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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

4.7.1 Stacking vectors

You may stack vectors row-wise or column-wise using vstack and column_stack, as illustrated in Figure 4.3:

Figure 4.3: Difference between vstack and column_stack

Note that hstack would produce the concatenation of v1 and v2. 

Let's consider the symplectic permutation as an example for vector stacking: we have a vector of size . We want to perform a symplectic transformation of a vector with an even number of components, that is, exchange the first half with the second half of the vector with sign change:

This operation is resolved in Python as follows:

# v is supposed to have an even length.
def symp(v):
    n = len(v) // 2 # use the integer division //
    return hstack([v[-n:], -v[:n]])
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