Saravanan666/pypi-org huggingface dataset:
Package: mapdump
mapdumpMapdump is a simple command-line tool that analyzes binaries to examine memory usage. It is built mainly for embedded
C code.installationYou can download & installmapdumpviapip:$ pip3 install mapdumpYou can also download and usemapdumpby cloning this repo:$ git clone https://github.com/sorjente/mapdump
$ cd mapdump/
$ python3 -m mapdump <my_binarys_map.map>usageNote: custom options coming soon!Usingmapdumpis very simple; you only need to provide it with the.mapfile corresponding to the binary you want
to analyze. Let's say I want to analysehello_world.map:$ mapdump hello_world.map
FLASH @ 0x0008000000 (524288 bytes)
****************************************
    build/startup_stm32l452.o                                                                                     [460 bytes, 31.94% of FLASH space used]
    lib_a-memcpy.o (/usr/lib/gcc/arm-none-eabi/6.3.1/../../../arm-none-eabi/lib/thumb/v7e-m/fpv4-sp/hard/libc.a)  [308 bytes, 21.39% of FLASH space used]
    rcc.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                                 [220 bytes, 15.28% of FLASH space used]
    lib_a-memset.o (/usr/lib/gcc/arm-none-eabi/6.3.1/../../../arm-none-eabi/lib/thumb/v7e-m/fpv4-sp/hard/libc.a)  [156 bytes, 10.83% of FLASH space used]
    build/main.o                                                                                                  [116 bytes, 8.06% of FLASH space used]
    gpio_common_f0234.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                   [68 bytes, 4.72% of FLASH space used]
    flash_common_all.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                    [36 bytes, 2.50% of FLASH space used]
    flash_common_idcache.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                [32 bytes, 2.22% of FLASH space used]
    rcc_common_all.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                      [26 bytes, 1.81% of FLASH space used]
    gpio_common_all.o (./libopencm3/lib/libopencm3_stm32l4.a)                                                     [18 bytes, 1.25% of FLASH space used]dev notesTests can (and should be!) run with the following command:$python3-munittestdiscover-smapdump/tests
Package: mapeathor
MapeathorMapeathor translates your mapping rules specified in spreadsheets to a mapping language.Mapeathoris a simple spreadsheet parser able to generate mapping rules in three mapping languages: R2RML, RML (with extension to functions from FnO) and YARRRML. It takes the mapping rules expressed in a spreadsheet and transforms them into the desired language. The spreadsheet template is designed to facilitate the mapping rules' writting, with the aim of being language independent, and thus, lowering the barrier of generating mappings for non-expert users.ExampleA more detailed explanation is provided in thewiki.First Step: Fill the xlsx template with the transformation rulesThe template has five mandatory sheets,Prefixes, Source, Subject PredicateObjectMapandFunctions. The last one can be left blank in case there are no functions. The spreadsheet can be in XLSX format or a Google Spreadsheet. Here is an example of the structure of the spreadsheet.Second Step: Choose the output languageOne of three options can be chosen: R2RML, RML or YARRRML.Third Step: Run it!The easiest way of running Mapeathor is using theweb serviceand theSwaggerinstance. For CLI lovers, the service is available as aPyPi packageand Docker image. The instructions of the latest can be found in thewiki.PublicationsIglesias-Molina, A., Pozo-Gilo, L., Dona, D., Ruckhaus, E., Chaves-Fraga, D., & Corcho, O. (2020, January).Mapeathor: Simplifying the Specification of Declarative Rules for Knowledge Graph Construction. In ISWC (Demos/Industry).Online versionIglesias-Molina, A., Chaves-Fraga, D., Priyatna, F., & Corcho, O. (2019). Towards the Definition of a Language-Independent Mapping Template for Knowledge Graph Creation.In Proceedings of the Third International Workshop on Capturing Scientific Knowledge co-located with the 10th International Conference on Knowledge Capture (K-CAP 2019)(pp. 33-36).Online versionAuthors and contactAna Iglesias-Molina-ana.iglesiasm@upm.esLuis PozoDaniel Doña
Package: mapel
MapelThis package's purpose is to install jointly the following (so far all) parts of
the mapel ecosystem:mapel-coremapel-electionsmapel-roommatesmapel-marriagesSee the documentation of the above for further information.AcknowledgmentsThis project is part of thePRAGMA projectwhich has received funding from theEuropean Research Council
(ERC)under the European Union’s Horizon 2020
research and innovation programme (grant agreement No
101002854).
Package: mapel-core
Mapel-coreThis package contains the necessary core parts of the mapel ecosystem. Mapel can
be used to draw maps of different kinds of computational problem instances based
on metrices defining distances between two instances.This package provides the framework that can be further extended. Some of the
extensions, regarding problems for Computational Social Choice, are already
available on PyPi:mapel-electionsmapel-roommatesmapel-marriagesFor the most recent version of mapel, visit itsgit
repo.InstallationFor a simple installation, type:pip install mapel-corein the console.AcknowledgmentsThis project is part of thePRAGMA projectwhich has received funding from theEuropean Research Council
(ERC)under the European Union’s Horizon 2020
research and innovation programme (grant agreement No
101002854).
Package: mapel-elections
Mapel-electionsThis pacakge is a plugin formapelextending
it with capabilities of drawing maps of various elections intances.For the most recent version of mapel, visit itsgit
repo.InstallationFor a simple installation, type:pip install mapel-electionsin the console.For more complicated variants of installation, refer to the readme of mapelhere.Extra dependenciesFor the full functionality of the package, it is recommended to also install
extra dependencies. Doing this is covered inthis
readme. The extra dependencies contain:cplex>=20.1.0.1
pulp~=2.5.1
abcvoting~=2.0.0b0
permanentwhich unlock approval based committee rules (which require solving I(L)P
programs) and sampling a matrix using a permanent-based approach.One can do it by invokingpip install mapel-elections[extras]:exclamation: Note that this library contains C++ extensions. So installing
this library from sources  might be a bit cumbersome. We will, one day, put
here an instruction how to do it.Testing InstallationIf the instalation was successfull, you should be able to mimic the following:(<virtual_envirnonment_name>) $ python
...
>>> import mapel.elections.metrics.cppdistances as d
...
>>> d.swapd([[0,1,2],[0,1,2]], [[0,1,2],[2,1,0]])
3
>>> exit()AcknowledgmentsThis project is part of thePRAGMA projectwhich has received funding from theEuropean Research Council
(ERC)under the European Union’s Horizon 2020
research and innovation programme (grant agreement No
101002854).
Package: mapel-marriages
Mapel-marriagesThis pacakge is a plugin formapelextending
it with necessary files to draw maps of Stable Marriage intances.For the most recent version of mapel, visit itsgit
repo.InstallationFor a simple installation, type:pip install mapel-marriagesin the console.For more complicated variants of installation, refer to the readme of mapelhere.AcknowledgmentsThis project is part of thePRAGMA projectwhich has received funding from theEuropean Research Council
(ERC)under the European Union’s Horizon 2020
research and innovation programme (grant agreement No
101002854).
Package: mapel-roommates
Mapel-marriagesThis pacakge is a plugin formapelextending
it with necessary files to draw maps of Stable Roommates intances.For the most recent version of mapel, visit itsgit
repo.InstallationFor a simple installation, type:pip install mapel-roommatesin the console.For more complicated variants of installation, refer to the readme of mapelhere.AcknowledgmentsThis project is part of thePRAGMA projectwhich has received funding from theEuropean Research Council
(ERC)under the European Union’s Horizon 2020
research and innovation programme (grant agreement No
101002854).
Package: maper
MaperMaper (may-per, like paper with an 'm', as inmeta-paper) is a minimal, easy to use, command line tool to download/manage scientific
papers in pdf-format by attaching a bibtex-file and updating its metadata using pikepdf.InstallInstall from PyPI:$pipinstallmaperAdditionally,pandocneeds to be installed and available.RunUse eitherpython -m maper <command>or ifPATHis set correctly,maper <command>.Currently available commands are:createshowbibtexcreateCreate a PDF file with an attached bibtex file and metadata title and authors set:$mapercreate<bibtex><pdf>[-o<output>]where<bibtex>and<pdf>may be either local files or URLs, and output is a filename, or a directory.
If<output>is not a directory, the resulting pdf will be stored in that file.
If<output>is an existing directory, the file will be stored in<output>/<FirstAuthorFamilyName><Year><FirstTitleWord>.pdf.
If<output>is omitted, the current directory is assumed.showShow a summary of the attached bibtex of one or multiple pdfs:mapershow[<pdf>...]bibttexShow the bibtex of one or combined of multiple pdfs (e.g. to create a full bibtex file)$maperbibtex[<pdf>...]ExampleThe following creates the pdfWeizenbaum1966ELIZA.pdf, shows a summary, and the full attached bibtex filed:$mapercreate'https://dblp.uni-trier.de/rec/journals/cacm/Weizenbaum66.bib?param=1''https://cse.buffalo.edu/~rapaport/572/S02/weizenbaum.eliza.1966.pdf'$mapershowWeizenbaum1966ELIZA.pdf
$maperbibtexWeizenbaum1966ELIZA.pdf
Package: mapexcept
importmapexcept# Basic usageforiinmapexcept(['1','2','3','FOOBAR','4'])[ValueError](int):print(i)# 1# 2# 3# 4print('')# With default valuesforiinmapexcept(['1','2','3','FOOBAR','4'])[ValueError:0](int):print(i)# 1# 2# 3# 0# 4print('')# As a decorator@mapexcept(['1','2','3','FOOBAR','4'])[ValueError:0]defconversion(val:str):returnint(val)foriinconversion:print(i)# 1# 2# 3# 0# 4
Package: mapextrackt
MapExtracktConvolutional Neural Networks Are BeautifulWe all take our eyes for granted, we glance at an object for an instant and our brains can identify with ease.
However distorted the information may be, we do a pretty good job at it.Low light, obscured vision, poor eyesight... There are a myriad of situations where conditions are poor but still we manage to understand what an object is.
Context helps, but we humans were created with sight in mind.Computers have a harder time, but modern advances with convolutional neural networks are making this task a reality.Computers are amazing, the neural networks and maps they create are beautiful.Why not have an explore?MapExtrakt makes viewing feature maps a breeze.Catch a glimpse of how a computer can see.MapExtrakt UsageFirst import your modelimporttorchvisionmodel=torchvision.models.vgg19(pretrained=True)Import MapExtract's Feature Extractor and load in the modelfromMapExtracktimportFeatureExtractorfe=FeatureExtractor(model)Set image to be analysed - input can be PIL Image, Numpy array or filepath. We are using the pathfe.set_image("pug.jpg")View Layersfe.display_from_map(layer_no=1)View Single Cells At a Timefe.display_from_map(layer_no=2,cell_no=4)Slice the class to get a range of cells  (Layer 2 Cells 0-9)fe[2,0:10]Or Export Layers To Videofe.write_video(out_size=(1200,800),file_name="output.avi",time_for_layer=60,transition_perc_layer=0.2)More ExamplesFor LOTS more - view the jupyter notebook.ExamplesInstallationIt's as easy as PyPIpip install mapextracktor build from source in terminalgit clone https://github.com/lewis-morris/mapextrackt &&\
cd mapextrackt &&\
pip install -e .Todo ListAdd the ability to slice the class i.e  FeatureExtractor[1,3]Show parameters on the imageFix video generationEnable individual cells to be added to videoAdd video parameters such as duration in seconds.Clean up codeMake speed improvementsAuthorCreated by me, initially to view the outputs for my own pleasure.If anyone has any suggestions or requests please send them over I'd be more than happy to consider.lewis.morris@gmail.com
Package: mapfish
MapFish=======MapFish is a Pylons-based web framework with GIS orientations.MapFish provides:* a geometry type which is to be used when mapping PostGIS tableswith SQLAlchemy* a paster command to generate model and controller modecorresponding to layers (PostGIS tables) defined in aconfiguration file* an implementation of a RESTful protocols for creating, reading,updating, and deleting geographic objects (features)MapFish relies on the geojson and shapely packages, seehttp://gispython.org.MapFish projects are Pylons projects, the project developertherefore fully benefits from the power of Pylons and itscompanion components (SQLAlchemy, Mako, etc.).Current status--------------MapFish 2.2 described in this page is the current stable version.Download and Installation-------------------------MapFish can be installed with `Easy Install<http://peak.telecommunity.com/DevCenter/EasyInstall>`_ by typing::> easy_install mapfish
Package: mapfish.plugin.client
MapFish Client Plugin=====================The client plugin of the MapFish framework. This plugin provides a Pastertemplate for installing the MapFish JavaScript toolbox and a defaultJavaScript web-mapping user interface within MapFish applications.Current status--------------MapFish Client Plugin 0.2 described in this page is the current stableversion.Download and Installation-------------------------The plugin can be installed with `Easy Install<http://peak.telecommunity.com/DevCenter/EasyInstall>`_ by typing::> easy_install mapfish.plugin.client
Package: mapflow-sdk
Mapflow SDKPython library for easy acceess toMapflow API.You can find it useful, ifIf you are a GIS researchers, you can use it in Jupyter notebooks or scriptsIf you are a Python developer and want to access Mapflow API from your applicationYou can use Mapflow API directly, but this SDK provides easier access from your Python code.RequirementsPython 3.8+python-requestsregistration atMapflow.aiInstallationpip install mapflow-sdkObtain API tokenVisitaccount/API sectionand get token to use it with APIBasic usageSee examplecdexample
python3example.py<YOUR_MAPFLOW_TOKEN>The example will create fileoutput.geojson
Package: mapfost
mapfostmapfost is a python implementation of the autofocus method MAPFoST introduced in the publication Binding J, Mikula S, Denk W. Low-dosage Maximum-A-Posteriori Focusing and Stigmation. Microsc Microanal. 2013InstallationUse the package manager pip to install mapfostpipinstallmapfostUsageimportmapfostasmfres=mf.est_aberr([test_im1,test_im2])Examplerun the scriptrun_sample.pymf.test_installation()Expected output is a scipy.optimize.minimize object (as shown below).Thexkey in the following object is the estimated aberration vector.hess_inv:<3x3LbfgsInvHessProductwithdtype=float64>jac:array([195312.50118701,-195312.50118701,390624.9980372])message:'CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH'nfev:24nit:5njev:6status:0success:Truex:array([-1.10622,-1.31151,0.58894])...InstallationTestSuccessful...[-1.10622-1.311510.58894]LicenseMIT
Package: mapfun
mapfunmapfunis a function that applies a mapping function to an infinite number of input elements, with options to skip certain elements and selectively apply the mapping to keys and/or values of objects.Themapfunfunction has been developed in JavaScript and Python, offering the same functionality in different programming languages.InstallpipinstallmapfunImportfrommapfunimportmapfunArgumentsfun: The mapping function that will be applied to each element in the input elements....X:The elements to be mapped .{ "skip" , "key" , "value" }: Object with three optional properties :"skip": specifies the elements to skip during the mapping process."key": boolean flag indicating whether to apply the mapping function to the keys of objects or not. The default value isfalse"value": boolean flag indicating whether to apply the mapping function to the values of objects or not. The default value istrueExamplesprint(mapfun(lambdaa:a+1,{},4))# Expected return : 5print(mapfun(lambdaa:a+1.0,{},4.2))# Expected return : 5.2print(mapfun(lambdaa:len(a),{},"Ziko"))# Expected return : 4print(mapfun(lambdaa:a+5,{},[1,2,3,[4]]))# Expected return : [6,7,8,[9]]print(mapfun(lambdaa:a+5,range(0,10)))print(mapfun(lambdaa:a+5,{},(1,2,3,[4])))print(mapfun(lambdaa:a+5,{},{1,2,3}))print(mapfun(lambdaa:a+5,frozenset({1,2,3})))print(mapfun(lambdaa:a+5,{},{"a":1,"b":2,"c":3}))print(mapfun(lambdax:x+1,{},1,2,3,[1,2]))print(mapfun(lambdax:x+1,{},1,2,3,[1,2]))Supported DatatypesDatatypeSupportint✅str✅Boolean✅float✅None✅memoryview✅list✅object✅dict✅set✅tuple✅bytes✅bytearray✅range✅complex❌AlternativesJavascriptLicenseThis projet is licensed under the terms of MIT License .
Package: mapillary
Mapillary Python SDKMapillary's official Python SDK for GeoSpatial DataExplore the docs »View Demo·Report Bug·Request Feature·See DocumentationTable Of ContentsMapillary Python SDKTable Of ContentsAboutGetting StartedInstallationDevelopmentFormatting/LintingDev SetupTrouble shootingUpgrading A PackagePossible IssuesContributingAcknowledgementsLinksMore About MapillaryLegalAboutMapillary's Python SDK provides an easy mechanism for accessing and retrieving information from Mapillary's web application.For more information, please visitMapillaryandMapillary's Blog.Getting StartedInstallationTo get started, simply installmapillaryby running,pipinstallmapillaryA quick demo,"""Getting image coordinates from a nearby set of coordinates"""# Importing mapillaryimportmapillary.interfaceasmly# JSON importimportjson# Get image points close to in the given coordinatesdata=mly.get_image_close_to(longitude=31,latitude=30).to_dict()# Save the data as JSONfile_name="get_image_close_to_1.json"withopen(file_name,mode="w")asf:json.dump(data,f,indent=4)You can check out all the implemented functionality from thedemo.Or you can check out thedocumentation!DevelopmentFormatting/LintingTo run the formatterblack, and the linterflake8, run,flake8mapillary&blackmapillaryOr, if you are on Linux, you can simply run,makestyleThis runs thestylepolicy from theMakefile.Dev SetupAliases for setting up the environment have been provided inMakefileto reduce burden of replication.The steps to execute are, in order of running them,make setup-dev: Install developer dependenciesmake build: Build the packagemake local-install: Install package locally in the dev environmentTo use the new package installed locally, first usepipenvto change into the environment that the package was installed into by running,pipenvshellThen run,python# assuming running python opens the Python3 shellThen import and use as required,# import package hereimportmapillary.interfaceasmly# more code to follow hereTrouble shootingIf you get messed up dependencies, feel free to delete thePipfile.lockfile withrm Pipfile.lock, then start again with the first step inDev Setup.If you ever need to start with a clean build, you can always runmake cleanwhich will clean thedistdirectory, then you can simply start again from the second step inDev Setup.Upgrading A PackageIf you upgrade any package inPipfile, be sure to runpipenv installto update thePipfile.lockas well.Possible IssuesFailed installation/dev setup because oflibgeos_c.soorlibgeos_c_1.soWith UNIX based systems, you would to install the `geos` package with you package manager.In debian systems,sudo apt install geos.
In arch based systems,sudo pacman -S geos.ContributingWe welcome contributions! SeeCONTRIBUTINGfor details on how to get started, and
ourcode of conduct.AcknowledgementsChristopher Beddow- for leading the projectSaif Ul Islam- for developing the SDK under MLH fellowshipOmar Ali- for developing the SDK under MLH fellowshipLinksMore About MapillaryWebsiteBlogA blog showcasing the SDKWeb AppForumAPI V4 - Mapillary BlogLegalFacebook Open Source Legal TermsFacebook Open Source Privacy Policy
Package: mapio
Introductionmapio is a project designed to provide a library of classes for dealing
with various grid formats, and performing some simple spatial
operations on the data.  The code is documented fairly well.CompatibilityThis library has been tested recent versions of Python (3.8+). If any errors are discovered, please contact the author.InstallationTo install this package:pip install mapioUninstalling and UpdatingTo uninstall:pip uninstall mapioTo update:pip install --upgrade mapio
Package: mapizy-property-analytics
Mapizy Property Analytics- Python APIProperty analytics is an machine learning based engine that helps industries like insurance, real-estate, and etc. Python API is useful to use Mapizy property analytics to developers simply using python language.UsageFirst please register and make an account inMapizy Studioand purchase the appropriate pricing plan or use the Free Tier mode.Go to API section and make API_key and API_url for your account.Install the package using
​pipinstallmapizy-property-analyticsIf you want to process analysis for a specific property you can use the following code:frommapizy_property_analyticsimportmapizy_property_analyticsAPI_url='<YOUR API URL>'API_key='<YOUR API KEY>'Address='<The property address>'# example: '5 Pele Ave Salisbury East Salisbury SA 5109'First_date='<First date in YYYY-MM-DD format>'# example: '2018-01-01Last_date='<Last date in YYYY-MM-DD format>'# example: '2020-01-01Frequency='<Image retrive frequency, can be 2-yearly, yearly, half-yearly, quarterly>'Analyse_type='<Analyse type can be change or latest>'# example: 'change'mapizy_property_analytics.development_API(API_url,API_key,Address=Address,\First_date=First_date,Last_date=Last_date,\Frequency=Frequency,Analyse_type=Analyse_type)All done
Package: mapjoin
The purpose of the mapjoin function is to have an alternative to str.join that
looks more like os.path.join (which takes*args).mapjoin()frommapjoinimportmapjoin,strjoinmapjoin('foo',2)# raise TypeErrormapjoin('foo',2,sep='_',key=str)# return 'foo_2'# you can also make mapjoin use your own callback with the key kwarg:your_formatter=lambdakey:str(key)ifkeyelse'Nothin!'mapjoin('foo',obj,2,None,sep='\n',key=your_formatter)strjoin()strjoin('foo',2)# 'foo-2'strjoin('foo',2,sep='_')# 'foo_2'But … why ?Initially, because I make to many mistakes when I write an os.path.join call and then a
str.join call in a row:>>>os.path.join('a','b')'a/b'# and 2 seconds later i'm doing this:>>>' '.join('a','b')TypeError:join()takesexactlyoneargument(2given)# and instead of "just fixit and move on", i decided try to make a jointBut also because I can’t get no satisfaction with my code when it looks like:readable=' '.join(map(str,['hello',f'__{name}__',something,]))# or:deffoo():readable=textwrap.dedent(f'''
    hello
    __{name}__
    ''').strip()Participate to the story