ipython-cache 0.2

# cache magic

This package adds %cache line-magic to ipython kernels in jupyter notebooks.

## installation

### install directly from notebook

1. open jupyter notebook

2. create new cell

3. enter !pip install cache-magic

4. execute

5. restart kernel

### install into conda-environment

conda create -n test source activate test pip install cache-magic jupyter notebook

## usage

Activate the magic by loading the module like any other module. Write into a cell import cache_magic and excecute it.

When you want to apply the magic to a line, just prepend the line with %cache

#### example

%cache myVar = someSlowCalculation(some, “parameters”)

Calculates someSlowCalculation(some, “parameters”) once. And in subsequent calls restores myVar from storage.

#### general form

%cache <variable> = <expression>

Variable: This Variable’s value will be fetched from cache.

Expression: This will only be excecuted once and the result will be stored to disk.

#### full form

%cache [–version <version>] [–reset] [–debug] variable [= <expression>]

-v or –version: either a variable name or an integer. Whenever this changes, a new value is calculated (instead of returning an old value from the cache).

if version is ‘*’ or omitted, the hashed expression is used as version, so whenever the expression changes, a new value is cached.

-r or –reset: delete the cached value for this variable. Forces recalculation, if <expression> is present

-d or –debug: additional logging

#### show cache

%cache

shows all variables in cache as html-table

#### full reset

%cache -r %cache –reset

deletes all cached values for all variables

## where is the cache stored?

In the directory where the kernel was started (usually where the notebook is located) in a subfolder called “.cache_magic”

# developer Notes

## editable import

Install into environment with -e:

!pip install -e .

reload after each change:

import cache_magic from imp import reload reload(cache_magic)

Alternatively (if you don’t want to install python, jupyter & co), you can use the docker-compose.yml for development:

cd ipython-cache docker-compose up

## create Conda Packet

todo: This does not work yet

conda install conda-build conda activate test