Saravanan666/pypi-org huggingface dataset:
Package: derivx
DerivXV0.3.7.1-Beta Build 20220121© 2021-2022 Xu Rendong. All Rights Reserved.Project SummaryDerivatives Pricing Engine.Plain Vanilla European / American options portfolio calculation.Plain Vanilla European / American option pricing and greek value calculation.Single Barrier / SharkFin option pricing and greek value calculation.Exotic Autocall Booster / Phoenix / Snowball option pricing and greek value calculation.Stochastic Diffusion Processes and Stochastic Volatility Models.Interfaces and examples for C++, Python, JavaScript.InstallC++:Python:pipinstallderivxJavaScript:npminstallderivxUsageC++:Python:importderivxprint(derivx.Version())For more usages please refer to examples inderivx_pyfolder.DerivX is not dependent on numpy, pandas and matplotlib, but if you want to run examples, you'd better install them with:pipinstallnumpypandasmatplotlibJavaScript:constderivx=require('derivx')console.log(derivx.Version())For more usages please refer to examples inderivx_jsfolder.DerivX is not dependent on numjs, but if you want to run examples, you'd better install it with:npminstallgit+https://github.com/nicolaspanel/numjs.gitChangesPlease refer tochangesfile.BenchmarkPlease refer tobenchmarkfile.Contact InformationQQ: 277195007, WeChat: xrd_ustc, E-mail:xrd@ustc.edu
Package: dermoscopic-symmetry
dermoscopic_symmetrydermoscopic_symmetry is a Python package aiming at study skin lesion's symmetry (regarding shape and textures)
and help the diagnose of diseases like melanomas.Basically, the symmetry study is divided into
two parts: shapes symmetry and textures symmetry. Here, shapes means the aspect of the outskirts of a lesion
and its global form whereas textures stand for colors and types of perceived textures.Note :The package has been built referring to thePH² Dataset.
See :Teresa Mendonça, Pedro M. Ferreira, Jorge Marques, Andre R. S. Marcal,
Jorge Rozeira. PH² - A dermoscopic image database for research and
benchmarking, 35th International Conference of the IEEE Engineering in
Medicine and Biology Society, July 3-7, 2013, Osaka, Japan.To be able to use it directly and properly, you must download and have
access to it.InstallationUsepipto install the package.pipinstalldermoscopic_symmetryThese are the Python files used to study the symmetry of skin lesions from the PH² Dataset
(see Usage section):shape_symmetry.py: containing functions to study the symmetry of shapes in a lesion's image.classifier_feeder.py: containing functions to create a classifier able to recognize if 2 patches taken in a lesion's
image are similar or not.A new dataset calledpatchesDataSet, derivating from the PH² Dataset, has been designed to
train this classifier. It is composed of patches pairs taken in the PH² Dataset images with
one half similar and the other non similar.patches_for_texture_symmetry.py: containing functions to take patches from a dermoscopic image and extract
features from them (textures and color).texture_symmetry.py: containing functions using the previous classifier and features
to study the symmetry of textures in a lesion's image.combined_classifier.py: containing functions using only shape features, only textures
features or both of them to train classifiers and be able to know which one is the best
according to expert diagnose in the PH² Dataset.Those classifiers are trained according to theShapesScores.csv,TextureScores.csvandShapeAndTextureScores.csvfiles contained in thedatarepository. The final models are saved asshapeModel.pkl,textureModel.pklandshapeAndTextureModel.pklin thedata/modelsrepository.Note :The code used to create the
patchesDataSet is given in thepatches_dataset_creator.pyfile. Theutils.pyfile contains the utilities
functions.UsageEach code script has an :example()function at the beginning aiming at presenting its functionalities. This function is run
as a defaultmain.LicenseMIT
Package: derm-tiff
Designed for DermAnnotationThis is an utility python package to deal with tiff images
created byDermAnnotation.Examples areexample/example.md.Release Note2.0.0: デフォルトの圧縮方式をtiff_adobe_deflateに変更1.0.6: 圧縮方式をtiff_adobe_deflateからtiff_lzwに変更1.0.5: パッケージ名をDermAnnoからDermTiffに変更
Package: derobertis-cv
derobertis-cvOverviewNick DeRobertis' CV Data and pyexlatex BuildGetting StartedInstallderobertis-cv:pip install derobertis-cvA simple example:importderobertis_cv# Do something with derobertis_cvSee amore in-depth tutorial here.LinksSee thedocumentation here.Development StatusThis project is currently in early-stage development. There may be
breaking changes often. While the major version is 0, minor version
upgrades will often have breaking changes.DevelopingSee thedevelopment guidefor development details.AuthorCreated by Nick DeRobertis. MIT License.
Package: derobertis-project-logo
derobertis-project-logoOverviewNick DeRobertis' Project Logo Collection and GeneratorGetting StartedInstallderobertis-project-logo:pip install derobertis-project-logoA simple example:importderobertis_project_logo# Do something with derobertis_project_logoLinksSee thedocumentation here.AuthorCreated by Nick DeRobertis. MIT License.
Package: derp
derpDerp (Deprecation Enforcement and Removal Planning) is a command-line tool for ensuring that deprecated code is removed from your python package in a timely manner.
Derp scans the application for deprecation flags and ensures that:All deprecations have a planned removal version.Deprecated objects are removed at the planned time.UsageInstall derp from pypi using pip:pip install derp.
Invoke derp from the command line, specifying the package or module to analyze and the current version of the software.
The command below will scan all modules insrc/my_appand catch any deprecated code that is supposed to be removed by version 1.0.0.derpsrc/my_app1.0.0The current version can also be read from a file.
Ifsrc/my_app/__version__.pycontains the version number, invoke the following.derpsrc/my_appsrc/my_app/__version__.pyIncluding this command as part of a CI/CD script will ensure that deprecations are done thoughtfully and that deprecated code is removed on schedule.Potentially Asked QuestionsWhy use derp?Derp was inspired by the belief that a lean codebase is more pleasant to work in,
and as such deprecated code should be cleaned out as soon as is feasible.
Including derp in your CI/CD pipeline pushes developers to clean out deprecated code.What if I don't know when I want to remove a deprecated item?"Next major version bump" is a good default.What if I need to keep a deprecated item around longer than expected?Increase the planned removal version.
It's fine for deprecated code to stick around longer than expected, but it should happen intentionally, not because nobody got around to clearing it out.What type of deprecations does derp catch?Currently, derp only works with thedeprecationlibrary.
It catches a single@deprecatedannotation on a class or method.What if I use a different deprecation tool or want to deprecate something that's neither a class nor a method?Tell me about your use case, and I might add it.
Alternatively, open a PR.
See "derp/deprecation.py" for a discussion of how to add more types of deprecations.What if I have multiple deprecation annotations on a single method?Don't do that.
Why are you doing that?OK fine, if there's a legitimate reason to do this, let me know and I'll think about supporting it.Couldn't I use the@fail_if_not_removeddecorator?Yeah, but that requires a developer to be conscientious every time they deprecate something.
You have to voluntarily point it to the version number, select a removal version, and add@fail_if_not_removedon all relevant tests.
It's easier to just slap@deprecated()with no arguments, move on, and forget about it.
Derp will chide you: "you need to select a removal version."
And when that version comes around it will poke you again: "time to remove this code."
Package: derpcache
derpcachederpcacheis a simple pickle-based caching utility.It's made for people who love nuking their Jupyter kernels.fromderpcacheimportcacheimportrequestscountries=['Afghanistan','Albania',# ...'Zambia','Zimbabwe']pages=[]forcountryincountries:url=f'https://en.wikipedia.org/wiki/{country}'annotation=f'Wikipedia page for{country}'page=cache(requests.get,url,_annotation=country)pages.append(page)Why should I usederpcache?You want a cache that will persist between kernel restarts.You want a simple functional interface—no IPython magics needed.You want a reasonable amount of visibility into what's in your cache.You want the ability to set expiration rules.Expiration>>> import time
>>> def long_running_func(*args, **kwargs):
...     time.sleep(1200)
...     print('running...')
...     return 'done'
...
>>> expires_after = 300
>>> cache(long_running_func, _expires_after=expires_after)
running...
done
>>> cache(long_running_func)
done
>>> time.sleep(expires_after)
>>> cache(long_running_func)
running...
doneViewing cached entriesfromderpcacheimportget_indexfrompprintimportpprintpprint(get_index(),sort_dicts=False)# or, pandas.DataFrame.from_records{"5e39b292":{"callable":"__main__.long_running_func","called_at":"2022-09-06T05:19:14.614796"},"b37ab1af":{"callable":"requests.api.get","called_at":"2022-09-06T05:21:35.157183","annotation":"Afghanistan"},"f0103017":{"callable":"requests.api.get","called_at":"2022-09-06T05:21:35.814452","annotation":"Albania"},"8861f226":{"callable":"requests.api.get","called_at":"2022-09-06T05:21:36.084777","annotation":"Zambia"},"19754ec0":{"callable":"requests.api.get","called_at":"2022-09-06T05:21:36.341655","annotation":"Zimbabwe"}}
Package: derpy
derpyFinancial derivatives and portfolio analysis tools for pythonFree software: MIT licenseDocumentation:https://derpy.readthedocs.io.How to get up and runningto include the module in your project, you can simply usepip install derpythen in your python projectimportderpyprint(derpy.__version__)# returns '0.0.1'Example usesBondsfromderpyimportbondasbdpx=95.0428face_val=100.0mat=1.5cpn_frq=2cpn_rate=5.25ytm=5.5print('    Price:{}'.format(bd.bond_price(face_val,mat,ytm,cpn_rate,cpn_frq)))print('    Yield:{}'.format(bd.bond_ytm(px,face_val,mat,cpn_rate,cpn_frq)))print('   ModDur:{}'.format(bd.bond_duration(px,face_val,mat,cpn_rate,cpn_frq)[0]))print('   MacDur:{}'.format(bd.bond_duration(px,face_val,mat,cpn_rate,cpn_frq)[1]))print('Convexity:{}'.format(bd.bond_convexity(px,face_val,mat,cpn_rate,cpn_frq)))Optionsfromderpy.optionsimportblack_scholes_mertonasbsm# usage method 1: use function wrapperinput=['call',20,21,0.20,0.1,0.0002,0]call_price=bsm.option_pricing(bsm.euro_option,input)call_gamma=bsm.option_pricing(bsm.gamma,input)# usage method 2: call individual functionsput_price=bsm.euro_option('put',20,21,0.2,0.1,0.0002)# div_yield is optionalput_gamma=bsm.gamma('put',20,21,0.2,0.1,0.0002,0.0001)print(call_price)# return 0.16384395..print(call_gamma)# return 0.23993880..print(put_price)# return 1.16342..print(put_gamma)# return 0.2399107..Portfolio analysisfromderpyimportportfolioasptsecurities=['AAA','BBB']positions=[[11,10],[12,10],[13,10],[13,11],[13,12]]prices=[[10,10],[11,10],[12,10],[12,10],[12,10]]dates=['2018-07-01','2018-08-01','2018-09-01','2018-10-01','2018-11-01']df_positions=pd.DataFrame(data=positions,columns=securities,index=dates)df_prices=pd.DataFrame(data=prices,columns=securities,index=dates)p=pt.Portfolio(names=securities,positions=df_positions,prices=df_prices)print(p.sec_values())print(p.sec_weights())print(p.portfolio_value())print(p.portfolio_returns())History0.1.0 (2018-09-19)Released option pricingReleased portfolio analysisAdded testing for Options and Portfolios
Package: des
DES (Data Encryption Standard)A pure Python implementation for the famous DES algorithm, supporting Python 2 and 3.InstallationUsingpip:$pipinstalldesOr manually download the archive and run the command after extracting the stuff inside:$pythonsetup.pyinstallUsageFirstly, define aDesKeyobject by passing your encryption / decryption key. The key should be of length 8, 16 or 24. The algorithm will be automatically chosen for you.
Note that the key should be written asbytesinPython 3.fromdesimportDesKeykey0=DesKey(b"some key")# for DESkey1=DesKey(b"a key for TRIPLE")# for 3DES, same as "a key for TRIPLEa key fo"key2=DesKey(b"a 24-byte key for TRIPLE")# for 3DESkey3=DesKey(b"1234567812345678REAL_KEY")# for DES, same as "REAL_KEY"You may know whether a key is forDESor3DESalgorithm by calling its methodis_single()oris_triple().key0.is_single()# -> Truekey1.is_triple()# -> Truekey2.is_single()# -> Falsekey3.is_triple()# -> FalseSecondly, encrypt messages by calling the methodencrypt()from theDesKeyobject, or decrypt them by callingdecrypt().
Note that the messages should be written asbytesinPython 3.key0.encrypt(b"any long message")# -> b"\x14\xfa\xc2 '\x00{\xa9\xdc;\x9dq\xcbr\x87Q"By default,ECB Modeis used. You may enableCBC Modeby passing the argumentinitial, as theInitial Value.
The argument may be either abytesobject of length 8 or an integer usingbig-endian.key0.encrypt(b"any long message",initial=0)# -> b"\x14\xfa\xc2 '\x00{\xa9\xb2\xa5\xa7\xfb#\x86\xc5\x9b"key0.encrypt(b"any long message",initial=b"\0"*8)# same as aboveTheDESalgorithm requires the message to be of any length that is a multiple of 8.
By default, the length of the message to encrypt / decrypt is assured by users.
You may choose to turn onPKCS5 Padding Mode(by passing the argumentpaddingwith a TRUTHY value), telling Python to do the padding before encryption for you.key0.encrypt(b"abc",padding=True)# -> b"%\xd1KU\x8b_A\xa6"While in decryption withPKCS5 Padding, the length of the message is still required to be a multiple of 8. But after decryption, Python will throw the padding characters away.key0.decrypt(b"%\xd1KU\x8b_A\xa6",padding=True)# -> b"abc"NoteBecause DES keys are 56 bits and the input key to this algorithm is 8, 16 or 24 bytes, this implementation ignores the least significant bit of each key byte.
Package: desafio-codigo
desafio_codigoDescription.
The package desafio-codigo is used to:
Desafio Inicial:
- Desafio da MÃ©dia
- Desafio do TriÃ¢ngulo
- Desafio da ConversÃ£o de Tempo
Desafio IntermediÃ¡rio:
- Desafio dos NÃºmeros Positivos
- Desafio Crescente e Decrescente
- Desafio do Resto de divisÃ£o
Desafio Final:
- Desafio dos NÃºmeros Primos
- Desafio do Vetor
- Desafio do Encaixa e NÃ£o encaixaInstallationUse the package managerpipto install desafi-codigopipinstalldesafio-codigoUsagefromdesafio-codigo.inicialimportmediamedia.calc_media(5.1,7.0)fromdesafio-codigo.inicialimporttriangulotriangulo.perimetro_area([6.0,4.0,2.0])fromdesafio-codigo.inicialimportconversao_tempoconversao_tempo.tempo_horas(556)fromdesafio-codigo.intermediarioimportcrescente_decrescentecrescente_decrescente.tipo_ordem()fromdesafio-codigo.intermediarioimportnumeros_positivosnumeros_positivos.quantidade_positivos(3):fromdesafio-codigo.intermediarioimportresto_divisaoresto_divisao.umeros_intervalo(10,18)fromdesafio-codigo.finalimportnumero_primonumero_primo.primo(3)fromdesafio-codigo.finalimportvetorvetor.vetor_dobro(4,10)fromdesafio-codigo.finalimportencaixaencaixa.encaixa(3)AuthorEric CunhaLicenseMIT
Package: desafio-processamento-de-imagem
desafio_processamento_de_imagemDescription.
The package package_name is used to:
-
-InstallationUse the package managerpipto install package_namepipinstallpackage_nameUsagefrompackage_name.module1_nameimportfile1_namefile1_name.my_function()AuthorMichelle TrigueiroLicenseMIT
Package: desafio-python-dio
desafio_python_dioDescription.
The package desafio_python_dio is used to:
Desafio Inicial:
- Desafio da Media
- Desafio do Triangulo
- Desafio da Conversao de Tempo
Desafio Intermediario:
- Desafio dos Numeros Positivos
- Desafio Crescente e Decrescente
- Desafio do Resto de divisao
Desafio Final:
- Desafio dos Numeros Primos
- Desafio do Vetor
- Desafio do Encaixa e Nao encaixaInstallationUse the package managerpipto install desafio_python_diopipinstalldesafio_python_dioUsagefromdesafio_python_dio.inicialimportmediamedia.calc_media(5.1,7.0)fromdesafio_python_dio.inicialimporttriangulotriangulo.perimetro_area([6.0,4.0,2.0])fromdesafio_python_dio.inicialimportconversao_tempoconversao_tempo.tempo_horas(556)fromdesafio_python_dio.intermediarioimportcrescente_decrescentecrescente_decrescente.tipo_ordem()fromdesafio_python_dio.intermediarioimportnumeros_positivosnumeros_positivos.quantidade_positivos(3)fromdesafio_python_dio.intermediarioimportresto_divisaoresto_divisao.umeros_intervalo(10,18)fromdesafio_python_dio.finalimportnumero_primonumero_primo.primo(3)fromdesafio_python_dio.finalimportvetorvetor.vetor_dobro(4,10)fromdesafio_python_dio.finalimportencaixaencaixa.encaixa(3)AuthorEric CunhaLicenseMIT
Package: desc
DESC is an unsupervised deep learning algorithm for clustering scRNA-seq data. The algorithm constructs a non-linear mapping function from the original scRNA-seq data space to a low-dimensional feature space by iteratively learning cluster-specific gene expression representation and cluster assignment based on a deep neural network. This iterative procedure moves each cell to its nearest cluster, balances biological and technical differences between clusters, and reduces the influence of batch effect. DESC also enables soft clustering by assigning cluster-specific probabilities to each cell, which facilitates the identification of cells clustered with high-confidence and interpretation of results.
Package: descartes
UseShapelyor GeoJSON-like geometric objects as matplotlib paths and patchesRequires: matplotlib, numpy, and optionally Shapely 1.2+.Example:from matplotlib import pyplot
from shapely.geometry import LineString
from descartes import PolygonPatch

BLUE = '#6699cc'
GRAY = '#999999'

def plot_line(ax, ob):
    x, y = ob.xy
    ax.plot(x, y, color=GRAY, linewidth=3, solid_capstyle='round', zorder=1)

line = LineString([(0, 0), (1, 1), (0, 2), (2, 2), (3, 1), (1, 0)])

fig = pyplot.figure(1, figsize=(10, 4), dpi=180)

# 1
ax = fig.add_subplot(121)

plot_line(ax, line)

dilated = line.buffer(0.5)
patch1 = PolygonPatch(dilated, fc=BLUE, ec=BLUE, alpha=0.5, zorder=2)
ax.add_patch(patch1)

#2
ax = fig.add_subplot(122)

patch2a = PolygonPatch(dilated, fc=GRAY, ec=GRAY, alpha=0.5, zorder=1)
ax.add_patch(patch2a)

eroded = dilated.buffer(-0.3)

# GeoJSON-like data works as well

polygon = eroded.__geo_interface__
# >>> geo['type']
# 'Polygon'
# >>> geo['coordinates'][0][:2]
# ((0.50502525316941682, 0.78786796564403572), (0.5247963548222736, 0.8096820147509064))
patch2b = PolygonPatch(polygon, fc=BLUE, ec=BLUE, alpha=0.5, zorder=2)
ax.add_patch(patch2b)

pyplot.show()See also: examples/patches.py.Descartes is not associated with the identically named and apparently defunct
project athttp://descartes.sourceforge.net/.
Package: descartes-bi
Descartes BIFree Open Source web based business intelligence softwareLicenseThis project is open sourced underApache 2.0 License.InstallationTo installDescartes BI, simply do:$virtualenvvenv$sourcevenv/bin/activate$pipinstalldescartes-bi==2.0rc1$descartes-bi.pyinitialsetup$descartes-bi.pyrunserverPoint your browser to 127.0.0.1:8000 and use the automatically created admin
account.ContributeCheck for open issues or open a fresh issue to start a discussion around a feature idea or a bug.Forkthe repositoryon GitHub to start making your changes to themasterbranch (or branch off of it).Write a test which shows that the bug was fixed or that the feature works as expected.Make sure to add yourself to thecontributors file.Send a pull requestAuthorRoberto Rosario -Twitterhttp://twitter.com/siloraptorE-mailroberto.rosario.gonzalez at gmail2.0 (2014-08-??)Updated to use Django 1.6.5Code cleanupsConversion of view to class based viewsCapitalization of stringImprove project files layoutImprove coding conventionsRemoval of included 3rd party modulesUpdate of required modules and libraries versionsRemoval of obsolete codeRemoval of obsolete scripts and instructionsLicense change, Descartes BI in now licensed under the Apache 2.0 LicensePyPI package, Django Bi in now available on PyPI:https://pypi.python.org/pypi/descartes-bi/
Package: descarteslabs
pipinstalldescarteslabs[complete]Documentation is available athttps://docs.descarteslabs.com.Source code and version information is athttps://github.com/descarteslabs/descarteslabs-python.The Descartes Labs Platform simplifies analysis ofglobal-scale raster databy providing:Access to a catalog of petabytes of disparate geospatial data,
all normalized and interoperable through onecommon interfaceA Python client library to access these systems
Package: descarteslabs-dynamic-compute
Dynamic-Compute 🗺️"It occurs to me that our survival may depend upon our talking to one another." — "Sol Weintraub",HyperionDynamic-Computeis amap computation engine. It enables users todynamicallygenerate maps from acomposableset of Python operations. Together, these properties enable data scientists in the building of complex GIS applications.Formal documentation for this library is available under theDescartes Labs API Documentation.Example notebooks to get started can be found underDescartes Labs Guides. Below is a very simple example to get you started using the map:First, we importdescarteslabs.dynamic_computeand instantiate the map, then set the zoom level and lat, long of the center:importdescarteslabs.dynamic_computeasdcm=dc.mapm.zoom=14m.center=(43.4783,-110.7506)mNext, we can create a layer from a Descartes Labs Catalog product by executing the following Python code:spot_rgb=(dc.Mosaic.from_product_bands("airbus:oneatlas:spot:v2","red green blue",start_datetime="20210101",end_datetime="2022101",))We can then visualize this on the map using by calling.visualizeon our layer:_=spot_rgb.visualize("SPOT",m,scales=[[0,256],[0,256],[0,256]])Only files included in__all__will be supported.