automatic import of python-discovery-transition-dsopeneuler20.03

3 files changed, 201 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
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--- a/.gitignore
+++ b/.gitignore
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+/discovery-transition-ds-4.14.3.tar.gz
diff --git a/python-discovery-transition-ds.spec b/python-discovery-transition-ds.spec
new file mode 100644
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--- /dev/null
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+%global _empty_manifest_terminate_build 0
+Name:		python-discovery-transition-ds
+Version:	4.14.3
+Release:	1
+Summary:	Data Science to production accelerator
+License:	BSD
+URL:		https://github.com/gigas64/discovery-transition-ds
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/3e/a8/b84da9b732aa3c79009c32f9b1bde7a875fc9971f66e4d7be248a0ce3e3e/discovery-transition-ds-4.14.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-aistac-foundation
+Requires:	python3-discovery-connectors
+Requires:	python3-pandas
+Requires:	python3-numpy
+Requires:	python3-matplotlib
+Requires:	python3-seaborn
+Requires:	python3-scikit-learn
+Requires:	python3-scipy
+Requires:	python3-boto3
+Requires:	python3-botocore
+Requires:	python3-fsspec
+Requires:	python3-s3fs
+Requires:	python3-pyyaml
+
+%description
+Project Hadron has been built to bridge the gap between data scientists and data engineers. More specifically between
+machine learning business outcomes and the final product.  It translates the work of data scientists into meaningful,
+production ready solutions that can be easily managed by product engineers.
+Project Hadron is a core set of abstractions that are the foundation of the three key elements that represent data
+science, those being: (1) feature engineering, (2) the construction of synthetic data with simulators, and generators
+(3) and statistics and machine learning algorithms for discovery and creating models. Project Hadron uniquely sees
+data as ‘all the same’ (lazyprogrammer (2020) https://lazyprogrammer.me/all-data-is-the-same/) , by which we mean
+its origin, shape and size stay independent throughout the disciplines so its content, form and structure can be
+removed as a factor in the design and implementation of the components built.
+Project Hadron has been designed to place data scientists in the familiar environment of machine learning and
+statistical tools, extracting their ideas and translating them automagicially into production ready solutions
+familiar to data engineers and Subject Matter Experts (SME’s).
+Project Hadron provides a clear separation of concerns, whilst maintaining the original intentions of the data
+scientist, that can be passed to a production team. It offers trust between the data scientists teams and product
+teams. It brings with it transparency and traceability, dealing with bias, fairness, and knowledge. The resulting
+outcome provides the product engineers with adaptability, robustness, and reuse; fitting seamlessly into a
+microservices solution that can be language agnostic.
+Project Hadron is designed using Microservices. Microservices - also known as the microservice architecture - is an
+architectural pattern that structures an application as a collection of component services that are:
+* Highly maintainable and testable
+* Loosely coupled
+* Independently deployable
+* Highly reusable
+* Resilient
+* Technically independent
+Component services are built for business capabilities and each service performs a single function. Because they are
+independently run, each service can be updated, deployed, and scaled to meet demand for specific functions of an
+application. Project Hadron microservices enable the rapid, frequent and reliable delivery of large, complex
+applications. It also enables an organization to evolve its data science stack and experiment with innovative ideas.
+At the heart of Project Hadron is a multi-tenant, NoSQL, singleton, in memory data store that has minimal code and
+functionality and has been custom built specifically for Hadron tasks in  mind. Abstracted from this is the component
+store which allows us to build a reusable set of methods that define each tenanted component that sits separately
+from the store itself. In addition, a dynamic key value class provides labeling so that each tenant is not tied to
+a fixed set of reference values unless by specificity. Each of the classes, the data store, the component property
+manager, and the key value pairs that make up the component are all independent, giving complete flexibility and
+minimum code footprint to the build process of new components.
+This is what gives us the Domain Contract for each tennant which sits at the heart of what makes the contracts
+reusable, translatable, transferable and brings the data scientist closer to the production engineer along with
+building a production ready component solution.
+
+%package -n python3-discovery-transition-ds
+Summary:	Data Science to production accelerator
+Provides:	python-discovery-transition-ds
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-discovery-transition-ds
+Project Hadron has been built to bridge the gap between data scientists and data engineers. More specifically between
+machine learning business outcomes and the final product.  It translates the work of data scientists into meaningful,
+production ready solutions that can be easily managed by product engineers.
+Project Hadron is a core set of abstractions that are the foundation of the three key elements that represent data
+science, those being: (1) feature engineering, (2) the construction of synthetic data with simulators, and generators
+(3) and statistics and machine learning algorithms for discovery and creating models. Project Hadron uniquely sees
+data as ‘all the same’ (lazyprogrammer (2020) https://lazyprogrammer.me/all-data-is-the-same/) , by which we mean
+its origin, shape and size stay independent throughout the disciplines so its content, form and structure can be
+removed as a factor in the design and implementation of the components built.
+Project Hadron has been designed to place data scientists in the familiar environment of machine learning and
+statistical tools, extracting their ideas and translating them automagicially into production ready solutions
+familiar to data engineers and Subject Matter Experts (SME’s).
+Project Hadron provides a clear separation of concerns, whilst maintaining the original intentions of the data
+scientist, that can be passed to a production team. It offers trust between the data scientists teams and product
+teams. It brings with it transparency and traceability, dealing with bias, fairness, and knowledge. The resulting
+outcome provides the product engineers with adaptability, robustness, and reuse; fitting seamlessly into a
+microservices solution that can be language agnostic.
+Project Hadron is designed using Microservices. Microservices - also known as the microservice architecture - is an
+architectural pattern that structures an application as a collection of component services that are:
+* Highly maintainable and testable
+* Loosely coupled
+* Independently deployable
+* Highly reusable
+* Resilient
+* Technically independent
+Component services are built for business capabilities and each service performs a single function. Because they are
+independently run, each service can be updated, deployed, and scaled to meet demand for specific functions of an
+application. Project Hadron microservices enable the rapid, frequent and reliable delivery of large, complex
+applications. It also enables an organization to evolve its data science stack and experiment with innovative ideas.
+At the heart of Project Hadron is a multi-tenant, NoSQL, singleton, in memory data store that has minimal code and
+functionality and has been custom built specifically for Hadron tasks in  mind. Abstracted from this is the component
+store which allows us to build a reusable set of methods that define each tenanted component that sits separately
+from the store itself. In addition, a dynamic key value class provides labeling so that each tenant is not tied to
+a fixed set of reference values unless by specificity. Each of the classes, the data store, the component property
+manager, and the key value pairs that make up the component are all independent, giving complete flexibility and
+minimum code footprint to the build process of new components.
+This is what gives us the Domain Contract for each tennant which sits at the heart of what makes the contracts
+reusable, translatable, transferable and brings the data scientist closer to the production engineer along with
+building a production ready component solution.
+
+%package help
+Summary:	Development documents and examples for discovery-transition-ds
+Provides:	python3-discovery-transition-ds-doc
+%description help
+Project Hadron has been built to bridge the gap between data scientists and data engineers. More specifically between
+machine learning business outcomes and the final product.  It translates the work of data scientists into meaningful,
+production ready solutions that can be easily managed by product engineers.
+Project Hadron is a core set of abstractions that are the foundation of the three key elements that represent data
+science, those being: (1) feature engineering, (2) the construction of synthetic data with simulators, and generators
+(3) and statistics and machine learning algorithms for discovery and creating models. Project Hadron uniquely sees
+data as ‘all the same’ (lazyprogrammer (2020) https://lazyprogrammer.me/all-data-is-the-same/) , by which we mean
+its origin, shape and size stay independent throughout the disciplines so its content, form and structure can be
+removed as a factor in the design and implementation of the components built.
+Project Hadron has been designed to place data scientists in the familiar environment of machine learning and
+statistical tools, extracting their ideas and translating them automagicially into production ready solutions
+familiar to data engineers and Subject Matter Experts (SME’s).
+Project Hadron provides a clear separation of concerns, whilst maintaining the original intentions of the data
+scientist, that can be passed to a production team. It offers trust between the data scientists teams and product
+teams. It brings with it transparency and traceability, dealing with bias, fairness, and knowledge. The resulting
+outcome provides the product engineers with adaptability, robustness, and reuse; fitting seamlessly into a
+microservices solution that can be language agnostic.
+Project Hadron is designed using Microservices. Microservices - also known as the microservice architecture - is an
+architectural pattern that structures an application as a collection of component services that are:
+* Highly maintainable and testable
+* Loosely coupled
+* Independently deployable
+* Highly reusable
+* Resilient
+* Technically independent
+Component services are built for business capabilities and each service performs a single function. Because they are
+independently run, each service can be updated, deployed, and scaled to meet demand for specific functions of an
+application. Project Hadron microservices enable the rapid, frequent and reliable delivery of large, complex
+applications. It also enables an organization to evolve its data science stack and experiment with innovative ideas.
+At the heart of Project Hadron is a multi-tenant, NoSQL, singleton, in memory data store that has minimal code and
+functionality and has been custom built specifically for Hadron tasks in  mind. Abstracted from this is the component
+store which allows us to build a reusable set of methods that define each tenanted component that sits separately
+from the store itself. In addition, a dynamic key value class provides labeling so that each tenant is not tied to
+a fixed set of reference values unless by specificity. Each of the classes, the data store, the component property
+manager, and the key value pairs that make up the component are all independent, giving complete flexibility and
+minimum code footprint to the build process of new components.
+This is what gives us the Domain Contract for each tennant which sits at the heart of what makes the contracts
+reusable, translatable, transferable and brings the data scientist closer to the production engineer along with
+building a production ready component solution.
+
+%prep
+%autosetup -n discovery-transition-ds-4.14.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-discovery-transition-ds -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 4.14.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..23e9d97
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+1b7cba3d9c04dbf248d31c635a72f5c1  discovery-transition-ds-4.14.3.tar.gz