MICCoM School 2022 Ex#0 : Introduction to Jupyter notebooks¶
We are going to review the core functionalities of Jupyter notebooks that will be used in the next tutorials.
0.0 : Do I need Jupyter notebooks in order to run WEST?¶
NO.
We are just using Jupyter Notebooks in order to make the tutorials more interactive.
0.1 : Run Python¶
The folowing cell will execute a simple Python command. In order to execute the cell: - Click the cell - Press SHIFT+ENTER
[1]:
print('Hello World!!!')
Hello World!!!
The output generated by the Python command is reported right after the cell.
0.2 : Run BASH¶
If instead of Python you want run BASH commands: the whole cell can be interpreted as BASH if the cell contains the header line %%bash.
[2]:
%%bash
echo 'Hello World!!!'
echo 'Any other line is again BASH!'
Hello World!!!
Any other line is again BASH!
0.3 : Run Python and BASH¶
In order to mix Python and BASH, start each BASH line with the character !, and leave Python as it is.
Comments start with #.
[3]:
# BASH (this is a comment)
!echo 'I love this school and this is BASH!'
# Python
print('I love this school and this is Python!')
I love this school and this is BASH!
I love this school and this is Python!
0.4 : Where does the Jupyter notebook run?¶
There’s nothing magical about Jupyter notebooks, we are simply invoking BASH and/or Python commands that are executed in your working directory.
[4]:
# where are we? show the local directory
!pwd
/home/user/MICCoM_School_2022/
[5]:
# list content of the local directory
!ls
miccom_000.ipynb
0.5 : Download a file from the web, give it a look then delete it¶
Let’s download the file Si_ONCV_PBE-1.2.upf from the URL http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.2.upf
[6]:
# download a file
!wget -q -N http://www.quantum-simulation.org/potentials/sg15_oncv/upf/Si_ONCV_PBE-1.2.upf
Let’s give a quick look at the file Si_ONCV_PBE-1.2.upf.
[7]:
# give a look
!cat Si_ONCV_PBE-1.2.upf
<UPF version="2.0.1">
<PP_INFO>
Si_ONCV_PBE-1.2.upf
This pseudopotential file has been produced using the code
ONCVPSP (Optimized Norm-Conservinng Vanderbilt PSeudopotential)
scalar-relativistic version 2.1.1, 03/26/2014 by D. R. Hamann
The code is available through a link at URL www.mat-simresearch.com.
Documentation with the package provides a full discription of the
input data below.
While it is not required under the terms of the GNU GPL, it is
suggested that you cite D. R. Hamann, Phys. Rev. B 88, 085117 (2013)
in any publication using these pseudopotentials.
Copyright 2015 The Regents of the University of California
This work is licensed under the Creative Commons Attribution-ShareAlike
4.0 International License. To view a copy of this license, visit
http://creativecommons.org/licenses/by-sa/4.0/ or send a letter to
Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.
This pseudopotential is part of the Schlipf-Gygi norm-conserving
pseudopotential library. Its construction parameters were tuned to
reproduce materials of a training set with very high accuracy and
should be suitable as a general purpose pseudopotential to treat a
variety of different compounds. For details of the construction and
testing of the pseudopotential please refer to:
M. Schlipf and F. Gygi, Computer Physics Communications (2015)
DOI: 10.1016/j.cpc.2015.05.011
http://dx.doi.org/10.1016/j.cpc.2015.05.011
http://www.quantum-simulation.org
We kindly ask that you include this reference in all publications
associated to this pseudopotential.
<PP_INPUTFILE>
# ATOM AND REFERENCE CONFIGURATION
# atsym z nc nv iexc psfile
Si 14.00 3 2 4 upf
#
# n l f energy (Ha)
1 0 2.00
2 0 2.00
2 1 6.00
3 0 2.00
3 1 2.00
#
# PSEUDOPOTENTIAL AND OPTIMIZATION
# lmax
1
#
# l, rc, ep, ncon, nbas, qcut
0 2.40480 -0.39736 5 8 5.11281
1 3.56481 -0.14998 5 8 3.50314
#
# LOCAL POTENTIAL
# lloc, lpopt, rc(5), dvloc0
4 5 1.27795 0.00000
#
# VANDERBILT-KLEINMAN-BYLANDER PROJECTORs
# l, nproj, debl
0 2 0.56800
1 2 1.00881
#
# MODEL CORE CHARGE
# icmod, fcfact
0 0.00000
#
# LOG DERIVATIVE ANALYSIS
# epsh1, epsh2, depsh
-5.00 3.00 0.02
#
# OUTPUT GRID
# rlmax, drl
6.00 0.01
#
# TEST CONFIGURATIONS
# ncnf
0
# nvcnf
# n l f
</PP_INPUTFILE>
</PP_INFO>
<!-- -->
<!-- END OF HUMAN READABLE SECTION -->
<!-- -->
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is_ultrasoft="F"
is_paw="F"
is_coulomb="F"
has_so="F"
has_wfc="F"
has_gipaw="F"
core_correction="F"
functional="PBE"
z_valence=" 4.00"
total_psenergy=" -3.74332968930E+00"
rho_cutoff=" 6.01000000000E+00"
l_max="1"
l_local="-1"
mesh_size=" 602"
number_of_wfc="0"
number_of_proj="4"/>
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5.0942997929E-02 5.0413544741E-02 4.9889564660E-02 4.9371004763E-02
4.8857810578E-02 4.8349926083E-02
</PP_RHOATOM>
</UPF>
Let’s remove the file Si_ONCV_PBE-1.2.upf.
[8]:
# remove the file
!rm Si_ONCV_PBE-1.2.upf
0.6 : Write/read a text file¶
We review how to write and read text files in Python.
[9]:
# open the file hello.dat in write mode
with open('hello.dat', 'w') as text_file :
text_file.write('Hello!!')
[10]:
# give a quick look at the file
!cat hello.dat
Hello!!
[11]:
# open the file hello.dat in read mode
with open('hello.dat', 'r') as text_file :
data = text_file.read()
# data holds the content read from the file
print(data) # show
Hello!!
0.7 : Convert Python data structures to/from a JSON file¶
We show how Python data structures can be easily dumped in/loaded from a JSON file.
Let’s create a Python data structure, similar to what we will be using in the next tutorial.
[12]:
# 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)
{'project': 'liquid_project', 'material': {'name': 'water', 'formula': 'H2O', 'density': 1, 'atomic_species': ['O', 'H']}}
Write the Python data in the JSON file data.json.
[13]:
import json
# write data to JSON file
with open('data.json', 'w') as file :
json.dump(data, file)
Give a quick look at the file data.json.
[14]:
# give a quick look at the file
!cat data.json
{"project": "liquid_project", "material": {"name": "water", "formula": "H2O", "density": 1, "atomic_species": ["O", "H"]}}
Read the Python data from the JSON file data.json.
[15]:
import json
# read data from JSON file
with open('data.json', 'r') as file :
d = json.load(file)
# show the data
print(d)
# print the data
print(json.dumps(d, indent=2))
{'project': 'liquid_project', 'material': {'name': 'water', 'formula': 'H2O', 'density': 1, 'atomic_species': ['O', 'H']}}
{
"project": "liquid_project",
"material": {
"name": "water",
"formula": "H2O",
"density": 1,
"atomic_species": [
"O",
"H"
]
}
}
0.8 : Convert Python data structures to/from a YAML file¶
We show how Python data structures can be easily dumped in/loaded from a YAML file.
Write the Python data in the YAML file data.yml.
[16]:
import yaml
# write data to YAML file
with open('data.yml', 'w') as file :
yaml.dump(data, file, sort_keys=False)
Give a quick look at the file data.yml.
[17]:
# give a quick look at the file
!cat data.yml
project: liquid_project
material:
name: water
formula: H2O
density: 1
atomic_species:
- O
- H
Read the Python data from the YAML file data.yml.
[18]:
import yaml
# read data from YAML file
with open('data.yml', 'r') as file :
d = yaml.load(file, Loader=yaml.FullLoader)
# show the data
print(d)
# print the data
print(yaml.dump(d, sort_keys=False))
{'project': 'liquid_project', 'material': {'name': 'water', 'formula': 'H2O', 'density': 1, 'atomic_species': ['O', 'H']}}
project: liquid_project
material:
name: water
formula: H2O
density: 1
atomic_species:
- O
- H
0.9 : Generate data and make a simple plot¶
We are going to generate the (X,Y) data points for the functions: \(Y=X\), and \(Y=X^2\), then plot the data points.
[19]:
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)
x : [0. 0.6 1.2 1.8 2.4 3. ]
y1 : [0. 0.6 1.2 1.8 2.4 3. ]
y2 : [0. 0.36 1.44 3.24 5.76 9. ]
[20]:
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()
You are good to go!