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+%global _empty_manifest_terminate_build 0
+Name:		python-data-warehouse-client
+Version:	3.0.2
+Release:	1
+Summary:	This package provides access to the e-Science Central data warehouse that can be used to store, access and analyse data collected in scientific studies, including for healthcare applications
+License:	Apache Software License
+URL:		https://github.com/e-science-central/data-warehouse-client
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/6f/a5/c5ec93e3a9a098d245cd20088be16e509bafe9be764b0091dda6fe3fbd2b/data-warehouse-client-3.0.2.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-more-itertools
+Requires:	python3-matplotlib
+Requires:	python3-psycopg2
+Requires:	python3-tabulate
+
+%description
+# Data Warehouse Client
+
+This package provides access to the e-Science Central data warehouse that can be used to store, access  and analyse 
+data collected in scientific studies, including for healthcare applications. The primary aim of the warehouse
+was to create a general system that enables users to explore data collected in a variety of forms. This might include
+data collected through questionnaires, data collected from sensors, 
+and features extracted from the analysis of sensor data (e.g. activity levels derived from raw accelerometer data). 
+Researchers might wish to slice, dice, visualise, analyse and explore this data in different ways, 
+e.g. all results for one participant,
+all results for one type of measure in a study, 
+changes in measurements over time. Others may wish to build models that can then be used in applications
+that make predictions about future values.
+
+Traditionally, data collected in studies has been stored in a collection of files, 
+often with metadata encoded in the filenames. 
+This makes it difficult, and time consuming, for researchers to explore, interpret and analyse the data.
+The data warehouse exploits modern database technology to vastly simplify this effort. 
+In doing this we have drawn heavily on the best practice for data warehouse design. 
+However, there is more variety in the types of healthcare data to be stored than there is in a typical warehouse,
+and so we have been forced to deviate from a conventional data warehouse in some aspect of the design.  
+There are three guiding principles behind the design:
+1.	The data warehouse must be able to store any type of data collected in a study without modifying the schema. 
+This means that when new types of data are collected in studies (e.g. from a new questionnaire, 
+a new data analysis program, or a new sensor) they can be stored in the warehouse without any changes to its design. 
+This has 3 main advantages: 
+firstly, it enables us to fix and optimise the schema for the tables in which the data is stored; 
+secondly it means that applications and tools (e.g. for analysis and visualisation) 
+built on the warehouse do not have to be updated when new types of data are added; 
+thirdly, a single, multi-tenant database server can support many studies. 
+This reduces the overall costs, the start-up time for a new study, and the overheads of managing the warehouse.
+2.	Descriptive information about the types of measurement is stored in the warehouse so that tools or humans 
+can interpret the data stored there.
+3.	The design is optimised for query performance. In several cases, this has led to denormalization
+ (duplication of data) to reduce the need for expensive joins.
+4.	It must support a security regime to restrict each user’s access 
+to the data collected in studies.
+
+
+For more information see: 
+P. Watson and H. Hiden, "The e-Science Central Study Data Platform"
+2022 IEEE 18th International Conference on e-Science (e-Science),
+Salt Lake City, UT, USA, 2022, pp. 55-64, doi: 10.1109/eScience55777.2022.00020.
+https://scholar.google.co.uk/citations?view_op=view_citation&hl=en&user=KQJg3lwAAAAJ&sortby=pubdate&citation_for_view=KQJg3lwAAAAJ:z0_F5_TITjQC
+
+For more documentation see [A Data Warehouse for Storing and Analysing Study Data](docs/data_warehouse_guide.pdf).
+
+# Running Instructions
+
+To install from PyPi, run:
+
+pip install data-warehouse-client
+
+In directory in which your executable is run, create a `db-credentials.json` file containing database 
+credentials (substituting all `<VARS>`):
+   ```
+   {"user": "<USER>", "pass": "<PASSWORD>", "IP": "<IP>", "port": <PORT>}
+   ```
+
+
+
+
+
+%package -n python3-data-warehouse-client
+Summary:	This package provides access to the e-Science Central data warehouse that can be used to store, access and analyse data collected in scientific studies, including for healthcare applications
+Provides:	python-data-warehouse-client
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-data-warehouse-client
+# Data Warehouse Client
+
+This package provides access to the e-Science Central data warehouse that can be used to store, access  and analyse 
+data collected in scientific studies, including for healthcare applications. The primary aim of the warehouse
+was to create a general system that enables users to explore data collected in a variety of forms. This might include
+data collected through questionnaires, data collected from sensors, 
+and features extracted from the analysis of sensor data (e.g. activity levels derived from raw accelerometer data). 
+Researchers might wish to slice, dice, visualise, analyse and explore this data in different ways, 
+e.g. all results for one participant,
+all results for one type of measure in a study, 
+changes in measurements over time. Others may wish to build models that can then be used in applications
+that make predictions about future values.
+
+Traditionally, data collected in studies has been stored in a collection of files, 
+often with metadata encoded in the filenames. 
+This makes it difficult, and time consuming, for researchers to explore, interpret and analyse the data.
+The data warehouse exploits modern database technology to vastly simplify this effort. 
+In doing this we have drawn heavily on the best practice for data warehouse design. 
+However, there is more variety in the types of healthcare data to be stored than there is in a typical warehouse,
+and so we have been forced to deviate from a conventional data warehouse in some aspect of the design.  
+There are three guiding principles behind the design:
+1.	The data warehouse must be able to store any type of data collected in a study without modifying the schema. 
+This means that when new types of data are collected in studies (e.g. from a new questionnaire, 
+a new data analysis program, or a new sensor) they can be stored in the warehouse without any changes to its design. 
+This has 3 main advantages: 
+firstly, it enables us to fix and optimise the schema for the tables in which the data is stored; 
+secondly it means that applications and tools (e.g. for analysis and visualisation) 
+built on the warehouse do not have to be updated when new types of data are added; 
+thirdly, a single, multi-tenant database server can support many studies. 
+This reduces the overall costs, the start-up time for a new study, and the overheads of managing the warehouse.
+2.	Descriptive information about the types of measurement is stored in the warehouse so that tools or humans 
+can interpret the data stored there.
+3.	The design is optimised for query performance. In several cases, this has led to denormalization
+ (duplication of data) to reduce the need for expensive joins.
+4.	It must support a security regime to restrict each user’s access 
+to the data collected in studies.
+
+
+For more information see: 
+P. Watson and H. Hiden, "The e-Science Central Study Data Platform"
+2022 IEEE 18th International Conference on e-Science (e-Science),
+Salt Lake City, UT, USA, 2022, pp. 55-64, doi: 10.1109/eScience55777.2022.00020.
+https://scholar.google.co.uk/citations?view_op=view_citation&hl=en&user=KQJg3lwAAAAJ&sortby=pubdate&citation_for_view=KQJg3lwAAAAJ:z0_F5_TITjQC
+
+For more documentation see [A Data Warehouse for Storing and Analysing Study Data](docs/data_warehouse_guide.pdf).
+
+# Running Instructions
+
+To install from PyPi, run:
+
+pip install data-warehouse-client
+
+In directory in which your executable is run, create a `db-credentials.json` file containing database 
+credentials (substituting all `<VARS>`):
+   ```
+   {"user": "<USER>", "pass": "<PASSWORD>", "IP": "<IP>", "port": <PORT>}
+   ```
+
+
+
+
+
+%package help
+Summary:	Development documents and examples for data-warehouse-client
+Provides:	python3-data-warehouse-client-doc
+%description help
+# Data Warehouse Client
+
+This package provides access to the e-Science Central data warehouse that can be used to store, access  and analyse 
+data collected in scientific studies, including for healthcare applications. The primary aim of the warehouse
+was to create a general system that enables users to explore data collected in a variety of forms. This might include
+data collected through questionnaires, data collected from sensors, 
+and features extracted from the analysis of sensor data (e.g. activity levels derived from raw accelerometer data). 
+Researchers might wish to slice, dice, visualise, analyse and explore this data in different ways, 
+e.g. all results for one participant,
+all results for one type of measure in a study, 
+changes in measurements over time. Others may wish to build models that can then be used in applications
+that make predictions about future values.
+
+Traditionally, data collected in studies has been stored in a collection of files, 
+often with metadata encoded in the filenames. 
+This makes it difficult, and time consuming, for researchers to explore, interpret and analyse the data.
+The data warehouse exploits modern database technology to vastly simplify this effort. 
+In doing this we have drawn heavily on the best practice for data warehouse design. 
+However, there is more variety in the types of healthcare data to be stored than there is in a typical warehouse,
+and so we have been forced to deviate from a conventional data warehouse in some aspect of the design.  
+There are three guiding principles behind the design:
+1.	The data warehouse must be able to store any type of data collected in a study without modifying the schema. 
+This means that when new types of data are collected in studies (e.g. from a new questionnaire, 
+a new data analysis program, or a new sensor) they can be stored in the warehouse without any changes to its design. 
+This has 3 main advantages: 
+firstly, it enables us to fix and optimise the schema for the tables in which the data is stored; 
+secondly it means that applications and tools (e.g. for analysis and visualisation) 
+built on the warehouse do not have to be updated when new types of data are added; 
+thirdly, a single, multi-tenant database server can support many studies. 
+This reduces the overall costs, the start-up time for a new study, and the overheads of managing the warehouse.
+2.	Descriptive information about the types of measurement is stored in the warehouse so that tools or humans 
+can interpret the data stored there.
+3.	The design is optimised for query performance. In several cases, this has led to denormalization
+ (duplication of data) to reduce the need for expensive joins.
+4.	It must support a security regime to restrict each user’s access 
+to the data collected in studies.
+
+
+For more information see: 
+P. Watson and H. Hiden, "The e-Science Central Study Data Platform"
+2022 IEEE 18th International Conference on e-Science (e-Science),
+Salt Lake City, UT, USA, 2022, pp. 55-64, doi: 10.1109/eScience55777.2022.00020.
+https://scholar.google.co.uk/citations?view_op=view_citation&hl=en&user=KQJg3lwAAAAJ&sortby=pubdate&citation_for_view=KQJg3lwAAAAJ:z0_F5_TITjQC
+
+For more documentation see [A Data Warehouse for Storing and Analysing Study Data](docs/data_warehouse_guide.pdf).
+
+# Running Instructions
+
+To install from PyPi, run:
+
+pip install data-warehouse-client
+
+In directory in which your executable is run, create a `db-credentials.json` file containing database 
+credentials (substituting all `<VARS>`):
+   ```
+   {"user": "<USER>", "pass": "<PASSWORD>", "IP": "<IP>", "port": <PORT>}
+   ```
+
+
+
+
+
+%prep
+%autosetup -n data-warehouse-client-3.0.2
+
+%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-data-warehouse-client -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 3.0.2-1
+- Package Spec generated