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