Saravanan666/pypi-org huggingface dataset:
Package: lapa-database
lapa_databaseaboutdatabase layer for my personal server.installationpip install lapa_database[all]usage (WIP)config.example.ini to config.ini.CREATE_SCHEMA = True to create database from scratch.LOG_FILE_NAME and configure loggerlink to lapa_database_structureconfigslapa_database\data\config.ini (can be created using lapa_database\data\config.example.ini)lapa_logger\data\config.inienvpython>=3.12.0changelogv0.0.8add module name in config.add first test case.add dependencies for testing.add GitHub workflow for testing.remove config from gitignore.v0.0.7use lapa_commons to read config.v0.0.6change default value of ignore_filters_and_get_all to False.v0.0.5make crud logic default to no rows when filters are empty.add new parameters to make it easy to select all rows for edit, delete and get.move logger to configuration.py to fix bug of multiple logs being created.v0.0.4rename to lapa database.fix bug in create_database that occurred in default data insertion.add logs to gitignore.change psycopg2 to psycopg2-binary in setup.py.v0.0.3created utils folder containing CommonOperations.py under which the common functions used across modules are stored.web_socket implemented for retrieving the data from the table when a new row is added/deleted/updated.it takes database_name, table_name and schema_name as input through query params.input for websocket/ws/<database_name>/<table_name>/<schema_name>E.g. /ws/game/player/publicinitially returns all the rows and if any update is made it returns the updated data.trigger creation is implemented once the websocket connection is made. it will first check if the trigger function
already exists or not and then only create.v0.0.2remove databases folder and enums related to tables and put in separate module for better version control.add proper error message display on errors in configuration.py.known bugs:creating engines everytime on fastapi route call is creating idle sessions.v0.0.1initial implementation.known bugs:creating engines everytime on fastapi route call is creating idle sessions.Feedback is appreciated. Thank you!
Package: lapa-database-helper
lapa_database_helperabouthelper to access the database layer for my personal server.installationpip install lapa_database_helperusagereference python fileconfigslapa_database_helper\data\config.inienvpython>=3.12.0changelogv0.0.5Configuration is now being read with the help of lapa_commons.v0.0.4change default value of ignore_filters_and_get_all to False.v0.0.3add extra parameters for ignore_filter_and_x_all.v0.0.2rename to lapa.v0.0.1initial implementation.Feedback is appreciated. Thank you!
Package: lapa-database-structure
lapa_database_structureaboutdatabase structure layer for my personal server.installationpip install lapa_database_structureusageto add a new databasecreate a package with package name as database name.to add a new schemaadd package in database_name package with schema name as package name.to add a new tablecreate /database_name/schema_name/tables.py file if not already created.create class corresponding to your new table add in /database_name/schema_name/tables.py file.to add default data in tableappend row objects containing your default data to the data_to_insert list inside the
/database_name/schema_name/tables.py file.do not forget to add new database_names, schema_names and/or table_names to main.py enums to make it accessible
through api calls.configsNoneenvpython>=3.12.0changelogv0.0.5file_storage -> public -> Filechange file_extension to file_content_type.file_system_relative_path default change from "." to "".v.0.0.4authentication database added with the below tablesuseruser_validation_statususer_registrationhashing_algorithmv0.0.3Changed package name to lapa_database_structure.v0.0.2Update table for file_storage -> File.v0.0.1initial implementation.Feedback is appreciated. Thank you!
Package: lapa-file-store
lapa_file_storeaboutfile storage layer for my personal server.installationpip install lapa_file_storeconfigslapa_file_store\data\config.inisquare_logger\data\config.inienvpython>=3.12.0changelogv0.0.5use lapa_commons to read config.v0.0.4Bug Fix in /download_file because of ignore_filters_and_get_all.v0.0.3MODULE_NAME added in the GENERAL configuration./ root endpoint added.Form data and description added for /upload_file endpoint for the below params.file_purposesystem_relative_pathValidation added while creating the directory in /upload_file endpoint./download_file endpoint changed from POST to GET.v0.0.2change default port to 10100.change default LOCAL_STORAGE_PATH to be relative and LOCAL_STORAGE_PATH.move logger to configuration.py.fix dependencies in setup.py.minor optimisations.add validation for LOCAL_STORAGE_PATH in config.ini.fix logic for upload_file.fix logic for download_file.v0.0.1Base version
Package: lapa-file-store-helper
lapa_file_store_helperabouthelper to access the file store layer for my personal server.installationpip install lapa_file_store_helperusagereference python fileconfigslapa_file_store_helper\data\config.inienvpython>=3.12.0changelogv0.0.3Status code check and error raise.v0.0.2Configuration is now being read with the help of lapa_commons.v0.0.1initial implementation.Feedback is appreciated. Thank you!
Package: lapart-python
# lapart-python
See documentation [here](http://lapart-python.readthedocs.io/en/latest)## Installation
TODO: Describe the installation process## Usage
TODO: Write usage instructions## Contributing
1. Fork it!
2. Create your feature branch:git checkout -b my-new-feature3. Commit your changes:git commit -am ‘Add some feature’4. Push to the branch:git push origin my-new-feature5. Submit a pull request :D## History
TODO: Write history## Credits
TODO: Write credits## License
Revised BSD.  Click [here](http://lapart-python.readthedocs.io/en/latest/license.html)
Package: lapels
Lapels remaps reads aligned to the in silico genome back to the reference
coordinate and annotates variants.Two files are taken as input:a MOD file that is used to generate the in silico genome anda BAM file that contains the in silico genome alignments.Lapels will generate a new BAM file with corrected read positions, adjusted
cigar strings, and annotated tags of variants (eg. SNPs, Insertions, and
Deletions).
Package: lapgm
lapgmlapgmis an image correction software package primarily used for MR debiasing and normalization. This package is generalized to work with any number of spatial dimensions and assumes smooth, multiplicative image corruption which is invariant through image channels. Derivations and results for the spatially regularized Gaussian mixture can be found atLapGM: A Multisequence MR Bias Correction and Normalization Model.InstallationPackagelapgmcan be installed using pip:pip install lapgmA CUDA accelerated version oflapgmis also available:pip install lapgm[gpu]ExamplesAn example overview on how to debias and normalize withlapgmis provided 'image_correction.ipynb' in the 'examples' subdirectory. For data, three biased presets from theBrainWebnormal dataset were used. Some of the debiased and normalized results have been provided below.T2 debiasing results:T1 normalization results:UsageBelow we list some common commands and procedures to help with the debiasing and normalization of MR images.lapgm's GPU compute can be turned on and off globally with the 'use_gpu' command. Returned values will be loaded off of the GPU.importlapgm# Set compute to GPUlapgm.use_gpu(True)# Set compute back to CPUlapgm.use_gpu(False)Before running for debiasing, LapGM's hyperparameters must be specified.# takes in optional downscale_factor and other saving meta-settingsdebias_obj=lapgm.LapGM()# required: inverse penalty strength 'tau' and number of class 'n_classes'debias_obj.set_hyperparameters(tau=tau,n_classes=n_classes)The cylindrical weighting scheme used byVinas et al.is provided as:# required: penalty relaxation alpha which goes as r^(-alpha)# optional: center, semi-major axes, and symmetries of the cylindrical ellipsedebias_obj.specify_cylindrical_decay(alpha=alpha)Debiasing can be run as:# before running, disambiguate channeled data from spatial data with 'to_sequence array'im_arr=lapgm.to_sequence_array([im_seq1,im_seq2])# retrieve estimated parametersparams=debias_obj.estimate_parameters(im_arr)# get debiased resultdb_arr=lapgm.debias(im_arr,params)Normalization can be run using the same debiased sequence data and estimated parameters from above:# specify a target intensity to achieveTRGT=1000.# normalize debiased array from before using estimated parameters and a target intensitynorm_arr=lapgm.normalize(db_arr,params,TRGT)ReferencesL. Vinas, A. A. Amini, J. Fischer, and A. Sudhyadhom. LapGM: A Multisequence MR Bias Correction
and Normalization Model. arXiv.org:2209.13619 [physics.med-ph], Sept. 2022.C.A. Cocosco, V. Kollokian, R.K.-S. Kwan, A.C. Evans. BrainWeb: Online Interface to a 3D MRI Simulated Brain Database. In: NeuroImage 5 (1997), p. 425.
Package: lapidary-render
Code generatorInstallationlapidary-render requires python 3.9 or higher to run.I recommend installing viapipxpipx install lapidary-renderYou can set python version for lapidary withpipx install --python [path-to-python] lapidary-render. Seepipx install --helpfor details.Usagelapidarycommand offers inline help and shell command completion. Seelapidary --helpfor details.lapidary initlapidary init [--[no-]format-strict] [--[no-]render] SCHEMA_PATH PROJECT_ROOT PACKAGE_NAMELapidary will createPROJECT_ROOT and all necessary directories,__init__.py files,pyproject.toml withpoetryconfigured,py.typedclient.pyi with function stubs for all operations and a client.py with an empty client class.Pydanticmodel classes for each schema.All python files are generated in PROJECT_ROOT/gen directory.If a directory PROJECT_ROOT/src/patches exists, Lapidary will read all yaml files and apply them as JSONPatch against the original openapi file.If the original openapi file is not compatible with Lapidary, runninglapidary init --no-render ...will generate only the project structure without any
models or stubs. Once you've prepared the patch, runlapidary update.lapidary updatelapidary update [--[no-]format-strict] [--[no-]cache] [PROJECT_ROOT]Default PROJECT_ROOT is the current directory.The commanddeletes PROJECT_ROOT/gen directory,re-applies patches to openapi fileand generates python fileslapidary versionlapidary versionPrints the programs version and exits.ConfigurationLapidary can be configured with a pyproject.yaml file, under [tool.lapidary] path.Onlypackagevalue is required, and it's set bylapidary init.package [str] - root package nameformat [bool] - whether to format the generated code with black [default = True].cache [bool] - whether to cache openapi and patches as pickle files. Only files larger than 50kB are cached [default = True].src_root [str] - sources root, in PROJECT_ROOT [default = 'src'].gen_root [str] = generated sources root, in PROJECT_ROOT [default = 'gen'].patches [str] = patches directory, under sources root [default = 'patches'].
Package: lapin-conf
Lapin: Simple Configurations with Zero Redundancylapinis a lightweight package that allows you to configure your classes with minimum
boilerplate. Configurations are easy to understand, quick to write, and intuitive to work with.RequirementsPython 3.6+InstallationThe recommended installation method is pip-installing into a virtual environment:$python-mpipinstalllapin-conflapinby ExampleimportlapinTBCwe will soon provide concrete examples!LicenseThis project is licensed under the terms of theApache License 2.0license.Citing LapinIf you use Lapin in your research please use the following BibTeX entry:@Misc{Eloff2022Lapin,author={Ryan Eloff},title={Lapin - Simple Configurations with Zero Redundancy},howpublished={Github},year={2022},url={https://github.com/rpeloff/lapin}}
Package: lapis-gen
Lapis - Python OpenAPI client generatorGenerate OpenAPI 3.0.3 client code that is easy to understand and debug.LeveragesPydanticas the base classes
andhttpxas the HTTP client.Usagelapis [--errata path] spec_file target_dir root_packageLapis will create target_dir, all necessary directories and pyproject.toml if they don't exist.Generated files will be placed in <target_dir>/gen/<root_package>Errata is a JSON Patch file in JSON or YAML format; it's applied to the spec before any other processing.Supported OpenAPI featuresParameter names: operation parameters are uniquely identified by their name the value of aninattribute. It is possible to have parameter namedparamin all of path, query, cookies and headers.Lapis uses Hungarian notation for method parameter names.Enums: [TODO] there's no limitation that enum schema cannot be an object or an array.Enums might need two python classes - a subclass ofenum.Enumand the schema class.oneOf: maps to typing.UnionAllOf: [TODO] maps to a separate class that uses all the schemas as superclasses.AnyOf: [TODO] maps to similar class as in case of AllOf, all fields should be non-required and the object should validate against at least one of superclasses.Recursive references between schemas: supported.References to other schemas: unsupported.Read- and write-only attributes: [TODO] Read-only attributes are considered non-existent when the object is validated before being sent to the server.Broken and incomplete API specificationserrata: use errata to update the specification document in cases where actual data doesn't match it, and the service provider is reluctant or slow to update it.TODO: pre- and postprocessing of API specification (dict- and pydantic-based models) with python code, errata (jsonpatch), etcBackwards compatibilityOnce stable, Lapis should generate code that is backwards compatible as long as the API specification is too. The following rules are used to ensure that.Names of interface elements (public functions, classes and methods) are fully deterministic and are derived only from their source elements and, in some cases, their parents.
For example, schemas of the same name are either placed in separate modules, or their names have the parent element name prepended.Each operation must have operationId which is used to create a unique package for its models.The structure of the API specification is roughly reflected in the generated code.
Each schema under #/components/schemas together with all inline parameter schemas will become a single module in<your_package>.components.schemas.
Each operation must defineoperationIdwhich will be used as a sub-package name for the module containing its all inline schemas. Operation packages will be placed under<your_package>.paths
Package: lapis.member
Content type and workflow for PloneFeaturesForum Page is added, it is hidden for everyone but owner and adminsForum page is ‘published’, it is visible for owner, admins and ALL MEMBERSComments can be enabled as usualThe content type does not show by default in menus (add it in the control panel, navigation settings if you want thisContent item should be hidden from ‘folder views’ for ‘not members’, I have not tested this test, but should work: (The permissions should be ‘access content information’, if I remember rightInstallationInstall lapis.member by adding it to your buildout:[buildout]

...

eggs =
    lapis.memberand then runningbin/buildoutContributeIssue Tracker:https://github.com/collective/lapis.member/issuesSource Code:https://github.com/collective/lapis.memberDocumentation:https://docs.plone.org/foo/barSupportIf you are having issues, please let us know.
We have a mailing list located at:project@example.comLicenseThe project is licensed under the GPLv2.ContributorsEspen Moe-Nilssen,espen@medialog.noChangelog1.0a1 (unreleased)Initial release.
[espenmn]
Package: lapis-mimeparse
This module provides basic functions for parsing mime-type names and matching them against a list of media-ranges.See section 14.1 of RFC 2616 (the HTTP specification) for a complete explanation.TestingThe format of the JSON test data file is as follows:
A top-level JSON object which has a key for each of the functions to be tested. The value corresponding to that key is a list of tests. Each test contains: the argument or arguments to the function being tested, the expected results and an optional description.PythonThe Python tests require either Python 2.6 or the installation of the SimpleJSON library.Installing SimpleJson can be done by:
sudo easy_install simplejsonRun the tests by typing:
PYTHONPATH=. python tests/mimeparse_test.py
Package: lapis-sim
Thelapislibrary provides a framework and runtime for simulating the scheduling and usage of opportunistic
and static resources.Command Line InterfaceCurrently the library provides a simple command line interface that allows three modes of operation:static provisioning of resources,dynamic provisioning of resources, andhybrid provisioning of resources.In the most simple case you can apply a given workload, e.g. downloaded from the parallel workload archive to a
static resource configuration:python3simulate.py--log-file-static--job-file<path-to-workload>swf--pool-file<path-to-pool-definition>htcondorThe output of simulation is given to stdout. You have further options you can explore viapython3simulate.py--helpand more specifically for the different operation modes withpython3simulate.pystatic--help
Package: lapjv
Linear Assignment Problem solver using Jonker-Volgenant algorithmThis project is the rewrite ofpyLAPJVwhich
supports Python 3 and updates the core code. The performance is twice as high as
the original thanks to the optimization of the augmenting row reduction phase
using Intel AVX2 intrinsics. It is a native Python 3 module and does
not work with Python 2.x, stick to pyLAPJV otherwise.Linear assignment problemis the bijection between two sets with equal cardinality which optimizes the sum
of the individual mapping costs taken from the fixed cost matrix. It naturally
arises e.g. when we want to fitt-SNEresults
into a rectangular regular grid.
See this awesome notebook for the details about why LAP matters:CloudToGrid.Jonker-Volgenant algorithm is described in the paper:R. Jonker and A. Volgenant, "A Shortest Augmenting Path Algorithm for Dense and Sparse Linear Assignment Problems,"Computing, vol. 38, pp. 325-340, 1987.This paper is not publicly available though a brief description exists onsciencedirect.com.
JV is faster in than theHungarian algorithmin practice,
though the complexity is the same - O(n3).The C++ source of the algorithm comes fromhttp://www.magiclogic.com/assignment.htmlIt has been reworked and partially optimized with OpenMP 4.0 SIMD.Installingpip3 install lapjvTested on Linux and Windows.macOS is not supported, please do not report macOS build errors in the issues.Feel free to PR macOS support, but I warn that it will be a rough ride.UsageRefer totest.pyfor the complete code.from lapjv import lapjv
row_ind, col_ind, _ = lapjv(cost_matrix)The assignment matrix by row isrow_ind: the value at n-th place is the assigned column index to the n-th row.col_indis the reverse ofrow_ind: mapping from columns to row indexes.Note: a bijection is only possible for sets with equal cardinality. If you need to map A vectors to B vectors,
derive the square symmetric (A+B) x (A+B) matrix: take the first A rows and columns from A and
the remaining [A..A+B] rows and columns from B. Set the A->A and B->B costs to some maximum distance value,
big enough so that you don't see assignment errors.Illegal instructionThis error appears if your CPU does not support the AVX2 instruction set. We do not ship builds for different CPUs so you need to build the package yourself:pip3 install git+https://github.com/src-d/lapjvNAN-sNAN-s in the cost matrix lead to completely undefined result. It is the caller's responsibility to check them.LicenseMIT Licensed,seeLICENSE
Package: laplaciannb
LaplacianNBCI/CDPackageMetaLaplacianNBis a Python module developed atNovartis AGfor Naive Bayes classifier for laplacian modified models based on scikit-learn Naive Bayes implementation.This classifier is suitable for binary/boolean data as it uses for prediction only indices of the positive bits. The algorithm was first implemented in Pipeline Pilot and KNIME.Literature:Nidhi; Glick, M.; Davies, J. W.; Jenkins, J. L. Prediction of biological targets
for compounds using multiple-category Bayesian models trained on chemogenomics
databases. J. Chem. Inf. Model. 2006, 46, 1124– 1133,
https://doi.org/10.1021/ci060003g

Lam PY, Kutchukian P, Anand R, et al. Cyp1 inhibition prevents doxorubicin-induced cardiomyopathy
in a zebrafish heart-failure model. Chem Bio Chem. 2020:cbic.201900741.
https://doi.org/10.1002/cbic.201900741=======AuthorsHuge thanks toFlorian Nigsch(florian.nigsch@novartis.com) for the first implementation of the algorithm in python andPeter Kutchukian(peter.kutchukian@novartis.net) for scientific guidance and validation.Author and maintainer:Bartosz Baranowski(bartosz.baranowski@novartis.com)InstallationDependencies:- Python       (>= 3.8)
- pandas       (>=1.4.2)
- numpy        (>=1.22.4)
- scikit-learn (>=1.1.1)
- scipy        (>=1.8.1)=======User installation:pip install laplaciannbChangelogv0.5.0- Initial release
Package: laporte
Laportea gateway for processing metrics with automation and statesacceptor for sensors, batch jobs or messageswith the ability to set actuatorscompatible with Prometheus for metric alerting, collecting and data visualizationSchema:Features:can evaluate, recalculate or correct metriccalculate new metric or state from other metricsautomation using a true algorithmization + data structures (python expression code)can set TTL for metrics (obsolete metric disappears when time is over)communicates via:RESTful APIrealtime, bidirectionally using Socket.IOexporting data as Prometheus metricslive www status pageoptional bridges (extensions):laporte-mqttto connect a large family of devices using MQTT protocollaporte-journalto store all events into other databaseInstallation:To install Laporte from PyPI:pip install laporteOr using container image from Docker Hub:docker pull vinklat/laporteQuick HOWTO:Example: push gateway for weather sensormanually simulate a weather sensor and send temperature and relative humidity data to this gateway using REST APIin this example the temperature will be corrected (slope +1% and shift + 0.5). Can be used in case of calibrate sensor, unit conversion, ...other two new metrics will be calculated (dew point temperature and absolute humidity)in the case of an inactive sensor, the data expires in 10 minutesa) run the docker imagedocker run -p 9128:9128 vinklat/laporte -c conf/example_weatherstation.yml(the content of this built-in config file can be seen here:example_weatherstation.yml)b) send metrics from sensor via REST APITry sending data several times using curl:curl http://localhost:9128/api/metrics/weather1 -d temp_celsius=37.5 -d hum_ratio=0.8 -X PUTc) watch statusLaporte status page:http://localhost:9128JSON response of REST API:http://localhost:9128/api/metrics/by_nodePrometheus metrics:http://localhost:9128/prom...more info on thewiki
Package: laporte-mqtt
laporte-mqttbidirectional MQTT connector forLaporte.it can take, convert and push metrics from MQTT sensorsimmediately publish changes (like actuators, relays) to MQTTSchema:Features:can subscribe/publish multiple topicsJSON and also single values in payloads are supportedtested withzigbee2mqtt,Tasmota, RFLink,nibe-mqttand othersInstallation:To install from PyPI:pip install laporte-mqttOr using container image from Docker Hub:docker pull vinklat/laporte-mqttQuick HOWTO:please wait a bit longer, examples are in development...
Package: lapostesdk
LaPosteSDK is a Python client library to work withLa Poste APIInstallationpip install lapostesdkRequirementsrequestsDev requirementsresponsesConfigurationfromlapostesdk.laposteapiimportLaPosteApiapi=LaPosteApi(api_key='YourApiKey')Test suitepython -m unittest testApisControlAdresseTheControlAdresse ApiAddress look upresponse=api.controladresse.search('116 avenue du Président Kennedy 75220 Paris Cedex 16')Address detailsadresse = api.controladresse.get(‘adresses/12280852’)
print adresse.codePostalSuiviTheSuivi Apisuivi=api.suivi.get('1111111111111')printsuivi.status