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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

Functions of two variables

Suppose  and  are vectors and we want to form the matrix  with elements . This would correspond to the function . The matrix  is merely defined by:

W=u.reshape(-1,1) + v

If the vectors  and  are and  respectively, the result is:

array([[2, 3, 4],
[3, 4, 5]])

More generally, suppose that we want to sample the function . Supposing that the vectors  and  are defined, the matrix  of sampled values is obtained with:

W = cos(x).reshape(-1,1) + sin(2*y)

Note that this is very frequently used in combination with ogrid. The vectors obtained from ogrid are already conveniently shaped for broadcasting. This allows for the following elegant sampling of the function :

x,y = ogrid[0:1:3j,0:1:3j] 
# x,y are vectors with the contents of linspace(0,1,3)
w = cos(x) + sin(2*y)

The syntax of ogrid needs some explanation: First, ogrid is not a function. It is an instance...

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