Saravanan666/pypi-org huggingface dataset:
Package: analyticViz
What is analyticViz?analyticVizis a Python package that allows user to plot basic visualization charts using plotly. It is suitable for people who do not wish to write many lines of code to plot graphs using plotly. Some of the graphs available at the moment includes bar chart, line plots, histograms, box plots etc.Main FeaturesHere are just a few of the things that pandas does well:Functions for plotting various visualizationsVisualizations formatted with a consistent color when used in jupyter notebookOption to plot multiple graphs using one functionWhere to get itThe source code is currently hosted on GitHub at:https://github.com/Adark-Amal/analyticVizBinary installers for the latest released version are available at thePython
Package Index (PyPI)DependenciesPlotly - Provides all the needed visualizationsInstallationTo install analyticViz via PyPIpipinstallanalyticVizLicenseMITDocumentationThe official documentation is yet to be providedBackgroundWork onanalyticVizstarted in 2022 and has been under active development since then.
Package: analytic-workspace-client
Библиотека для Analytic WorkspaceПолучение токенаПерейдите по ссылкеhttps://aw.example.ru/data-master/get-token(вместоhttps://aw.example.ru/укажите адрес вашего сервера Analytic Workspace).Значение токена лучше всего сохранить в отдельном файл или поместить в переменную окруженияAW_DATA_TOKEN.Пример использованияfromaw_clientimportSessionwithopen('aw_token','rt')asf:aw_token=f.read()session=Session(token=aw_token,aw_url='https://aw.example.ru')# Если токен доступа указан в переменной окружения AW_DATA_TOKEN, то объект сессии можно создавать# без явного указания параметра token: session = Session(aw_url='https://aw.example.ru')df=session.load()# df: pandas.DataFramedisplay(df)
Package: analytixhero
AnalytiXHeroCurrent Version:0.0.1Description:Everything That Needs To Be Done While Preprocessing Data, May It Be Outlier Handling, Skewness/Kurtosis Minimization, Treating Null Spaces Etc. Can Be Done With Pre-Defined State-Of-The-Art Features.Dependencies:NumPy,Pandas,Scikit-Learn,SciPy,MatPlotLib,Seaborn,Python-DateutilThese Dependencies Are Subjected To Current VersionFor Contribution:Check It HereDocumentation:Check It HereThere Are Many Libraries That One Can Use LikeScikit-Learn,NumPyOrPandasBut This Library,AnalytiXHeroCan Make Preprocessing A Task Of Just One Line
Package: analyzefit
[![PyPI](https://img.shields.io/pypi/v/analyzefit.svg)](https://pypi.python.org/pypi/analyzefit/)[![Build Status](https://travis-ci.org/wsmorgan/analyzefit.svg?branch=master)](https://travis-ci.org/wsmorgan/analyzefit)[![codecov](https://codecov.io/gh/wsmorgan/analyzefit/branch/master/graph/badge.svg)](https://codecov.io/gh/wsmorgan/analyzefit)[![Code Health](https://landscape.io/github/wsmorgan/analyzefit/master/landscape.svg?style=flat)](https://landscape.io/github/wsmorgan/analyzefit/master)# analyzefitAnalyze fit is a python package that performs standard analysis on thefit of a regression model. The analysis class validate method willcreate a residuals vs fitted plot, a quantile plot, a spread locationplot, and a leverage plot for the model provided as well as print theaccuracy scores for any metric the user likes. For example:![alt_text](../master/support/images/validation.png)If a detailed plot is desired then the plots can also be generatedindividually using the methods res_vs_fit, quantile, spread_loc, andleverage respectively. By default when the plots are createdindividually they are rendered in an interactive inverontment usingthe bokeh plotting package. For example:![alt text](../master/support/images/interactive.pdf)This allows the user to determine which points the model is failing topredict.Full API Documentation available at: [github pages](https://wsmorgan.github.io/analysefit/).## Installing the codeTo install analyzefit you may either pip install:```pip install analyzefit```or clone this repository and install manually:```python setup.py install```# Validating a ModelTo use analyze fit simply pass the feature matrix, target values, andthe model to the analysis class then call the validate method, (or anyother plotting method). For example:```import pandas as pdimport numpy as npfrom sklearn.linear_model import LinearRegressionfrom sklearn.metrics import mean_squared_error, r2_scorefrom sklearn.model_selection import train_test_splitfrom analyzefit import Analysisdf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data', header=None,sep="\s+")df.columns = ["CRIM","ZN","INDUS","CHAS","NOX","RM","AGE","DIS","RAD","TAX","PTRATIO","B","LSTAT","MEDV"]X = df.iloc[:,:-1].valuesy = df[["MEDV"]].valuesX_train, X_test,y_train,y_test = train_test_split(X,y, test_size=0.3,random_state=0)slr = LinearRegression()slr.fit(X_train,y_train)an = Analysis(X_train, y_train, slr)an.validate()an.validate(X=X_test, y=y_test, metric=[mean_squared_error, r2_score])an.res_vs_fit()an.quantile()an.spread_loc()an.leverage()```## Python Packages Used- numpy- matplotlib- bokeh- sklearn
Package: analyze-html
Analyze-HTMLIntroductionAnalyze-HTML is a project that read your html tag and count for appearenceHow it workingIt is using Requests to get the HTML of an URL then using BeautifulSoup4 to parse the HTML tag contained.Typer CLIThere are several upgrade function on Typer CLIgenerate-htmlReturn html, to save locally add --save-to-local=Truecount-tag-from-localcount-tag-from-url
Package: analyze-objects
analyze_objectscontains command line tools that analyze compile object files (.o, .obj). It is a wrapper around the
platform specific toolsnm(linux) ordumpbin(windows).Currently, it consists of the single shell commandfind_symbols.InstallingInstall from pip:python -m pip install analyze_objectsUsageIf binaries of installed python packages are added to the PATH, you can callfind_symbolsdirectly from the shell:find_symbolsOtherwise, it can be invoked using python:python -m analyze_objects.find_symbolsExamplesUse the following command to search the object filesfoo.oandbar.ofor undefined symbols that match the
regular expression"foo":find_symbols --undef_regex foo foo.o bar.oUsing this command requires thatnm(linux) ordumpbin(windows) are available in the PATH. If that is not the
case, you can use the--nm_exeor--dumpbin_exearguments to pass their location tofind_symbols. For
convenience, you may pass--store_configin addition to--nm_exeor--dumpbin_exe, so that this path will be
used in all subsequent calls tofind_symbols. The stored configuration can be cleared using--clear_config.Use--def_regexinstead of--undef_regexto search for defined symbols. It is
possible to combine both arguments and search for both defined and undefined symbols.Thefind_symbolscommand accepts an arbitrary number of object files. It is possible to use placeholders**and*in the object file paths.
Package: analyzere
This is a Python wrapper for a Analyze Re REST API. It allows you to easily
utilize the PRIME Re platform in your applications.Installationpip install analyzereUsagePlease seehttp://docs.analyzere.net/?pythonfor the most up-to-date
documentation.TestingWe currently commit to being compatible with Python 2.7. 3.4, 3.5, and 3.6. In
order to run tests against against each environment we usetoxandpy.test. You’ll
need an interpreter installed for each of the versions of Python we test.
You can find these via your system’s package manager oron the Python site.To start, install tox:pip install toxThen, run the full test suite:toxTo run tests for a specific module, test case, or single test, you can pass
arguments to py.test through tox with--. E.g.:tox -- tests/test_base_resources.py::TestReferences::test_known_resourceSeetox--helpandpy.test--helpfor more information.TaggingInstalltwineandwheel:pip install twine wheelIncrement version number insetup.pyaccording toPEP 440.Increment the version number in theuser_agentvariable inanalyzere/__init__.py.Commit your change tosetup.pyand create a tag for it with the version
number. e.g.:git tag 0.5.1
git push origin 0.5.1.pypircfileCreate a.pypircfile with your production and test server accounts in yourHOMEdirectory. This file should look as follows:[distutils]
index-servers=
    pypi
    testpypi

[testpypi]
repository = https://test.pypi.org/legacy/
username = <username>
password = <password>

[pypi]
repository = https://upload.pypi.org/legacy/
username = <username>
password = <password>Note thattestpypiandpypirequire separate registration.Testing Publication1. Ensure you have tagged the master repository according to the tagging
instructions above.Package source and wheel distributions:python setup.py sdist bdist_wheelCheck format:twine check dist/*Upload to PyPI with twine:twine upload dist/* -r testpypiTest that you can install the package from testpypi:pip install -i https://testpypi.python.org/pypi analyzerePublishing1. Ensure you have tagged the master repository according to the tagging
instructions above, testing publication before publication.Package source and wheel distributions:python setup.py sdist bdist_wheelUpload to PyPI with twine:twine upload dist/* -r pypi
Package: analyze_site
analyze_site is a python application to crawl a site and return a count of the keywords, provided in a file, found in the web pages of the site. The application will also return counts of the most used verb, nouns, adverbs and adjectives.analyze_site requires Python version 3 and the following libraries:nltk - Natural Language Toolkit with maxent_treebank_pos_taggerusage: analyze_site.py [-h] [-d DEPTH] [-r PATH_REGEX] [–verbose]keywords_file urlpositional arguments:keywords_file         Path to keywords file
url                   URL to crawloptional arguments:-h,--helpshow this help message and exit-dDEPTH,--depthDEPTHDepth to crawl-rPATH_REGEX,--path_regexPATH_REGEXRegular expression to match URL--verboseIncrease logging level
Package: analyzr-sdk-python
Python SDK for the Analyzr APIOverviewThis Python client will give you access to the Analyzr API. See files in theexamplesfolder
for examples showing how to use the client. Note that aclient_idshould always be provided when querying the API; it is used for reporting purposes.For general information please seehttps://analyzr.ai.For help and support seehttps://support.analyzr.ai.For SDK reference documentation seehttps://analyzr-sdk-python.readthedocs.io.Installation instructionsGetting the client set up will require the following:Install the latest version of the client on your local machine:pip install analyzr-sdk-pythonGet an API username and password from your Analyzr admin (you may need SSO credentials from your local admin instead).Confirm you are able to connect to the API, and check the API version
as follows from a Python session:>>> from analyzrclient import Analyzer
>>> analyzer = Analyzer(host="<your host>")
>>> analyzer.login()
Login successful
>>> Analyzer().version()
{'status': 200, 'response': {'version': 'x.x.xxx', 'tenant': <your tenant name>, 'copyright': '2023 (c) Go2Market Insights Inc. All rights reserved.'}}Testing instructionsIf you are developing the SDK and would like to test the repo, clone it locally using git then
run the following from the root directory:python -m unittest tests.test_all -v   # all tests
python -m unittest tests.test_quick -v # quick testsMake sure you update theconfig.jsonfile first to include the name of your API tenant.
To run a single test case do:python -m unittest tests.test_all.PropensityTest.test_logistic_regression_classifier -v
Package: anaml-helper
OverviewA helper library for working with features and featuresets programmatically leveraging the existing anaml_client SDK.Example Usagefrom anaml_client import Anaml ##import Anaml as normal.
from anaml_helper import AnamlHelper ##import AnamlHelper 

anaml_client = Anaml(url=,apikey=,secret=,ref=) ##create Anaml class
anaml_helper = AnamlHelper(anaml_client) ##pass Anaml to AnamlHelperThis will provide access to the helper methods for interacting with Features and FeatureSets via anaml sdk. N.B -The library is currently limited to working only with Features and FeatureSet objects.The below contains some examples and a list of available methods.Feature Methodscreate_featureupdate_feature_aggregateupdate_feature_attributesupdate_feature_descriptionupdate_feature_entityRestrictionsupdate_feature_filterupdate_feature_labelsupdate_feature_nameupdate_feature_postAggregateExprupdate_feature_selectupdate_feature_tableupdate_feature_templateupdate_feature_to_DayWindowupdate_feature_to_OpenWindowupdate_feature_to_RowWindow#Creating EventFeature
anaml_helper.create_feature("feature_name", 
               attributes=[{"key": "OWNER", "value": "C11_HH360"},
                           {"key": "CREATOR", "value": "my_email@domain.com"}],
               labels=["label1", "label2"],
               select="accnt_key",
               description="feature description",
               template=None,
               table=383,
               filter="accnt_key is not null",
               aggregate="count",
               post_aggregate=None,
               entity_ids=[4])

#Updating feature labels
anaml_helper.update_feature_labels(feature_id=3467,labels=['label1','label2','label3'])

#Updating feature filter
anaml_helper.update_feature_filter(feature_id=3467, filter="accnt_id is not null")

#Updating feature name
anaml_helper.update_feature_name(feature_id=3457, name="modified_feature_name")

#Updating feature window to DayWindow
anaml_helper.update_feature_to_DayWindow(feature_id=3467, days=30)

#Updating feature window to RowWindow
anaml_helper.update_feature_to_RowWindow(feature_id=3467, rows=10_000)FeatureSet Methodscreate_featuresetupdate_featureset_attributesupdate_featureset_descriptionupdate_featureset_entityupdate_featureset_featuresupdate_featureset_labelsupdate_featureset_name#Creating FeatureSet
anaml_helper.create_featureset(
    name="featureset_name",
    entity=4,
    description="featureset description.",
    labels=["featureset_label1","featureset_label2"],
    attributes=[{"key": "OWNER", "value": "C11_HH360"}],
    features=[3467, 3459, 3462]
)

#Updating featureset name
anaml_helper.update_featureset_name(featureset_id=440,name="new_featureset_name")

#Updating featureset description
anaml_helper.update_featureset_description(featureset_id=440,description="new featureset description")

#Updating featureset entity
anaml_helper.update_featureset_entity(featureset_id=333, entity_id=3)
Package: anamnesis
anamnesisThis repository contains the anamnesis python module.  This is a python module
which enables easy serialisation of python objects to/from HDF5 files as well
as between machines using the Message Passing Interface (MPI) via mpi4py.anamnesis was originally part of the Neuroimaging Analysis Framework (NAF)
which is available fromhttps://vcs.ynic.york.ac.uk/naf.  It was split out as a
separate module in order to allow for its use in other modules (such as the
replacement for NAF, YNE:https://github.com/sails-dev/yne.AuthorsMark Hymersmark.hymers@hankel.co.ukLicenseThis project is currently licensed under the GNU General Public Licence 2.0
or higher.  For alternative license arrangements, please contact the authors.DependenciesSee therequirements.txtfile.Some of these aren't strict dependencies, but are instead what we develop
against (i.e. we don't guarantee not to use features which only exist from that
release onwards).
Package: ananimlib
AnAnimlib was inspired byManimLibby Grant
Sanderson of3Blue1Brown.  The aim of AnAnimlib is to facilitate the creation
of mathematically preceise animations through an intuitive and extensible API.As a simple example, the following code spins a square it as it moves across the canvas.importananimlibasalrect=al.Rectangle([1,1])al.Animate(al.AddAnObject(rect),al.MoveTo(rect,[-3.0,0.0]),al.RunParallel(al.Move(rect,[6,0],duration=1.0),al.Rotate(rect,2*3.1415,duration=1.0),),al.Wait(1.0))al.play_movie()Installation instructions:> pip install ananimlibDocumentation available atRead The Docs
Package: ananke-causal
AnankeVisit thewebsiteto find out more.Ananke, named for the Greek
primordial goddess of necessity and causality, is a python package for
causal inference using the language of graphical modelsContributorsRohit BhattacharyaJaron LeeRazieh NabiPreethi PrakashRanjani SrinivasanInterested contributors should check out theCONTRIBUTING.mdfor further details.InstallationIf graph visualization is not required then install viapip:pip install ananke-causalAlternatively, the package may be installed from gitlab by cloning andcdinto the directory. Then,poetry(seehttps://python-poetry.org) can be used to install:poetry installInstall with graph visualizationIf graphing support is required, it is necessary to installgraphviz.Non M1 Mac instructionsUbuntu:sudoaptinstallgraphvizlibgraphviz-devpkg-configMac (Homebrew):brewinstallgraphvizFedora:sudoyuminstallgraphvizOnce graphviz has been installed, then:pipinstallananke-causal[viz]# if pip is preferredpoetryinstall--extrasviz# if poetry is preferredM1 Mac specific instructionsIf on M1 see thisissue. The fix is to run the following before installing:brewinstallgraphviz
python-mpipinstall\--global-option=build_ext\--global-option="-I$(brew--prefixgraphviz)/include/"\--global-option="-L$(brew--prefixgraphviz)/lib/"\pygraphviz
Package: ananse
AnanseThis project is a collaboration between Dr. Effah Antwi,Research Scientist, Natural Resources Canada, Canadian Forest Service
and Dr Wiafe Owusu-Banahene, Department of Computer Engineering, School of Engineering Sciences, University of Ghana, Legon, Accra, Ghana.
TheAnansepackage is a python package  designed to partially automate search term selection and writing search strategies for systematic reviews. Read the documentation atbaasare.github.io/ananseandananse.readthedocs.io/SetupAnanse requires python 3.7 or higherUsing pippipinstallananseDirectly from the repositorygitclonehttps://github.com/baasare/ananse.git
pythonananse/setup.pyinstallQuick startWriting your own scriptfromananseimportAnansemin_len=1# minimum keyword lengthmax_len=4# maximum keyword length# Create an instance of the packagetest_run=Ananse()# Import your naive search results from the current working directoryimports=test_run.import_naive_results(path="./")# Columns to deduplicate imported search resultscolumns=['title','abstract']#de-duplicate the imported search resultsdata=test_run.deduplicate_dataframe(imports,columns)#extract keywords from article title and abstract as well as author and database tagged keywordsall_terms=test_run.extract_terms(data,min_len=min_len,max_len=max_len)#create Document-Term Matrix, with columns as terms and rows as articlesdtm,term_column=test_run.create_dtm(data.text,keywords=all_terms,min_len=max_len,max_len=max_len)#create co-occurrence network using Document-Term Matrixgraph_network=test_run.create_network(dtm,all_terms)#plot histogram and node strength of the networktest_run.plot_degree_histogram(graph_network)test_run.plot_degree_distribution(graph_network)#Determine cutoff for the relevant keywordscutoff_strengths=test_run.find_cutoff(graph_network,"spline","degree",degrees=3,knot_num=1,percent=0.879956,diagnostics=True)#get suggested keywords and save to a csv filesuggested_keywords=test_run.get_keywords(graph_network,"degree",cutoff_strengths,save_keywords=True)#Print suggested keywordsforwordinsuggested_keywords:print(word)Using Ananse Test Scriptpythontests/ananse_testReferencesThis is a python implementation of the R package as mentioned in paperAn automated approach to identifying search terms for systematic reviews using keyword co‐occurrence networks by Eliza M. Grames, Andrew N. Stillman  Morgan W. Tingley and Chris S. Elphick
Package: anansescanpy
AnanseScanpypackage: implementation of scANANSE for Scanpy objects in PythonInstallationThe most straightforward way to install the most recent version of AnanseScanpy is via conda using PyPI.Install package through CondaIf you have not used Bioconda before, first set up the necessary channels (in this order!).
You only have to do this once.$ conda config --add channels defaults
$ conda config --add channels bioconda
$ conda config --add channels conda-forgeThen install AnanseScanpy with:$ conda install anansescanpyInstall package through PyPI$ pip install anansescanpyInstall package through GitHub$ git clone https://github.com/Arts-of-coding/AnanseScanpy.git
$ cd AnanseScanpy
$ conda env create -f requirements.yaml
$ conda activate AnanseScanpy
$ pip install -e .Install Jupyter Notebook$ pip install jupyterStart using the packageRun the package either in the console$ python3Or run the package in jupyter notebook$ jupyter notebookFor extended documentation see our ipynb vignette with PBMC sample dataOf which the sample data can be downloaded$ wget https://zenodo.org/record/7446267/files/rna_PBMC.h5ad -O scANANSE/rna_PBMC.h5ad
$ wget https://zenodo.org/record/7446267/files/atac_PBMC.h5ad -O scANANSE/atac_PBMC.h5adinstalling and running anansnakeFollow the instructions its respective github page,https://github.com/vanheeringen-lab/anansnakeNext automatically use the generated files to run GRN analysis using your single cell cluster data:snakemake --use-conda --conda-frontend mamba \
--configfile scANANSE/analysis/config.yaml \
--snakefile scANANSE/anansnake/Snakefile \
--resources mem_mb=48_000 --cores 12Thanks to:Jos Smits and his Seurat equivalent of this packagehttps://github.com/JGASmits/AnanseSeuratSiebren Frohlich and his anansnake implementationhttps://github.com/vanheeringen-lab/anansnake
Package: anansi-market-data-handler
Anansi Market Data HandlerPacote python cujo objetivo é servir como abstração para aquisição,
armazenamento, leitura e apresentação de dados de mercado, procurando
ser agnóstico quanto à fonte dados[^1].AAPIdeste pacote fornece uma interface para os objetos de dados
cujosmétodosprocuram refletir a necessidade de quem lida,
usualmente, com dados deste tipo - ostraders- abstraindo, por
exemplo, a complexidade matemática da implementação dos indicadores de
mercado, como médias móveis, bandas de bollinger e afins sobre um
conjunto decandlesticks, por exemplo.[^1]: Corretoras,exchanges,brokerssão nomenclaturas comuns para
essas fontes.
Package: anantmishra
# Anant Mishra’s ModuleA simple package for doing calculations.## InstallationTo install Anant Mishra’s Module, use pip:
pip install anantmishra## API###calculate(x, y, operation)Perform a calculation based on the value ofoperation.operationcan be one of the following strings: “add”, “subtract”, “multiply”, “divide”, “exponentiate”, “modulo”, or “square root”.Returns the result of the calculation.###about()Prints out some ASCII art and a description of the package.## CreditsAnant Mishra
Package: anapioficeandfire
anapioficeandfire-pythonA Python helper library for anapioficeandfire.com - The world’s greatest API for quantified and structured data from the universe of Ice and Fire.Free software: BSD licenseDocumentation:https://anapioficeandfire-python.readthedocs.org.FeaturesTODOHistory0.1.0 (2016-3-3)First release on PyPI.0.1.1 (2016-3-3)Fixed a few things0.1.2 (2016-3-3)Importing submodules in init