Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Scientific Computing with Python 3
  • Table Of Contents Toc
Scientific Computing with Python 3

Scientific Computing with Python 3

By : Claus Führer, Claus Fuhrer, Olivier Verdier, Jan Erik Solem
4 (2)
Buy this Book
close
close
Scientific Computing with Python 3

Scientific Computing with Python 3

4 (2)
By: Claus Führer, Claus Fuhrer, Olivier Verdier, Jan Erik Solem
Buy this Book

Overview of this book

Python can be used for more than just general-purpose programming. It is a free, open source language and environment that has tremendous potential for use within the domain of scientific computing. This book presents Python in tight connection with mathematical applications and demonstrates how to use various concepts in Python for computing purposes, including examples with the latest version of Python 3. Python is an effective tool to use when coupling scientific computing and mathematics and this book will teach you how to use it for linear algebra, arrays, plotting, iterating, functions, polynomials, and much more.
Table of Contents (17 chapters)
close
close
close
Preface
Icon
Preface
Icon
What this book covers
Icon
What you need for this book
Icon
Who this book is for
Icon
Other Python literature
Icon
Conventions
Icon
Reader feedback
Icon
Customer support
Lock Free Chapter
1
1. Getting Started
Icon
1. Getting Started
Icon
Installation and configuration instructions
Icon
Program and program flow
Icon
Basic types
Icon
Repeating statements with loops
Icon
Conditional statements
Icon
Encapsulating code with functions
Icon
Scripts and modules
Icon
Interpreter
Icon
Summary
2
2. Variables and Basic Types
Icon
2. Variables and Basic Types
Icon
Variables
Icon
Numeric types
Icon
Booleans
Icon
Strings
Icon
Summary
Icon
Exercises
3
3. Container Types
Icon
3. Container Types
Icon
Lists
Icon
Arrays
Icon
Tuples
Icon
Dictionaries
Icon
Sets
Icon
Container conversions
Icon
Type checking
Icon
Summary
Icon
Exercises
4
4. Linear Algebra – Arrays
Icon
4. Linear Algebra – Arrays
Icon
Overview of the array type
Icon
Mathematical preliminaries
Icon
The array type
Icon
Accessing array entries
Icon
Functions to construct arrays
Icon
Accessing and changing the shape
Icon
Stacking
Icon
Functions acting on arrays
Icon
Linear algebra methods in SciPy
Icon
Summary
Icon
Exercises
5
5. Advanced Array Concepts
chevron up
Icon
5. Advanced Array Concepts
Icon
Array views and copies
Icon
Comparing arrays
Icon
Boolean operations on arrays
Icon
Array indexing
Icon
Performance and Vectorization
Icon
Broadcasting
Icon
Sparse matrices
Icon
Summary
6
6. Plotting
Icon
6. Plotting
Icon
Basic plotting
Icon
Formatting
Icon
Meshgrid and contours
Icon
Images and contours
Icon
Matplotlib objects
Icon
Making 3D plots
Icon
Making movies from plots
Icon
Summary
Icon
Exercises
7
7. Functions
Icon
7. Functions
Icon
Basics
Icon
Parameters and arguments
Icon
Variable number of arguments
Icon
Return values
Icon
Recursive functions
Icon
Function documentation
Icon
Functions are objects
Icon
Anonymous functions - the  lambda keyword
Icon
Functions as decorators
Icon
Summary
Icon
Exercises
8
8. Classes
Icon
8. Classes
Icon
Introduction to classes
Icon
Attributes and methods
Icon
Attributes that depend on each other
Icon
Bound and unbound methods
Icon
Class attributes
Icon
Class methods
Icon
Subclassing and inheritance
Icon
Encapsulation
Icon
Classes as decorators
Icon
Summary
Icon
Exercises
9
9. Iterating
Icon
9. Iterating
Icon
The for statement
Icon
Controlling the flow inside the loop
Icon
Iterators
Icon
Convergence acceleration
Icon
List filling patterns
Icon
When iterators behave as lists
Icon
Iterator objects
Icon
Infinite iterations
Icon
Summary
Icon
Exercises
10
10. Error Handling
Icon
10. Error Handling
Icon
What are exceptions?
Icon
Finding Errors: Debugging
Icon
Summary
11
11. Namespaces, Scopes, and Modules
Icon
11. Namespaces, Scopes, and Modules
Icon
Namespace
Icon
Scope of a variable
Icon
Modules
Icon
Summary
12
12. Input and Output
Icon
12. Input and Output
Icon
File handling
Icon
NumPy methods
Icon
Pickling
Icon
Shelves
Icon
Reading and writing Matlab data files
Icon
Reading and writing images
Icon
Summary
13
13. Testing
Icon
13. Testing
Icon
Manual testing
Icon
Automatic testing
Icon
Using unittest package
Icon
Parameterizing tests
Icon
Assertion tools
Icon
Float comparisons
Icon
Unit and functional tests
Icon
Debugging
Icon
Test discovery
Icon
Measuring execution time
Icon
Summary
Icon
Exercises
14
14. Comprehensive Examples
Icon
14. Comprehensive Examples
Icon
Polynomials
Icon
The polynomial class
Icon
Newton polynomial
Icon
Spectral clustering
Icon
Solving initial value problems
Icon
Summary
Icon
Exercises
15
15. Symbolic Computations - SymPy
Icon
15. Symbolic Computations - SymPy
Icon
What are symbolic computations?
Icon
Basic elements of SymPy
Icon
Elementary Functions
Icon
Symbolic Linear Algebra
Icon
Examples for Linear Algebra Methods in SymPy
Icon
Substitutions
Icon
Evaluating symbolic expressions
Icon
Converting a symbolic expression into a numeric function
Icon
Summary
16
References
Icon
References

Array indexing

We have already seen that one may index arrays by combinations of slices and integers, this is the basic slicing technique. There are, however, many more possibilities, which allow for a variety of ways to access and modify array elements.

Indexing with Boolean arrays

It is often useful to access and modify only parts of an array, depending on its value. For instance, one might want to access all the positive elements of an array. This turns out to be possible using Boolean arrays, which act like masks to select only some elements of an array. The result of such an indexing is always a vector. For instance, consider the following example:

B = array([[True, False],
           [False, True]])
M = array([[2, 3],
           [1, 4]])
M[B] # array([2,4]), a vector

In fact, the M[B] call is equivalent to M.flatten()[B]. One may then replace the resulting vector by another vector. For instance, one may replace all the elements by zero (refer to section Broadcasting...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Scientific Computing with Python 3
End of Section 5
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon