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@@ -0,0 +1 @@ +/nbautoeval-1.7.0.tar.gz diff --git a/python-nbautoeval.spec b/python-nbautoeval.spec new file mode 100644 index 0000000..7a8cfac --- /dev/null +++ b/python-nbautoeval.spec @@ -0,0 +1,395 @@ +%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. + +[](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. + +[](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. + +[](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 @@ -0,0 +1 @@ +3cdfbf4285b05c42f952d8c8ac4c8889 nbautoeval-1.7.0.tar.gz |