Saravanan666/pypi-org huggingface dataset:
Package: transnet-generator
TransNet GeneratorTransNet Generator is a Python library for generating transport networks from GTFS data.
The library provides a convenient and efficient way to generate transport network graphs
from one or multiple GTFS directories to a graph in Pajek or GML format, which can be used
for network analysis and visualization.InstallationTo install TransNet Generator, you can use pip:pipinstalltransnet-generatorUsageTo use TransNet Generator, you can import thetransnet_generatormodule and use thegenerate_graphfunction:fromtransnet_generatorimportgenerate_graphinput_GTFS=["path/to/gtfs/directory1","path/to/gtfs/directory2"]output_path="path/to/output/graph"generate_graph(input_GTFS,output_path)Thegenerate_graphfunction takes two required arguments:input_GTFS- A list containing the paths to the directories containing the GTFS files.output_path- The path to the output graph file.Supported Output FormatsTransNet Generator supports the following output formats:Pajek (.net)GML (.gml)To specify the output format, simply pass the desired format as a string to theoutput_formatparameter
of thegenerate_graphfunction.ExamplesHere's an example of how to generate a graph file in Pajek format from one GTFS directory:fromtransnet_generatorimportgenerate_graphinput_GTFS=["path/to/gtfs/directory"]output_path="path/to/output/graph.net"generate_graph(input_GTFS,output_path,format="pajek")Here's an example of how to generate a graph file in GML format from three GTFS directories:fromtransnet_generatorimportgenerate_graphinput_GTFS=["path/to/gtfs/directory1","path/to/gtfs/directory2","path/to/gtfs/directory3"]output_path="path/to/output/graph.gml"generate_graph(input_GTFS,output_path,format="gml")LicenseTransNet Generator is released under the MIT License. See theLICENCEfile for details.
Package: transparencyEpias
transparencyEpiastransparencyEpias is a package which consumes Epias Transparency Rest Web Services, mainly scopes to provide easy access to users public Turkish Electricity Market data.CoversConsumption Data ServicesNatural Gas Data ServicesMarket Data Services-Dayahead Market Data-Intraday Market Data-Balancing Power Market Data-Ancillary Services DataProduction Data ServicesTools (Which includes useful functions like exporting excel, creating basic report etc. )InstallationtransparencyEpias requires python3 or latest versions to run.
Also following packages need to be installed.pandasrequestsdatetimejsonxlsxwriterInstall the dependencies and install transparencyEpias.$python-mpipinstalltransparencyEpiasSample UsageTo be able to reach file user should follow instructions below.MCP Values Example:fromtransparency_epias.marketsimportdayaheadClientmcp_list=dayaheadClient.dayahead.mcp(startDate='2020-05-10',endDate='2020-05-10')[1]>>>[249.99,322.22,321.82,321.81,310.01,214,79.96,14.04,1,0,0,0,0.87,1,4,13.99,97.45,227.57,299.99,323.23,321.85,308.56,289.8,284.99]MCP Excel Export Example:fromtransparency_epias.toolsimportreportClientreportClient.reports.mcp_excel_export(startDate='2020-05-10',endDate='2020-05-10')>>>UsershouldgetanxlsxdocumentincludesdailyMCPpricevaluestothepaththatpythonruns.Simple Price Comparison Report Example:fromtransparency_epias.toolsimportreportClientreportClient.reports.compare_price()>>>UsershouldgetaxlsxdocumentincludesMCP,SMP,andWeightedAvaragePricevaluestothepaththatpythonruns.LicenseMITSorucehttps://seffaflik.epias.com.tr/transparency/https://seffaflik.epias.com.tr/transparency/technical/en/
Package: transparentemail
Transparent EmailTransparent Email clears aliases from email address. EmailJohn.Doe+alias@gmail.comwill be transformed tojohndoe@gmail.com.Inspired by:bkrukowski/transparent-emailWhy?To detect multi-accounts on your website.Supported mailboxesgmail.com33mail.comoutlook.comyahoo.comInstallationpip install transparentemailUsagefromtransparentemail.srcimportget_primary_emailfromtransparentemail.services.Emails.emailExceptionimportInvalidEmailExceptiontry:transformed_email=get_primary_email('John.Doe+alias@gmail.com')print(transformed_email)# John.Doe@gmail.comexceptInvalidEmailException:print('Invalid Email')Yahoo.comAliases work different on Yahoo than on Gmail. On Gmail part after plus is skipped.
For example message sent tojanedoe+alias@gmail.comwill be redirected tojanedoe@gmail.com.Yahoo uses the following pattern*:baseName-keyword@yahoo.combaseName- value defined by the user, different than email login;keyword- one from a list of keywords defined by the user.Therefore we do not know what is the real email, so in this case result will bebaseName@yahoo.com,
which actually does not exist.
Package: transparent-keras
Transparent Keras aims to provide a very simple way to look under the hood
during training of Keras models by defining an extra set of outputs that will
be returned bytrain_on_batchortest_on_batch.The API is extremely simple all that is provided is aTransparentModelthat
accepts an extra constructor keyword argumentobserved_tensors. The created
model should behave exactly like a Keras model except for the functions(train|test)_on_batch, which return the extra tensors as after their normal
return values.Examplefromkeras.layersimportActivation,Dense,Dropout,Inputimportnumpyasnpfromtransparent_kerasimportTransparentModelx0=Input(shape=(10,))x=Dense(10,activation="relu")(x0)x=Dropout(0.5)(x)y_extra=x=Dense(10)(x)x=Activation("relu")(x)x=Dropout(0.5)(x)y=Dense(1)(x)m=TransparentModel(inputs=[x0],outputs=[y],observed_tensors=[y_extra])m.compile(optimizer="sgd",loss="mse")x_random=np.random.rand(128,10)y_random=np.random.rand(128,1)loss,y_extra=m.train_on_batch(x_random,y_random)
Package: transparentlogs-pymerkle
pymerkleMerkle-tree cryptographyDocumentation found atpymerkle.readthedocs.org.This library provides a Merkle-tree implementation in Python. It supports
multiple combinations of hash functions and encding schemas with defense against
second-preimage attack enabled.Installpip3installpymerkleUsagefrompymerkleimportMerkleTree,verify_inclusion,verify_consistencytree=MerkleTree()# Populate tree with some entriesfordatain[b'foo',b'bar',b'baz',b'qux',b'quux']:tree.append_entry(data)# Prove and verify inclusion of `bar`proof=tree.prove_inclusion(b'bar')verify_inclusion(b'bar',tree.root,proof)# Save current statesublength=tree.lengthsubroot=tree.root# Append further entriesfordatain[b'corge',b'grault',b'garlpy']:tree.append_entry(data)# Prove and verify previous stateproof=tree.prove_consistency(sublength,subroot)verify_consistency(subroot,tree.root,proof)SecurityThis is currently a prototype requiring security review. However, some steps have
been made to this direction:Defense against second-preimage attackThis consists in the following standard technique:Upon computing the hash of a leaf, prepend its data with0x00.Upon computing the hash of an interior node, prepend the hashes of its
children with0x01.Referhereto see how to perform second preimage
attack against the present implementation.Defense against CVE-2012-2459 DOSContrary to thebitcoinspecification for Merkle-trees, lonely leaves are not duplicated while the tree is growing.
Instead, when appending new leaves, a bifurcation node is created at the rightmost branch.
As a consequence, the present implementation should be invulnerable to the DOS attack reported asCVE-2012-2459(see alsoherefor explanation).Tree structureThe topology turns out to be that of a binarySakura tree.Developmentpip3 install -r requirements-dev.txtTests./test.sh [pytest options]to run tests against the limited set of encoding schemas UTF-8, UTF-16 and
UTF-32. To run tests against all possible combinations of hash algorithms and
encoding schemas, run./test.sh --extendedDocumentationpymerkle.readthedocs.org.Build locallyDocumentation is built withsphinx:pip3 install -r requirements-doc.txtOnce installed, build docs with./build-docs.sh [--help]and browse atdocs/target/build/html/index.htmlto view them.
Package: transparent-rotate
Rotates images, makes the created background transparent$pipinstalltransparent-rotatefromtransparent_rotateimportrotate_with_adjusting,rotate_without_adjustingi1=r"https://github.com/hansalemaos/screenshots/raw/main/splitted1.png"i2=rotate_without_adjusting(img="https://github.com/hansalemaos/screenshots/raw/main/papag_00000008.png",angle=60,save_path=None,)# The picture can easily be merged with other picturesfromcv2watermarkimportmerge_image_percentage_width_positionmerg3=merge_image_percentage_width_position(back=i1,front=i2,percentx=20,percenty=20,front_percentage_width=30,save_path="f:\\cv2mergepics\\rotate1.png",)i1=r"https://github.com/hansalemaos/screenshots/raw/main/splitted1.png"i2=rotate_with_adjusting(img="https://github.com/hansalemaos/screenshots/raw/main/papag_00000008.png",angle=60,save_path=None,)merg3=merge_image_percentage_width_position(back=i1,front=i2,percentx=20,percenty=20,front_percentage_width=30,save_path="f:\\cv2mergepics\\rotate2.png",)
Package: transpeg
Wanna transparent JPEG?
Unfortunately there is no such thing except mostly unsupported JPEG 2000.
But you can convert your transparent image into SVG containing a color information as JPEG and an alpha channel as a greyscale mask.
This simple tool does exactly this.
The logo above is an example.Usagetranspeg input.png output.svgInstallationpip install transpegAlternativesWebP. Unfortunatelly WebP is not well supperted in Safari yet.https://github.com/tannerhodges/gulp-zorrosvghttps://github.com/gribnoysup/jpng.svgWhyIt is dead simple in PythonI needed a PNG palette for a mask
Package: transphire
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4302359.svg)](https://doi.org/10.5281/zenodo.4302359)The documentation of TranSPHIRE moved to [here](https://transphire.readthedocs.io).# TransphireCryo-EM on the fly processing pipeline.If you have any questions contact:markus.stabrin@mpi-dortmund.mpg.de## InstallationYou can find a description how to install TranSPHIRE here: [Installation instructions](https://github.com/MPI-Dortmund/transphire/wiki/Installation)## Getting startedYou can find an extensive Tutorial in how to use TranSPHIRE:
[Getting started with TranSPHIRE](https://github.com/MPI-Dortmund/transphire/wiki/getting-started)## FAQPlease refere to this link: [https://github.com/MPI-Dortmund/transphire/wiki/FAQ](https://github.com/MPI-Dortmund/transphire/wiki/FAQ)
Package: transphire-transform
[![Build Status](https://travis-ci.org/MPI-Dortmund/transphire_transform.svg?branch=master)](https://travis-ci.org/MPI-Dortmund/transphire_transform) [![Coverage Status](https://coveralls.io/repos/github/MPI-Dortmund/transphire_transform/badge.svg)](https://coveralls.io/github/MPI-Dortmund/transphire_transform)# transphire_transform
Utilites to change between SPHIRE, RELION and to modify the data
Package: transplanttoolbox-allan
Transplant tool box-ALLAN:  HLA molecular typing data to antigen conversion for virtual crossmatchDescriptionThe organ allocation computer matching system used by United Network Organ Sharing (UNOS)
requires molecular HLA typing data of transplant candidates and donor organs to be reported as
UNOS antigen equivalents for entry into UNet. This web tool maps ambiguous or unambiguous
HLA typing data to respective antigens based on UNOS OPTN guidelines. A list of UNOS antigen
equivalents is returned as output. Ambiguous typings use National Marrow Donor Program (NMDP)
allele frequencies to calculate antigen probabilities and assign the most probable antigens for each
locus.
Package: transplanttoolbox-victor
Virtual Crossmatch for molecular HLA typing dataDescriptionVirtual crossmatch for solid organ transplantation is done by cross referencing candidate’s unacceptable antigens
with donor’s HLA typing. This manual process is laborious when donor’s HLA typing is ambiguous and candidate has
allele specific antibodies. VICTOR: VIrtual CrossmaTch for mOlecular typing data is a web application that computes
virtual crossmatch for unambiguous and ambiguous HLA typing data and antigen specific or allele specific alloantibodies.
Transplanttoolbox-victor is VICTOR’s python package available under GNU General Public License.
Package: transpo-dir
transpo-dirtranspois a tool to "transpose" directory structure.Have you ever needed to create a directory structure that looks like a two-dimensional spreadsheet? For example, let's say you have product sales data stored in a directory where the first level is the product name and the second level is the month.
Now you want to convert this to a directory structure where the first level is the month and the second level is the product name.
The tooltransposupports such a change of directory structure.InstallationBy installing the packagetranspo-dirwith pip, an executabletranspowill be installed on your system.pipinstalltranspo-dirIf you get the errorModuleNotFoundError: No module named 'docopt'when runningtranspo, install the package docopt-ng or docopt.pipinstalldocopt-ngUsagetranspo [options] <src> -d DESTOptions-d DEST       Destination directory.
-i INDICES, --index-order=INDICES     The order of indices for transpose [default: 21].Walkthrough$cdtest-data/test-depth-2

$tree
.
├──a
│├──1││└──a-1.txt
│└──2│└──a-2.txt
└──b├──1│└──b-1.txt└──2└──b-2.txt6directories,4files

$transpo.-dhoge#!/bin/bashset-ex

mkdirhoge
mkdir-phoge/1
mv./a/1hoge/1/a
mkdir-phoge/2
mv./a/2hoge/2/a
mv./b/1hoge/1/b
mv./b/2hoge/2/b
rm-rf./a
rm-rf./b

$transpo.-dhoge|bash-
+mkdirhoge
+mkdir-phoge/1
+mv./a/1hoge/1/a
+mkdir-phoge/2
+mv./a/2hoge/2/a
+mv./b/1hoge/1/b
+mv./b/2hoge/2/b
+rm-rf./a
+rm-rf./b

$tree
.
└──hoge├──1│├──a││└──a-1.txt│└──b│└──b-1.txt└──2├──a│└──a-2.txt└──b└──b-2.txt
Package: transport-analysis
Transport AnalysisLatest releaseStatusCommunityA Python package to compute and analyze transport properties.Transport Analysis is bound by aCode of Conduct.InstallationTo build Transport Analysis from source,
we highly recommend using virtual environments.
If possible, we strongly recommend that you useAnacondaas your package manager.
Below we provide instructions both forcondaand
forpip.With condaEnsure that you havecondainstalled.Create a virtual environment and activate it:conda create --name transport_analysis
conda activate transport_analysisInstall the development and documentation dependencies:conda env update --name transport_analysis --file devtools/conda-envs/test_env.yaml
conda env update --name transport_analysis --file docs/requirements.yamlBuild this package from source:pip install -e .If you want to update your dependencies (which can be risky!), run:conda update --allAnd when you are finished, you can exit the virtual environment with:conda deactivateWith pipTo build the package from source, run:pip install -e .If you want to create a development environment, install
the dependencies required for tests and docs with:pip install -e ".[test,doc]"CopyrightThe Transport Analysis source code is hosted athttps://github.com/MDAnalysis/transport-analysisand is available under the GNU General Public License, version 2 (see the fileLICENSE).Copyright (c) 2023, Xu Hong ChenAcknowledgementsProject based on theMDAnalysis Cookiecutterversion 0.1.
Please citeMDAnalysiswhen using Transport Analysis in published work.
Package: transportapi-python
transportapi-python 🚆🚌🚲Unofficial Python 3.7 API wrapper for theTransportAPI.Dependencies[dev-packages]
black = "*"

[packages]
requests = "*"

[requires]
python_version = "3.7"Installtransportapi-pythonusing either:pip3 install transportapi-python,pipenv install,pip3 install -r requirements.txt,python setup.py install.Example UsageObtain anAPI Key.Seetransportapi_python/transportapi.pyfor other parameters.Accessing the Train endpoints.fromtransportapi_pythonimportTrainfrompprintimportpprint# HTTP(S) proxies are supported: https://2.python-requests.org/en/master/user/advanced/#proxiestrain=Train(APP_ID="Your app ID here.",API_KEY="Your API key here.")# Uses the default value station_code: "LBG".pprint(train.train_timetable())Accessing Bus endpoints.fromtransportapi_pythonimportBusfrompprintimportpprint# HTTP(S) proxies are supported: https://2.python-requests.org/en/master/user/advanced/#proxiesbus=Bus(APP_ID="Your app ID here.",API_KEY="Your API key here.")# Uses the default value station_code: "LBG".pprint(bus.bus_service_info())Usage forPublic,CarandBicycleclasses are exactly the same as above.Additional InformationTransportAPI official API referenceChangelog0.0.1 - Inital release of transportapi-python. Covered all endpoints of the API.Authors -- ContributorsDextroz-Author-DextrozLicenseThis project is licensed under the MIT License - see theLICENSEfor details.DisclaimerThis API wrapper isunofficialmeaning it has no affiliation withTransportAPI. When using their API, you consent to theirterms & conditionsandprivacy policy.
Package: transport-cba
transport-cbaA Python module for cost-benefit analysis of infrastructure projects.Provides a consistent way to evaluate economic efficiency
of road projects with well-defined inputs and parameters.Main benefits compared to traiditional Excel-based approach:several orders of magnitude faster and cheaperwider options for analysis of alternative scenariossignificantly lower margin for errorInstallationFrom pip:pip install transport-cbaOr directly from git:pip install git+https://github.com/transport-cba/transport-cba.gitInputsLoad project inputs as an Excel file with following sheet names:road_params, capex, intensities_0, intensities_1, velocities_0, velocities_1Meaning of required inputs:capital expenditures (CAPEX) with pre-defined itemsparameters of road sections (length, width, number of lanes etc)vehicle intensities in variant 0 and 1 (without and with the project) by road segmentvehicle velocities in variant 0 and 1 by segmentFor illustration, please download the sample input (see below).OutputsDataframe of costs and benefitsEconomic indicators:economic net present value (ENPV)economic internal rate of return (ERR)benefit to cost ratio (BCR)dataframes with breakdown of relevant benefits by yearsExampleNB: Values might differ slightly.>>>fromtransport_cbaimportRoadCBA>>>fromtransport_cba.sample_projectsimportload_sample_bypass>>>bypass=load_sample_bypass()>>>cba=RoadCBA(2020,"svk")>>>cba.read_project_inputs(...bypass["road_params"],...bypass["capex"],...bypass["intensities_0"],...bypass["intensities_1"],...bypass["velocities_0"],...bypass["velocities_1"]...)>>>cba.economic_analysis()>>>cba.economic_indicatorsQuantityUnitValue0ENPVM EUR3.3361ERR%5.622BCR1.076
Package: transport-dependency
Transport DependencyThis package provides a few simple functions to compute the transport correlation and other optimal transport based dependency measures as defined here:Transport dependency: Optimal transport based dependency measures.Pip installationSince the library relies on thePython Optimal Transport packageit is necessesary to first install
some dependencies separately with the comand:pip install numpy scipy CythonYou can then install the package by typing:pip install transport_dependencyShort example# Compute transport correlation and test for independencefromtransport_dependencyimporttcor,permutation_testx=[[1,2,3],[4,5,6],[7,8.,9]]y=[1,0,0.3]res=tcor(x,y)test=permutation_test(x,y,coef=tcor)p_val=test["p_val"]References[1] . G. Nies, T. Staudt, and A. Munk.Transport dependency: Optimal transport based dependency measuresarXiv Preprint, 2021.
Package: transport-energy
Tools for the iTEM databasesThis repository contains tools for two databases maintained byiTEM, theInternational Transport Energy Modelingconsortium:Ahistorical databaseto form a common, public, “best available” baseline for model calibration and projections.
The historical database is under continuous development.Amodel databaseof transport energy projections assembled as part of the iTEM model intercomparison projects (MIPs) linked toiTEM workshops.
To meet the intellectual property concerns of workshop participants, the model database is currently not public, and only available on request; however, the tools used to prepare it are public.
These tools are developed periodically, during sequential MIPs.For details on installation, usage, contributing, etc., see theonline documentationathttps://transportenergy.readthedocs.io, automatically built from the contents of this repository.LicenseCopyright © 2017–2021,iTEM contributorsLicensed under the GNU General Public License, version 3.
The full text of the license is available in the fileLICENSE.See the online documentation forcitation of this softwarein scientific publications that use the softwareorthe resulting database.Related repositoriestransportenergy/item_mip_data_processing: tools for iTEM MIP3.transportenergy/metadata: shared metadata about models and historical data sources.
Package: transporthours
Transport Hours (Python version)Read-meTransportHours is a Python (2.7+) library for easy reading/writing ofpublic transport hourspresent in OpenStreetMap. It interprets string values from OSM tags, and also converts your objects into string values to go back into OSM.InstallFrom pipJust run the following command in your development environment :pipinstalltransporthoursUsing this repositoryDownload this repository, then embed the library into your own project :fromtransporthours.mainimportMainUsageAn example of usage is available inexample.pyfile.For more details about available functions, seeAPI documentation.Build & developFor start developing on this library, run the following commands :# Prepare python 2.7 environmentvirtualenvenv-p/usr/bin/python2.7source./env/bin/activate# Install dependenciesmakedeps# Run unit testsmaketest# Generate documentationmakedocs# Following commands are optional, but can be useful sometimes# Run a single unit testpythontests/test_main.pyMainTest.test_name_of_the_testPublish on PyPI# Put your credentials in .pypirc (only first time you want to publish)vim~/.pypirc# Install Twine (only first time you want to publish)pipinstalltwine# Be sure to have updated package version numbervimsetup.py# Build archivespythonsetup.pysdistbdist_wheel# Check buildstwinecheckdist/*# Test upload on TestPyPItwineuploaddist/*-rtestpypi# Real upload on PyPItwineuploaddist/*-rpypiLicenseCopyright 2019 Jungle Bus &Adrien PAVIESeeLICENSEfor complete LGPL3 license.TransportHoursPython is free software: you can redistribute it and/or modify
it under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.TransportHoursPython 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 Lesser General Public License for more details.You should have received a copy of the GNU Lesser General Public License
along with TransportHoursPython. If not, seehttp://www.gnu.org/licenses/.