automatic import of python-nbautoevalopeneuler20.03

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+/nbautoeval-1.7.0.tar.gz
diff --git a/python-nbautoeval.spec b/python-nbautoeval.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-nbautoeval
+Version:	1.7.0
+Release:	1
+Summary:	A mini framework to implement auto-evaluated exercises in Jupyter notebooks
+License:	CC BY-SA 4.0
+URL:		https://github.com/parmentelat/nbautoeval
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/3f/6e/f07bd2278329709c2a76fddc6c7d11f9235dd141a9b10ce904f0e07a4fde/nbautoeval-1.7.0.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-ipython
+Requires:	python3-ipywidgets
+Requires:	python3-numpy
+Requires:	python3-PyYAML
+Requires:	python3-myst-parser
+
+%description
+# `nbautoeval`
+
+`nbautoeval` is a very lightweight python framework for creating **auto-evaluated**
+exercises inside a jupyter (python) notebook.
+
+two flavours of exercises are supported at this point :
+
+* code-oriented : given a text that describes the expectations, students are invited to
+  write their own code, and can then see the outcome on teacher-defined data samples,
+  compared with the results obtained through a teacher-provided solution, with a visual
+  (green/red) feedback
+* quizzes : a separate module allows to create quizzes
+
+At this point, due to lack of knowledge/documentation about open/edx (read: the
+version running at FUN), there is no available code for exporting the results as
+grades or anything similar (hence the `autoeval` name).
+
+There indeed are provisions in the code to accumulate statistics on all
+attempted corrections, as an attempt to provide feedback to teachers.
+
+# Try it on `mybinder`
+
+Click the badge below to see a few sample demos under `mybinder.org` - it's all
+in the `demo-notebooks` subdir.
+
+**NOTE** the demo notebooks ship under a `.py` format and require `jupytext` to be
+installed before you can open them in Jupyter.
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/parmentelat/nbautoeval/master?filepath=demo-notebooks)
+
+
+# History
+
+This was initially embedded into a [MOOC on
+python2](https://github.com/parmentelat/flotpython) that ran for the first time on [the
+French FUN platform](https://www.france-universite-numerique-mooc.fr/) in Fall 2014. It
+was then duplicated into a [MOOC on
+bioinformatics](https://github.com/parmentelat/flotbioinfo) in Spring 2016 where it was
+named `nbautoeval` for the first time, but still embedded in a greater git module.
+
+The current git repo is created in June 2016 from that basis, with the intention
+to be used as a git subtree from these 2 repos, and possibly others since a few
+people have proved interested.
+
+# Installation
+
+```
+pip install nbautoeval
+```
+
+# Overview
+
+## code-oriented
+
+Currently supports the following types of exercises
+  * `ExerciseFunction` : the student is asked to write a function
+  * `ExerciseRegexp` : the student is asked to write a regular expression
+  * `ExerciseGenerator` : the student is asked to write a generator function 
+  * `ExerciseClass` : tests will happen on a class implementation
+
+A teacher who wishes to implement an exercise needs to write 2 parts :
+
+* One python file that defines an instance of an exercise class; this in a nutshell
+  typically involves
+  * providing one solution (let's say a function) written in Python
+  * providing a set of input data
+  * plus optionnally various tweaks for rendering results
+
+* One notebook that imports this exercise object, and can then take advantage of it to
+  write jupyter cells that typically
+  * invoke `example()` on  the  exercise  object to show examples of the expected output
+  * invite the student to write their own code
+  * invoke `correction()` on  the  exercise  object to display the outcome.
+
+## quizzes
+
+Here again there will be 2 parts at work :
+
+* The recommended way is to define quizzes in YAML format :
+  * one YAML file can contain several quizzes - see examples in the `yaml/` subdir
+  * and each quiz contain a set of questions
+  * grouping questions into quizzes essentially makes sense wrt the maximal number of
+    attempts
+  * mostly all the pieces can be written in markdown (currently we use `myst_parser`)
+
+* then one invokes `run_yaml_quiz()` from a notebook to display the test
+  * this function takes 2 arguments, one to help locate the YAML file
+  * one to spot the quiz inside the YAML file
+  * run with `debug=True` to pinpoint errors in the source
+
+## results and storage
+
+Regardless of their type all tests have an `exoname` that is used to store information
+about that specific test; for quizzes it is recommended to use a different name than 
+the quiz name used in `run_yaml_quiz()` so that students cant guess it too easily.
+
+stuff is stored in 2 separate locations :
+
+* `~/.nbautoeval.trace` contain one JSON line per attempt (correction or submit)
+* `~/.nbautoeval.storage` for quizzes only, preserves previous choices, number of attempts
+
+# Known issues
+
+see https://github.com/parmentelat/nbautoeval/issues
+
+
+
+
+%package -n python3-nbautoeval
+Summary:	A mini framework to implement auto-evaluated exercises in Jupyter notebooks
+Provides:	python-nbautoeval
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-nbautoeval
+# `nbautoeval`
+
+`nbautoeval` is a very lightweight python framework for creating **auto-evaluated**
+exercises inside a jupyter (python) notebook.
+
+two flavours of exercises are supported at this point :
+
+* code-oriented : given a text that describes the expectations, students are invited to
+  write their own code, and can then see the outcome on teacher-defined data samples,
+  compared with the results obtained through a teacher-provided solution, with a visual
+  (green/red) feedback
+* quizzes : a separate module allows to create quizzes
+
+At this point, due to lack of knowledge/documentation about open/edx (read: the
+version running at FUN), there is no available code for exporting the results as
+grades or anything similar (hence the `autoeval` name).
+
+There indeed are provisions in the code to accumulate statistics on all
+attempted corrections, as an attempt to provide feedback to teachers.
+
+# Try it on `mybinder`
+
+Click the badge below to see a few sample demos under `mybinder.org` - it's all
+in the `demo-notebooks` subdir.
+
+**NOTE** the demo notebooks ship under a `.py` format and require `jupytext` to be
+installed before you can open them in Jupyter.
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/parmentelat/nbautoeval/master?filepath=demo-notebooks)
+
+
+# History
+
+This was initially embedded into a [MOOC on
+python2](https://github.com/parmentelat/flotpython) that ran for the first time on [the
+French FUN platform](https://www.france-universite-numerique-mooc.fr/) in Fall 2014. It
+was then duplicated into a [MOOC on
+bioinformatics](https://github.com/parmentelat/flotbioinfo) in Spring 2016 where it was
+named `nbautoeval` for the first time, but still embedded in a greater git module.
+
+The current git repo is created in June 2016 from that basis, with the intention
+to be used as a git subtree from these 2 repos, and possibly others since a few
+people have proved interested.
+
+# Installation
+
+```
+pip install nbautoeval
+```
+
+# Overview
+
+## code-oriented
+
+Currently supports the following types of exercises
+  * `ExerciseFunction` : the student is asked to write a function
+  * `ExerciseRegexp` : the student is asked to write a regular expression
+  * `ExerciseGenerator` : the student is asked to write a generator function 
+  * `ExerciseClass` : tests will happen on a class implementation
+
+A teacher who wishes to implement an exercise needs to write 2 parts :
+
+* One python file that defines an instance of an exercise class; this in a nutshell
+  typically involves
+  * providing one solution (let's say a function) written in Python
+  * providing a set of input data
+  * plus optionnally various tweaks for rendering results
+
+* One notebook that imports this exercise object, and can then take advantage of it to
+  write jupyter cells that typically
+  * invoke `example()` on  the  exercise  object to show examples of the expected output
+  * invite the student to write their own code
+  * invoke `correction()` on  the  exercise  object to display the outcome.
+
+## quizzes
+
+Here again there will be 2 parts at work :
+
+* The recommended way is to define quizzes in YAML format :
+  * one YAML file can contain several quizzes - see examples in the `yaml/` subdir
+  * and each quiz contain a set of questions
+  * grouping questions into quizzes essentially makes sense wrt the maximal number of
+    attempts
+  * mostly all the pieces can be written in markdown (currently we use `myst_parser`)
+
+* then one invokes `run_yaml_quiz()` from a notebook to display the test
+  * this function takes 2 arguments, one to help locate the YAML file
+  * one to spot the quiz inside the YAML file
+  * run with `debug=True` to pinpoint errors in the source
+
+## results and storage
+
+Regardless of their type all tests have an `exoname` that is used to store information
+about that specific test; for quizzes it is recommended to use a different name than 
+the quiz name used in `run_yaml_quiz()` so that students cant guess it too easily.
+
+stuff is stored in 2 separate locations :
+
+* `~/.nbautoeval.trace` contain one JSON line per attempt (correction or submit)
+* `~/.nbautoeval.storage` for quizzes only, preserves previous choices, number of attempts
+
+# Known issues
+
+see https://github.com/parmentelat/nbautoeval/issues
+
+
+
+
+%package help
+Summary:	Development documents and examples for nbautoeval
+Provides:	python3-nbautoeval-doc
+%description help
+# `nbautoeval`
+
+`nbautoeval` is a very lightweight python framework for creating **auto-evaluated**
+exercises inside a jupyter (python) notebook.
+
+two flavours of exercises are supported at this point :
+
+* code-oriented : given a text that describes the expectations, students are invited to
+  write their own code, and can then see the outcome on teacher-defined data samples,
+  compared with the results obtained through a teacher-provided solution, with a visual
+  (green/red) feedback
+* quizzes : a separate module allows to create quizzes
+
+At this point, due to lack of knowledge/documentation about open/edx (read: the
+version running at FUN), there is no available code for exporting the results as
+grades or anything similar (hence the `autoeval` name).
+
+There indeed are provisions in the code to accumulate statistics on all
+attempted corrections, as an attempt to provide feedback to teachers.
+
+# Try it on `mybinder`
+
+Click the badge below to see a few sample demos under `mybinder.org` - it's all
+in the `demo-notebooks` subdir.
+
+**NOTE** the demo notebooks ship under a `.py` format and require `jupytext` to be
+installed before you can open them in Jupyter.
+
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/parmentelat/nbautoeval/master?filepath=demo-notebooks)
+
+
+# History
+
+This was initially embedded into a [MOOC on
+python2](https://github.com/parmentelat/flotpython) that ran for the first time on [the
+French FUN platform](https://www.france-universite-numerique-mooc.fr/) in Fall 2014. It
+was then duplicated into a [MOOC on
+bioinformatics](https://github.com/parmentelat/flotbioinfo) in Spring 2016 where it was
+named `nbautoeval` for the first time, but still embedded in a greater git module.
+
+The current git repo is created in June 2016 from that basis, with the intention
+to be used as a git subtree from these 2 repos, and possibly others since a few
+people have proved interested.
+
+# Installation
+
+```
+pip install nbautoeval
+```
+
+# Overview
+
+## code-oriented
+
+Currently supports the following types of exercises
+  * `ExerciseFunction` : the student is asked to write a function
+  * `ExerciseRegexp` : the student is asked to write a regular expression
+  * `ExerciseGenerator` : the student is asked to write a generator function 
+  * `ExerciseClass` : tests will happen on a class implementation
+
+A teacher who wishes to implement an exercise needs to write 2 parts :
+
+* One python file that defines an instance of an exercise class; this in a nutshell
+  typically involves
+  * providing one solution (let's say a function) written in Python
+  * providing a set of input data
+  * plus optionnally various tweaks for rendering results
+
+* One notebook that imports this exercise object, and can then take advantage of it to
+  write jupyter cells that typically
+  * invoke `example()` on  the  exercise  object to show examples of the expected output
+  * invite the student to write their own code
+  * invoke `correction()` on  the  exercise  object to display the outcome.
+
+## quizzes
+
+Here again there will be 2 parts at work :
+
+* The recommended way is to define quizzes in YAML format :
+  * one YAML file can contain several quizzes - see examples in the `yaml/` subdir
+  * and each quiz contain a set of questions
+  * grouping questions into quizzes essentially makes sense wrt the maximal number of
+    attempts
+  * mostly all the pieces can be written in markdown (currently we use `myst_parser`)
+
+* then one invokes `run_yaml_quiz()` from a notebook to display the test
+  * this function takes 2 arguments, one to help locate the YAML file
+  * one to spot the quiz inside the YAML file
+  * run with `debug=True` to pinpoint errors in the source
+
+## results and storage
+
+Regardless of their type all tests have an `exoname` that is used to store information
+about that specific test; for quizzes it is recommended to use a different name than 
+the quiz name used in `run_yaml_quiz()` so that students cant guess it too easily.
+
+stuff is stored in 2 separate locations :
+
+* `~/.nbautoeval.trace` contain one JSON line per attempt (correction or submit)
+* `~/.nbautoeval.storage` for quizzes only, preserves previous choices, number of attempts
+
+# Known issues
+
+see https://github.com/parmentelat/nbautoeval/issues
+
+
+
+
+%prep
+%autosetup -n nbautoeval-1.7.0
+
+%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-nbautoeval -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.7.0-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..081f7fd
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+3cdfbf4285b05c42f952d8c8ac4c8889  nbautoeval-1.7.0.tar.gz