path: root/python-akernel.spec

%global _empty_manifest_terminate_build 0
Name:		python-akernel
Version:	0.0.22
Release:	1
Summary:	An asynchronous Python Jupyter kernel
License:	MIT
URL:		https://github.com/davidbrochart/akernel
Source0:	https://mirrors.aliyun.com/pypi/web/packages/e6/19/adac21ddbdcd5da5d54c3ccd65df029ed2702cea82f2edd52024b4e8bb15/akernel-0.0.22.tar.gz
BuildArch:	noarch

Requires:	python3-ipyx
Requires:	python3-zict
Requires:	python3-pyzmq
Requires:	python3-typer
Requires:	python3-click
Requires:	python3-dateutil
Requires:	python3-colorama
Requires:	python3-gast
Requires:	python3-zict
Requires:	python3-ipyx
Requires:	python3-mypy
Requires:	python3-flake8
Requires:	python3-black
Requires:	python3-pytest
Requires:	python3-pytest-asyncio
Requires:	python3-types-python-dateutil
Requires:	python3-kernel-driver

%description
[![Build Status](https://github.com/davidbrochart/akernel/workflows/CI/badge.svg)](https://github.com/davidbrochart/akernel/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/davidbrochart/akernel/HEAD?urlpath=lab%2Ftree%2Fexamples%2Freactivity.ipynb)

# akernel

An asynchronous Python Jupyter kernel, with optional reactive programming.

## Install

```bash
pip install akernel
```

If you want to be able to use reactive programming:

```bash
pip install akernel[react]
```

Note that it will just add [ipyx](https://github.com/davidbrochart/ipyx), which you could also
install later if you want (`pip install ipyx`).

You can parameterize akernel's execution mode (you will need to restart your kernel):

```bash
akernel install  # default (async)
akernel install react  # reactive mode
akernel install cache  # cache mode
akernel install multi  # multi-kernel mode
akernel install cache-multi-react  # cache mode + multi-kernel mode + reactive mode
```

## Motivation

[ipykernel](https://github.com/ipython/ipykernel) offers the ability to
[run asynchronous code from the REPL](https://ipython.readthedocs.io/en/stable/interactive/autoawait.html).
This means you can `await` at the top-level, outside of an async function. Unfortunately, this will still
block the kernel.

akernel changes this behavior by launching each cell in a task.

akernel now also supports reactive programming, although it is still experimental!

## Features

### Asynchronous execution

akernel allows for asynchronous code execution. What this means is that when used in a Jupyter
notebook, you can run cells concurrently if the code is cooperative. For instance, you can run a
cell with the following code:

```python
# cell 1
for i in range(10):
    print("cell 1:", i)
    await asyncio.sleep(1)
```

Since this cell is `async` (it has an `await`), it will not block the execution of other cells.
So you can run another cell concurrently, provided that this cell is also cooperative:

```python
# cell 2
for j in range(10):
    print("cell 2:", j)
    await asyncio.sleep(1)
```

If cell 2 was blocking, cell 1 would pause until cell 2 was finished. You can see that by changing
`await asyncio.sleep(1)` into `time.sleep(1)` in cell 2.

You can make a cell wait for the previous one to be finished with:

```python
# cell 3
await __task__()  # wait for cell 2 to be finished
print("cell 2 has run")
```

### Reactivity

One feature other notebooks offer is the ability to have variables react to other variables'
changes. [Observable notebooks](https://observablehq.com/@observablehq/how-observable-runs) are a
good example of this, and it can give a whole new user experience. For instance, you can run cells
out of order:

```python
# cell 1
a = b + 1  # "b" is not defined yet
a
```

Executing cell 1 won't result in an "undefined variable" error. Instead, the *result* of the
operation is undefined, and the output of cell 1 is `None`. You can then continue with the
definition of `b`:

```python
# cell 2
b = 2  # triggers the computation of "a" in cell 1
```

Now `a`, which depends on `b`, is automatically updated, and the output of cell 1 is `3`.

You can of course define much more complex data flows, by defining variables on top of other ones.

![screencast](https://user-images.githubusercontent.com/591645/131855258-35118507-6be2-44cb-9329-143ad8509405.gif)

### Caching

With this mode, cell execution is cached so that the next time a cell is run, its outputs are retrieved from cache (if its inputs didn't change). Inputs and outputs are inferred from the cell code.

### Multi-kernel

This mode emulates multiple kernels inside the same kernel. Kernel isolation is achieved by using the session ID of execution requests. You can thus connect multiple notebooks to the same kernel, and they won't share execution state.

This is particularly useful if cells are async, because they won't block the kernel. The same kernel can thus be "shared" and used by potentially a lot of notebooks, greatly reducing resource usage.

## Limitations

It is still a work in progress, in particular:

- `stdout`/`stderr` redirection to the cell output is only supported through the `print` function.
- No rich representation for now, only the standard `__repr__` is supported. This means no
  matplotlib figure yet :-( But since ipywidgets work, why not using
  [ipympl](https://github.com/matplotlib/ipympl)? :-)




%package -n python3-akernel
Summary:	An asynchronous Python Jupyter kernel
Provides:	python-akernel
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-akernel
[![Build Status](https://github.com/davidbrochart/akernel/workflows/CI/badge.svg)](https://github.com/davidbrochart/akernel/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/davidbrochart/akernel/HEAD?urlpath=lab%2Ftree%2Fexamples%2Freactivity.ipynb)

# akernel

An asynchronous Python Jupyter kernel, with optional reactive programming.

## Install

```bash
pip install akernel
```

If you want to be able to use reactive programming:

```bash
pip install akernel[react]
```

Note that it will just add [ipyx](https://github.com/davidbrochart/ipyx), which you could also
install later if you want (`pip install ipyx`).

You can parameterize akernel's execution mode (you will need to restart your kernel):

```bash
akernel install  # default (async)
akernel install react  # reactive mode
akernel install cache  # cache mode
akernel install multi  # multi-kernel mode
akernel install cache-multi-react  # cache mode + multi-kernel mode + reactive mode
```

## Motivation

[ipykernel](https://github.com/ipython/ipykernel) offers the ability to
[run asynchronous code from the REPL](https://ipython.readthedocs.io/en/stable/interactive/autoawait.html).
This means you can `await` at the top-level, outside of an async function. Unfortunately, this will still
block the kernel.

akernel changes this behavior by launching each cell in a task.

akernel now also supports reactive programming, although it is still experimental!

## Features

### Asynchronous execution

akernel allows for asynchronous code execution. What this means is that when used in a Jupyter
notebook, you can run cells concurrently if the code is cooperative. For instance, you can run a
cell with the following code:

```python
# cell 1
for i in range(10):
    print("cell 1:", i)
    await asyncio.sleep(1)
```

Since this cell is `async` (it has an `await`), it will not block the execution of other cells.
So you can run another cell concurrently, provided that this cell is also cooperative:

```python
# cell 2
for j in range(10):
    print("cell 2:", j)
    await asyncio.sleep(1)
```

If cell 2 was blocking, cell 1 would pause until cell 2 was finished. You can see that by changing
`await asyncio.sleep(1)` into `time.sleep(1)` in cell 2.

You can make a cell wait for the previous one to be finished with:

```python
# cell 3
await __task__()  # wait for cell 2 to be finished
print("cell 2 has run")
```

### Reactivity

One feature other notebooks offer is the ability to have variables react to other variables'
changes. [Observable notebooks](https://observablehq.com/@observablehq/how-observable-runs) are a
good example of this, and it can give a whole new user experience. For instance, you can run cells
out of order:

```python
# cell 1
a = b + 1  # "b" is not defined yet
a
```

Executing cell 1 won't result in an "undefined variable" error. Instead, the *result* of the
operation is undefined, and the output of cell 1 is `None`. You can then continue with the
definition of `b`:

```python
# cell 2
b = 2  # triggers the computation of "a" in cell 1
```

Now `a`, which depends on `b`, is automatically updated, and the output of cell 1 is `3`.

You can of course define much more complex data flows, by defining variables on top of other ones.

![screencast](https://user-images.githubusercontent.com/591645/131855258-35118507-6be2-44cb-9329-143ad8509405.gif)

### Caching

With this mode, cell execution is cached so that the next time a cell is run, its outputs are retrieved from cache (if its inputs didn't change). Inputs and outputs are inferred from the cell code.

### Multi-kernel

This mode emulates multiple kernels inside the same kernel. Kernel isolation is achieved by using the session ID of execution requests. You can thus connect multiple notebooks to the same kernel, and they won't share execution state.

This is particularly useful if cells are async, because they won't block the kernel. The same kernel can thus be "shared" and used by potentially a lot of notebooks, greatly reducing resource usage.

## Limitations

It is still a work in progress, in particular:

- `stdout`/`stderr` redirection to the cell output is only supported through the `print` function.
- No rich representation for now, only the standard `__repr__` is supported. This means no
  matplotlib figure yet :-( But since ipywidgets work, why not using
  [ipympl](https://github.com/matplotlib/ipympl)? :-)




%package help
Summary:	Development documents and examples for akernel
Provides:	python3-akernel-doc
%description help
[![Build Status](https://github.com/davidbrochart/akernel/workflows/CI/badge.svg)](https://github.com/davidbrochart/akernel/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/davidbrochart/akernel/HEAD?urlpath=lab%2Ftree%2Fexamples%2Freactivity.ipynb)

# akernel

An asynchronous Python Jupyter kernel, with optional reactive programming.

## Install

```bash
pip install akernel
```

If you want to be able to use reactive programming:

```bash
pip install akernel[react]
```

Note that it will just add [ipyx](https://github.com/davidbrochart/ipyx), which you could also
install later if you want (`pip install ipyx`).

You can parameterize akernel's execution mode (you will need to restart your kernel):

```bash
akernel install  # default (async)
akernel install react  # reactive mode
akernel install cache  # cache mode
akernel install multi  # multi-kernel mode
akernel install cache-multi-react  # cache mode + multi-kernel mode + reactive mode
```

## Motivation

[ipykernel](https://github.com/ipython/ipykernel) offers the ability to
[run asynchronous code from the REPL](https://ipython.readthedocs.io/en/stable/interactive/autoawait.html).
This means you can `await` at the top-level, outside of an async function. Unfortunately, this will still
block the kernel.

akernel changes this behavior by launching each cell in a task.

akernel now also supports reactive programming, although it is still experimental!

## Features

### Asynchronous execution

akernel allows for asynchronous code execution. What this means is that when used in a Jupyter
notebook, you can run cells concurrently if the code is cooperative. For instance, you can run a
cell with the following code:

```python
# cell 1
for i in range(10):
    print("cell 1:", i)
    await asyncio.sleep(1)
```

Since this cell is `async` (it has an `await`), it will not block the execution of other cells.
So you can run another cell concurrently, provided that this cell is also cooperative:

```python
# cell 2
for j in range(10):
    print("cell 2:", j)
    await asyncio.sleep(1)
```

If cell 2 was blocking, cell 1 would pause until cell 2 was finished. You can see that by changing
`await asyncio.sleep(1)` into `time.sleep(1)` in cell 2.

You can make a cell wait for the previous one to be finished with:

```python
# cell 3
await __task__()  # wait for cell 2 to be finished
print("cell 2 has run")
```

### Reactivity

One feature other notebooks offer is the ability to have variables react to other variables'
changes. [Observable notebooks](https://observablehq.com/@observablehq/how-observable-runs) are a
good example of this, and it can give a whole new user experience. For instance, you can run cells
out of order:

```python
# cell 1
a = b + 1  # "b" is not defined yet
a
```

Executing cell 1 won't result in an "undefined variable" error. Instead, the *result* of the
operation is undefined, and the output of cell 1 is `None`. You can then continue with the
definition of `b`:

```python
# cell 2
b = 2  # triggers the computation of "a" in cell 1
```

Now `a`, which depends on `b`, is automatically updated, and the output of cell 1 is `3`.

You can of course define much more complex data flows, by defining variables on top of other ones.

![screencast](https://user-images.githubusercontent.com/591645/131855258-35118507-6be2-44cb-9329-143ad8509405.gif)

### Caching

With this mode, cell execution is cached so that the next time a cell is run, its outputs are retrieved from cache (if its inputs didn't change). Inputs and outputs are inferred from the cell code.

### Multi-kernel

This mode emulates multiple kernels inside the same kernel. Kernel isolation is achieved by using the session ID of execution requests. You can thus connect multiple notebooks to the same kernel, and they won't share execution state.

This is particularly useful if cells are async, because they won't block the kernel. The same kernel can thus be "shared" and used by potentially a lot of notebooks, greatly reducing resource usage.

## Limitations

It is still a work in progress, in particular:

- `stdout`/`stderr` redirection to the cell output is only supported through the `print` function.
- No rich representation for now, only the standard `__repr__` is supported. This means no
  matplotlib figure yet :-( But since ipywidgets work, why not using
  [ipympl](https://github.com/matplotlib/ipympl)? :-)




%prep
%autosetup -n akernel-0.0.22

%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-akernel -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.22-1
- Package Spec generated