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 -->
  <!--                               -->
    <PP_HEADER
       generated="Generated using ONCVPSP code by D. R. Hamann"
       author="Martin Schlipf and Francois Gygi"
       date="150415"
       comment=""
       element="Si"
       pseudo_type="NC"
       relativistic="scalar"
       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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</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()
../../_images/tutorials_MICCoM_School_2022_miccom_000_41_0.png

You are good to go!