Saravanan666/pypi-org huggingface dataset:
Package: agc
AGC - Area under Gain CurvesPython code to compute and plot (truncated, weighted) area under gain curves (AGC)For binary classification, gain curves are a nice alternative to ROC curves in that they can naturally betruncatedto focus on the top scoring points only.
Moreover, the data points can haveweights. In this code, we provide two functions:agc_score: Compute the area under the gain curve (AGC) for binary labelled datagain_curve: Compute the proportion of data points and true positive rate for all thresholds, for plottingThe first function returns thenormalized areaby default (improvement over random, so this could be negative).
The functions can be imported from the suppliedagc.pyfile, or installed viapip install agc.A simple example## create toy binary labels and scores for illustration
labels = np.concatenate((np.repeat(1,100),np.repeat(0,900)))
scores = np.concatenate((np.random.uniform(.4,.8,100),np.random.uniform(.2,.6,900)))

## compute (normalized) area under the gain curve
print(agc_score(labels, scores))

## compute (un-normalized) area under the gain curve
print(agc_score(labels, scores, normalized=False))

## now the area for the top scoring 10% of the points
print(agc_score(labels, scores, truncate=0.1))

## or top scoring 100 points
print(agc_score(labels, scores, truncate=100))More details in Notebooks:For a quick introduction, see the following notebook:https://github.com/ftheberge/agc/blob/main/agc/agc_intro.ipynbalso available in markup format:https://github.com/ftheberge/agc/blob/main/intro/agc_intro.mdFor more details, see the notebook:https://github.com/ftheberge/agc/blob/main/agc/agc.ipynbalso available in markup format:https://github.com/ftheberge/agc/blob/main/example/agc.md
Package: agci
agciThis is a tutorial on how to debut a PyPI application and how to validate software reproducibility.
This agci is renamed and modified from "Symmetric GC integration.py" athttps://github.com/1mikegrn/pyGC/blob/master/JCE-supplemental/Asymmetric%20GC%20integration.pyThe modification is used to show how to debut a PyPI application and how to validate software reproducibility
via Code Ocean.how to install agci$ pip install agcihow to run agciThe result picture will be saved in result.png.$ agci "csv data filename"$ agci Sample\ GC\ data\ 1\ pt\ acetone\ 1\ pt\ cyclohexane.csv
Package: agcod
Amazon Gift Code On-Demand (AGCOD)This is a tool for working with the AGCOD service and can be used for easily creating, cancelling
and checking the status of Amazon gift codes.Installpip install agcodQuickstartfromagcodimportclientclient.sandbox=True# default is True, False will use production URLsclient.debug=False# default is Falseclient.aws_region_name='us-east-1'# Default is us-east-1client.partner_id='<Your Partner ID>'client.aws_key_id='<Your AWS Key ID>'client.aws_secret_key='<Your AWS Secret Key>'# Request ID must begin with Partner IDrequest_id=client.partner_id+'EXAMPLE'amount=1.00currency='USD'# Create a new gift coderesult=client.create_gift_card(request_id,amount,currency)# Example response# {#   "cardInfo": {#     "cardNumber": null,#     "cardStatus": "Fulfilled",#     "expirationDate": null,#     "value": {#       "amount": 1.0,#       "currencyCode": "USD"#     }#   },#   "creationRequestId": "MyidEXAMPLE",#   "gcClaimCode": "ABCD-12345-WXYZ",#   "gcExpirationDate": null,#   "gcId": "AA11BB22CC33DD",#   "status": "SUCCESS"# }# Cancel that gift codeclient.cancel_gift_card(request_id,result['gcId'])# Get account balanceclient.get_available_funds()# Example response# {#   "availableFunds": {#     "amount": 1250.00,#     "currencyCode": "USD"#   },#   "status": "SUCCESS",#   "timestamp": "20180802T155339Z"# }
Package: agcounts
agcountsA python package for extracting actigraphy counts from accelerometer data.InstallpipinstallagcountsTestDownload test data:curl-Lhttps://github.com/actigraph/agcounts/files/8247896/GT3XPLUS-AccelerationCalibrated-1x8x0.NEO1G75911139.2000-01-06-13-00-00-000-P0000.sensor.csv.gz--outputdata.csv.gzRun a simple testimportpandasaspdimportnumpyasnpfromagcounts.extractimportget_countsdefget_counts_csv(file,freq:int,epoch:int,fast:bool=True,verbose:bool=False,time_column:str=None,):ifverbose:print("Reading in CSV",flush=True)raw=pd.read_csv(file,skiprows=0)iftime_columnisnotNone:ts=raw[time_column]ts=pd.to_datetime(ts)time_freq=str(epoch)+"S"ts=ts.dt.round(time_freq)ts=ts.unique()ts=pd.DataFrame(ts,columns=[time_column])raw=raw[["X","Y","Z"]]ifverbose:print("Converting to array",flush=True)raw=np.array(raw)ifverbose:print("Getting Counts",flush=True)counts=get_counts(raw,freq=freq,epoch=epoch,fast=fast,verbose=verbose)delrawcounts=pd.DataFrame(counts,columns=["Axis1","Axis2","Axis3"])counts["AC"]=(counts["Axis1"]**2+counts["Axis2"]**2+counts["Axis3"]**2)**0.5ts=ts[0:counts.shape[0]]iftime_columnisnotNone:counts=pd.concat([ts,counts],axis=1)returncountsdefconvert_counts_csv(file,outfile,freq:int,epoch:int,fast:bool=True,verbose:bool=False,time_column:str=None,):counts=get_counts_csv(file,freq=80,epoch=60,verbose=True,time_column=time_column)counts.to_csv(outfile,index=False)returncountscounts=get_counts_csv("data.csv.gz",freq=80,epoch=60)counts=convert_counts_csv("data.csv.gz",outfile="counts.csv.gz",freq=80,epoch=60,verbose=True,time_column="HEADER_TIMESTAMP",)
Package: agct
agct: Another Genome Conversion ToolA drop-in replacement for thepyliftovertool, using the St. Jude'schainfilecrate. Enables significantly faster chainfile loading from cold start (seeanalysis/).InstallationInstall fromPyPI:python3-mpipinstallagctUsageInitialize a class instance:fromagctimportConverterc=Converter("hg38","hg19")If a chainfile is unavailable locally, it's downloaded from UCSC and saved using thewags-tailspackage -- see theconfiguration instructionsfor information on how to designate a non-default storage location.Callconvert_coordinate():c.convert_coordinate("chr7",140453136,"+")# [['chr7', 140152936, '+']]DevelopmentTheRust toolchainmust be installed.Create a virtual environment and install developer dependencies:python3-mvirtualenvvenvsourcevenv/bin/activate
python3-mpipinstall-e'.[dev,tests]'Be sure to install pre-commit hooks:pre-commitinstallThis installs Python code as editable, but after any changes to Rust code,maturin developmust be run:maturindevelopCheck Python style withruff:python3-mruffformat.&&python3-mruffcheck--fix.Usecargo fmtto check Rust style (must be run from within therust/subdirectory):cdrust/
cargofmtRun tests withpytest:pytest
Package: agd
Adaptive Grid Discretizations using Lattice Basis Reduction (AGD-LBR)A set of tools for discretizing anisotropic PDEs on cartesian gridsThis repository containsthe agd library (Adaptive Grid Discretizations), written in Python® and cuda®a series ofjupyter notebooksin the Python® language (onlinestaticandinteractiveview), reproducing my research in Anisotropic PDE discretizations and their applications.abasic documentation,
generated withpdoc.The AGD libraryThe recommended ways to install arepip install agdor alternatively (but this option does not include the GPU eikonal solver)conda install agd -c agd-lbrReboot of the git history (february 8th 2024)The whole notebooks, including images and videos, were previously saved in the git history, which as a result had grown to approx 750MB. After some unsuccessful attempts with BFG, I eventually had to delete and recreate the repository.The notebooksYou may :Visualize the notebooks online using nbviewer.Note: prefer to use this link to view the notebooks, rather than the present repository, which contains some notebooks in a partially evaluated state.Run and modify the notebooks online using GoogleColab.Note: some notebooks require turning on the GPU acceleration in GoogleColab (typical error: cannot import cupy) : Modify->Notebook parameters->GPU.The notebooks are intended as documentation and testing for the adg library. They encompass:Anisotropic fast marching methods, for shortest path computation.Non-divergence form PDEs, including non-linear PDEs such as Monge-Ampere.Divergence form anisotropic PDEs, often encountered in image processing.Algorithmic tools, related with lattice basis reduction methods, and automatic differentiation.For offline consultation, please download and installanacondaorminiconda.Optionally, you may create a dedicated conda environnement by typing the following in a terminal:conda env create --file agd-hfm.yamlconda activate agd-hfmIn order to open the book summary, type in a terminal:jupyter notebook Summary.ipynbThen use the hyperlinks to navigate within the notebooks.Matlab usersRecent versions of Matlab are able to call the Python interpreter, and thus to use the
agd library. See Notebooks_FMM/Matlab for examples featuring the CPU and GPU eikonal solvers.
Package: agda
Agda Python DistributionA project that packages Agda as a Python package, which allows you to install Agda from PyPI:pipinstallagdaThe PyPI package versions follow thePvPversion numbers of Agda releases.Binary wheels are provided for the following platforms:PlatformReleaseArchitecturemacOS≥10.10x86_64≥13.0ARM64Linuxlibc ≥2.17x86_64libc ≥2.28aarch64musl ≥1.1x86_64WindowsAMD64For more information, see:The Agda Source CodeThe Agda User Manual
Package: agda-kernel
agda-kernelAn experimental Agda kernel for Jupyter.
Used atNextjournal[nextjournal kernel].ExamplesYou can launch the following examples directly via the mybinder interface:example/LabImp.ipynb[binder].Alternatively, if you havebinder,
then you can userepo2dockerlocally:repo2docker https://github.com/lclem/agda-kernelInstallationpip install agda_kernel
python -m agda_kernel.installSyntax highlightingSyntax highlighting is done separately byCodemirror,
but unfortunately there is no Agda mode packaged with it.
A rudimentary Agda mode for Codemirror can be found incodemirror-agda/agda.js.
In order to install it, typemake codemirror-installAgda extensionIn order to improve the Jupyter interface,
it is strongly recommended to also installagda-extension.FunctionalityEach code cell must contain a line of the formmodule A.B.C where.
For instance:moduleA.B.Cwhereid:{A:Set}→A→A
idx=xUpon execution, the fileA/B/C.agdais created containing the cell's contents,
and it is fed to the Agda interpreter (viaagda --interaction).
The results of typechecking the cell are then displayed.After a cell has been evaluated, one canRun Agsy (auto) by putting the cursor next to a goal?and hitting TAB.
The hole?is replaced by the result returned by Agsy, if any,
or by{! !}if no result was found.
If there is more than one result, the first ten of them are presented for the user to choose from.Refine the current goal by putting the cursor next to a goal{! !}and hitting TAB.
An optional variable can be provided for case-splitting{! m !}.Show information about the current context, goal, etc.: putting the cursor near a goal/literal and hit SHIFT-TAB.EditingInputting common UNICODE characters is facilitated by the code-completion feature of Jupyter.When the cursor is immediately to the right of one of thebase formsymbols
hitting TAB will replace it by the correspondingalternate form.
Hitting TAB again will go back to the base form.base formalternate form->→\λ<⟨B𝔹>⟩=≡top⊤/=≢bot⊥alphaα/\∧eε/∨emptyset∅neg¬qed∎forall∀SigmaΣexists∃PiΠ[=⊑
Package: agdistispy
============AGDISTIS Py============Python bindings for AGDISTIS web service: http://agdistis.aksw.org/demo/Usage=========In [1]: from agdistispy.agdistis import AgdistisIn [2]: ag = Agdistis()In [3]: ag.disambiguate("<entity>Austria</entity>")Out[3]:[{u'disambiguatedURL': u'http://dbpedia.org/resource/Austria',u'namedEntity': u'Austria',u'offset': 7,u'start': 0}]Contributors=========* Ivan Ermilov (AKSW/BIS/UPB)Notes=========The source code is available from:https://github.com/earthquakesan/agdistispy
Package: agd_tools
﻿[![PyPi version](https://img.shields.io/pypi/v/agd_tools.svg)](https://pypi.python.org/pypi/agd_tools/)# AGD ToolsUseful functions to work with data### Documentationhttp://agd-tools.readthedocs.org/fr/latest/### LicenceCette librairie est libre sous licence [GNU Affero General Public License](http://www.gnu.org/licenses/agpl.html) version 3 ou ultérieure.
Package: age
pyagepyage is an experimental implementation of @FiloSottile and @Benjojo12 's project "age".
The spec is currently available as seven-page Google doc atage-encryption.org/v1.This project is still work-in-progress.⚠️ pyage is not intended to be a secure age implementation!
My original intention was to better understand the spec, find mistakes early and provide a redundant implementation for validation. I'm not a cryptographer (IANAC) and did not (yet) find the time to address  implementation-specific security issues (such as DoS attacks or side-channel attacks).So:Use at your own risk.Quick StartInstall from pip:pip install ageGenerate a key pair:mkdir -p ~/.config/age
pyage generate > ~/.config/age/keys.txtEncrypt a file:pyage encrypt -i hello.txt -o hello.age pubkey:<recipient public key>Decrypt a file (uses~/.config/age/keys.txt):pyage decrypt -i hello.ageFor a real tutorial, seethe Tutorial section in the documentation.DocumentationThe full documentation can be found atpyage.readthedocs.io.
Package: age3d
age3dA Python Library to age 3d models by simulating the effects of weatherOverviewAge3D is a Python Library that allows for eroding of 3d models. It uses the.stlfile format and incorporates Open3D functionality, allowing users to simulate material removal.Features:Simplified Workflow for.stl$\rightarrow$TriangleMesh$\rightarrow$PointCloud&BitMaskVisualization ofTriangleMesh&PointCloudCalculate Metric ofTriangleMeshErosion Method for AgingCustomizable Number of Passes of Simulated Erosion ParticlesCustomizable lifetime of Simulated Erosion ParticlesDependenciesage3d requires theopen3dPython Library which is installed during libray installation.InstallationThe recommended way to install age3d is through pip.pip install age3dUsageImport the library:import age3dImport a.stlmodel wherefile_pathpoints to the location:mesh = age3d.import_mesh(file_path)ErosionIf themeshis low-poly, run withnumber_of_subdivisions > 0:mesh = age3d.mesh_subdivision(mesh, iterations = number_of_subdivisions)Erode themesh:eroded_mesh = age3d.erode(mesh)If Erosion with customized Passes and Max Particle Lifetime:updated_vertices, eroded_mesh = age3d.erode(mesh, iterations = 2, erosion_lifetime = 10)Point Cloud CreationMake aPointCloudwith Red Color Points:point_cloud = age3d.make_point_cloud(mesh, color = [255, 0, 0])VisualizationVisualize Eroded Mesh:eroded_mesh.compute_vertex_normals()
age3d.visualize(mesh)oreroded_mesh.compute_vertex_normals()
age3d.visualize([mesh])Visualize Mesh & Point Cloud:eroded_mesh.compute_vertex_normals()
age3d.visualize([mesh, point_cloud])Visualize Mesh & Point Cloud with Wireframe:eroded_mesh.compute_vertex_normals()
age3d.visualize([mesh, point_cloud], show_wireframe = True)ContributingIf you encounter an issue, please feel free to raise it by opening an issue. Likewise, if you have resolved an issue, you are welcome to open a pull request.See more atCONTRIBUTING.md
Package: agea-tools
Agea toolsEste paquete tiene como objetivo ser utilizado por el equipo de Big Data de Agea para las diferentes tareas diarias que sea realizan.InstallationSe puede istalar el paquete desdePyPI:pip install agea_toolsPor el momento solo estÃ¡ testeado para Python 3.6.7.How to useA continuaciÃ³n se darÃ¡n ejemplos de como utilizarlo:You can also call the Real Python Feed Reader in your own Python code, by importing from thereaderpackage:>>> from reader import feed
>>> feed.get_titles()
['How to Publish an Open-Source Python Package to PyPI', ...]
Package: agecalc
SynopsisCalculates birth information based on a specified Date of Birth.Installationpip install agecalcWhat’s InsideClassesAgeCalcThis stores the DOB data into a class. You can then use the methods
below to get data from this.FunctionsageDisplays a DOB’s age in years.age_daysDisplays a DOB’s age in days.age_hoursDisplays a DOB’s age in hours.age_monthsDisplays a DOB’s age in months.age_weeksDisplays a DOB’s age in weeks.age_weeks_daysDisplays a DOB’s age in weeks/days. Will return a dictionary with the
‘weeks’ and ‘days’ keys, and their values.age_years_monthsDisplays a DOB�s age in years/months. Will return a dictionary with the
‘years’ and ‘months’ keys, and their values.dating_agesDisplays the socially acceptable dating ages for a person. Will return a
dictionary with the “max”, “min” and “original” keys, with their values.day_of_birthDisplays the DAY of birth of a DOB.last_birthdayDisplays the days since a DOB’s last birthdaynext_birthdayDisplays the days until a DOB’s next birthdayExample (age function)With AgeCalc classimportagecalcdob=agecalc.AgeCalc(1,1,2000)printdob.ageWith Functionsimportagecalcprintagecalc.age(1,1,2000)NotesAll functions/classes take only these three arguments:dd: Daymm: Monthyy: YearDates should be passed as if they were integers. If the Date/Month
contains a ‘0’ before the integer, the ‘0’ should be ommitted.E.G. DOB ‘01/01/2000’ should be passed as:dd: 1mm: 1yy: 2000Submitting an IssueIf you wish to submit an issue with this module, or suggest any changes,
you can either use theGitHub Issue Tracker, or email me atalir6716@gmail.comCopyright/LicenseCopyright (C) 2015, Ali RajaThis program is free software: you can redistribute it and/or modify it
under the terms of the GNU General Public License as published by the
Free Software Foundation, either version 3 of the License, or (at your
option) any later version.This program is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
Public License for more details.You should have received a copy of the GNU General Public License along
with this program. If not, seehttp://www.gnu.org/licenses/.
Package: agecal-mick
agecalThis library providessay "Hi Everyone"Compare age between user and I whether who is younger or olderIt takes user's age as an input.Installationpip install agecal-mickHow to use it?import agecalfrom agecal import greetingfrom agecal import compareageTo print "Hi everyone"greeting.sayHi()Comparing the age between user and I. It takes user's age as an input and print younger or older as an output.compareage.compare_my_age()LicenseCopyright 2021 Mickzaa
Package: age-cda
Community-Detection-ModularityEigenvector-based community detection is a method used to identify communities or groups within a network by analyzing the eigenvectors of the network's adjacency matrix. The basic idea behind this approach is that nodes that belong to the same community will be more strongly connected to each other than to nodes in other communities.The method starts by calculating the adjacency matrix of the network, which represents the connections between nodes. Next, the eigenvalues and eigenvectors of this matrix are calculated. The eigenvectors with the largest eigenvalues are then used to assign nodes to communities.The basic idea is that nodes that belong to the same community will have similar eigenvector values for these dominant eigenvectors. By grouping nodes with similar eigenvector values together, communities can be identified.The method starts by calculating the adjacency matrix of the network, which represents the connections between nodes. Next, the eigenvalues and eigenvectors of this matrix are calculated. The eigenvectors with the largest eigenvalues are then used to assign nodes to communities.The basic idea is that nodes that belong to the same community will have similar eigenvector values for these dominant eigenvectors. By grouping nodes with similar eigenvector values together, communities can be identified.InstallationInstall via PIPpip install age-cdaBuild from Sourcesudo apt-get update
sudo apt-get install libeigen3-dev
git clone https://github.com/Munmud/Community-Detection-Modularity
cd Community-Detection-Modularity
python setup.py installConfigure GSL - GNU Scientific LibraryUnit Testpython -m unittest test_community.pyInstructionimportfromage_cdaimportGraphCreate Graphnodes=[0,1,2,3,4,5]edges=[[0,1],[0,2],[1,2],[2,3],[3,4],[3,5],[4,5]]g=Graph.Graph()g.createGraph(nodes,edges)Nodes :anyEdges :2d array : adjacency list`Each element within Nodes arrayGenerate Community Assignmentres=g.get_community()Output Format[[3,4,5],[0,1,2]]List communityEach community has list of nodesSamplesCreating GraphZachary's karate clubReferenceFinding community structure in networks using the eigenvectors of matricesModularity and community structure in networksStatistical Mechanics of Community Detection
Package: ageliaco.p10userdata
IntroductionThis product adds some properties to the user data to exploit to its best the values
there are in the p10 ldap.This product is dependant onplone.app.ldap, because it does set the basic properties.InstallationGo to admin > Site Setup > Add-onsActivate plone.app.ldapActivate ageliaco.p10userdataGo to ZMI > acl_users > ldap-plugin > acl_users
** reset LDAP Server
** reset “Configure” to fit your needs (filter and groups)There is a bug concerning plone.app.ldap => when the ldap server is set
it doesn’t set properly the port number, and the ldap filter is not set either.This product may contain traces of nuts.Authors“AGELIACO”, Serge Renfermailto:serge.renfer@gmaildot comChangelog1.0dev (unreleased)Initial release
Package: ageliaco.rd
IntroductionThis product is intended for a service management in Geneva School Departement (DIP).
This service is “Ressources & Développement” and it brings a support for educational projects
in the upper secondary schools (colleges).This product is dependant onageliaco.p10userdata, because it does set the basic properties
in user data conforming to our ldap settings.InstallationGo to admin > Site Setup > Add-onsActivate plone.app.ldapActivate ageliaco.p10userdataGo to ZMI > acl_users > ldap-plugin > acl_users
** reset LDAP Server
** reset “Configure” to fit your needs (filter and groups)Activate ageliaco.rdThere is a bug concerning plone.app.ldap => when the ldap server is set
it doesn’t set properly the port number, and the ldap filter is not set either.This product may contain traces of nuts.Authors“AGELIACO”, Serge Renfermailto:serge.renfer@gmaildot comChangelog0.1dev (unreleased)Initial release
Package: ageliaco.rd2
IntroductionThis product helps managing a service in the post-obligatory schools of Geneva.
“Ressource & Developpement” is a service that manages resources (hours of teaching used
to produce a pedagogic project) to help teachers bring new teaching resources.This is an experimental project, to see how far I can push this dexterity/Plone matter ;-)Changelog0.1dev (unreleased)Initial release