Saravanan666/pypi-org huggingface dataset:
Package: umami
What is it?Umami is a package for calculating objective functions or objective function
components for Earth surface dynamics modeling. It was designed to work well
withterrainbentoand other models built with theLandlab Toolkit. Examples can be
found in thenotebooksdirectory (or on Binder).Umami offers two primary classes:aResidual,
which represents the difference between model and data, andaMetric,
which is a calculated value on either model or data.The set of currently supported calculations are found in theumami.calculationssubmodule.What does it do well?Umami was designed to provide an input-file based interface for calculating
single-value landscape metrics for use in model analysis. This supports
reproducible analysis and systematic variation in metric construction. When
used withterrainbentoone input file can describe the model run, and one
input file can describe the model assessment or model-data comparison. This
streamlines model analysis applications. Umami also provides multiple output
formats (YAML and Dakota), the latter of which is designed to interface with
Sandia National Laboratory'sDakota package.To get a sense of how it is meant to be used, check out thenotebooks on Binderand theAPI documentation.Where to get itTo install the release version of umami (this is probably what you want) we
supportcondaandpippackage management.Using condaOpen a terminal and execute the following:$ conda config --add channels conda-forge
$ conda install umamiUsing pipOpen a terminal and execute the following:$ pip install umamiFrom source codeThe source code is housed onGitHub.
To install the umami from source code we recommend creating a conda environment.$ git clone https://github.com/TerrainBento/umami.git
$ cd umami
$ conda env create -f environment-dev.yml
$ conda activate umami-dev
$ python setup.py installIf you are interested in developing umami, please check out thedevelopment practicespage.Read the documentationDocumentation is housed onReadTheDocs.LicenseMITReport issues and get helpUmami uses Github Issue as a single point of contact for users and developers.
To ask a question, report a bug, make a feature request, or to get in touch for
any reason, please makean Issue.Contribute to umamiAll contributions are welcome and appreciated. Feel free to:Makean issueto ask a
question. Your question will help others in the future.Makean issueto report a
bug or a potential improvement. We will work to fix it. If you have an idea
about how to fix it, please feel free to propose it, or make a Pull Request.Fork the repository, make
changes to the source code on adevelopment branch,
and submit a Pull Request to have your changes brought into the umami
repository.No contribution to the code base or documentation is too small.Contributors and maintainers to this project are are expected to abide theContributor Code of Conduct.Cite umamiUmami is described by a Journal of Open Source Software paper. If you use umami in your research, please cite it.
Package: umansysprop
UManSysProp is an online facility for calculating the properties of individual
organic molecules and ensemble mixtures. Built using open source chemical
informatics, the facilities provided are divided according to broad
applications.The code is licensed under theGPL v2or above. Packages can bedownloadedfrom PyPI. Thesource codecan be obtained from GitHub, which
also hosts thebug tracker. Thedocumentation(which includes
installation and quick start examples) can be read on ReadTheDocs.
Package: umapi-client
umapi-client.pyThis is a Python client for the User Management API from Adobe, aka theUMAPI.The User Management API is an Adobe-hosted network service which provides Adobe
Enterprise customers the ability to manage their users. This client makes it
easy to access the UMAPI from a local Python application.See theuser guidefor
more information.This client is open source, maintained by Adobe, and distributed under the terms
of the OSI-approved MIT license. Copyright (c) 2016-2023 Adobe Inc.CompatibilityThe3.xrelease branch is actively maintained. New users of the UMAPI client
should use the latest 3.x release. The2.xbranch is still supported, but new
2.x releases will only contain bug fixes. New features will only be developed
for 3.x.Pull requests for 3.x should be made against thev3branch (the default
branch). Anything related to 2.x bugfixes should targetv2.Installationumapi-client.pyis published to the Python Packaging Index.https://pypi.org/project/umapi-client/It can be installed with pip:$ pip install umapi-clientOr a dependency manager such as Poetry:$ poetry add umapi-clientBuildingPoetryis required to build the package. Follow
the instructions documented on Poetry's website to install it on your system.Clone this repository$ git clone https://github.com/adobe-apiplatform/umapi-client.py
$ cd umapi-client.pyInstall dependencies to virtual environment.$ poetry installThebuildcommand will create a source package (.tar.gz) and a wheel file
(.whl) in thedistdirectory.$ poetry build
$ ls dist
umapi-client-3.0.1.tar.gz  umapi_client-3.0.1-py3-none-any.whlSome of the packages required by this module use encryption, and so may
require you to do local builds of modules that use SSL. Typically, this will
require you to have a python installed that supports compiling extensions.Run tests withpytest.$ poetry run pytestUsageUsage documentation, as well as information about how to get client
credentials for use of the UMAPI, can be found in theuser guide,
whose sources are in thedocsdirectory of this repository.LicenseThis project is licensed under the MIT License. SeeLICENSEfor more
information.
Package: umapy
umapyA simple API wrapper of Unofficial MCOC API(UMA).Installpipinstallgit+https://github.com/Rexians/umapy.gitBasic UsageGet Champ Info-fromumapyimportClientclient=Client()abom=x.get_champ_info('ABOMINATION',6,3)print(abom.name)Docs soon!!ContributionFeel free to contribute by either opening an issue or a pull request.
Package: umarkdown
Ultra Markdown ⚡Ultra Markdown, an ultra fast (high performance) Markdown parser compliant with the markdownspecwritten in pure C with bindings for Python 3.8+. Unlike others,Ultra Markdownis written using Python's C API and usesCMark, an ultra fast Markdown parser written in C.Source:https://github.com/kumaraditya303/umarkdownDocs:https://umarkdown.netlify.app/Features 🚀Fast - Very high performance, One of the fastest Markdown Parser available for Python.Intuitive - Great IDE support as it ships with stubs out of the box.Standards - Based on CMark C library which is fully compliant with the markdownspec.Command Line Interface - Ships with a Cli based onclickfor Cli usage.Support - Fully supported on Windows, Linux, MacOS.Installation ✔Install with pip:$python-mpipinstallumarkdown# Or Install with cli$python-mpipinstallumarkdown[cli]Usage 🚀Can be used as a drop in replacement for most Markdown parsers.>>>fromumarkdownimportmarkdown>>>print(markdown("# Hello World!"))<h1>HelloWorld!</h1>BenchmarksHigher score is better.
Package: umat
Configuration file contents:[main]api_key=YOUR_API_KEY[export]advertiser_id=YOUR_ADVERTISER_IDtimeout=300# secondedelay=30# secondelimit=2000000Using:# -*- coding: utf-8 -*-fromumatimport(AdvertiserReportCohortValuesAdvertiserReportLogInstalls)fromdatetimeimportdatetimeconfig_filename='config.ini'log_endpoint=AdvertiserReportLogInstalls(config_filename)log_endpoint.params.start_date=datetime(2016,1,10,9)log_endpoint.params.end_date=datetime(2016,1,10,14)log_endpoint.params.timezone='utc'log_endpoint.params.fields=['publisher_id','publisher.name','revenue_usd']result_df=log_endpoint.get_dataframe()cohort_endpoint=AdvertiserReportCohortValues(filename)cohort_endpoint.params.end_date=datetime(2016,1,10)cohort_endpoint.params.start_date=datetime(2016,1,10)cohort_endpoint.params.timezone='UTC'cohort_endpoint.params.fields=['publisher_id','publisher.name','revenues_usd']cohort_endpoint.params.update({'aggregation_type':'cumulative','cohort_type':'install','interval':'year_day',})result_df=cohort_endpoint.get_dataframe()
Package: umaudemc
Unified Maude model-checking toolUniform interface for model checking LTL, CTL, CTL*, and μ-calculus properties
on standard andstrategy-controlledMaudespecifications using built-in and
external backends. Models can also be extended with quantitative information
and be applied probabilistic and statistical model-checking techniques.This tool can be used from the command line, from a graphical user interface,
and as a Python library. See therepositoryfor additional information,
documentation, and examples.
Package: umb
ContentsRequirementsInstallationUsageUMB information system’s private APIHomepage:https://github.com/fadhiilrachman/UMBPrivateAPIRequirementsThe UMB module only requires Python 3InstallationInstallation is simple. It can be installed from pip using the following
command:$ pip install umbOr from the terminal:$ python setup.py installUsage>>> from umb import *
>>> umb = UMB()
>>> login = umb.login('username', 'password')
>>> log(login.json)All examples can be foundhere.
Package: umbitlib
UmbitlibThis is a library containing commonly used functions and constants so that they can be centrally hosted, sourced, tested, and managedFunctions being added to the library shouldInclude a clear description of the function's purposeInclude parameter definitions for easy access in the tooltipInclude descriptive comments and formatting to aid in reading the codeInclude specific unit tests for the function in the corresponding tests fileFollow DRY (Don't Repeat Yourself) coding practices where possibleInclude input cleaning and/or explicit parameter-typing  to prevent end-users from entering incorrect valuesFor an example submission including tests see the 'add()' function in umbitlib/helpers.py and tests/test_helpers.py
Package: umbrage-evals
Umbrage Evals Python SDKOverviewThis is the Python SDK for Umbrage Evals.InstallationTo install the package, run the following command:pipinstallumbrage-evalsDependenciesPython 3.9+Example usage# TODO: Add example usageLICENSECopyright (c) 2023 Umbrage Studios, LLCPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to use the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: umbral-pre
Python bindings forumbral-preThis repo contains the Python bindings for themain Rust project.BuildYou will need to havesetuptools-rustinstalled. Then, for development you can just dopip install -e .as usual.Building Linux wheels must be done via Docker (makefile under construction).dockerpullquay.io/pypa/manylinux2014_x86_64
dockerrun--rm-v`pwd`/..:/ioquay.io/pypa/manylinux2014_x86_64/io/umbral-pre-python/build-wheels.shDevelopmentUpdateumbral-pre-python/stubtest-allowlist.txtwith:cdumbral-pre-python
python-mmypy.stubtestumbral_pre--generate-allowlist>stubtest-allowlist.txt
Package: umbrella-py
umbrella-py 🚧Pure Python implementation for the Umbrella project - a tool to instpect static
runtime information of Objective-C and Swift binaries. As of now, MachO and PE
binaries will be accepted by the API. There is also support for parsing Java
class files.Plase follow thedocumentationfor more details on how to use this library. Python 3.12 support is in preparation and will be published as soon as LIEF
uploads wheels for 3.12.InstallationTo install the Python package you can use pip:pipinstallumbrella-py# orpipinstallgit+https://github.com/MatrixEditor/umbrella-py.gitThe documentation can be build using Make and required dependencies defined indocs/requirements.txt:pipinstall-rdocs/requirements.txt&&cddocs&&makehtmlLicenseDistributed under the Apache 2.0 License. See LICENSE for more information.
Package: umbridge
UM-BridgeUM-Bridge (the UQ and Model Bridge) is a unified interface for numerical models that is accessible from virtually any programming language or framework. It is primarily intended for coupling advanced models (e.g. simulations of complex physical processes) to advanced statistical or optimization methods.Documentation including tutorials and UQ benchmark library can be found here:Documentation.Instructions for contacting the team, bug reports and contributions can be found inCONTRIBUTING.md.
Package: umb-zd-tools
zd_tools PackageThis package can be used to reduce the amount of copy/pasting required for scripts that make api calls to Zendesk.zd_requests includes PUT, GET, DEL and POST functions, as well as a Batch Request function that can be called with any of the HTTP functions using objects=[zd_id1, zd_id2,...], call_type="http_del" etc.It also includes a class that can facilitate Endpoint construction. EndPointBuilder.Domain.zd runs scripts on the prod instance of Zendesk. EndPointBuilder.Domain.sb runs on the Sandbox. Specific endpoints have been added as needed.
Package: umclient
# University of Michigan Requests ClientA wrapper around the Python [Requests](http://www.python-requests.org/en/latest/) library
that allows you to access authenticated University of Michigan resources.## InstallWith pip:pip install umclientIf you don’t have pip you can get it with:easy_install pip## QuickstartCreate a new requests client:from umclient import AuthenticatedUMClientclient = AuthenticatedUMClient(username, password)Request the authenticated resource:resp = client.get(’http://wolverineaccess.umich.edu’)
print resp # returns a requests response object
<Response [200]>The client automatically logged the user in and returned the
appropriate resource.## LicenseLicensed under the MIT LicenseTHE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
Package: umdriver
UM DriverAn extension of the selenium webdriver bindings for python with U-M weblogin baked in.UsageUse UMDriver as you would a normal Selenium webdriver. UMDriver provides the login method to help facilitate logging into authenticated resources. If you have two-factor authentication enabled, the login method will wait for you to authorize the login before proceeding to the next lines of code.withUMDriver()asdriver:driver.login(username='uniqname',password='secret')driver.get('https://url.for.protected.resource')If you are logging into any test environments and need to authenticate with the test identity provider, you can add an optionalenvparameter to your login call:driver.login(username,password,env='test')
Package: umdsubmit
umdsubmitThis package allows submitting to the UMD Submit Server from the command line, without the Eclipse Plugin.InstallationYou can install the package via pip:pipinstallumdsubmitUpdatingThe package can be upgraded via pip:pipinstallumdsubmit--upgradeUsageAfter installing navigate to the root of the project directory in the terminal and run the command umdsubmit. Enter your UMD Directory ID and password as requested. Ensure that you run the command from the directory containing the .submit file for the project.How It WorksInitialization:Theumdsubmitcommand is executed, initiating the submission process.Zip Archive Creation:Theshutil.make_archivefunction is used to create a zip archive of the current working directory.File Reading - .submit:Theget_infofunction is called to gather necessary submission data from the.submitfile in the current directory.CVS Account Retrieval:Theget_cvs_accountfunction is called to obtain the CVS account.If the.submitUserfile exists, it is read to get the account information.If the file doesn't exist, theauthfunction is triggered to authenticate the user, which creates a.submitUserfile storing information for later submissions.User Authentication (if needed):Theauthfunction prompts the user to enter their UMD Directory ID and password.A POST request is made to the server to negotiate a one-time password.The server's response is written to the.submitUserfile and printed to the console.Submission:A POST request is made to the submit URL with the zip file and the gathered data.The server's response is printed to the console.
Package: ume_application_model
ume_application_modelGenerate application score for a new customer.Installation$pipinstallume_application_modelUsageTODOContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.Licenseume_application_modelwas created by Wesllen Sousa Lima. It is licensed under the terms of the Proprietary license.Creditsume_application_modelwas created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: umemcache
ultramemcacheis an ultra fast Memcache client written in highly optimized
C++ with Python bindings.To install it just run Pip as usual:$ pip install umemcacheultramemcacheprovides aClientclass you can use to interact with
Memcache:>>> import umemcache
>>> c = umemcache.Client('127.0.0.1:11211')
>>> c.connect()
>>> c.set('key', 'value')
'STORED'
>>> c.get('key')[0]
'value'By design,ultramemcachelimits the size of Memcache items to 1000*1000
bytes, but you can change this limitation by using themax_item_sizeargument when creating aClientclass.
Package: umetrix
UNet segmentation metricsWORK IN PROGRESSSimple Python 3 tools to assess the performance of UNet segmentation networks
(or any other segmentation method) by comparing the prediction to a ground truth
image.Use it to calculate:Jaccard metric for object detectionIntersection over Union (IoU) for object segmentation accuracyLocalization (positional) error for estimating MOTP during trackingPixel identityTODO:Add strict matching with IoU thresholdAdd confusion matrix for multi-label/classification type tasksSingle image usageimportumetrixfromskimage.ioimportimready_true=imread('true.tif')y_pred=imread('pred.tif')# can now make the calculation strict, by only considering objects that have# an IoU above a theshold as being true positivesresult=umetrix.calculate(y_true,y_pred,strict=True,iou_threshold=0.5)print(result.results)returns:============================
 Segmentation Metrics (n=1)
============================
Strict: True (IoU > 0.5)
n_true_labels: 354
n_pred_labels: 362
n_true_positives: 345
n_false_positives: 10
n_false_negatives: 0
IoU: 0.999
Jaccard: 0.972
pixel_identity: 0.998
localization_error: 0.010Batch processingimportumetrix# provide a list of file pairs ('true', 'prediction')files=[('true0.tif','pred0.tif'),('true1.tif','pred1.tif'),('true2.tif','pred2.tif')]batch_result=umetrix.batch(files)Returns aggregate statistics over the batch. Jaccard index is calculated over
all found objects, while other metrics are the average IoU etc.InstallationFirst clone the repo:$gitclonehttps://github.com/quantumjot/unet_segmentation_metrics.git(Optional, but advised) Create a conda environment:$condacreate-numetrixpython=3.9
$condaactivateumetrixInstall the package$cdunet_segmentation_metrics
$pipinstall.
Package: umierrorcorrect
# umierrorcorrectPipeline for analyzing barcoded amplicon sequencing data with Unique molecular identifiers (UMI)ReferenceUMIErrorCorrect has been published in Clinical Chemistry.[Link to the Umierrorcorrect paper](https://doi.org/10.1093/clinchem/hvac136)Osterlund T., Filges S., Johansson G., Stahlberg A. UMIErrorCorrect and UMIAnalyzer: Software for Consensus Read Generation, Error Correction, and Visualization Using Unique Molecular Identifiers, Clinical Chemistry, 2022;, hvac136InstallationTo run Umierrorcorrect via Docker, see the [Docker documentation](doc/docker.md).To install the UMI-errorcorrect pipeline from source, open a terminal and type the following:` pip install umierrorcorrect `After installation, try to run the pipeline:` run_umierrorcorrect.py-h`DependenciesUmi-errorcorrect runs using Python 3 and requires the following programs/libraries to be installed (if you run through docker all dependencies are already handled):Python-libraries (should be installed automatically):pysam (v 0.8.4 or greater)External programs:bwa (bwa mem command is used)
Either of gzip or pigz (parallel gzip)Install the external programs and add them to the path.Since the umierrorcorrect pipeline is usingbwafor mapping of reads, a bwa-indexed reference genome is needed. Index the reference genome with the commandbwa index -a bwtsw reference.fa.UsageExample syntax for running the whole pipeline:run_umierrorcorrect.py -r1 read1.fastq.gz -r2 read2.fastq.gz -ul umi_length -sl spacer_length -r reference_fasta_file.fasta -o output_directoryTherun_umierrorcorrect.pypipeline performs the following steps:Preprocessing of fastq files (remove the UMI and spacer and puts the UMI in the header)Mapping of preprocessed fastq reads to the reference genomePerform UMI clustering, then error correcion of each UMI clusterCreate consensus reads (one representative read per UMI cluster written to a BAM file)Create a consensus output file (collapsed counts per position)Perform variant calling.It is also to possible to run the pipeline step-by-step.To see the options for each step, type the following:` preprocess.py-hrun_mapping.py-humi_error_correct.py-hget_consensus_statistics.py-hcall_variants.py-hfilter_bam.py-hfilter_cons.py-h`Tutorial
——–[Link to the Umierrorcorrect tutorial](https://github.com/stahlberggroup/umierrorcorrect/wiki/Tutorial)Example of UMI definition options[UMI definition options](https://github.com/stahlberggroup/umierrorcorrect/wiki/UMI-definition-options)
Package: uminifier
uminifierScript to convert Python files into .mpy (Micropython)Usageusage: compilation_test.py [-h] [-o OUTPUT] [-k] [-v] filename

Convert python files into .mpy (Micropython) files.

positional arguments:
  filename

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output path or output filename
  -k, --keep            Keep any intermediate file generated.
  -v, --verbose         Print verbose output.RequirementsModules needed installation:pipinstallpyminifier>=2.1mpy-cross>=1.12Running the testspytest
Package: umipy
UMIPYInteraction with umi blockchainInstallfor stable versionpip install umipyfor dev versionpip install https://github.com/RoyFractal/PyUmi/archive/master.zipUsageYou can find more examples -examples/simple getting a address dataimportasynciofromumipyimportUmiPyasyncdefmain():umi=UmiPy()balance=awaitumi.get_balance("umi17ymaed9h9hq7s5pc2f5fhmlzpmsk3qtc6g2cgm360zysz0uvq44qnxlsuz")print(balance)trans=awaitumi.get_transactions("umi17ymaed9h9hq7s5pc2f5fhmlzpmsk3qtc6g2cgm360zysz0uvq44qnxlsuz")print(trans)awaitumi.close()asyncio.run(main())