powerxrd 2.0.0

Simple tools to handle powder XRD (and XRD) data with Python.

powerxrd

Simple tools to handle powder XRD (and XRD) data

Installation

pip install powerxrd

Usage

On your Terminal ("command line"), copy-paste the following lines:

cd Desktop   		# go to your Desktop
mkdir pxrd		# create a folder called pxrd

cd pxrd 		# go inside that folder
touch example.py  	# create example.py file
wget https://raw.githubusercontent.com/andrewrgarcia/powerxrd/main/sample1.xy	# download sample1.xy file

Plots from tests/test_module

Code shows basic application of this module. On a blank Python file, type the following:

import powerxrd as xrd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

def test_backsub():
    
    df = pd.read_csv('sample1.xy', sep='\t', header=None)   #'https://www.statology.org/pandas-read-text-file/'
    x,y = np.array(df).T

    chart = xrd.Chart(x,y)

    chart.emission_lines(show=True)
    plt.plot(x,y,label='no backsub')
    plt.plot(*chart.backsub(),label='backsub')
    plt.xlabel('2 $\\theta$')
    plt.legend()
    plt.show()

'''[OUT]
-Gaussian fit results-
y-shift 11692.125692754556
amplitude 2095353.5051754152
mean 33.62661656530919
sigma 15.966392102035668
covariance matrix 
[[ 8.46197862e+07 -6.62161996e+09 -2.35993662e+03 -2.92153344e+04]
 [-6.62161996e+09  5.72042049e+11  1.44541382e+05  2.60212877e+06]
 [-2.35993662e+03  1.44541382e+05  4.03327433e+00  3.38864534e-01]
 [-2.92153344e+04  2.60212877e+06  3.38864534e-01  1.46624777e+01]]

FWHM == sigma*2*sqrt(2*ln(2)): 37.597980168697426 degrees
K (shape factor): 0.9
K-alpha: 0.15406 nm 
max 2-theta: 19.91162984576907 degrees
Scherrer Width == K*lmda / (FWHM*cos(theta))

SCHERRER WIDTH: 0.2145261012981139 nm
'''

def test_sch():
    
    df = pd.read_csv('sample1.xy', sep='\t', header=None)   #'https://www.statology.org/pandas-read-text-file/'
    x,y = np.array(df).T

    chart = xrd.Chart(x,y)

    chart.backsub(tol=1.0,show=True)
    chart.SchPeak(show=True,xrange=[18,22])
    plt.xlabel('2 $\\theta$')
    plt.title('backsub and Scherrer width calculation')
    plt.show()

def test_mav():
    
    df = pd.read_csv('sample1.xy', sep='\t', header=None)   #'https://www.statology.org/pandas-read-text-file/'
    x,y = np.array(df).T

    chart = xrd.Chart(x,y)

    chart.backsub()
    n = 20
    plt.plot(*chart.mav(n))
    plt.xlabel('2 $\\theta$')
    plt.title('backsub and {}-point moving average'.format(n))
    plt.show()

Contributors

• Andrew Garcia - creator and maintainer

Contributing

1. Fork it (https://github.com/your-github-user/tensorscout/fork)
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request