Saravanan666/pypi-org huggingface dataset:
Package: descarteslabs-vector
Vector ⥂"Vector! That's me, 'cause I'm committing crimes with both direction and magnitude. Oh, yeah!" — "Vector",Despicable MeVectoris acatalog for vector data. It enables users to store, query, and display vector data — which includes everything from fault lines to thermal anomalies to material spectra.Formal documentation for this library is available under theDescartes Labs API Documentation. Below is a very simple example to get you started with uploading and querying data to and from Vector.To start using the Vector client, first import it:fromdescarteslabs.vectorimportTable,TableOptions,models,propertiesaspimportjsonimportgeopandasasgpdimportipyleafletNext, we can create a Vector Table by executing the following Python code:classCustomModel(models.PolygonBaseModel):name:strtable=Table.create(product_id="my-favourite-shapes",name="My favourite shapes",description="This is just one of my favorite shapes",model=CustomModel)This product will contain only the most appealing shapes. Vector Tables consist of features, which themselves consist of a geometry and attributes.
Let's create a feature that contains a triangle geometry, and give it a name by adding a property:geojson={"type":"FeatureCollection","features":[{"type":"Feature","properties":{"name":"The coldest lake"},"geometry":{"coordinates":[[[-110.40972704675957,54.464841702835145],[-109.69305293767921,54.41100418392534],[-110.00659786040178,54.75020711805794],[-110.40972704675957,54.464841702835145]]],"type":"Polygon"}}]}...and then add it to the Vector table we just created by executing the following Python code:gdf=gpd.GeoDataFrame.from_features(geojson["features"],crs="EPSG:4326")table.add(gdf)We can retrieve this feature by querying the product in a few different ways. First, by its name:table.options.property_filter=p.name=="The coldest lake"gdf=table.collect()...and second, by an AOI which intersects with the geometry of our feature. The AOI is defined as a GeoJSON geometry:# Define the AOI...aoi={"coordinates":[[[-110.7588318166646,54.941440389459956],[-110.7588318166646,54.14613049153121],[-109.34594259898329,54.14613049153121],[-109.34594259898329,54.941440389459956],[-110.7588318166646,54.941440389459956]]],"type":"Polygon"}# ...and query the producttable.reset_options()table.options.aoi=aoigdf=table.collect()Since, in this case, our feature has a geometry, we can also visualise it on a map! Let's do this now, usingipyleaflet:# Instantiate and configure the ipyleaflet Mapm=ipyleaflet.Map()m.center=54.549829,-110.060936m.zoom=9# Display the mapdisplay(m)# Visualize the vector tile layer on the maptable.visualize("My favourite shapes",m)Which should yield the feature we just created, outlining the coldest lake on Earth:The Vector service also provides for more advanced product and feature management and querying. You can read more about what can be done with Vector in theDescartes Labs API Documentation.
Package: descartes-rpa
descartes_rpaPython pipeline to extract pathway activity from single-cell clusters in a systematic manner, annotating each cluster with which pathway(s) it represents.Pathways are annotated by extracting the differentialy expressed genes found in each clusters. Then, the pathways represented by these genes are retrieved using the Reactome analysis tools (explainedhere).QuickstartHereyou can see examples of Single-Cell cluster pathway annotation using a 10x Genomics data set from themain Scanpy tutorial, or anexampleon Mouse Single-Cell data from theScanpy hematopoiesis in mouse tutorial.You can also check examples using the Descartes data set from human Single-Celltissues, such asPancreasandLiver.InstallingLocallyInstall the module using pippipinstalldescartes_rpaDockerBuild imagedocker-composebuilddescartes_rpaRun the imagedocker-composerun--rmdescartes_rpaDescartes pathway dataSingle-Cell cluster pathways data for each tissue in the descartesHuman Gene Expression During Developmentdatabase can be found inthis link.
Package: desc-bpz
DESC_BPZ:This repo contains a python3 port of the popular BPZ software package (https://www.stsci.edu/~dcoe/BPZ/).  The code was modified from the original bpz-1.99.3 version available at the given URL, upgraded to be python3 compatible, and modified to read and write hdf5 files rather than straight ascii.  The code is messy in many places, having gone through multiple iterations by multiple authors.  No promises are made as to functionality of multiple subsystems (e.g. plotting), so use with caution!If you use this code, please citeBenitez (2000)andCoe et al. (2006).In order to run, you should set the environment variableBPZDATAPATHto point to the directory housing the SED, FILTER, and AB files.  Default SED and FILTER files are available in thesrc/desc_bpz/data_files/FILTERandsrc/desc_bpz/data_files/SEDdirectoriesTo run traditional command-lineBPZuse thebpz.pyfile insrc/desc_bpz/scripts, e.g.:python src/desc_bpz/scripts/bpz.py yourasciicatalog.cat -P yourparameterfile.parsDan Coe's STScI webpage (listed above) contains a nice BPZ user guide, for more detail on running the code, please check that website.However, this package will mainly be used as a library for running "bpz_lite" in the DESC PZ RAIL Package, see:https://github.com/LSSTDESC/RAIL
Package: desciclopedia
Wikipediais a Python library that makes it easy to access and parse
data from Wikipedia.Search Wikipedia, get article summaries, get data like links and images
from a page, and more. Wikipedia wraps theMediaWiki
APIso you can focus on using
Wikipedia data, not getting it.>>>importwikipedia>>>printwikipedia.summary("Wikipedia")# Wikipedia (/ˌwɪkɨˈpiːdiə/ or /ˌwɪkiˈpiːdiə/ WIK-i-PEE-dee-ə) is a collaboratively edited, multilingual, free Internet encyclopedia supported by the non-profit Wikimedia Foundation...>>>wikipedia.search("Barack")# [u'Barak (given name)', u'Barack Obama', u'Barack (brandy)', u'Presidency of Barack Obama', u'Family of Barack Obama', u'First inauguration of Barack Obama', u'Barack Obama presidential campaign, 2008', u'Barack Obama, Sr.', u'Barack Obama citizenship conspiracy theories', u'Presidential transition of Barack Obama']>>>ny=wikipedia.page("New York")>>>ny.title# u'New York'>>>ny.url# u'http://en.wikipedia.org/wiki/New_York'>>>ny.content# u'New York is a state in the Northeastern region of the United States. New York is the 27th-most exten'...>>>ny.links[0]# u'1790 United States Census'>>>wikipedia.set_lang("fr")>>>wikipedia.summary("Facebook",sentences=1)# Facebook est un service de réseautage social en ligne sur Internet permettant d'y publier des informations (photographies, liens, textes, etc.) en contrôlant leur visibilité par différentes catégories de personnes.Note: this library was designed for ease of use and simplicity, not for advanced use. If you plan on doing serious scraping or automated requests, please usePywikipediabot(or one of the other more advancedPython MediaWiki API wrappers), which has a larger API, rate limiting, and other features so we can be considerate of the MediaWiki infrastructure.InstallationTo install Wikipedia, simply run:$ pip install wikipediaWikipedia is compatible with Python 2.6+ (2.7+ to run unittest discover) and Python 3.3+.DocumentationRead the docs athttps://wikipedia.readthedocs.org/en/latest/.QuickstartFull APITo run tests, clone therespository on GitHub, then run:$ pip install -r requirements.txt
$ bash runtests  # will run tests for python and python3
$ python -m unittest discover tests/ '*test.py'  # manual stylein the root project directory.To build the documentation yourself, after installing requirements.txt, run:$ pip install sphinx
$ cd docs/
$ make htmlLicenseMIT licensed. See theLICENSE
filefor
full details.Creditswiki-apiby
@richardasaurus for inspiration@nmoroze and @themichaelyang for feedback and suggestionsTheWikimedia
Foundationfor giving
the world free access to data
Package: descmap
TheDescriptor-BasedMicrokineticAnalysisPackage (DescMAP) is a Python library developed by the Vlachos
Research Group at the University of Delaware. This code was developed to
automate descriptor selection and volcano curve generation for
heterogeneous catalysis using empirical and semi-empirical approaches
coupled with microkinetic modeling. Both electronic and geometric
descriptors are supported. Inputting data via spreadsheets and
controlling program behavior via template files increases flexibility
and supported capabilities.DocumentationSee ourdocumentation pagefor
docstrings and more details.Getting StartedInstall using pip (see documentation for full instructions)$pipinstalldescmapLook at the provided examplesDevelopersJonathan Lym (jonathanlym@gmail.com)Xue Zong (xzong@udel.edu)DependenciesPython >= 3.9Numpy>= 1.24.2: Used for vector and matrix operationsPandas>= 1.5.3: Used to import data from Excel or CSV filesScipy>= 1.10.0: Used for curve fittingScikit-Learn>= 1.2.1: Choose
descriptors based on DFT dataRDKit>= 2022.9.4: Used for
constructing feasible chemical structures required by pGraddMatplotlib>= 3.6.3: Used for generating plotsPlotly>= 5.13.0: Plots interactive volcano curvesChart-studio>= 1.1.0: Provide utilities
for interfacing with Plotly’s Chart Studio servicepMuTT>= 1.3.2: Generates input files for OpenMKMpGradd>= 2.9.5: Estimate
thermodynamic properties of moleculesVUnits>= 0.0.4: Unit conversion and constantsxlsxwriter>= 3.0.8: Create Excel xlsx filespyDOE>= 0.3.8: Experimental design package to
provide sampling methodLicenseThis project is licensed under the MIT License - see the LICENSE.md file
for details.ContributingIf you have any suggestion or find a bug, please post to our Issues page
on GitHub.QuestionsIf you have any question or run into any issue, please post to our
Issues page on GitHub.FundingThis material is based upon work supported by the Department of Energy’s
Office of Energy Efficient and Renewable Energy’s Advanced Manufacturing
Office under Award Number DE-EE0007888-9.5.
Package: desc-opt
Stellarator Optimization PackageDESC solves for and optimizes 3D MHD equilibria using pseudo-spectral numerical methods and automatic differentiation.The theoretical approach and implementation details used by DESC are presented in these papers[1][2][3][4]and documented atTheory.
Please cite our work if you use DESC![1]Dudt, D. & Kolemen, E. (2020). DESC: A Stellarator Equilibrium Solver. [Physics of Plasmas]    [pdf][2]Panici, D. et al (2023). The DESC Stellarator Code Suite Part I: Quick and accurate equilibria computations. [JPP]    [pdf][3]Conlin, R. et al. (2023). The DESC Stellarator Code Suite Part II: Perturbation and continuation methods. [JPP]    [pdf][4]Dudt, D. et al. (2023). The DESC Stellarator Code Suite Part III: Quasi-symmetry optimization. [JPP]    [pdf]Quick StartThe easiest way to install DESC is from pypi:pip install desc-optFor more detailed instructions on installing DESC and its dependencies, seeInstallation.
The code is run using the syntaxdesc <path/to/inputfile>and the full list of command line options are given inCommand Line Interface. (Note that you may have to prepend the command withpython -m)DESC can be ran in two ways:The first is through an input file specifying the equilibrium and solver options, this way can also can also accept VMEC input files.The second is through a python script, where the equilibrium and solver options are specified programmatically, this method offers much more flexibility over what types of equilibrium solution, optimization and analysis can be performed.
See the tutorialScript Interfacefor more detailed information.Refer toInputsfor documentation on how to format the input file.
The equilibrium solution is output in a HDF5 binary file, whose format is detailed inOutputs.As an example usage of the input file method, to use DESC to solve for the equilibrium of the high-beta, D-shaped plasma described with the DSHAPE input file, the command from theDESCdirectory isdesc -p desc/examples/DSHAPE, where the-pflag tells DESC to plot the results once it finishes.An example of the script usage to solve and optimize an equilibrium, refer to the python scriptdesc/examples/precise_QA.py, which can be run from theDESCdirectory from the command line withpython3 desc/examples/precise_QA.pyRepository Contentsdesccontains the source code including the main script and supplemental files. Refer to theAPIdocumentation for details on all of the available functions and classes.docscontains the documentation files.examplescontains example input files along with corresponding DESC solutions, which are also accessible using thedesc.examples.getfunction.testscontains routines for automatic testing.ContributeContributing guidelinesIssue TrackerSource CodeDocumentation
Package: describe_it
Describe-it is a nose plugin that allows you to write unit tests that are more
like executable examples of how a component should work than just being tests.The other benefit is the ability to describe how a component behaves in certain
contexts, where one context may build on a previously defined context by using
nesting.Installing$pipinstalldescribe-itWritingAny module that ends with ‘spec’ is considered to contain specifications/tests
for describe_it.$vimmyfirst_spec.py…and the content:fromgameimportGamefromdescribe_itimportdescribe,it,before_each,Fixture@describe# This declares a test context.defa_game():f=Fixture()# Fixture is a hack to get around# Python's implementation of closures.# You can use other methods, such as# nonlocal if you like.@before_each# Will be called before each 'it'defsetup():f.game=Game()@after_each# Will be called after each 'it'defteardown():perform_post_test_cleanup_if_needed()# This should rarely be needed!@it# This marks a test method.defis_player_ones_turn():assertf.game.current_player==1# describe_it doesn't come with an# assertion lib. Pick any one you like.@describe# This is a nested context thatdefin_second_round():# augments the context above.@before_each# Before each 'it' method, anydefsetup():# before_each in outer contexts willf.game.play_round()# be called first. Then this method# will be called.@itdefis_player_twos_turn():assertf.game.current_player==2@xit# You can skip individual test methodsdefskips_tests():# by using '@xit' or '@it_skip'assertTrue@with_data([1,2,3],# You can parameterize tests with[3,4,7])# different combinations of inputsdefadds_numbers(term_1,term_2,expected):assertterm_1+term_2==expected@xdescribe# You can skip whole contexts bydefthis_context_is_marked_as_skipped():# using '@xdescribe' or '@describe_skip'@itdefthis_test_will_be_skipped():assertTrueRunning$nosetests--with-describe-it
Package: describer-ml
Descriptive Statistics and Hypothesis TestsThe goal of this library is to provide a common interface for existing hypothesis tests across several frameworks as well as implement some new ones.  Additionally this library is here to provide a set of descriptive statistics that may be overlooked.Implemented NotionsTimeseries DataThis is data with a timeseries component, therefore this data is collected over time.  Time will be one of the variables collected, on some scaleGeo Spatial DataThis is data with a geospatial component, therefore this data is collected with coordinates of some kind.  It is likely latitude and longitude or some variant there of will be present in data sets of this kind.Numeric DataThis is data without time or geospatial structure.  For now it is the blanket for all other data.  If a better name is decided upon, it will be used in this names place.Image DataThis is data from images that has been transformed via some feature engineering to numbers or possibly the raw pixels in some scale, likely RGB or Grey scale.Text DataThis is textual data that has been transformed via some feature engineering to numbers.Known Installation IssuesIf you getModuleNotFoundError: No module named 'tkinter':Since this library relies on pysal, it relies on tkinter.  Tkinter is a weird library in that it isn't pip installable.  On some systems it comes pre-installed, on others it does not.  For ubuntu the fix is:sudo apt-get install idle3This is the only way I've found to install tkinter for python 3.  This library does not offer python 2 support so I won't discuss installation for that version.
Package: describetable
A very simple input for Pandas.describe() method. The size and layout should work out of the box but the colors were designed with the plotly 'plasma' scheme in mind, but everything except the most critical should be easily overwritten without the !important flag./ ## Example Usage/ import describetable.Describetable as dTable/ dTable(id="innercontainerId", data=df.describe().to_html(), buttonText='Click to unfold')
Package: describr
README.mddescribr is a Python library that provides functionality for descriptive statistics and outlier detection in pandas DataFrames.InstallationYou can install describr using pip:pipinstalldescribrExample usageimportpandasaspdimportnumpyasnpfromdescribrimportFindOutliers,DescriptiveStatsCreate a sample dataframenp.random.seed(0)n=500data={'MCID':['MCID_'+str(i)foriinrange(1,n+1)],'Age':np.random.randint(18,90,size=n),'Race':np.random.choice(['White','Black','Asian','Hispanic',''],size=n),'Educational_Status':np.random.choice(['High School','Bachelor','Master','PhD',''],size=n),'Gender':np.random.choice(['Male','Female',''],size=n),'ER_COST':np.random.uniform(500,5000,size=n),'ER_VISITS':np.random.randint(0,10,size=n),'IP_COST':np.random.uniform(5000,20000,size=n),'IP_ADMITS':np.random.randint(0,5,size=n),'CHF':np.random.choice([0,1],size=n),'COPD':np.random.choice([0,1],size=n),'DM':np.random.choice([0,1],size=n),'ASTHMA':np.random.choice([0,1],size=n),'HYPERTENSION':np.random.choice([0,1],size=n),'SCHIZOPHRENIA':np.random.choice([0,1],size=n),'MOOD_DEPRESSED':np.random.choice([0,1],size=n),'MOOD_BIPOLAR':np.random.choice([0,1],size=n),'TREATMENT':np.random.choice(['Yes','No'],size=n)}df=pd.DataFrame(data)Parametersdf: name of dataframeid_col: Primary key of the dataframe; accepts string or integer or float.group_col: A Column to group by, It must be a binary column. Strings or integers are acceptable.positive_class: This is the response value for the primary outcome of interest. For instance, positive value for a Treatment cohort is 'Yes' or 1 otherwise 'No' or 0, respectively. Strings or integers are acceptable.continuous_var_summary: User specifies measures of central tendency, only mean and median are acceptable. This parameter is case insensitive.Example usage of FindOutliers ClassThis returns a dataframe (outliers_flag_df) with outlier_flag column (outlier_flag =1: record contains one or more ouliers). Tukey's IQR method is used to detect outliers in the dataoutliers_flag=FindOutliers(df=df,id_col='MCID',group_col='TREATMENT')outliers_flag_df=outliers_flag.flag_outliers()This example counts number of rows with outliers stratified by a defined grouping variableoutliers_flag.count_outliers()This example removes all outliersdf2=outliers_flag.remove_outliers()df2.shapeExample usage of DescriptiveStats Classdescriptive_stats=DescriptiveStats(df=df,id_col='MCID',group_col='TREATMENT',positive_class='Yes',continuous_var_summary='median')Gets statistics for binary and categorical variables and returns a dataframe.binary_stats_df=descriptive_stats.get_binary_stats()Gets mean and standard deviation for continuous variables and returns a dataframe.continuous_stats_mean_df=descriptive_stats.get_continuous_mean_stats()Gets median and interquartile range for continuous variables and returns a dataframe.continuous_stats_median_df=descriptive_stats.get_continuous_median_stats()Computes summary statistics for binary and continuous variables based on defined measure of central tendency. Method returns a dataframe.descriptive_stats.compute_descriptive_stats()summary_stats=descriptive_stats.summary_stats()
Package: descripstats
descripstatsThis is a small package to help add more descriptive statistics to the defaultdescribe()of Pandas, which include:mad: mean absolute deviationvariance: variancesem: standard error of the meansum: sumskewness: skewnesskurtosis: kurtosisDeveloped by Shouke Wei from Deepsim Academy, Deepsim Intelligence Technology Inc. (c) 2022Install the packagepipinstalldescripstatsimport the packagefromdescripstatsimportDescribethen use theDescribe()directly. Orimportdescripstatsasdsthen useds.Discribe()DocumentAn example:https://github.com/shoukewei/descripstats/blob/main/docs/example.ipynb
Package: descript
python-descript-ionSimple library fordescript.ionfiles manipulationExamples:import descript.ion

#Read description
with descript.ion.open(filename) as f:
    print f.description

#Write description
with descript.ion.open(filename) as f:
    f.description = self.description

#Remove description
with descript.ion.open(filename) as f:
    del f.description
Package: descript-audiotools
AudioToolsObject-oriented handling of audio signals, with fast augmentation routines, batching, padding, and more.Installationpip install git+https://github.com/descriptinc/audiotoolsORgitclonehttps://github.com/descriptinc/audiotoolscdaudiotools
pipinstall.DocumentationFor documentation, seethe docs.Deploying documentationTo build the documentation, do:cddocs/
makehtml
open_build/html/index.htmlOnce you're satisfied with your docs, push them to the gh-pages branch viacddocs
bashpublish_docs.shQuickstartimportaudiotoolsfromaudiotoolsimportAudioSignalsignal=AudioSignal("tests/audio/spk/f10_script4_produced.wav",offset=5,duration=5)signal.play()# Play back the signal in your terminal using ffplaysignal.low_pass(8000)# Low-pass the signalsignal.play()# Play back the low-passed version of the signalFor more, see thedocumentation.Install hooksFirst install the pre-commit util:https://pre-commit.com/#installpip install pre-commit  # with pip
brew install pre-commit  # on MacThen install the git hookspre-commit install
# check .pre-commit-config.yaml for details of hooksUpongit commit, the pre-commit hooks will be run automatically on the stage files (i.e. added bygit add)N.B. By default, pre-commit checks only run on staged filesIf you need to run it on all files:pre-commit run --all-files
Package: description-generator
Description GeneratorThis application uses GPT-3 to generate personal descriptions for users using their GitHub usernames. Simply provide the username, and the application will generate a description of the user based on their public profile and activity on GitHub.How to useTo use the application, you will need to install the required dependencies and obtain an API key for GPT-3.fromdescription_generatorimportdescription_generatorkey="openai api key"description=description_generator(key,"octocat")InputOutputoctocatOctocat is a fun-loving individual who loves open source and helping others. They are a active member of the GitHub community, often helping others with their code and projects. Octocat is also known for their love of cats, often sharing photos and stories of their own feline friends.DependenciesDependencyVersionPython3.7+OpenAI0.11.0Requests2.25.1DisclaimerPlease note that the descriptions generated by this model are based on the public information available on GitHub and may not always be accurate. Use the generated descriptions at your own risk.