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Scientific Computing with Python

Scientific Computing with Python

By : Führer, Claus Fuhrer, Solem, Verdier
4.5 (15)
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Scientific Computing with Python

Scientific Computing with Python

4.5 (15)
By: Führer, Claus Fuhrer, Solem, Verdier

Overview of this book

Python has tremendous potential within the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python. This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing, and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms within the realm of scientific computing.
Table of Contents (23 chapters)
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20
About Packt
22
References

14.6 Reading and writing images

The module PIL.Image comes with some functions for handling images. The following will read a JPEG image, print the shape and type, and then create a resized image, and write the new image to a file:

import PIL.Image as pil   # imports the Pillow module

# read image to array
im=pil.open("test.jpg") print(im.size) # (275, 183)
# Number of pixels in horizontal and vertical directions # resize image im_big = im.resize((550, 366)) im_big_gray = im_big.convert("L") # Convert to grayscale

im_array=array(im)
print(im_array.shape)
print(im_array.dtype) # unint 8
# write result to new image file im_big_gray.save("newimage.jpg")

 

PIL creates an image object that can easily be converted to a NumPy array. As an array object, images are stored with pixel values in the range 0...255 as 8-bit unsigned integers (unint8). The third shape value shows how many color channels the image...

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