Saravanan666/pypi-org huggingface dataset:
Package: adaptix
An extremely flexible and configurable data model conversion library.[!IMPORTANT]
Adaptix is ready for production!
The beta version only means there may be some backward incompatible changes, so you need to pin a specific version.📚DocumentationTL;DRInstallpipinstalladaptix==3.0.0b2Use for model loading and dumping.fromdataclassesimportdataclassfromadaptiximportRetort@dataclassclassBook:title:strprice:intauthor:str="Unknown author"data={"title":"Fahrenheit 451","price":100,}# Retort is meant to be global constant or just one-time createdretort=Retort()book=retort.load(data,Book)assertbook==Book(title="Fahrenheit 451",price=100)assertretort.dump(book)==dataUse for converting one model to another.fromdataclassesimportdataclassfromsqlalchemy.ormimportDeclarativeBase,Mapped,mapped_columnfromadaptix.conversionimportget_converterclassBase(DeclarativeBase):passclassBook(Base):__tablename__='books'id:Mapped[int]=mapped_column(primary_key=True)title:Mapped[str]price:Mapped[int]@dataclassclassBookDTO:id:inttitle:strprice:intconvert_book_to_dto=get_converter(Book,BookDTO)assert(convert_book_to_dto(Book(id=183,title="Fahrenheit 451",price=100))==BookDTO(id=183,title="Fahrenheit 451",price=100))Use casesValidation and transformation of received data for your API.Conversion between data models and DTOs.Config loading/dumping via codec that produces/takes dict.Storing JSON in a database and representing it as a model inside the application code.Creating API clients that convert a model to JSON sending to the server.Persisting entities at cache storage.Implementing fast and primitive ORM.AdvantagesSane defaults for JSON processing, no configuration is needed for simple cases.Separated model definition and rules of conversion
that allow preservingSRPand have different representations for one model.Speed. It is one of the fastest data parsing and serialization libraries.There is no forced model representation, adaptix can adjust to your needs.Supportdozensof types,
including different model kinds:@dataclass,TypedDict,NamedTuple,attrsandsqlalchemyWorking with self-referenced data types (such as linked lists or trees).Savingpathwhere an exception is raised (including unexpected errors).Machine-readableerrorsthat could be dumped.Support for user-defined generic models.Automatic name style conversion (e.g.snake_casetocamelCase).Predicate systemthat allows to concisely and precisely override some behavior.Disabling additional checks to speed up data loading from trusted sources.No auto casting by default. The loader does not try to guess value from plenty of input formats.
Package: adaptkeybert
AdaptKeyBERTKeyBERT is a minimal and easy-to-use keyword extraction technique that leverages BERT embeddings to create keywords and keyphrases that are most similar to a document.AdaptKeyBERT expands the aforementioned library by integrating semi-supervised attention for creating a few-shot domain adaptation technique for keyphrase extraction. Also extended the work by allowing zero-shot word seeding, allowing better performance on topic relevant documentsBasic Use:Take a look atrunner.pyfromadaptkeybertimportKeyBERTdoc="""Supervised learning is the machine learning task of learning a function thatmaps an input to an output based on example input-output pairs. It infers afunction from labeled training data consisting of a set of training examples.In supervised learning, each example is a pair consisting of an input object(typically a vector) and a desired output value (also called the supervisory signal).A supervised learning algorithm analyzes the training data and produces an inferred function,which can be used for mapping new examples. An optimal scenario will allow for thealgorithm to correctly determine the class labels for unseen instances. This requiresthe learning algorithm to generalize from the training data to unseen situations in a'reasonable' way (see inductive bias). But then what about supervision and unsupervision, what happens to unsupervised learning."""kw_model=KeyBERT()keywords=kw_model.extract_keywords(doc,top_n=10)print(keywords)kw_model=KeyBERT(domain_adapt=True)kw_model.pre_train([doc],[['supervised','unsupervised']],lr=1e-3)keywords=kw_model.extract_keywords(doc,top_n=10)print(keywords)kw_model=KeyBERT(zero_adapt=True)kw_model.zeroshot_pre_train(['supervised','unsupervised'],adaptive_thr=0.15)keywords=kw_model.extract_keywords(doc,top_n=10)print(keywords)kw_model=KeyBERT(domain_adapt=True,zero_adapt=True)kw_model.pre_train([doc],[['supervised','unsupervised']],lr=1e-3)kw_model.zeroshot_pre_train(['supervised','unsupervised'],adaptive_thr=0.15)keywords=kw_model.extract_keywords(doc,top_n=10)print(keywords)
Package: adaptmesh
adaptmeshCreate triangular meshes by the adaptive process.The user feeds in a polygon and a low quality mesh is created.  Then the low
quality mesh gets improved by adaptive finite elements and mesh smoothing.  The
approach is detailedin the following paper:@article{adaptmesh,
    title={A simple technique for unstructured mesh generation via adaptive finite elements},
    author={Gustafsson, Tom},
    volume={54},
    doi={10.23998/rm.99648},
    number={2},
    journal={Rakenteiden Mekaniikka},
    year={2021},
    pages={69--79}
}adaptmeshships with customized versions of the following packages:tri v0.3.1.dev0(ported to Python 3; Copyright (c) 2015 Martijn Meijers; MIT;source)optimesh v0.6.3(trimmed down version with minor changes to the edge
flipping; Copyright (c) 2018-2020 Nico Schlömer; the last version with MIT;source)meshplex v0.12.3(trimmed down version with minor changes, i.e. removal of
unnecessary imports; Copyright (c) 2017-2020 Nico Schlömer; the last version with MIT;source)Installationpip install adaptmeshDependenciesnumpyscipymatplotlibscikit-femExamplesThe mesh generator is called through the functionadaptmesh.triangulate.Square with default settingsfromadaptmeshimporttriangulatem=triangulate([(0.,0.),(1.,0.),(1.,1.),(0.,1.),])# m.p are the points# m.t are the elementsNon-convex shapefromadaptmeshimporttriangulatem=triangulate([(0.0,0.0),(1.1,0.0),(1.2,0.5),(0.7,0.6),(2.0,1.0),(1.0,2.0),(0.5,1.5),],quality=0.95)# default: 0.9Holesm=triangulate([(0.,0.),(1.,0.),(1.,1.),(0.,1.),],holes=[[(.25,.25),(.75,.25),(.75,.75),(.25,.75)]])Subdomainsm1=triangulate([(0.,0.),(1.,0.),(.7,1.),(0.,1.),],split=[(1,8),(2,6)],quality=0.91)m2=triangulate([(0.,2.),(2.,2.),(2.,0.),(1.,0.),(.7,1.),(0.,1.)],split=[(3,8),(4,6)],quality=0.91)m=m1+m2Multiple meshes can be joined to emulate subdomains.  However, the nodes
must match.  Above, segments are splitted to facilitate the matching, e.g.,[(1, 8), (2, 6)]means that the second and the third segments are split
using eight and six equispaced extra nodes, respectively.LicensingThe main source code ofadaptmeshis distributed under the MIT License.The licenses of the included packages can be found also inLICENSE.mdand the
respective subdirectories, i.e../adaptmesh/*/LICENSE. SeeLICENSE.mdfor
more information.ChangelogUnreleased[0.3.3] - 2022-02-04Fixed: Properly respect segments in the initial triangulation.[0.3.2] - 2021-09-28Fixed: Rendering of README in pypi.[0.3.1] - 2021-09-28Fixed: Support forscikit-fem>=4.[0.3.0] - 2021-06-22Fixed: Support forscikit-fem>=3. Dependency update broke the mesh refinement.[0.2.0] - 2021-01-20Added: keyword argumentsplitoftriangulateallows further splitting
the provided segments.  This is useful because the segment endpoints are
always preserved in the final mesh.Added: keyword argumentholesoftriangulateallows specifying additional
polygonal areas inside the domain that will be free of triangles in the final
mesh.
Package: adaptpath
# Description
convinent script to adapt python’s sys.path# Installpip install adaptpath# UsageSay we have dir-tree below:a / b / c.py
x / y / z.pyNow suppose we are in “z.py” and we want to do this:from a.b import cWe can put the lines below ahead of “z.py”from adaptpath import adaptpath
adaptpath.adapt_from_path(2, __file__)# Testpytest test
Package: adapt.py
adapt.pyOfficial wrapper around Adapt's API for Python.Installationpipinstalladapt.pyFrom GitHub:pipinstallgit+https://github.com/AdaptChat/adapt.pyUsageimportadaptclassClient(adapt.Client):"""My example client"""@adapt.once# This event will only be called onceasyncdefon_ready(self,ready:adapt.ReadyEvent)->None:print(f"Logged in as{ready.user}!")asyncdefon_message(self,message:adapt.Message)->None:ifmessage.content=="!ping":awaitmessage.channel.send("Pong!")if__name__=="__main__":client=Client()client.run("token")Using adapt.py with a custom Adapt instanceAdapt.py defaults to use the official Adapt instance athttps://adapt.chat. If you want to use a custom instance,
pass anAdaptServerinstance to theserverkwarg when constructing the client.AdaptServer.local()can be used as a shortcut to create a server instance for a local instance of Adapt:fromadaptimportAdaptServer,Clientclient=Client(server=AdaptServer.local())# Use a local instance of Adapt...Or, you can manually pass in URLs:fromadaptimportAdaptServer,Clientserver=AdaptServer(api="https://my-adapt-instance.com/api",harmony="https://my-adapt-instance.com/harmony",convey="https://my-adapt-instance.com/convey",)client=Client(server=server)...
Package: adaptwms
adaptwmsLightweight OpenStreetMap adapter for WMS servicesWhat it is?Basically it is adapter translating requests in so calledSlippy Map format(used by
OpenStreetMap, takes zoom level and XY tile indexes) into requests to external
WMS service (takes bounding box in form of two points in some coordinate
system of area to render).To give an example , let's say you need to supply to external map viewer an URL in format:https://tile.openstreetmap.org/{z}/{x}/{y}.pngAll x,y and z are natural numbers. But the service you want to access gives you interface that requires this format:https://external.service/wms?BBOX=0.0%2C0.0%2C1.0%2C1.0&SRS=EPSG%3A3857&WIDTH=256&HEIGHT=256&SERVICE=WMSHowever in this case BBOX contains set of 4 floating point numbers for 2 (x0, y0) and (x1, y1) points limiting the area to render. This is a kind of problems this program is trying to solve.What is supported?These are the known constraints:WMS service you use must allow unauthenticated access (there is no way for passing credentials in any form at the moment)it must supportEPSG:3857coordinate systemHow to use it......if I don't have experience with DjangoThis makes use of this Github project and demo project it supplies. Best for testing, or temporary deployement.Make sure you have Python and pip installedClone repo:git clone git@github.com:v3l0c1r4pt0r/adaptwms.gitand enter it:cd adaptwmsInstall all requirements:pip install -r requirements.txtRun development server with:./manage.py runserverOpenhttp://127.0.0.1:8000/in your browserTODO: write...if I do have some experience with DjangoThis makes use of pip package available on PyPI and Django project that you have, or want to set up. Best for production environments.Make sure you have Python and pip installedStart new django project:django-admin startproject demo ., where demo is its name, or use existing oneInstall adaptwms from PyPI:pip install adaptwmsAddadaptwmsto yourINSTALLED_APPSin settings.pyAddpath("adaptwms/", adaptwms.views.adapter_view),to your urlpatterns in urls.py (same could be done foradaptwms.views.InterfaceView.as_view()if you want to have interactive generator for adaptwms URLs)Run development server with:./manage.py runserverOpenhttp://127.0.0.1:8000/in your browser
Package: adapya
What is adapya?---------------With adapya you can access Adabas databases from Python programs usingthe Adabas API.It comes with sample programs to show its use and demonstrates newAdabas features like reading and storing of large binary objects usingthe extended Adabas API (ACBX).Adabas is a commercial database system that runs on Windows,Unix and mainframe systems. For more information seehttp://en.wikipedia.org/wiki/Adabas andhttp://www.softwareag.com/corporate/products/adabas/default.aspIn the download area you may also find an Adabas community version forWindows.Here is the Adabas product documentationhttp://documentation.softwareag.com/adabas/Python is a scripting language that allows rapid prototyping,object-oriented or functional style programming. It is opensource and is used in a large number of projects.Note: adapya does not implement a SQL interface as defined with thePython DBAPI. You may do Adabas DBAPI access with the productADABAS SQL Gateway via the ODBC interface.adapya requires a good knowledge of the Adabas API.adapya is a pure Python package: it does not require compilation ofextensions. It should work on all platforms where CPython and Adabas are available.It has been tested on Windows, Solaris and z/Linux.It can access local Adabas databases and with the product NET-WORKremote Adabas databases on all platforms including mainframez/OS, VSE, BS2000).Prerequisite for adapya is Python version 2.5, or Python version 2.3 and2.4 where it requires the extra ctypes package.adapya can be downloaded fromhttp://tech.forums.softwareag.com/viewforum.php?f=171&C=11adapya license--------------Licensed under the Apache License, Version 2.0 (the "License");you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, softwaredistributed under the License is distributed on an "AS IS" BASIS,WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions andlimitations under the License.
Package: adapya-adabas
adapya-adabas implements the Adabas database API for Python.It can access local and remote Adabas databases.
adapya-adabas comes with scripts and sample programs to show its features.
It is being used on Linux, mainframe z/OS, Solaris and Windows.Prerequisites for adapya-adabas are Python version 2.7, 3.5 or higher
and the adapya-base package.Installationpip install adapya-adabasLinksDetails of adapya-adabas:https://softwareag.github.io/adapya-adabas/index.htmlAdabas at Software AG:https://www.softwareag.comAdabas forum:http://tech.forums.softwareag.com/techjforum/forums/show/171.pageAdabas documentation:http://techcommunity.softwareag.comLicenseCopyright 2004-ThisYear Software AGLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions and
limitations under the License.These tools are provided as-is and without warranty or support.
They do not constitute part of the Software AG product suite.
Users are free to use, fork and modify them, subject to the license agreement.
While Software AG welcomes contributions, we cannot guarantee to include every
contribution in the master project.
Package: adapya-base
adapya-base is a Python package that provides the foundations for
the other adapya packages, especially for adapya-adabas,
the Adabas API for Python.adapya-base comes with scripts and sample programs to show its features.
It is being used on Linux, z/OS mainframe, Solaris and Windows.Prerequisite for adapya-base is Python version 2.7 or 3.5 and above.Installationpip install adapya-baseLinksDetails of adapya-base:https://softwareag.github.io/adapya-base/index.htmlAdabas at Software AG:https://www.softwareag.comAdabas forum:http://tech.forums.softwareag.com/techjforum/forums/show/171.pageAdabas documentation:http://techcommunity.softwareag.comLicenseCopyright 2004-ThisYear Software AGLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions and
limitations under the License.These tools are provided as-is and without warranty or support.
They do not constitute part of the Software AG product suite.
Users are free to use, fork and modify them, subject to the license agreement.
While Software AG welcomes contributions, we cannot guarantee to include every
contribution in the master project.
Package: adapya-entirex
adapya-entirexis a services library for persistent messaging using the Advanced Communication Interface (ACI) with the
EntireX Broker from Software AG.EntireX is a component in the webMethods high-performance communication infrastructure.adapya-entirex is part of a set of Python packages including adapya-adabas
a client library for Adabas database access.adapya-entirexis apurePython package: it does not require compilation
of extensions.It has been used on Linux, Solaris and Windows.Prerequisite for adapya is Python version 2.7 or 3.5 and above.InstallationInstall and update with the Python package manager:pip install -U adapya-entirexLinksDetails of adapya-entirex:https://softwareag.github.io/adapya-entirex/index.htmladapya in the Adabas forum:http://tech.forums.softwareag.com/techjforum/forums/show/171.pageAbout webMethods EntireX:https://resources.softwareag.com/application-modernization/webmethods-entirexwebMethods EntireX documentation:http://techcommunity.softwareag.com/ecosystem/documentation/webmethods/entirex/entirex_vers.htmLicenseCopyright 2004-ThisYear Software AGLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions and
limitations under the License.These tools are provided as-is and without warranty or support.
They do not constitute part of the Software AG product suite.
Users are free to use, fork and modify them, subject to the license agreement.
While Software AG welcomes contributions, we cannot guarantee to include every
contribution in the master project.
Package: adapya-era
adapya-era- services library of the Event Replicator for Adabasadapya-eraadapya-erais a services library of the Event Replicator for Adabas.The Event Replicator for Adabas is an add-on product to Adabas that allows replicating
database data to other systems. Client programs (also called target adapters) receive
event replication data through a messaging system like MQ series or EntireX Broker.adapya-era can be used to write target adapters in Python.The package also consists of scripts that can send requests to the Replicator and
receive event data via the EntireX Broker messaging system.adapya-era requires the following adapya packages: adapya.adabas, adapya.base
and adapya.entirexPrerequisite for adapya is Python version 2.7 or 3.5 and above.Installationpip install adapya-eraLinksDocumentation for adapya-era:https://softwareag.github.io/adapya-era/index.htmlAbout Event Replicator for Adabas:https://resources.softwareag.com/adabas-natural/event-replicator-for-adabas-on-the-mainframeCommunity forum:http://tech.forums.softwareag.com/techjforum/forums/show/171.pageEvent Repliator documentation:http://techcommunity.softwareag.com/ecosystem/documentation/adabas/a_distribution/event_replicator_vers.htm(free registered access)LicenseCopyright 2004-ThisYear Software AGLicensed under the Apache License, Version 2.0 (the “License”);
you may not use this file except in compliance with the License.You may obtain a copy of the License athttp://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an “AS IS” BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.See the License for the specific language governing permissions and
limitations under the License.These tools are provided as-is and without warranty or support.
They do not constitute part of the Software AG product suite.
Users are free to use, fork and modify them, subject to the license agreement.
While Software AG welcomes contributions, we cannot guarantee to include every
contribution in the master project.
Package: adasamp-pareto
Adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. The algorithm aims to find the Pareto-optimal solution of\begin{equation*}
max [ y(x) ] s.t. f(x) = feasible
\end{equation*}in an iterative procedure. Here,\(y(x)\)denotes the multi-dimensional goals and\(f(x)\)the binary feasibility of the problem (in the sense that certain design variables\(x\)lead to invalid goals). All technical details can be found in the paper “Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification” (https://arxiv.org/abs/2008.12005).InstallationInstall viapipor clone this repository. In order to usepip, type:$pipinstalladasamp-paretoUsageThe classAdaptiveSampleris used to define and solve a problem instance. Simple example:fromadasampimportAdaptiveSampler# Create instancesampler=AdaptiveSampler(func,# Problem definition: function returns (goals Y, feasibility f)X_limits,# Design variable limits to search solution inY_ref,# Reference point, has to be dominated by any goal Yiterations,# Number of solver iterationsY_model,# Regression model to predict goals Yf_model)# Classification model to predict feasibility f# Return the sampling suggestions X, the corresponding goals Y, and the corresponding feasibilities f.X,Y,f=sampler.sample()Demo notebooks can be found in theexamples/directory.DocumentationComplete documentation is available:https://adasamp-pareto.readthedocs.io/en/latest.📖CitationIf you find this code useful in your research, please consider citing:@misc{heesebortzCITE2020,
              title={Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification},
              author={Raoul Heese and Michael Bortz},
              year={2020},
              eprint={2008.12005},
              archivePrefix={arXiv},
              primaryClass={stat.ML}
     }
Package: a-data-processing
# Data Processing## Current Version Main FeaturesData Processing is used for data processing through MinIO, databases, Web APIs, etc. The data types handled include:
- txt
- json
- doc
- html
- excel
- csv
- pdf
- markdown
- ppt### Current Text Type ProcessingThe data processing process includes: cleaning abnormal data, filtering, de-duplication, and anonymization.## Design![Design](../../docs/images/data-process.drawio.png)## Local Development
### Software RequirementsBefore setting up the local data-process environment, please make sure the following software is installed:Python 3.10.x### Environment SetupInstall the Python dependencies in the requirements.txt file### RunningRun the server.py file in the src directory# isort
isort is a tool for sorting imports alphabetically within your Python code. It helps maintain a consistent and clean import order.## install`shell pip install isort `## isort a file`shell isort src/server.py `## isort a directory`shell isort . `