Saravanan666/pypi-org huggingface dataset:
Package: xl2py
An Excel (XL) 2 Python (Py) structure retriever for optimization. Convert the I/O of XL files into Python.DescriptionConvert an XL structure to Py and use any optimization algorithm of your willNow, with object-oriented formulasThe current project makes use of the XL COM interface (win32com library) to:Read an objective function cellRecursively build its dependent structure as of its formulaThe XL structure is represented in Py as a dict() objectThe structure is referenced to as:dictobj[Workbook number as int][Worksheet number as int][Row as int][Column as int]Whereby it handles:multi-XL workbook/worksheet referencessingle worksheet multirange retrievalXL cell formulas are translated toobject orientedcalculation blocks (no moreevalsas of this update).The calculation structure is determinded by cell-dependency trees, which have been already stored during the conversion (2)Handling of circular referencesOngoing development: A simple evolutionary algorithm that runs based off the abovementioned structure.FeaturesConversion LibraryThe following XL functions can be currently handled by xl2py.
xl2py is capable of undertakingsingle-cells,arraysandarray/matrix operationsStandard operators: +, -, /, *, ^Logical operators: <, >, <=, >=, <>, =IFAVERAGESTDEV.PTRANSPOSEABSMMULTIFERRORSUMCOUNTSQRTTackled in the latest updateNo moreevals-> formulas are object oriented (Calculation-, Formula- and Reference- and Numeric-Blocks)by-operand handlingOver the latest update development, by-operand handling of formulas took place of RPN (reverse-polish notation). For additional details, viz. github repositoryOn the wayObject serializationCVS outputsA conceptual example with corresponding XL file. (reach me for further assistance)InstructionsInstallationpipinstallxl2py==version_noExample: I/O object creationimportxl2pyBuilder=xl2py.builder()# creates a xl2py builder object# place the path of your XL filepath=r'C:\\User\\DEFAULT\\WHATEVER\\...'# define your XL file password (if it exists)pwd='password'# opens up a XL COM interface and attach it to the Builder objectBuilder.connect_com(path,pwd)# declare your input cell/range referencesinputs=xl2py.xlref(<Workbookstrorint>,\<Worksheetint>,<A1-orR1C1-typeXLreferences>)# inputs include other inputs to the xlref objectinputs+=xl2py.xlref(<strorint>,<int>,<str>)# output must be a single cell referenceoutput=xl2py.xlref(<strorint>,<int>,<str>)# Now you are all set. You shall translate the XL structure to python.Builder.set_structure(inputs,output)# If you want to change the input cell/range values...# vals must be of the shape of the inputs# and must be parsed as a list of lists or numpy arraysBuilder.set_input_values(vals)# grab the output (objective fun) value as numpy arrayoutput_val=Builder.get_output_value()# Grab the new output valueYou can find me @ Gabriel S. Gusmão <gusmaogabriels@gmail.com>https://www.researchgate.net/profile/Gabriel_Gusmaohttps://github.com/gusmaogabriels
Package: xl2sitemap
Excel to Sitemap (xl2sitemap)Xl2sitemapis a command line tool to generate sitemaps using data in an excel sheet. Xl2sitemap reads data from an excel sheet and converts the data intoSEO friendlysitemaps that can be submitted to search engines directly after uploading.Generates a .xml fileGenerates a .xml.gz file (compressed)Gives you flexibility with the number of urls in a single fileNew Features!Ability to create multiple sitemaps based on classifiers. Classifiers are nothing but different groups for which it is ideal to create different sitemaps. This makes it easier forindexation problem debuggingas mentioned on the blogXML Sitemapsby MozYou can also:Add attributes such as priority, changefreq, lastmod to your urlsetsRequirementsDillinger uses a number of open source projects to work properly:Python 3- Python 3 and aboveA well structured excel file with appropriate column names as mentioned belowStructuring your excel sheetThe columnurlis acompulsory columnin your excel sheet. This contains the urls of your websiteTheclassifiercolumn is an optional column. This contains the classifier based on which the sitemap file will be split into multiple files. If you are including this column in your excel sheet, make sure you use the-cflagThelastmodcolumn is an optional column. This contains the last modified date of the corresponding url in DD/MM/YYYY format. If you are including this column in your excel sheet, make sure you use the-lflagThechangefreqcolumn is an optional column. This contains the last change frequency of the corresponding url. If you are including this column in your excel sheet, make sure you use the-fflagTheprioritycolumn is an optional column. This contains the priority of the corresponding url. If you are including this column in your excel sheet, make sure you use the-pflagInstallationInstallation of xl2sitemap requires running the following command form your command line utilitypip install xl2sitemapRunning from command lineRunning xl2sitemap with the basic default configuration requires running the following commandxl2sitemap example-input.xlsxTheexample-input.xlsxcan be any excel sheet with the appropriate columns in itOther options that can be enabled areFlagUsage-fWill add<changefreq>tag in your sitemap-pWill add<priority>tag in your sitemap-lWill add<lastmod>tag in your sitemap-cWill split sitemap into multiple files based on the classifier column-m 50000Will add a maximum of 50,000 urls only in a single sitemap. If urls are greater than 50,000 then multiple files will be generatedExamplexl2sitemap example-input.xlsx -m 40000 -p -f -lThis will generate sitemaps with 40,000 urls in each file with<changefreq>,<priority>,<lastmod>attributes for each<urlset>.DevelopmentWant to contribute? Great!
Open your favorite Terminal and run these commands.git clone https://github.com/antiproblemist/excel-to-sitemap.gitLicenseBSD 3-ClauseAuthorFollow the author onLinkedinFree Software, Hell Yeah!
Package: xl3335currency
MGGY-8411-HW1This package is for currency convertion and calculation.Installtion$ pip install xl3335currencyAUDUSD_returnDefine the AUDUSD_return class - each instance will store one row from the dataframe.GBPEUR_returnDefine the GBPEUR_return class - each instance will store one row from the dataframe.USDCAD_returnDefine the USDCAD_return class - each instance will store one row from the dataframe.portfolioThis class defies a nobject for keeping track of our current investments/profits for each currency pair.
Package: xlab
xLab is an experiment execution tool that automates tasks in research-oriented projects. It provides a simple interface that can be integrated to project executables with minimum modifications. xLab enables the following functionalities:Maintaining a folder structure for runs.Caching results saved after each run.Accessing cached results from a Python dictionary of arguments.Installationpip install xlabQuickstartRunxlab project initon the root directory of your project.Wrap the code of your executables within awithclause such that it resembles the following pattern:# Import libraries...importxlab.experimentasexp# ...parser=get_parser()withexp.setup(parser)assetup:args=setup.argsdir=setup.dir# Use args to run experiment and# dir as a directory to save results# ...Call your program either directly from the terminal or indirectly through another script.If you executable could be run on the terminal before using xLab, now you can continue running the program in the same way as before, but now with results cached and automatically saved in a folder structure.You can also run the executable within a Python script by using the following code structure:# Import libraries...importxlab.experimentasexp# ...e=exp.Experiment(executable,required_args,command)print(e.args)# Prints all arguments (both required and optional)# Modify e.args as you wish...e.run()
Package: xl-aliyun-fc2
OverviewThe SDK of this version is dependent on the third-party HTTP libraryrequests.Running environmentPython 3.6+InstallationInstall the official release version through PIP (taking Linux as an example):$pipinstallxl-aliyun-fc2You can also install the unzipped installer package directly:$sudopythonsetup.pyinstallGetting started# -*- coding: utf-8 -*-importfc2importzipfileimportbase64# To know the endpoint and access key id/secret info, please refer to:# https://help.aliyun.com/document_detail/52984.htmlclient=fc2.Client(endpoint='<Your Endpoint>',accessKeyID='<Your AccessKeyID>',accessKeySecret='<Your AccessKeySecret>')# Create service.client.create_service('demo')zipFileBase64=''withopen('file.zip','rb')asf:content=f.read()zipFileBase64=base64.b64encode(content).decode()# Create function.client.create_function('demo',{'functionName':'test_func','runtime':'python3','handler':'index.handler','code':{'zipFile':zipFileBase64},'environmentVariables':{'testKey':'testValue'}})# Invoke function synchronously.client.invoke_function('demo','test_func')More resourcesAliyun FunctionCompute docsContacting usLinksLicenseMIT
Package: xlambda-helper
X-Lambda Helper (Python version)A Python library to help handle warm-up requests generated byX-Lambda.X-LambdapredictsAWS Lambdademand and keeps a fleet of containers warm to mitigate cold-start latency. Warm invocations sent by X-Lambda are received by a Lambda function, which needs to respond accordingly.ThisX-Lambda Helperproject helps to handle warm invocations in Python functions.RequirementsPython 3.6+AWS accountQuick startInstall X-Lambda Helper:pip install xlambda_helper.Decorate yourLambda handlerfollowing the example below:Deploy your code in AWS Lambda.Usage exampleimportxlambda_helper@xlambda_helper.warm()defhandler(event,context):# Your original code goes herex_men=[{'Charles':'Professor Xavier'},{'Logan':'Wolverine'},{'Jean':'Phoenix'},{'Scott':'Cyclops'}]returnx_menAboutX-Lambda Helper will be executed every time your handler is invoked by AWS Lambda. It will check whether the invocation is a warming request coming from X-Lambda. If not, it will run your handler code normally. If yes, it will short-circuit to return a default answer and will not execute the handler function.When handling warm requests, X-Lambda Helper will automatically adjust your function behavior:Check whether it was a cold startSleep for a period of time, if neededDefer to your original handler function, if invocation is not a warm request
Package: xlandsat
Analyze Landsat remote sensing images using xarrayDocumentation(latest)•Contributing(how you can help)•CompGeoLabAboutxlandsatis Python library for loading and analyzing Landsat scenes
downloaded fromUSGS EarthExplorerwith
the power ofxarray.
We take care of reading the metadata from the*_MTL.txtfiles provided by
EarthExplorer and organizing the bands into a singlexarray.Datasetdata
structure for easier manipulation, processing, and visualization.ExampleHere's a quick example of loading and plotting thisLandsat 9 scene from the city of Manaus, Brazil,
which is where the Solimões (brown water) and Negro (black water) rivers merge
to form the Amazon river:importxlandsatasxlsimportmatplotlib.pyplotasplt# Download a sample Landsat 9 scene in EarthExplorer formatpath_to_scene_file=xls.datasets.fetch_manaus()# Load the data from the file into an xarray.Datasetscene=xls.load_scene(path_to_scene_file)# Make an RGB composite as an xarray.DataArrayrgb=xls.composite(scene,rescale_to=[0.02,0.2])# Plot the composite using xarray's plotting machineryrgb.plot.imshow()# Annotate the plot with the rich metadata xlandsat adds to the sceneplt.title(f"{rgb.attrs['title']}\n{rgb.attrs['long_name']}")plt.axis("scaled")plt.show()Project goalsLoading single scenes in the EarthExplorer format.Provide some calculation, like composites, but leave most of the rest to the
user and xarray.Our goal isnotto provide a solution for large-scale data processing.
Instead, our target is smaller scale analysis done on individual computers.For cloud-based data processing, see thePangeo Project.For other satellites and more powerful features, useSatpy.Project statusxlandsat is ready for use but still changing.This means that we sometimes break backwards compatibility as we try to
improve the software based on user experience, new ideas, better design
decisions, etc. Please keep that in mind before you update xlandsat to a newer
version.We welcome feedback and ideas!This is a great time to bring new ideas on
how we can improve the project.
Submitissues on GitHub.LicenseThis is free software: you can redistribute it and/or modify it under the terms
of theMIT License. A copy of this license is provided inLICENSE.txt.
Package: xlavir
xlavir - Informative Excel reports from viral sequencing Nextflow analysisExcel report from viral sequencing data analysis output from thenf-core/viralreconorCFIA-NCFAD/nf-virontusNextflowpipelines.UsageCreate an Excel report from a Nextflow pipeline run:# e.g. run nf-core/viralrecon Nextflow pipeline against SARS-CoV-2 samples# sequenced by Illumina using the ARTIC V4.1 protocolnextflowrunnf-core/viralrecon\-profiledocker\--inputsamplesheet.csv\--platformillumina\--protocolamplicon\--genome'MN908947.3'\--primer_setartic\--primer_set_version'4.1'\--skip_assembly\--outdirviralrecon-results# create Excel report from Nextflow pipeline runxlavirviralrecon-resultsxlavir-report-viralrecon-results.xlsxSeeexample reportfrom test data intests/data/tools.FeaturesCollect sample results from anf-core/viralreconorCFIA-NCFAD/nf-virontusinto an Excel reportSamtoolsread mapping stats (flagstat)Mosdepthread mapping coverage informationVariant calling information (SnpEffandSnpSiftresults, VCF file
information) from the following variant callersBcftoolsiVarMedakaNanopolishClair3Consensus sequencesQA/QC of sample analysis results (basic PASS/FAIL based on minimum genome
coverage and depth)Nextflowworkflow execution informationPrepend worksheets from other Excel documents into the report (e.g. cover
page/sheet, sample sheet, lab results)Add custom images into worksheets with custom names and descriptions (e.g.
phylogenetic tree figure PNG)LicenseDistributed under the terms of theMITlicense, "xlavir" is free and open
source software.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: xlayers
xlayers: Python implementation of MITgcm’s layer packagexlayers allows you to transform your data from a regular vertical grid (like depth) to a vertical grid defined based on contours of some variable (like density). xlayers uses FORTRAN code from the layers package of the MITgcm, which was written by Ryan Abernathey.We recommend using this package on xarrays, but it can also be applied to numpy
arrays. Thisexamplemay be helpful in getting started.Why a new vertical regridding package?xlayers has many advantages over other similar tools. It conserves the total quantity of the input variable in the water column: this is particularly important when performing volume or heat budgets in density space. xlayers outputs the thickness-weighted variable in density space, which makes thickness-weighted averaging easier. This output is mapped in a smooth and sensible way. xlayers is also very fast and parallelizable.Installationxlayers can be installed from PyPI with pip:python-mpipinstallxlayersIt is also available fromconda-forgefor conda installations:condainstall-cconda-forgexlayersTo install xlayers from source repository, the fortran-compiler package is required. The easiest way to install the fortran-compiler is viaconda:condainstall-cconda-forgefortran-compilerunsetLDFLAGSOnce the compiler is installed, now we can install xlayers from Github:gitclonehttps://github.com/cspencerjones/xlayers.gitcdxlayerspythonsetup.pyinstallFeaturesxlayers isproperty conserving, meaning that the total amount of your variable in the water column will not change when it is transformed into the new coordinate system.xlayers acheives this using binning: it bins the variable onto a much finer vertical grid, estimates the depth of the new coordinate surfaces, and adds up these bins in the new space.InputsThis package expects your initial coordinate system to be on a structured grid. i.e. it will not take density as an initial vertical variable, but depth is ok.Before runninglayers_xarray, you must runfinegridto define some of the binning parameters needed.finegridtakes 2 inputs (which should both be numpy arrays): the vertical depth of the gridcells, and the vertical distance between the centers of the grid cells. The second variable should be loneger by 1 than the first, because it includes the distance between the cell center of the top cell and the sea surface, and the distance between the cell center of the bottom cell and the domain bottom.The first two inputs tolayers_xarrayshould be on the same gridpoints.data_inis the variable to be remapped andtheta_inis the variable that defines the location of the new coordinate system.OutputsThe output oflayers_xarrayisthickness weighted, i.e. if the input variable is salinity, the output variable is the salinity in each layermultiplied by the depth of the layer. In order to get an output that has the same units as the input, divide by the thickness of the layer (which you can get by inputting an array of ones intolayers_xarrayinstead of you input variabledata_in).Get in touchReport bugs, suggest features or view the source codeon GitHub.
Package: xl-bert
DocumentationThis is the repository of the xl_bert package. This library helps you find sentence embedding for your sentence using SOTA language models such as Bert and XLNET.This library takes in an input seven paramters :

**sentence_list,model_name,model_dir,token_model_dir,n_layers,strategy,max_len**. Each parameter has been explained below.

**sentence_list** : List of sentences you want to get embedding of. 

**model_name** : Name of model which you want to use, currently can be 'bert' or 'xlnet'.

**model_dir** : Directory path of pretrained/finetuned Bert/XLNet language model. Default is 'xlnet-base-cased'. Pretrained language models can be seen from here:


<http://huggingface.co/transformers/pretrained_models.html>`_


**token_model_dir** : Directory path of tokenizer. Default is 'xlnet-base-cased'


**n_layers** :  Number of layers you want to use to get sentence embedding.Default is 1

**Strategy** : This is where it gets interesting. Strategy is categorised in four choices.

    'avg': We average each layer individually and then average n_layers.
    'cat': We concatenate each layer individually, then we concatenate n_layers
    'avgcat': We average each layer individually and then concat n_layers
    'catavg': We concat each individual layer and then average n_layers. 

**max_len** : Maximum length of sentence you want. Default 50================
Installationpip install xl_bertUsage with Bert as well as XLNetget_sentence_embedding(['I am playing','let me dance'],model_name='xlnet',model_dir='xlnet- 
large-cased',token_model_dir='xlnet-large-cased',n_layers=2,strategy='avg',max_len=50)ContributionPackage author and current maintainer is Shivam Panwar (panwar.shivam199@gmail.com); You are more than welcome to approach him for help. Contributions are very welcomed, especially since this package is very much in its infancy.Created by Shivam Panwar (panwar.shivam199@gmail.com)
Package: xlbridge
XlBridge Python User LibraryXlBridge enables writing Excel user-defined formulas (UDFs) in C#, Python (or any programming language that
supports gRPC and protobufs).The XlBridge Python User Library provides the functionality to expose Python formulas to the XlBridge Excel add-in.The XlBridge Excel add-in is non-free software, seehttps://xlbridge.qaplix.sefor pricing and trial version availability.
Package: xlcompare
xlcompareCompare two Excel files (old vs new) where each row has auniqueID (identifier). Ideal for comparing requirements, bill of materials, invoices, exported databases, etc. Generates differences Excel file showing changes from old to new (red strikeout for deletions, blue for insertions). Supports both.xlsand.xlsxformats as inputs.Easy To UseGenerate differences Excel filediff.xlsxwith:xlcompareold.xlsnew.xlsFeaturesSupports both.xlsand.xlsxfile formats for input filesGenerates output Excel file containing differences (default:diff.xlsx)Output is autofiltered to show differences at a glanceChanges in each cell are marked with red strikeout for deletions, blue for insertionsDeleted rows will be at the bottom in red strikeoutPure Python (usesxlrd,pylightxl,XlsxWriterpackages)Excel File Format AssumptionsFirst row is assumed to contain column headingsColumns that are common between the two files will be compared (others are ignored)Column containing unique IDs is labeled "ID" (can override with the--idoption)Limitations:Only compares first sheet of each Excel fileCompares cells as textPython 3.6 or laterInstallationpython -m pip install xlcompareUsageusage:xlcompare[-h][--idID][--outfileOUTFILE][--colwidthmaxCOLWIDTHMAX]oldfilenewfile

ComparesExcel.xlsor.xlsxfiles(firstsheetonly)withheadersanduniquerowIDs;generatesdiff.xlsx.

positionalarguments:oldfileoldExcelfilenewfilenewExcelfile

optionalarguments:-h,--helpshowthishelpmessageandexit--idIDIDcolumnheading(default:ID)--outfileOUTFILE,-oOUTFILEoutput.xlsxfileofdifferences(default:diff.xlsx)--colwidthmaxCOLWIDTHMAXmaximumcolumnwidthinoutputfile(default:50)Examplesxlcompareold.xlsnew.xls# Generates diff.xlsxxlcompareold.xlsnew.xlsx# Generates diff.xlsxxlcompareold.xlsxnew.xls# Generates diff.xlsxxlcompareold.xlsnew.xls-omydiff.xlsx# Generates mydiff.xlsxxlcompareold.xlsxnew.xls--idMYID# Uses "MYID" as the ID column
Package: xlcrf
xlcrfUn tool (libreria Python + script) per creare file utili per la
raccolta dati (con validazione dell'input) a partire da una descrizione
della struttura del dataset desiderato (mediante un file.xlsx).UtilizzoLo strumento si può utilizzare via webquioppure da linea di comando, come segue:xlcrf file_struttura.xlsxQuesto produrràfile_struttura_CRF.xlsx.File di strutturaPer indicazioni su come compilarlo segui le istruzioniqui.
Package: xlcsv
xlcsvA Python micropackage for consuming Excel as CSV.Build CSVStringIOfrom Excel files.importxlcsvbuffer=xlcsv.to_csv_buffer("my-file.xlsx")Read Excel files without Excel using aDataFramelibrary.# ...importpandasaspddf=pd.read_csv(buffer)importpolarsaspldf=pl.read_csv(buffer)ContributingSeeCONTRIBUTING.md.
Package: xldeploy-py
Python SDK forXL-Deploy.Usageimportxldeployconfig=xldeploy.Config(protocol="http",host="localhost",port="4516",context_path="deployit",username="admin",password="admin")# If you are using urlconfig=xldeploy.Config.initialize(url="http://localhost:4516/deployit",username="admin",password="admin")# If you are using proxiesconfig=xldeploy.Config(protocol="http",host="localhost",port="4516",context_path="deployit",username="admin",password="admin",proxy_host="localhost",proxy_port=8080,proxy_username="proxyUsername",proxy_password="proxyPassword")# orconfig=xldeploy.Config()client=xldeploy.Client(config)# repositoryrepository=client.repositoryprint(repository.exists("Applications/EC2/1.0/ec2"))print(repository.exists("Applications/EC2/1.0/wrong"))ci=repository.read("Applications/EC2/1.0/ec2")print(ci.amiId)# deploymentdeployment=client.deploymentdeploymentRef=deployment.prepare_initial("Applications/NIApp/1.0","Environments/awsEnv")depl=deployment.prepare_auto_deployeds(deploymentRef)task=deployment.create_task(depl)task.start()print(task.task_id)# Deployfile## Apply Deployfile script.importrefromosimportpathdeployfile=client.deployfiledeploy_file=open('deploy_file_path','rb').read()file_names=re.findall('upload\([\'|"](.*)[\'|"]\)',deploy_file.decode("utf-8"))files_to_upload=[path.abspath(path.join(path.abspath(path.join(file_path,"..")),name))fornameinfile_names]deployfile.apply('deploy_file_path',files_to_upload)## POST of multiple multipart-encoded binary filesBasedonPython[requests](https://pypi.python.org/pypi/requests)module,see[docs](http://docs.python-requests.org/en/master/user/advanced/#advanced)## Generate Deployfile script.deployfile=client.deployfiledeployfile.generate([Environments/directory1,Environments/directory1])Installing from the PyPi repository$ pip install xldeploy-pyInstalling package directly from source$ cd xldeploy-py
$ pip install --editable .