#!/usr/bin/env python
# coding: utf-8

# <h1>Introduction to Jupyter Notebooks</h1>
# <p>We are going to review core functionalities of Jupyter notebooks.</p>
# <p><i>~ Expert users may skip this introductory tutorial! ~</i></p>

# <h2>Execute Python code</h2>
# <p>The folowing cell will execute a simple python command.<br>
# In order to execute the cell:
# <nl>
#   <li>Click the cell</li>
#   <li>Press SHIFT+ENTER</li>
# </nl>
# </p>

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print("Hello World!!!")


# <p>The output generated by the Python command is reported right after the cell</p>

# <h2>Execute BASH code</h2>
# <p>If instead of Python you want to use BASH: the cell must begin with the header line <tt><b>%%bash</b></tt>. In this way the whole cell is interpreted as BASH.</p>

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get_ipython().run_cell_magic('bash', '', 'echo "Hello World!!!"\necho "Any other line is again BASH!"\n')


# <h2>Execute both Python and BASH</h2>
# <p>In order to mix Python and BASH, start each BASH line with the character "<tt><b>!</b></tt>", and leave Python as it is. <br>
# Comments start with "<tt><b>#</b></tt>".</p>

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# bash (this is a comment)
get_ipython().system('echo "I love this school and this is BASH!"')
# python
print("I love this school and this is Python!")


# <h2>Where does the Jupyter notebook run?</h2>
# <p>Jupyter Notebooks is simply invoking commands that are executed in your local directory.</p>

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# where are we? show the local directory
get_ipython().system('pwd')


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# list content of the local directory 
get_ipython().system('ls')


# <h2>Download a file from the web, give it a look and delete it</h2>
# <p>Let's download the file <br>
# <tt><b>Si_ONCV_PBE-1.1.upf</b></tt><br>
# from the URL <tt>http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.1.upf<tt></p>

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# download a file
get_ipython().system('wget -N http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.1.upf')


# <p>Let's give a quick look at the file <tt><b>Si_ONCV_PBE-1.1.upf</b></tt><p>

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# give a look
get_ipython().system('cat Si_ONCV_PBE-1.1.upf')


# <p>Let's remove the file <tt><b>Si_ONCV_PBE-1.1.upf</b></tt><p>

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# remove the file 
get_ipython().system('rm Si_ONCV_PBE-1.1.upf')

# list content of the local directory 
get_ipython().system('ls')


# <h2>Write/Read a text file</h2>
# <p>We review how I/O operations with text files work in Python.</p>

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# open the file hello.dat in write mode
with open("hello.dat", "w") as file :
    file.write("Hello!!") # write


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# list content of the local directory 
get_ipython().system('ls')

# give a quick look at the file 
get_ipython().system('cat hello.dat')


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# open the file hello.dat in read mode 
with open("hello.dat", "r") as file :
    data = file.read() # read 

# data holds the content read from the file
print(data) # show 


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# remove the file 
get_ipython().system('rm hello.dat')


# <h2>Convert Python data structures to/from a JSON file</h2>
# <p>We show how Python data structures can be easily dumped in/loaded from a JSON file.</p>
# <p>Let's create a Python data structure.</p>

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# a dictionary contains keys and values
data={}
data["project"]="liquid_project"

# it can be nested 
data["material"]={}
data["material"]["name"]="water"
data["material"]["formula"]="H2O"
data["material"]["density"]=1

# lists contain information which can be iterated
data["material"]["atomic_species"]=["O","H"]

# show the data 
print(data)


# <p>Write the Python data in the JSON file <tt><b>data.json</b></tt>

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import json

# write data to JSON file 
with open('data.json', 'w') as file:
    json.dump(data, file)


# <p>Give a quick look at the file <tt><b>data.json</b></tt></p>

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# give a quick look at the file 
get_ipython().system('cat data.json')


# <p>Read the Python data from the JSON file <tt><b>data.json</b></tt>

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import json

# read data from JSON file 
with open('data.json',"r") as file:
    d = json.load(file)

# show the data 
print(d) 
# pretty print the data
print(json.dumps(data, indent=2))


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# remove the file 
get_ipython().system('rm data.json')


# <h2>Generate data and create a simple plot</h2>
# <p>We are going to generate the (X,Y) data points for the functions: $Y=X$, and $Y=X^2$, then plot the data points.</p>

# Generate data

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import numpy as np

# generate 6 points equally spaced from 0 to 3.
x = np.linspace(0,3,6) 

# generate y points for each x (implicit iteration)
y1 = x
y2 = x*x

# show data
print("x  : ", x)
print("y1 : ", y1)
print("y2 : ", y2)


# Plot data

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import matplotlib.pyplot as plt

# plot 
fig, ax = plt.subplots(1, 1)
ax.plot(x, y1, 'bo-', label="$y=x$" )
ax.plot(x, y2, 'ro-', label="$y=x^2$" )

# set labels 
plt.xlabel('x')
plt.ylabel('y')

# set legend
plt.legend()

# set title 
plt.title("Functions")

# show the plot
plt.show()


# <h3>Hi 5! You are no longer a toddler!</h3>

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