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# Motivation

>>>
As a scientist you need to produce plots.

The question is how to plot? 
>>>

**plot and compare subsets of large data sets**

>>>
You usually face large data sets,
which you want to process in different manner
to extract and plot a single aspect of this data set.

But you may also want to compare a single aspect 
of different data sets, or on the contrary different aspects 
of a single data set.
>>>

**plot fast and interactively**

>>>
And you may want to do in a fast and interactive 
manner.
>>>

**be sure what you plot**

>>>
But the most important for a scientist is to control what 
you do and be sure of what you plot.
>>>

**clear and transparent way to plot**

>>>
Plots are the key value of your work. 
You want to spend time on the quality of the content and form. 
Not on the technical aspect of how to plot what.
>>>

**reuse, improve, and combine your plots**

>>>
After spending time on designing the content and the 
form of a single plot you want to be able to use it for 
various data sets without loosing consistence between 
versions of the plot. 
>>>

 **keep track of your plots over long periods**

>>>
Finally, when you come back on some former work, 
you don't want to rethink about the technique, 
you want to start from the same point as before 
from another point of view.
>>>

**These are the basic features of `myplotlib` and much more.**



# The power of `myplotlib`

`myplotlib` allows you to design a plot, that consist 
of a single file. 
It can then plotted alone or combined with any other plot.

This plot contains it own data processing method and 
can be used for any compatible raw data you provide. 

A plot can be called interactively from a python interpreter, 
or from the command line. 

The same plot can be drawn on screen or directly in a file 
making sure that what you see is what you get.

Producing files of the same aspect of large number of 
data sets is possible thanks to the scripting mode.

Because a plot is in a unique file, any changes will 
be spread, allowing fast and consistent corrections.



## `myplotlib` can:

   - interactive mode
   - script mode
   - consistent corrections
   - versatile dataset
   - full user control on IOs and data processing 
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     making it fast and safe



# Install from source **(without an account at gitlab.aip.de)**

### STEP 1: download source of the module myplotlib

- [zip](https://gitlab.aip.de/yfournier/myplotlib/repository/archive.zip?ref=v3.0.1a)
- [tar.gz](https://gitlab.aip.de/yfournier/myplotlib/repository/archive.tar.gz?ref=v3.0.1a)

extract and rename like `/home/user/src/python/myplotlib`

### STEP 2: Install

Set the python path
```
export PYTHONPATH="/home/user/src/python/"
```

Create your config file (in `/home/user/src/python/myplotlib/`)
```
cp config-default.py config.py
```

### STEP 3: testing

in `/home/user/src/python/`

```
python2.6 -m myplotlib.__main__ -t
python2.7 -m myplotlib -t
python3 -m myplotlib -t
```

This will execute a series of tests, they should all be successful.

> **REMARK**: If you get some errors, try the minimal configuration: replace config.py with myplotlib/.configs/minimal-config.py

## How to use myplotlib

- [get started](how to use interactively)
- [how to use myplotlib](how to use myplotlib)