ALBERT

Note: Contents: 1 Computing with Formulas. - 1.1 The First Programming Encounter: a Formula. - 1.1.1 Using a Program as a Calculator. - 1.1.2 About Programs and Programming. - 1.1.3 Tools for Writing Programs. - 1.1.4 Writing and Running Your First Python Program. - 1.1.5 Warning About Typing Program Text. - 1.1.6 Verifying the Result. - 1.1.7 Using Variables. - 1.1.8 Names of Variables. - 1.1.9 Reserved Words in Python. - 1.1.10 Comments. - 1.1.11 Formatting Text and Numbers. - 1.2 Computer Science Glossary. - 1.3 Another Formula: Celsius-Fahrenheit Conversion. - 1.3.1 Potential Error: Integer Division. - 1.3.2 Objects in Python. - 1.3.3 Avoiding Integer Division. - 1.3.4 Arithmetic Operators and Precedence. - 1.4 Evaluating Standard Mathematical Functions. - 1.4.1 Example: Using the Square Root Function. - 1.4.2 Example: Computing with sinh x. - 1.4.3 A First Glimpse of Rounding Errors. - 1.5 Interactive Computing. - 1.5.1 Using the Python Shell. - 1.5.2 Type Conversion. - 1.5.3 IPython. - 1.6 Complex Numbers. - 1.6.1 Complex Arithmetics in Python. - 1.6.2 Complex Functions in Python. - 1.6.3 Unified Treatment of Complex and Real Functions. - 1.7 Symbolic Computing. - 1.7.1 Basic Differentiation and Integration. - 1.7.2 Equation Solving. - 1.7.3 Taylor Series and More. - 1.8 Summary. - 1.8.1 Chapter Topics. - 1.8.2 Example: Trajectory of a Ball. - 1.8.3 About Typesetting Conventions in This Book. - 1.9 Exercises. - 2 Loops and Lists. - 2.1 While Loops. - 2.1.1 A Naive Solution. - 2.1.2 While Loops. - 2.1.3 Boolean Expressions. - 2.1.4 Loop Implementation of a Sum. - 2.2 Lists. - 2.2.1 Basic List Operations. - 2.2.2 For Loops. - 2.3 Alternative Implementations with Lists and Loops. - 2.3.1 While Loop Implementation of a for Loop. - 2.3.2 The Range Construction. - 2.3.3 For Loops with List Indices. - 2.3.4 Changing List Elements. - 2.3.5 List Comprehension. - 2.3.6 Traversing Multiple Lists Simultaneously. - 2.4 Nested Lists. - 2.4.1 A table as a List of Rows or Columns. - 2.4.2 Printing Objects. - 2.4.3 Extracting Sublists. - 2.4.4 Traversing Nested Lists. - 2.5 Tuples. - 2.6 Summary. - 2.6.1 Chapter Topics. - 2.6.2 Example: Analyzing List Data. - 2.6.3 How to Find More Python Information. - 2.7 Exercises. - 3 Functions and Branching. - 3.1 Functions. - 3.1.1 Mathematical Functions as Python Functions. - 3.1.2 Understanding the Program Flow. - 3.1.3 Local and Global Variables. - 3.1.4 Multiple Arguments. - 3.1.5 Function Argument or Global Variable?. - 3.1.6 Beyond Mathematical Functions. - 3.1.7 Multiple Return Values. - 3.1.8 Computing Sums. - 3.1.9 Functions with No Return Values. - 3.1.10 Keyword Arguments. - 3.1.11 Doc Strings. - 3.1.12 Functions as Arguments to Functions. - 3.1.13 The Main Program. - 3.1.14 Lambda Functions. - 3.2 Branching. - 3.2.1 If-else Blocks. - 3.2.2 Inline if Tests. - 3.3 Mixing Loops, Branching, and Functions in Bioinformatics Examples. - 3.3.1 Counting Letters in DNA Strings. - 3.3.2 Efficiency Assessment. - 3.3.3 Verifying the Implementations. - 3.4 Summary. - 3.4.1 Chapter Topics. - 3.4.2 Example: Numerical Integration. - 3.5 Exercises. - 4 User Input and Error Handling. - 4.1 Asking Questions and Reading Answers. - 4.1.1 Reading Keyboard Input. - 4.2 Reading from the Command Line. - 4.2.1 Providing Input on the Command Line. - 4.2.2 A Variable Number of Command-Line Arguments. - 4.2.3 More on Command-Line Arguments. - 4.3 Turning User Text into Live Objects. - 4.3.1 The Magic Eval Function. - 4.3.2 The Magic Exec Function. - 4.3.3 Turning String Expressions into Functions. - 4.4 Option-Value Pairs on the Command Line. - 4.4.1 Basic Usage of the Argparse Module. - 4.4.2 Mathematical Expressions as Values. - 4.5 Reading Data from File. - 4.5.1 Reading a File Line by Line. - 4.5.2 Alternative Ways of Reading a File. - 4.5.3 Reading a Mixture of Text and Numbers. - 4.6 Writing Data to File. - 4.6.1 Example: Writing a Table to File. - 4.6.2 Standard Input and Output as File Objects. - 4.6.3 What is a File, Really?. - 4.7 Handling Errors. - 4.7.1 Exception Handling. - 4.7.2 Raising Exceptions. - 4.8 A Glimpse of Graphical User Interfaces. - 4.9 Making Modules. - 4.9.1 Example: Interest on Bank Deposits. - 4.9.2 Collecting Functions in a Module File. - 4.9.3 Test Block. - 4.9.4 Verification of the Module Code. - 4.9.5 Getting Input Data. - 4.9.6 Doc Strings in Modules. - 4.9.7 Using Modules. - 4.9.8 Distributing Modules. - 4.9.9 Making Software Available on the Internet. - 4.10 Making Code for Python 2 and 3. - 4.10.1 Basic Differences Between Python 2 and 3. - 4.10.2 Turning Python 2 Code into Python 3 Code. - 4.11 Summary. - 4.11.1 Chapter Topics. - 4.11.2 Example: Bisection Root Finding. - 4.12 Exercises. - 5 Array Computing and Curve Plotting. - 5.1 Vectors. - 5.1.1 The Vector Concept. - 5.1.2 Mathematical Operations on Vectors. - 5.1.3 Vector Arithmetics and Vector Functions. - 5.2 Arrays in Python Programs. - 5.2.1 Using Lists for Collecting Function Data. - 5.2.2 Basics of Numerical Python Arrays. - 5.2.3 Computing Coordinates and Function Values. - 5.2.4 Vectorization. - 5.3 Curve Plotting. - 5.3.1 MATLAB-Style Plotting with Matplotlib. - 5.3.2 Matplotlib; Pyplot Prefix. - 5.3.3 SciTools and Easyviz. - 5.3.4 Making Animations. - 5.3.5 Making Videos. - 5.3.6 Curve Plots in Pure Text. - 5.4 Plotting Difficulties. - 5.4.1 Piecewisely Defined Functions. - 5.4.2 Rapidly Varying Functions. - 5.5 More Advanced Vectorization of Functions. - 5.5.1 Vectorization of StringFunction Objects. - 5.5.2 Vectorization of the Heaviside Function. - 5.5.3 Vectorization of a Hat Function. - 5.6 More on Numerical Python Arrays. - 5.6.1 Copying Arrays. - 5.6.2 In-Place Arithmetics. - 5.6.3 Allocating Arrays. - 5.6.4 Generalized Indexing. - 5.6.5 Testing for the Array Type. - 5.6.6 Compact Syntax for Array Generation. - 5.6.7 Shape Manipulation. - 5.7 High-Performance Computing with Arrays. - 5.7.1 Scalar Implementation. - 5.7.2 Vectorized Implementation. - 5.7.3 Memory-Saving Implementation. - 5.7.4 Analysis of Memory Usage. - 5.7.5 Analysis of the CPU Time. - 5.8 Higher-Dimensional Arrays. - 5.8.1 Matrices and Arrays. - 5.8.2 Two-Dimensional Numerical Python Arrays. - 5.8.3 Array Computing. - 5.8.4 Matrix Objects. - 5.9 Some Common Linear Algebra Operations. - 5.9.1 Inverse, Determinant, and Eigenvalues. - 5.9.2 Products. - 5.9.3 Norms. - 5.9.4 Sum and Extreme Values. - 5.9.5 Indexing. - 5.9.6 Transpose and Upper/Lower Triangular Parts. - 5.9.7 Solving Linear Systems. - 5.9.8 Matrix Row and Column Operations. - 5.9.9 Computing the Rank of a Matrix. - 5.9.10 Symbolic Linear Algebra. - 5.10 Plotting of Scalar and Vector Fields. - 5.10.1 Installation. - 5.10.2 Surface Plots. - 5.10.3 Parameterized Curve. - 5.10.4 Contour Lines. - 5.10.5 The Gradient Vector Field. - 5.11 Matplotlib. - 5.11.1 Surface Plots. - 5.11.2 Contour Plots. - 5.11.3 Vector Field Plots. - 5.12 Mayavi. - 5.12.1 Surface Plots. - 5.12.2 Contour Plots. - 5.12.3 Vector Field Plots. - 5.12.4 A 3D Scalar Field and Its Gradient Field. - 5.12.5 Animations. - 5.13 Summary. - 5.13.1 Chapter Topics. - 5.13.2 Example: Animating a Function. - 5.14 Exercises. - 6 Dictionaries and Strings. - 6.1 Dictionaries. - 6.1.1 Making Dictionaries. - 6.1.2 Dictionary Operations. - 6.1.3 Example: Polynomials as Dictionaries. - 6.1.4 Dictionaries with Default Values and Ordering. - 6.1.5 Example: Storing File Data in Dictionaries. - 6.1.6 Example: Storing File Data in Nested Dictionaries. - 6.1.7 Example: Reading and Plotting Data Recorded at Specific Dates. - 6.2 Strings. - 6.2.1 Common Operations on Strings. - 6.2.2 Example: Reading Pairs of Numbers. - 6.2.3 Example: Reading Coordinates. - 6.3 Reading Data fromWeb Pages. - 6.3.1 About Web Pages. - 6.3.2 How to Access Web Pages