your folder of component definitions which will generate a PDF in that folder namedquality_view.gv.pdf:quality-viewer ./my_componentsIf you run these regularly, you probably want to label each PDF. Customise the label on the graph with the--labelargument:quality-viewer --label "Quality Views - Christmas 2019" ./my_componentsA fairly elaborate example, which uses dated quality views can be found at (other repo)[more work]. This is a good repo to get started from.DevelopmentThis repository is setup to validate your work easily, running tests is as simple as:make testAnd checking your coding style is:make checkTo automagically conform to the above, run:make format
Package: qualkit
DACT Qualitative Analysis Toolkit (qualkit)This project is a collection of utilities for conducting qualitative
analysis.It currently consists of the following modules:clean: a utility for cleaning up text prior to use with other toolssentiment: a wrapper around SciKit's SentimentIntensityAnalyzeranchored_topic_model: creates topic models using the Corex algorithm (Gallagher et. al., 2017) with user-supplied anchors to 'steer' the model using domain knowledgestopwords: a standard set of stopwordstopics: a wrapper around SciKit's LatentDirichletAllocationkeywords: a wrapper around NLTK's RAKE (Rapid Keyword Extraction) algorithm for finding
keywords in text.For more details on each module, see the 'docs' folder.Installing the toolkit and its requirementsInstall using:pip install qualkitOr add 'qualkit' to your requirements.txt file, or add as
a dependency in project properties in PyCharm.User ControlA user has control over the following aspects when using this toolkit which will influence outputs.Anchoring strategiesAnchor StrengthNumber of topicsLabelling True/False for each topic instead of dichotomisingHow data is preprocessed before topic modelling, redaction, tfidr vectoriser etecReferencesGallagher, R. J., Reing, K., Kale, D., and Ver Steeg, G. "Anchored Correlation Explanation: Topic Modeling with Minimal Domain Knowledge." Transactions of the Association for Computational Linguistics (TACL), 2017.
Package: qualm
QualmQualm is an esolang, which uses a cell-based memory model.InstallationYou can install Qualm from PyPi:python3 -m pip install --user qualmOr clone the repository:$ git clone https://github.com/wilfreddv/qualm.git
$ cd qualm
$ python3 -m pip install --user .DocumentationData operations:
|<delimiter>    split string on delimiter
@               index of, e.g.: "'apples pears bananas:@'apples
$               indexing
i               turn into integer
c               turn into character (chr())
o               turn into ascii value (ord())

.               read from STDIN
!               write to STDOUT
w               working cell
s<number>       swap <number> var with w
><number>       push w to <number>
<<number>       pop <number> to w
<operation>><number> write output of previous operation to <number>
<number> can also be `w`, like sw
v<data>         put data in w
'...:           string data
{<condition>{...} loop
<number>+<number> add
<number>-<number> sub
<number>*<number> mul
<number>/<number> div
<number>%<number> mod
<number>=<number> equals
<expr>!=<expr>    neq
<expr><=<expr>    smaller
<expr>>=<expr>    biggerExamplesHello worldHello world:
v'Hello World!:!

v write into working cell
' denotes string data
! printsFibonacci sequencev2>0v1>1{s1<=23416728348467685{!iv' :!<1+<0s0}
Package: qualname
Python module to emulate the__qualname__attribute for classes and methods
(Python 3.3+) in older Python versions. SeePEP 3155for details.Installationpip install qualnameUsageAssume these definitions:class C:
    def f():
        pass

    class D:
        def g():
            passIn Python 3.3+, classes have a__qualname__property:>>> C.__qualname__
'C'
>>> C.f.__qualname__
'C.f'
>>> C.D.__qualname__
'C.D'
>>> C.D.g.__qualname__
'C.D.g'Unfortunately, Python 2 and early Python 3 versions do not have an (obvious)
equivalent.qualnameto the rescue:from qualname import qualname

>>> qualname(C)
'C'
>>> qualname(C.f)
'C.f'
>>> qualname(C.D)
'C.D'
>>> qualname(C.D.g)
'C.D.g'Victory!How does it work?Glad you ask.This module uses source code inspection to figure out how (nested) classes and
functions are defined in order to determine the qualified names for them. That
means parsing the source file, and traversing the AST (abstract syntax tree).
This sounds very hacky, and it is, but the Python interpreter itself does not
have the necessary information, so this justifies extreme measures.Now that you know how it works, you’ll also understand that this module only
works when the source file is available. Fortunately this is the case in most
circumstances.LicenseBSD.Feedback? Issues?https://github.com/wbolster/qualname
Package: qualtop-conversational-analysis
Conversational analysis based on communication with the OpenAI API (regardless of the API Base)Implementation of several methods for communication with the OpenAI API, embedding-based retrieval and data preparationGetting StartedCan be installed directly with pip (asetup.pyfile is provided, if needed).Notably the module contains theChatGPTMemoryclass, which enables communication with the OpenAI API (through the ChatCompletion endpoint) while managing conversation memory.Data preparation modulesThis project was not originally intended for production. A lot of the scripts contained here are mainly for data preparation (to demonstrate different use cases). However, some functions are being used by both thequaltop-llmapiand the QOPA-LLMdemosides of the project.In the future, this module should be replaced by a communication module. Also, any existent calculation functions should be passed to the server.
Package: qualtop-llmapi
Implementation of the OpenAI API for local LLM useImplementation of the OpenAI API. Currently supports two end points (ChatCompletion and Embeddings) with the following models:ChatCompletion models:'mistral-7b': 7B Instruct model trained by MistralAI.'llama-13b': 13B Llama 2 model trained by Meta.'llama-7b': 7B Instruct Llama 2 model trained by Together AI (32K context).'codellama-13b': 13B CodeLlama model trained by Meta.Embeddings models:'mistral-7b' : Encodes documents into 4096-dimension vectors.'bert' : Encodes documents into 386-dimension vectors.Getting StartedCan be installed directly with pip (asetup.pyfile is provided, if needed).Once installed, the FastAPI server (Uvicorn) can be started with:run_serverPromptsCurrently, two kinds of prompts are supported.Open ended questions to engage in conversation with the model.Instruct prompts for vector-based retrieval over some test collections.Some considerationsThis project is by no means production ready. The ChatCompletion endpoint in particular has been tailored to communicate with the QOPA-LLMdemo. A lot of work is needed security-wise to avoid data leaking.
Package: qualtran
# QᴜᴀʟᴛʀᴀɴQᴜᴀʟᴛʀᴀɴ (quantum algorithms translator) is a set of abstractions for representing quantum
programs and a library of quantum algorithms expressed in that language to support quantum
algorithms research.Note:Qualtran is an experimental preview release. We provide no backwards compatibility
guarantees. Some algorithms or library functionality may be incomplete or contain inaccuracies.
Open issues or contact the authors with bug reports or feedback.Subscribe to [qualtran-announce@googlegroups.com](https://groups.google.com/g/qualtran-announce)
to receive the latest news and updates!## DocumentationDocumentation is available athttps://qualtran.readthedocs.io/## InstallationQualtran is being actively developed. We recommend installing from source:git clonehttps://github.com/quantumlib/Qualtran.gitcd Qualtran/
pip install -e .You can also install the latest tagged release usingpip:pip install qualtran
Package: qualtrics-api
qualtrics_apiA Python-based project that performs a query to a Qualtrics API.InstallationIn order to install this package, you can use the following command:pipinstallqualtrics-apiUsageOn terminal you can use it in the following way:qualtrics-api-t<token_api>-d<datacenter_id>-s<survey_id>This will return the survey with all the responses on JSON format.Powered by: Hugo Angulo
Package: qualtrutils
QualtrUtils - A package to create questions from templates with Qualtrics (v3) APIThis package allows the creation of questions based on an existing template (i.e., a question created with the Qualtrics interface. The operations that this package supports are:Creating blockCopying an existing questionReplacing keywordsChanging multiple choice answersChanging the initial position of the sliderChanging a question JS codeInstallationUsing PIP:pipinstallqualtrutilsUsing CONDA:condainstall-cemanuele-albiniqualtrutilsFor developersTo use the package in editable mode use instead the following.gitclonehttps://github.com/emanuele-albini/qualtrutils.git
pipinstall--editablequaltrutilsConfiguration (optional)Global configuration is in~\.qualtrutils\qualtrics.toml. Example:API_URL="https://yourdatacenter.qualtrics.com/API/v3/"API_TOKEN="your_token"LIBRARY_ID="UR_XXXXXXXXXXXXX"SURVEY_ID="SV_XXXXXXXXXXXXX"The configuration saved in~\.qualtrutils\qualtrics.tomlwill be used as default inQualtricsSurveyconstructor.This configuration it's optional, the settigs can be directly passed toQualtricsSurveyat runtime.Usage examplefromqualtrutilsimportQualtricsSurveysurvey=QualtricsSurvey()# Get a question from an existing surveyquestion=survey.get_question_by_name('QuestionName','MyNewQuestion')# The following will replace (using regex) all the occurences# of 'SOMETHING' in the question text and multiples choice answers (if any)# with 'SOMETHING_ELSE'question.text_sub('SOMETHING','SOMETHING_ELSE')# The following will set the multiple choice answersquestion.set_choices(['First Answer','Second Answer','Third Answer'])# The flowwing will set the Javascript code of the questionquestion.set_js('var hello = 1;')# Add this new question to the survey in a block called 'Block A'# If the block does not exists it will be createdsurvey.create_question(question,'Block A')DocumentationSeeherefor the complete documentation with all the functionalities.
Package: qualytics-cli
Qualytics CLIThis is a CLI tool for working with Qualytics API. With this tool, you can manage your configurations, export checks, import checks, and more. It's built on top of the Typer CLI framework and uses the rich library for fancy terminal prints.RequirementsPython 3.7+Packages:typerosjsonrequestsurllib3rerichInstallationpipinstallqualytics-cliUsageHelpqualytics--helpInitializing the ConfigurationYou can set up your Qualytics URL and token using theinitcommand:qualyticsinit--url"https://your-qualytics.qualytics.io/"--token"YOUR_TOKEN_HERE"OptionTypeDescriptionDefaultRequired--urlTEXTThe URL to be set. Example:https://your-qualytics.qualytics.io/NoneYes--tokenTEXTThe token to be set.NoneYesQualytics init helpqualyticsinit--helpDisplay ConfigurationTo view the currently saved configuration:qualyticsshow-configExport ChecksYou can export checks to a file using thechecks exportcommand:qualyticschecksexport--datastoreDATASTORE_ID[--containersCONTAINER_IDS][--tagsTAG_NAMES][--outputLOCATION_TO_BE_EXPORTED]By default, it saves the exported checks to./qualytics/data_checks.json. However, you can specify a different output path with the--outputoption.OptionTypeDescriptionDefaultRequired--datastoreINTEGERDatastore IDNoneYes--containersList of INTEGERContainers IDsNoneNo--tagsList of TEXTTag namesNoneNo--outputTEXTOutput file path./qualytics/data_checks.jsonNoExport Check TemplatesEnables exporting check templates to the_export_check_templatestable to an enrichment datastore.qualyticschecksexport-templates--enrichment_datastore_idENRICHMENT_DATASTORE_ID[--check_templatesCHECK_TEMPLATE_IDS]OptionTypeDescriptionDefaultRequired--enrichment_datastore_idINTEGERThe ID of the enrichment datastore where check templates will be exported.Yes--check_templatesTEXTComma-separated list of check template IDs or array-like format. Example: "1, 2, 3" or "[1,2,3]".NoImport ChecksTo import checks from a file:qualyticschecksimport--datastoreDATASTORE_ID_LIST[--inputLOCATION_FROM_THE_EXPORT]By default, it reads the checks from./qualytics/data_checks.json. You can specify a different input file with the--inputoption.Note: Any errors encountered during the importing of checks will be logged in./qualytics/errors.log.OptionTypeDescriptionDefaultRequired--datastoreTEXTComma-separated list of Datastore IDs or array-like format. Example: 1,2,3,4,5 or "[1,2,3,4,5]"NoneYes--inputTEXTInput file pathHOME/.qualytics/data_checks.jsonNoneSchedule Metadata Export:Allows you to schedule exports of metadata from your datastores using a specified crontab expression.qualyticsschedule_appexport-metadata--crontab"CRONTAB_EXPRESSION"--datastore"DATASTORE_ID"[--containers"CONTAINER_IDS"]--options"EXPORT_OPTIONS"OptionTypeDescriptionRequired--crontabTEXTCrontab expression inside quotes, specifying when the task should run. Example: "0 * * * *"Yes--datastoreTEXTThe datastore IDYes--containersTEXTComma-separated list of container IDs or array-like format. Example: "1, 2, 3" or "[1,2,3]"No--optionsTEXTComma-separated list of options to export or "all". Example: "anomalies, checks, field-profiles"Yes
Package: quanbit
QuanbitAboutQuanbitis a pythonic package for simulating quantum computer. It faciliate basic quantum algorithm exploration on a classic computer.PlatformQuanbitis a pure python package supportingWindows,LinuxandMacOS.Example:Quantum TeleportationfromquanbitimportX,Y,Z,BellBasisfromquanbitimportcubit,bell_state,Measureimportnumpyasnp# Alice has an unknown qubit Ctheta,phi,alpha=np.random.rand(3)qubit_C=cubit(theta,phi,alpha)# To teleport the cubit, Alice and Bob need to share a maximally entangled state# Anyone of the four states is sufficient, we choose 1/sqrt(2) (|00> + |11>) herequbit_AB=bell_state(0)# Now the total state is, where @ represent tensor product:total_state=qubit_C@qubit_AB# Project the state of Alice's two qubits as a superpositions of the Bell basistotal_state=BellBasis(total_state,[0,1])# Measuring her two cubits in Bell basisCA,B_state=Measure(total_state,indices[0,1])# Rotate Bob's state based on the measurement result# if CA == (0, 0), no change need to be made# when CA is in state \Psi+ifCA==(0,1):B_state=X(B_state)# when CA is in state \Psi-elifCA==(1,0):B_state=Z(X(B_state))# when CA is in state \Phi-elifCA==(1,1):B_state=Z(B_state)# Now Bob's state is the same as Alice initial qubit_C.LicenseQuanbitis distributed underBSD 3-Clause.DependenceInstallation requirements:numpyTesting requirement:pytestQA requirement:toxInstallationTo run tests:pytestquanbitTo install Quanbit to system:pipinstall.Local build and TestingTo install editable Quanbit locally:pipinstall-e.To run tests:pytesttestsQuality AssuranceTo run QA locally:tox
Package: quanestimation
QuanEstimationDocumentationQuanEstimation is a Python-Julia based open-source toolkit for quantum parameter estimation, which consist in the calculation of the quantum metrological tools and quantum resources, and the optimizations with respect to the probe states, the controls or the measurements, as well as comprehensive optimizations in quantum metrology. Futhermore, QuanEstimation can generate not only optimal quantum parameter estimation schemes, but also adaptive measurement schemes.DocumentationThe documentation of QuanEstimation can be foundhere.InstallationRun the command in the terminal to install QuanEstimation:pip install quanestimationCitationIf you use QuanEstimation in your research, please cite the following paper:[1] M. Zhang, H.-M. Yu, H. Yuan, X. Wang, R. Demkowicz-Dobrza艅ski, and J. Liu,
QuanEstimation: An open-source toolkit for quantum parameter estimation,arXiv:2205.15588.=======
History0.1.0 (2022-06-04)
0.1.3 (2022-06-08)
0.1.4 (2022-06-19)
0.1.5 (2022-07-07)
0.2.0 (2022-09-01)First release on PyPI.
Package: quanfima
Quanfima(quantitativeanalysis offibrousmaterials)
is a collection of useful functions for morphological analysis and visualization
of 2D/3D data from various areas of material science. The aim is to simplify
the analysis process by providing functionality for frequently required tasks
in the same place.More examples of usage you can find in the documentation.Analysis of fibrous structures by tensor-based method in 2D / 3D datasets.Estimation of structure diameters in 2D / 3D by a ray-casting method.Counting of particles in 2D / 3D datasets and providing a detailed report in
pandas.DataFrame format.Calculation of porosity measure for each material in 2D / 3D datasets.Visualization in 2D / 3D using matplotlib, visvis packages.InstallationThe easiest way to install the latest version is by using pip:$ pip install quanfimaYou may also use Git to clone the repository and install it manually:$ git clone https://github.com/rshkarin/quanfima.git
$ cd quanfima
$ python setup.py installUsageOpen a grayscale image, perform segmentation, estimate porosity, analyze fiber
orientation and diameters, and plot the results.importnumpyasnpfromskimageimportio,filtersfromquanfimaimportmorphologyasmrphfromquanfimaimportvisualizationasvisfromquanfimaimportutilsimg=io.imread('../data/polymer_slice.tif')th_val=filters.threshold_otsu(img)img_seg=(img>th_val).astype(np.uint8)# estimate porositypr=mrph.calc_porosity(img_seg)fork,vinpr.items():print'Porosity ({}):{}'.format(k,v)# prepare data and analyze fibersdata,skeleton,skeleton_thick=utils.prepare_data(img_seg)cskel,fskel,omap,dmap,ovals,dvals=\mrph.estimate_fiber_properties(data,skeleton)# plot resultsvis.plot_orientation_map(omap,fskel,min_label=u'0°',max_label=u'180°',figsize=(10,10),name='2d_polymer',output_dir='/path/to/output/dir')vis.plot_diameter_map(dmap,cskel,figsize=(10,10),cmap='gist_rainbow',name='2d_polymer',output_dir='/path/to/output/dir')>>Porosity(Material1):0.845488888889
Package: quanmsms
quanmsms_Python[中文] |English介绍python版本要求:py3.x
安装：pip install quanmsms
最近更新:2023/12/24使用# 我们在sdk中提供了example.py(最小示例文件)# 但您仍可参考本文档# 请确认在当前环境安装了最新版sdk：pip install quanmsmsfromquanmsmsimportsdk# openID、Apikey以及模板都可以在 https://dev.quanmwl.com/ability_sms 查看到# 其中，模板可以在测试接口成功后申请自定义模板sms=sdk.SDK('2',# OpenID'wd4wa8d4a98w94d89wefwsef4ae9f7ad59ae46s7te49g7t4g9y65h'# Apikey)#                        手机号      模板id   模板参数sendOK,info,apiStatus=sms.send('19954761564',0,{'code':12344})print(sendOK)# 是否成功(布尔值)print(apiStatus)# api状态码print(info)# 描述信息
Package: quanp
Quanp – Quantitative Analysis in PythonQuanp is a scalable toolkit for analyzing cross-sectional and longitudinal/time-series
quantitative data. It was first inspired byscanpyand
jointly built withanndata. It includes
preprocessing, visualization, clustering, features selection/importance.Read thedocumentation. If you’d like to contribute by opening an issue or creating a pull request,
please take a look at ourcontributing guide.
If Quanp is useful for your research, consider being a contributor.
Package: quantadex
# Python Library for BitShares![](https://img.shields.io/pypi/v/bitshares.svg?style=for-the-badge)
![](https://img.shields.io/github/release/bitshares/python-bitshares.svg?style=for-the-badge)
![](https://img.shields.io/github/downloads/bitshares/python-bitshares/total.svg?style=for-the-badge)
![](https://img.shields.io/pypi/pyversions/bitshares.svg?style=for-the-badge)
![](https://img.shields.io/pypi/l/bitshares.svg?style=for-the-badge)
![](https://cla-assistant.io/readme/badge/bitshares/python-bitshares)Stable[![docs master](https://readthedocs.org/projects/python-bitshares/badge/?version=latest)](http://python-bitshares.readthedocs.io/en/latest/)
[![Travis master](https://travis-ci.org/bitshares/python-bitshares.png?branch=master)](https://travis-ci.org/bitshares/python-bitshares)
[![codecov](https://codecov.io/gh/bitshares/python-bitshares/branch/master/graph/badge.svg)](https://codecov.io/gh/bitshares/python-bitshares)Develop[![docs develop](https://readthedocs.org/projects/python-bitshares/badge/?version=develop)](http://python-bitshares.readthedocs.io/en/develop/)
[![Travis develop](https://travis-ci.org/bitshares/python-bitshares.png?branch=develop)](https://travis-ci.org/bitshares/python-bitshares)
[![codecov develop](https://codecov.io/gh/bitshares/python-bitshares/branch/develop/graph/badge.svg)](https://codecov.io/gh/bitshares/python-bitshares)—## DocumentationVisit the [pybitshares website](http://docs.pybitshares.com/en/latest/) for in depth documentation on this Python library.## Installation### Install with pip3:$ sudo apt-get install libffi-dev libssl-dev python-dev python3-dev python3-pip
$ pip3 install bitshares### Manual installation:$ git clonehttps://github.com/bitshares/python-bitshares/$ cd python-bitshares
$ python3 setup.py install –user### Upgrade$ pip3 install –user –upgrade bitshares## Contributingpython-bitshares welcomes contributions from anyone and everyone. Please
see our [guidelines for contributing](CONTRIBUTING.md) and the [code of
conduct](CODE_OF_CONDUCT.md).### Discussion and DevelopersDiscussions around development and use of this library can be found in a
[dedicated Telegram Channel](https://t.me/pybitshares)### LicenseA copy of the license is available in the repository’s
[LICENSE](LICENSE.txt) file.
Package: quantagonia-api-client
Quantagonia API ClientThis package contains Quantagonia's CLI client and client-side APIs for using HybridSolver in the cloud.InstallationPython >= 3.8 is required. Then, dopython -m pip install quantagonia-api-clientIn order to use HybridSolver through Quantagonia's cloud system, you'll need to sign up for an API key and set it system-wide byexport QUANTAGONIA_API_KEY=<your API key here>Quick start: using the CLI clientThis package provides the scripthybridsolverto interact with the
cloud solver. In order to solve a MIP (supported formats:.mps, .lp, .mps.gz, .lp.gz) or QUBO (spported formats:.qubo, .qubo.gz), just
usehybridsolver solve path/to/example.mpswhich will automatically set all parameters and solver controls to their default value, which is a good compromise for many middle-sized
optimization problems out there.Thesolvecall has a few options (tryhybridsolver solve --help) and will, after submitting the problem, stream the logs and output the
solution vector if one is found.To start a job without attaching to its log stream and leave it running in the background, usehybridsolver submit path/to/example.mpswhich supports the same parameters assolveand returns ajob_id.
using this ID, logs, results and job status as well as time billed can
be retrieved usinghybridsolver (logs | solution | status | time_billed) job_idLastly, in order to cancel a runnning job and avoid unnecessary
costs, usehybridsolver cancel job_id. To listkactive and/or past jobs,
usehybridsolver list --n=k.Using the API client in your own codeThe capabilities of the API client include, but are not limited to:a plugin for PuLP to solve PuLP-generated models locally and in the client,a simple API to model QUBOs locally and solve them in the cloud andan importer to convert PyQUBO models into our format - allowing to solve PyQUBO models in QUantagonia's cloud.For starters, you can run the examples inexamples/through, e.g.,QUANTAGONIA_API_KEY="<redacted>" python examples/submit_qubo.pywith a valid Quantagonia API key.For more details, we refer to ourAPI reference.