Saravanan666/pypi-org huggingface dataset:
Package: xshape
xshapeTools for working with shapefiles, topographies, and polygons in xarrayFree software: MIT licenseDocumentation:https://xshape.readthedocs.io.FeaturesRead a shapefile and obtain an xarray DataArray of field recordsDraw shapefile boundaries on gridded dataPlot xarray DataArray data indexed by shapefile records as a choroplethUsageGetting records for fields in a shapefileIn[1]:importxshapeIn[2]:fields,polygons=xshape.parse_shapefile(...:'tests/data/shapefiles/CA_counties/CA_counties',...:encoding='latin1')In[3]:fieldsOut[3]:<xarray.Dataset>Dimensions:(shape:58)Coordinates:*shape(shape)int640123456789101112131415161718...Datavariables:STATEFP(shape)<U22'06''06''06''06''06''06''06''06''06''06'...COUNTYFP(shape)<U22'107''009''047''079''097''041''023''051'...COUNTYNS(shape)<U22'00277318''01675885''00277288''00277304'...GEOID(shape)<U22'06107''06009''06047''06079''06097''06041'...NAME(shape)<U22'Tulare''Calaveras''Merced''San Luis Obispo'...NAMELSAD(shape)<U22'Tulare County''Calaveras County'...LSAD(shape)<U22'06''06''06''06''06''06''06''06''06''06'...CLASSFP(shape)<U22'H1''H1''H1''H1''H1''H1''H1''H1''H1''H1'...MTFCC(shape)<U22'G4020''G4020''G4020''G4020''G4020''G4020'...CSAFP(shape)<U22'''''''''488''488''''''''''488''472'...CBSAFP(shape)<U22'47300''''32900''42020''42220''41860'...METDIVFP(shape)<U22'''''''''''41884''''''''''36084'''''...FUNCSTAT(shape)<U22'A''A''A''A''A''A''A''A''A''A''A''A'...ALAND(shape)<U22'12494707314''2641820029''5011554680'...AWATER(shape)<U22'37391604''43810423''112760479''820974619'...INTPTLAT(shape)<U22'+36.2288317''+38.1846184''+37.1948063'...INTPTLON(shape)<U22'-118.7810618''-120.5593996''-120.7228019'...Drawing shape boundaries on gridded dataIn[4]:importxarrayasxr,numpyasnp,xshape# generate sample dataIn[5]:da=xr.DataArray(...:np.cos((...:np.arange(41*45).reshape((41,45))*np.arange(41*45).reshape((45,41)...:).T)/4e5),...:dims=('lat','lon'),...:coords={...:'lon':np.linspace(-125,-114,45),...:'lat':np.linspace(32,42,41)})...:In[6]:da.xshape.overlay(...:'tests/data/shapefiles/CA_counties/CA_counties',...:encoding='latin1',...:cmap='YlGnBu');...:Plotting regional data in a choroplethUsing the xarray extension, we can plotDataArraydata directly:In[7]:importxshape,xarrayasxr,pandasaspdIn[8]:df=pd.read_csv('tests/data/datasets/co-est2016.csv',encoding='latin1')...:ca=df[(df['STATE']==6)&(df['COUNTY']>0)].copy()...:ca['fips']=df['STATE']*1000+df['COUNTY']...:da=ca.set_index(['fips'])['POPESTIMATE2016'].to_xarray()...:da.coords['GEOID']=('fips',),list(map('{:05}'.format,da.fips.values))...:da=da.swap_dims({'fips':'GEOID'})In[9]:da.xshape.plot(...:'tests/data/shapefiles/CA_counties/CA_counties',...:encoding='latin1',...:cmap='YlGnBu');...:We can also combine the information from the fields with the data contained in the DataArray:In[10]:land_area=(....:fields....:.set_coords('GEOID')....:.swap_dims({'shape':'GEOID'})....:.ALAND.astype(float))In[11]:np.log(da/land_area).xshape.plot(....:'tests/data/shapefiles/CA_counties/CA_counties',....:encoding='latin1',....:cmap='YlGnBu');....:TODOUse shapefiles to reshape gridded/pixel dataHistory0.1.0 (2018-01-13)First release on PyPI.
Package: xshl-target
Python Library for XSHL TargetJSON Schemaimportjsonfromxshl.targetimportTarget,Referencet=Reference(["project:[\"mcode-cc\",\"xshl\"]@pypi.org/xshl-target/#https://xshl.org/schemas/1.1/definitions/target.json","https://github.com/mcode-cc/py-xshl-target","https://en.wikipedia.org/wiki/Object_database","https://translate.yandex.ru?value.lang=en-ru&value.text=Targets","https://en.wikipedia.org/wiki/Object_database"],unique=True)t.insert(0,Target("https://github.com/mcode-cc/py-xshl-target"))t.append(Target(**{"@id":"https://xshl.org/schemas/1.1/definitions/target.json","@type":"/xshl-target/","base":"pypi.org","entity":["mcode-cc","xshl"],"spot":"project"}))print(json.dumps(t.dictionaries,ensure_ascii=False,sort_keys=True,indent=4))[{"@id":"https://xshl.org/schemas/1.1/definitions/target.json","@type":"/xshl-target/","base":"pypi.org","entity":["mcode-cc","xshl"],"spot":"project"},{"@type":"/mcode-cc/py-xshl-target","base":"github.com","spot":"https"},{"@type":"/wiki/Object_database","base":"en.wikipedia.org","spot":"https"},{"@context":{"value":{"lang":"en-ru","text":"Targets"}},"base":"translate.yandex.ru","spot":"https"}]
Package: xslearn
xslearnxslearn is a machine learning toolkit implemented by Python. (keep updating...)model examples(below are implemented models)- linear_modelPerceptronLogistic Regression- neighborsKNeighborsClassifier- naive_bayesNaiveBayesGaussianNaiveBayes- svmSVC- treeDecisionTreeClassifierID3C4.5CartDecisionTreeRegressor- ensembleBoostingAdaboostClassifierBaggingBaggingClassifierBaggingRegressorRandomForestClassifierRandomForestRegressor
Package: xs-lib
personal python liblegacy python lib seexsthunder/python-lib: useful python pieces of codecode and releasecode atxsthunder/xs_lib at masterupdatexs_lib/version.py at master· xsthunder/xs_libmerge to releae and travis will deploy to pipTODOFeaturesFull test with traivis to make sure things are on rail.xs_lib.ismain.main(__name__, main)to define entrancexs_lib.common.IN_TRAVISandxs_lib.common.IN_JUPYTERto tell current state, also allow bash env setting totrueorfalseto overwrite.xs_lib.common.CLI_TESTis read from bash env to control cli.Install and RunInstall viaxs-lib · PyPIpip install xs-libUse in Codeuse for singleipynbfileexport theipynbfileimportxs_lib.commonascommon# this will choose con tqdmforiincommon.tqdm(range(3)):print(i)nbe=common.NBExporter()nbe('./pdb.ipynb',to='./')use for projcetclonexsthunder/jupyter_dev_templateUse in clinb2py --helpoptional pakage tqdmconda install tqdmUsage suggestionadd#test_exportto top of the code cell which will be exported to test and standard file.add#exportto top ot the code cell which will be exported to standard file.useSure 1.4.7 - Documentation — sure 1.4.7 documentationfor#test_export.DevelopmentEnvironment Setupbash./config/create-env.shfor condadepsnot alldepsare necessary. only ipython are set in thesetup.py/deps.xs_lib.commonsupports dynamic import. feel free to import.to import other modules, please install corresponding deps first or you may come across import error.It's recommanded to install all packages listed increate-env.shcode structuresRefdeployment - How can I use setuptools to generate a console_scripts entry point which callspython -m mypackage? - Stack OverflowPackaging Python Projects — Python Packaging User Guidenotebook2scriptfromcourse-v3/nbs/dl2 at master · fastai/course-v3
Package: xslide
xslideTool for creating presentations.Accepts content specified in:markdowngrot- graphviz syntax overlay for generating graphsplain python stringsxplantsyntax (can build arbitrary html code)Outputs:statichtmlBasic usage:importxslideslide=xslide.XSlide("Title of example from README")slide.markdown("""
# xslide

- Can accept a `markdown`
- Can draw graphs in `grot` (`graphviz` overlay)
- Can use HTML in `xplant`

Author: [Michal Kaczmarczyk](mailto:michal.s.kaczmarczyk@gmail.com),

""")slide.next("Header of the next slide")slide.markdown("This one contains a graph:")withslide.make_graph("this_dot_name",html_style="max-width: 55%;")asg:stage_1=g.node("Stage 1",shape="box3d")stage_2=g.node("Stage\n2",shape="circle",penwidth="3.1")g.edge(stage_1,stage_2,penwidth="2.6")sink=g.node("This\nsinks\nall")fornin["alfa","beta","gamma","delta"]:ifn=="gamma":g.edge(stage_2,n,sink,penwidth="2.6",color="#314289")else:g.edge(stage_2,n,sink,color="#aabbcc",style="dashed")slide.flush()# makes a breakslide.markdown("""
*Markdown* with `nice_code` formatting. This example generates such a files:

\`\`\`
    >$ tree XSLIDE/examples/output/readme_example
    XSLIDE/examples/output/readme_example
    |-- index.html
    |-- readme_example_01.html
    |-- readme_example_01.html_this_dot_name.dot
    |-- readme_example_01.html_this_dot_name.dot.svg
    |-- readme_example_02.html
    |-- serve.py
    `-- xslide.css
\`\`\`
""")# don't forget to:slide.store()Result can be seen in gitlab in:examples/output/readme_example/index.html
Package: xslots
xslotsExample usage: Crack an n-digit codefromdatetimeimporttimedeltaimporttimefromxslotsimportSlotsslots=Slots(4,0,9,0)password="939614"start=time.time()whileTrue:print(slots)s="".join(str(slots)))ifs==password:end=time.time()print(f"Hacked code{s}in{timedelta(seconds=end-start)}")breakslots.increase()
Package: xslproxy
Xsl transforming reverse proxy based ontwistedandlxmlInstallationpython3 -m pip install xslproxyUsageUsage: twist [options] plugin [plugin_options] xslproxy [options]
Options:
      --backend=  Url to backend, no trailing slash [default: http://localhost]
      --help      Display this help and exit.
      --listen=   Listen port (strports syntax) [default: tcp:8080]
      --path=      [default: A directory with xsl files]
      --version   Display Twisted version and exit.Requests to one of the proxy endpoints will be forwarded to the backend. When a
successful result is returned from the backend, content is transformed using
the specified XSL stylesheets and sent to the client. The following proxy
endpoints are available:/transform/{XSLPARAMS}All stylesheets and parameters specified in XSLPARAMS are applied and to the
backend response and the result is returned to the client withContent-Typeheader set toapplication/xml,text/htmlortext/plainaccording to themethodspecified in the<xsl:output>element of the last stylesheet in the chain.TheXSLPARAMSpath segement takes the followingkey=valuepairs. Each
pair separated by the ampersand (&) character:xsl[]Relative path to a stylesheet on the server (without.xslor.xsltextension). This parameter can be specified multiple times.xa[stylesheet-key]Stylesheet alias. Relative path to a stylesheet on the server (without.xslor.xslt. This parameter is usefull if the same stylesheet should
be applied multiple times with different parameters.xp[stylesheet-key][param-key]XPath parameter with the nameparam-keyfor the stylesheet specified instylesheet-key. The latter need to match one of the values specified viaxsl[]parameter.sp[stylesheet-key][param-key]String parameter with the nameparam-keyfor the stylesheet specified instylesheet-key. The latter need to match one of the values specified viaxsl[]parameter.LicenseThe software is subject to theAGPLv3or later license.
Package: xsmiles
XSMILES visualizations in Jupyter LabA Custom Jupyter Lab Widget to visualize SMILES-based and atom-based scores.Please CiteIf you use XSMILES, the use cases, its code, or the generated explanations, please cite our article:Article in preparation, a preprint should appear mid September!Heberle, H., Zhao, L., Schmidt, S., Wolf, T., & Heinrich, J. (2022). XSMILES: interactive visualization for molecules, SMILES and XAI scores. Article in preparation.@article{Heberle2022XSMILES,author={Heberle, Henry and Zhao, Linlin and Schmidt, Sebastian and Wolf, Thomas and Heinrich, Julian},doi={},journal={Article in preparation},month={},number={},pages={},title={{XSMILES: interactive visualization for molecules, SMILES and XAI scores}},volume={},year={2022}}Availability and examplesXSMILES main pageTBD Demo websiteHOW TO use XSMILES (JupyterLab notebook)More examples with JupyterLabTBD PiPY repositoryKNIME component? Check the main pageExample Notebook with a how-toTo use XSMILES with Jupyter Lab you need to install the package with pipand have internet connectionwhile using it.RDKit MinimalLib is downloaded by your browser.Please check this notebook to seehow to usethe tool:NotebookNote that it is only being tested withJupyter Lab. A reason for you to use Lab is the space available for visualizations, which is much better than in regular Jupyter notebooks. We also had problems with the installation with Jupyter notebooks.InstallationQuick example - create conda environment and installs xsmilescondacreate--namexsmiles_envpython=3.7-cconda-forge# create conda env with python 3.7condaactivatexsmiles_env# activate the created envcondainstall-cconda-forgejupyterlab# install jupyter labpipinstallxsmiles# if xsmiles is not in pip yet, try pip install xsmiles-0.2.2-py2.py3-none-any.whl# Download the most up to date whl from this repository's releases)jupyterlabexamples/xsmiles_examples.ipynb# run a notebook with jupyter labUninstallpip uninstall xsmilesrm -r <path-to:>/share/jupyter/labextensions/xsmilesjupyter labextension listDevelopmentPlease check theDEVELOPMENT.mdfile.
Package: xsm-parser
Executable State Model ParserParses an *.xsm file (Executable State Model) to yield an abstract syntax tree using python named tuplesWhy you need thisYou need to process an *.xsm file in preparation for populating a database or some other purposeInstallationCreate or use a python 3.11+ environment. Then% pip install xsm-parserAt this point you can invoke the parser via the command line or from your python script.From your python scriptYou need this import statement at a minimum:from xsm-parser.parser import StateModelParserYou then specify a path as shown:result = StateModelParser.parse_file(file_input=path_to_file, debug=False)Check the code inparser.pyto verify I haven't changed these parameters on you wihtout updating the readme.In either case,resultwill be a list of parsed scrall statements. You may find the header of thevisitor.pyfile helpful in interpreting these results.From the command lineThis is not the intended usage scenario, but may be helpful for testing or exploration. Since the parser
may generate some diagnostic info you may want to create a fresh working directory and cd into it
first. From there...% xsm cabin.xsmThe .xsm extension is not necessary, but the file must contain xsm text. See this repository's wiki for
more about the xsm language. The grammar is defined in thestate_model.pegfile. (if the link breaks after I do some update to the code,
just browse through the code looking for the state_model.peg file, and let me know so I can fix it)You can also specify a debug option like this:% xsm cabin.xsm -DThis will create a diagnostics folder in your current working directory and deposit a couple of PDFs defining
the parse of both the state model grammar:state_model_tree.pdfand your supplied text:state_model.pdf.You should also see a file namedxsm-parser.login a diagnostics directory within your working directory
Package: xsmtplib
An extension of standard smtplib, which supports proxy tunneling.Package works on Python 2.7+ and Python 3.5+.UsingPySocks.InstallationYou can installxsmtplibfromPyPIby running:pip install xsmtplibOr you can just download tarball / clone the repository and run:python setup.py installAlternatively, include justxsmtplib.pyin your project.Usagexsmtplibextends standard python smtplib, so it can be used instead without any compatibility issues.Connection to SMTP server via proxy can be done during instance initialization:from xsmtplib import SMTP

server = SMTP(host="smtp.example.com", proxy_host="proxy.example.com")
server.sendmail("user@example.com", "admin@example.com", "I have an issue. Please help!")
server.quit()Alternatively, you can connect to SMTP server manually when you need to:from xsmtplib import SMTP

server = SMTP(timeout=30)
server.set_debuglevel(1)
server.connect_proxy(proxy_host="proxy.example.com", host="smtp.example.com")
server.helo("user@example.com")
server.sendmail("user@example.com", "admin@example.com", "I have an issue. Please help!")
s.quit()Known issuesSMTPS (SSL SMTP) and LMTP connections via proxy are not supported yet.LicenseSeeLICENSEfile for more details.
Package: xsnippet-cli
xsnippet-cliis a simple command line interface for interacting withxsnippetservice. By means this script, you can easily post and receive
snippets directly from your terminal.UsageIt’s very easy to use. You can paste the snippet this way$ xsnippet /path/to/fileor this way$ cat /path/to/file | xsnippetAs you can see the last method posts a some command output. It’s very usefull
to post the last few lines from logfile this way$ tail -n 5 nginx.log | xsnippetIt’s important to note that you can specify a snippet language or tags.
Thats can be done by the following command$ cat setup.py | xsnippet -l python -t setuptools testIf you want to receive snippet just do the following command$ xsnippet -r snippet_idInstallation$ (sudo) pip install xsnippet-clior$ (sudo) easy_install xsnippet-clior$ wget http://git.io/xsnippet-cli.zip -O xsnippet-cli.zip
$ unzip xsnippet-cli.zip && cd xsnippet-cli-master
$ (sudo) python ./setup.py installMetaAuthor: Igor Kalnitsky <igor@kalnitsky.org>License: BSD License
Package: xso
xarray-simlab-odeThexsoframework for building and solving models based on ordinary differential equations (ODEs), an extension ofxarray-simlab.Xarray-simlab provides a generic framework for building computational models in a modular fashion and anxarrayextension for setting and running simulations using xarray'sDatasetstructure.Xarray-simlab-ode (XSO) extends the framework with a set of variables, processes and a solver backend, suited towards ODE-based models. It is designed for flexible, interactive and reproducible modeling workflows.Installation$pipinstallxsoIn a nutshellA highly simplified model based on ordinary differential equations is shown below.Create new model components by writing compact Python classes:importxso@xso.componentclassVariable:var=xso.variable(description='basic state variable',attrs={'units':'µM'})@xso.componentclassLinearGrowth:var_ext=xso.variable(foreign=True,flux='growth',description='external state variable')rate=xso.parameter(description='linear growth rate',attrs={'units':'$d^{-1}$'})@xso.fluxdefgrowth(self,var_ext,rate):returnvar_ext*rateCreate a new model just by providing a dictionary of model components:model=xso.create({'Var':Variable,'Growth':LinearGrowth},time_unit='d')Create an input xarray.Dataset, run the model and get an output xarray.Dataset:importnumpyasnpinput_ds=xso.setup(solver='solve_ivp',model=model,time=np.arange(1,10,.1),input_vars={'Var':{'value_label':'X','value_init':1},'Growth':{'var_ext':'X','rate':1.},})withmodel:output_ds=input_ds.xsimlab.run()4.Perform model setup, pre-processing, run, post-processing and visualization in a functional style, using method chaining:withmodel:batchout_ds=(input_ds.xsimlab.update_vars(input_vars={'Growth':{'rate':('batch',[0.9,1.0,1.1,1.2])}}).xsimlab.run(parallel=True,batch_dim='batch').swap_dims({'batch':'Growth__rate'}).Var__var_value.plot.line(x='time'))DocumentationDocumentation is hosted on ReadTheDocs:https://xarray-simlab-ode.readthedocs.ioContributingThe package is in the early stages of development, and contributions are very welcome. See GitHub Issues for specific issues, or raise your own.
Code contributions can be made via Pull Requests on GitHub.
Check out the contributing guidelines for more specific information.Licensexarray-simlab-ode was created by Benjamin Post.
It is licensed under the terms of the BSD 3-Clause license.CreditsXarray-simlab-ode is an extension ofxarray-simlab, created by Benoît Bovy.
Package: xsocs
The X-ray Strain Orientation Calculation Software (X-SOCS) is a user-friendly software,
developed for automatic analysis of 5D sets of data recorded during continuous mapping measurements.
X-SOCS aims at retrieving strain and tilt maps of nanostructures, films, surfaces or even embedded structures.DocumentationInstallationX-SOCS runs on Linux, Windows and macOS withPython>=3.7 and can be installed with:pip install xsocs[gui]or with conda:conda install -c conda-forge xsocsSeeHow to install XSocsfor details.Using XSOCSOnce installed, you can run X-SOCS from the console with either:xsocsOr:python -m xsocsSeeUsing X-Socs documentationand
thevideo tutorials.LicenseThe source code of X-SOCS is licensed under theMIT license.
Package: xson
XML Encoding for JSONXSONis a Python package that supports the serialization of Python objects to
XML documents according to theJSONxspecification (draft), as well as the
deserialization of JSONx documents to Python objects. The implementation aims at
being API and CLI-compatible with Python’s standardJSONpackage.RequirementsPython>= 3.5InstallTo useXSONin another project, it can be added tosetup.cfgas an install
requirement (if usingsetuptoolswith declarative config):[options]install_requires=xsonTo installXSONmanually, e.g., into a virtual environment, usepip:pip install xsonThe above approaches install the latest release ofXSONfromPyPI.
Alternatively, for the development version, clone the project and perform a
local install:pip install .UsageAPIExample:>>> import xson
>>> out = xson.dumps({'foo': 42, 'bar': [3.14, 'baz', True, None]}, indent=4)
>>> print(out)  #doctest: +NORMALIZE_WHITESPACE
<?xml version="1.0" encoding="UTF-8"?>
<json:object xmlns:json="http://www.ibm.com/xmlns/prod/2009/jsonx">
    <json:number name="foo">42</json:number>
    <json:array name="bar">
        <json:number>3.14</json:number>
        <json:string>baz</json:string>
        <json:boolean>true</json:boolean>
        <json:null/>
    </json:array>
</json:object>
>>> dct = xson.loads(out)
>>> print(dct)
{'foo': 42, 'bar': [3.14, 'baz', True, None]}CLIA command line tool is available to validate, pretty-print, or convert between
JSONx and JSON objects:xson-tool --helpor:python -m xson.tool --helpCopyright and LicensingLicensed under the BSD 3-ClauseLicense.
Package: xsorted
xsortedLikesortedbut using external sorting so that large data sets can be sorted, for example:>>> from random import random
>>> from six.moves import xrange
>>> from xsorted import xsorted
>>> nums = (random() for _ in xrange(pow(10, 7)))
>>> for x in xsorted(nums): passThe only restriction is that the items must be pickleable (or you can provide your own serializer for externalizing
partitions of items).MotivationIt is sometimes necessary to sort a dataset without having to load the entire set into memory. For example, if you
want to group a very large csv file by one of it’s columns. There are several ways in which this can be achieved, a
common solution is to use the unix commandsort. However unixsortdoes not offer the flexibility of the python
csv module.xsortedattempts to generalize external sorting of any python iterable in a similar way in whichsortedgeneralises the sorting of any iterable.Installation$ pip install xsortedUsageJust likesorted…>>> from xsorted import xsorted
>>> ''.join(xsorted('qwertyuiopasdfghjklzxcvbnm'))
'abcdefghijklmnopqrstuvwxyz'Withreverse…>>> ''.join(xsorted('qwertyuiopasdfghjklzxcvbnm', reverse=True))
'zyxwvutsrqponmlkjihgfedcba'And a customkey…>>> list(xsorted(('qwerty', 'uiop', 'asdfg', 'hjkl', 'zxcv', 'bnm'), key=lambda x: x[1]))
['uiop', 'hjkl', 'bnm', 'asdfg', 'qwerty', 'zxcv']The implementation details ofxsortedcan be customized using the factoryxsorter(in order to provide
the same interface assortedthe partition_size is treated as an implementation detail):>>> from xsorted import xsorter
>>> xsorted_custom = xsorter(partition_size=4)
>>> ''.join(xsorted_custom('qwertyuiopasdfghjklzxcvbnm'))
'abcdefghijklmnopqrstuvwxyz'
Package: xspear.btas2015
Vulnerability of Speaker Verification under Realistic AttacksThis package is an extension to thebob.bio.spearpackage.It contains functionality to run speaker recognition experiments on theAVspoofdatabase.It is an extension to thebob.bio.spearpackage, which provides the basic scripts.For further information aboutbob.bio.spear, please readits Documentation.InstallationTo install this package – alone or together with otherPackages of Bob– please read theInstallation Instructions.
ForBobto be able to work properly, some dependent packages are required to be installed.
Please make sure that you have read theDependenciesfor your operating system.DocumentationFor further documentation on this package, please read theStable Versionor theLatest Versionof the documentation.
For a list of tutorials on this or the other packages ofBob, or information on submitting issues, asking questions and starting discussions, please visit its website.