get-started.md

-# Install the template module
+* (Install the module mypltemplate)[https://gitlab.aip.de/yfournier/mypltemplate/wikis/get-started#install-the-module-mypltemplate]
+* (Plotting some Examples)[https://gitlab.aip.de/yfournier/mypltemplate/wikis/get-started#plotting-some-examples]
+
+
+# Install the module mypltemplate
 
 > **NOTES:** STEP 3 has two version with and without an account at gitlab.aip.de
 
@@ -47,61 +51,64 @@ This will execute a series of tests, they should all be successful.
 Now you can start to enjoy the power of `myplotlib`
 
 
-# Plotting the interactively some Exemples
-
-
----- STEP 1: launch python2.7
+# Plotting some Examples
 
->$ python2.7
 
+## STEP 1: launch python2.7
 
----- STEP 2: Import the module. 
+```
+python2.7
+```
 
->>> # IMPORT the MODULE
->>> from mymodule import *
+## STEP 2: Import the module. 
 
-This imports all variables, functions and classes from "mymodule/__init__.py"
+```python
+# IMPORT the MODULE
+from mymodule import *
+```
 
+This imports all variables, functions and classes from `mymodule/__init__.py`
 
----- STEP 3: Open and Access some data
+## STEP 3: Open and Access some data
 
-The template modules has some dummy data for demonstration in "mymodule/data"
+The template modules has some dummy data for demonstration in `mymodule/data`
 This data are of two type:
-- some structured data set (run1.txt, run2.txt, ...)
-- some database (serie1.txt, serie2.txt)
+- some **structured datasets** (run1.txt, run2.txt, ...)
+- some **database-like datasets** (serie1.txt, serie2.txt)
 
 In python (this is a general remark) it exists two powerful objects for
 storing and accessing these types of data.
 
-- for structured data sets the python dictionaries are suitable (https://docs.python.org/2/tutorial/datastructures.html#dictionaries)
-- for database data sets the numpy.recarray are the perfect tool (https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html)
+- for **structured datasets** the python **dictionaries** are suitable [doc](https://docs.python.org/2/tutorial/datastructures.html#dictionaries)
+- for **database-like datasets** the **numpy.recarray** are the perfect tool [doc](https://docs.scipy.org/doc/numpy/reference/generated/numpy.recarray.html)
 
-In "mymodule/myIOs" are two files containing the functions readRun, and readSeries
-- the function readRun reads a run-text-file (structured data set) and stores the information into a python dictionary.
-- the function readSeries reads a series-text-file (database data set) and stores the data into a numpy recarray.
+In `mymodule/myIOs` are two files containing the functions `readRun`, and `readSeries`
+- the function `readRun` reads a run-text-file (structured data set) and stores the information into a python dictionary.
+- the function `readSeries` reads a series-text-file (database data set) and stores the data into a numpy recarray.
 
-Both function returns the data container of myplotlib, MyData
+Both function returns the data container of myplotlib, `MyData`
 
->>> # READ Some data 
->>> run1 = readRun('./mymodule/data/serie1/run1.txt')
-
-you can now type
-
->>> run1
-
-it will return:
+```python
+# READ Some data 
+run1 = readRun('./mymodule/data/serie1/run1.txt')
+```
 
+now you can now type
+```python
+run1
 >>> <mymodule.myplotlib.myData.MyData instance at ...>
+```
 
-as you can see the object run1 is an object of type MyData. The ... shows the address of these object in the memory
-this identifier is unique for any instance (again this is a general remark for python)
-
-Now you can call
+As you can see the object `run1` is an instance of type `MyData`. The `...` shows the address of the instance in the memory.
+This identifier is unique (again this is a general remark for python).
 
->>> run1.data['name']
->>> run1.data['input1']
->>> run1.data['results']
+Now you can access the name of the run, the vaue of input1 or the matrix of the results by simply typing:
+```python
+run1.data['name']
+run1.data['input1']
+run1.data['results']
 ...
+```
 
 
 ---- STEP 4: Visualising some RUN data