# Calysto Scheme
You can try Calysto Scheme without installing anything by clicking on the following button:
Calysto Scheme is a real Scheme programming language, with full support for continuations, including call/cc. It can also use all Python libraries. Also has some extensions that make it more useful (stepper-debugger, choose/fail, stack traces), or make it better integrated with Python. For more details on using Calysto Scheme, see:
In Jupyter notebooks, because Calysto Scheme uses [MetaKernel](https://github.com/Calysto/metakernel/blob/master/README.rst), it has a fully-supported set of “magics”—meta-commands for additional functionality. This includes running Scheme in parallel. See all of the [MetaKernel Magics](https://github.com/Calysto/metakernel/blob/master/metakernel/magics/README.md).
Calysto Scheme is written in Scheme, and then translated into Python (and other backends). The entire functionality lies in a single Python file: https://github.com/Calysto/calysto_scheme/blob/master/calysto_scheme/scheme.py However, you can easily install it (see below).
Calysto Scheme in use:
[CS245: Programming Languages - 2014, Fall](https://jupyter.brynmawr.edu/services/public/dblank/CS245%20Programming%20Languages/2014-Fall/Programming%20Languages,%20Syllabus.ipynb)
[CS245: Programming Languages - 2016, Fall](https://jupyter.brynmawr.edu/services/public/dblank/CS245%20Programming%20Languages/2016-Fall/Syllabus.ipynb)
## Parallel Processing
To use Calysto Scheme in parallel, do the following:
Make sure that the Python module ipyparallel is installed. In the shell, type:
` pip install ipyparallel `
To enable the extension in the notebook, in the shell, type:
` ipcluster nbextension enable `
To start up a cluster, with 10 nodes, on a local IP address, in the shell, type:
` ipcluster start --n=10 `
or listening on a specific address:
` ipcluster start --n=10 --ip=192.168.1.108 `
Initialize the code to use the 10 nodes, inside the notebook from a host kernel (can be any metakernel kernel), type:
` %parallel calysto_scheme CalystoScheme `
Run code in parallel, inside the notebook, type:
Execute a single line, in parallel:
` %px (+ 1 1) `
Or execute the entire cell, in parallel:
` %%px (* cluster_rank cluster_rank) `
Results come back in a Scheme vector, in cluster_rank order. Therefore, the above would produce the result:
`scheme #10(0 1 4 9 16 25 36 49 64 81) ` You can get the results back in the host Scheme by accessing the variable _ (single underscore).
Notice that you can use the variable cluster_rank to partition parts of a problem so that each node is working on something different.
In the examples above, use -e to evaluate the code in the host Scheme as well. Note that cluster_rank is not defined on the host machine, and that this assumes the host kernel is the same as the parallel machines.
A full notebook example can be found here: [Mandelbrot.ipynb](https://github.com/Calysto/metakernel/blob/master/examples/Mandelbrot.ipynb)
## Install
Ensure that your system has jupyter installed, and pyQt and qtconsole if you want to use the qtconsole option.
You can install Calysto Scheme with Python3:
` pip3 install --upgrade calysto-scheme --user python3 -m calysto_scheme install --user `
or in the system kernel folder with:
` sudo pip3 install --upgrade calysto-scheme sudo python3 -m calysto_scheme install `
You can also use the –sys-prefix to install into your virtualenv.
Change pip3/python3 to use a different pip or Python. The version of Python used will determine how Calysto Scheme is run.
Use it in the Jupyter console, qtconsole, or notebook:
` jupyter console --kernel calysto_scheme jupyter qtconsole --kernel calysto_scheme jupyter notebook `
You can also just use the Python program, but it doesn’t have a fancy Read-Eval-Print Loop. Just run:
` python calysto_scheme/scheme.py `
## Requires
Python3
metakernel (installed automatically)
Calysto Scheme can also be un under PyPy for increased performance.
## Features
Calysto Scheme supports:
JIT - just in time compilation
continuations
use of all Python libraries
choose/fail - built in fail and try again
produces stack trace (with line numbers), like Python
test suite
Planned:
Object-oriented class definitions and instance creation
create a full set of Scheme functions (one can fall back to Python functions for now)
Limitations:
Runs faster on PyPy
1 maintainer
Metadata
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Release files for calysto-scheme 2.1.9
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Total release size: 662.4 kB
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