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Scientific Computing with Python 3

You're reading from   Scientific Computing with Python 3 An example-rich, comprehensive guide for all of your Python computational needs

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Product type Paperback
Published in Dec 2016
Publisher Packt
ISBN-13 9781786463517
Length 332 pages
Edition 1st Edition
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Authors (4):
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Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
Olivier Verdier Olivier Verdier
Author Profile Icon Olivier Verdier
Olivier Verdier
Claus Führer Claus Führer
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Claus Führer
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Table of Contents (17) Chapters Close

Preface 1. Getting Started FREE CHAPTER 2. Variables and Basic Types 3. Container Types 4. Linear Algebra – Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Error Handling 11. Namespaces, Scopes, and Modules 12. Input and Output 13. Testing 14. Comprehensive Examples 15. Symbolic Computations - SymPy References

Stacking


The universal method to build matrices from a couple of (matching) submatrices is concatenate. Its syntax is:

concatenate((a1, a2, ...), axis = 0)

This command stacks the submatrices vertically (on top of each other) when axis=0 is specified. With the axis=1 argument, they are stacked horizontally, and this generalizes according to arrays with more dimensions. This function is called by several convenient functions, as follows:

  • hstack: Used to stack matrices horizontally
  • vstack: Used to stack matrices vertically
  • columnstack: Used to stack vectors in columns

Stacking vectors

One may stack vectors row-wise or column-wise using vstack and column_stack, as illustrated in the following figure:

Tip

hstack would produce the concatenation of v1 and v2. 

Let us consider the symplectic permutation as an example for vector stacking: We have a vector of size 2n. 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...

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