Search in book...
Toggle Font Controls
Create new playlist

Name your new playlist

Playlist description (optional)
Sign In

Email address

Password

Forgot Password?

or

Continue with Facebook

Continue with Google
Sign Up

Full Name

Email address

Confirm Email Address

Password

or

Continue with Facebook

Continue with Google

Previous Chapter

1. Introduction

Albert Danial

Python for MATLAB Development

Extend MATLAB with 300,000+ Modules from the Python Package Index

Logo of the publisher

Albert Danial

Redondo Beach, CA, USA

ISBN 978-1-4842-7222-0e-ISBN 978-1-4842-7223-7

https://doi.org/10.1007/978-1-4842-7223-7

This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed.

The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This Apress imprint is published by the registered company APress Media, LLC part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

To Ashley, Theodore, Mimi

Preface

In 2018, I was chatting with a young engineer who had recently earned an engineering master’s degree. MATLAB came up and she spoke of her frustration with license shortages when projects came due at school. I had the same frustrations—25 years earlier. Had nothing changed?

Of course, a lot had changed.

By then, we had already found our separate ways to Python to do the kind of work we used to do in MATLAB. Why were so few MATLAB users aware of the power and freedom Python could bring them?

I began assembling notes comparing Python solutions to their MATLAB equivalents and shortly afterward learned of MATLAB’s py module. A binary API to Python?! Too good to be true. It was too good to be true, in a sense; early versions couldn’t use critical modules such as NumPy.

The MathWorks improved py with each MATLAB release, though, and today MATLAB can run code from NumPy, SciPy, Pandas, matplotlib, statsmodels, dask, even modules compiled with Cython, Numba, Pythran, and f2py. The MATLAB + Python combination offers astounding possibilities to both languages, yet few MATLAB developers know of this capability or how to take advantage of it. Fertile ground for a new book, I thought.

Acknowledgments

Rachel Rybarczyk’s comments about MATLAB and Python inspired me to start the journey that led to this book.

Alain Sei, although bowing out as my coauthor after realizing the magnitude of the work ahead, nonetheless stayed on as my first reader. At our weekly meetings in building R2, Alain put thought-provoking spin on things to keep my perspective fresh.

Rocco Samuele helped elevate the literary quality of the text. While I don’t have Rocco’s chops for the written word, his edit suggestions clarified my writing.

Ravi Narasimhan’s critique of an early draft felt more like a mugging than a review. After the bruises faded, it was clear he’d given me a goldmine of improvement suggestions. Implementing them led to a more balanced tone and more convincing assertions. Ravi also provided the MATLAB examples for the point and line plots on maps shown in Section 12.4.

When Curtis Webb first told me about Numba four years ago, I dismissed his claims that it could make Python functions run 10 ×, even 30 × faster—without using a C/C++/Fortran compiler. Impossible! Yet somehow, like a magical alien technology, Numba does just that. Best tip ever, thanks, Curtis!

Parker Hudnut gave valuable “big picture” suggestions on the book’s overall structure. Thanks! The beer’s on me the next time we’re at building H.

Thank you Steven Millett, Petra Poschmann, Drew Swalley, and Mark Vaughn for tips on missing or incomplete topics.

Thanks to Professor John Hedengren for letting me copy his predband() function in my section on prediction intervals (Section 11.7.3). His website¹ has excellent videos on statistical computations in Python.

Thanks to Professor James Doyle for advice on, and a technical review of, the frequency response section, Section 14.13.7. His classic “red book” on structural wave propagation [1] remains one of my favorite technical reads.

Dale Williamson’s insights on structural dynamics were inspiring and educational, and for that I’m grateful; thanks Dale. Errors with the formulation of equations and procedures are entirely mine.

My technical reviewers, Phillip Feldman and Darrell Yocom, are the unsung heroes behind the operational aspects of the code and examples. Both found numerous code errors and saved me from embarrassingly wrong explanations. Phillip guided me through the confidence and prediction intervals in the regression section, Section Section 11.7.3. Darrell was the original “observant reader” mentioned at the end of the TCP Recipe, Section 7.18, and first altered me to MATLAB’s type downcast behavior for mixed-type math, Section 15.2.

To the Apress team, thank you: Steve Anglin for taking a chance on me; Mark Powers for shepherding the book from early draft to production; Sherly Nandha for the extensive copy editing and typesetting work needed to turn my draft into a book.

Finally, and most importantly, thanks to Ashley, Theodore, and Mimi for believing in me and giving me time and space to write.

A.N.D.

November 2021

Table of Contents

Chapter 1: Introduction 1

1.1 Learn Python Through MATLAB Equivalents 2

1.2 Is Python Really Free? 4

1.3 What About Toolboxes? 5

1.4 Why Python Won’t Replace MATLAB 5

1.5 Contents at a Glance 6

1.6 I Already Know Python. How Do I Call Python Functions in MATLAB? 8

1.7 The Recipes Don’t Work! MATLAB Crashes! (and What to Do About It) 8

Chapter 2: Installation 11

2.1 Downloads 11

2.1.1 Match Your Python and MATLAB Versions! 12

2.1.2 Verify That Python Runs 12

2.2 Post-Install Configuration and Checkout 12

2.3 Creating and Running a Python Program 14

2.4 The Curse of Choice 15

2.5 Virtual Environments 15

2.5.1 matpy, the Virtual Environment Used in This Book 16

2.5.2 Commands to Manage Virtual Environments 17

2.5.3 Keeping Your Virtual Environment Current 18

2.6 ipython, IDEs 20

2.6.1 Autoload Modules When ipython Starts 20

2.7 Python and MATLAB Versions Used in This Book 21

Chapter 3: Language Basics 23

3.1 Assignment 23

3.1.1 Assignment with = 23

3.1.2 In-Place Updates with +=, -=, and Others 25

3.1.3 Walrus Operator, = 26

3.2 Printing 27

3.3 Indentation 28

3.3.1 Tabs 30

3.4 Indexing 30

3.4.1 Brackets vs. Parentheses 30

3.4.2 Zero-Based Indexing and Index Ranges 30

3.4.3 Start, End, and Negative Indices 31

3.4.4 Index Strides 32

3.4.5 Index Chaining 32

3.5 for loops 33

3.5.1 Early Loop Exits 36

3.5.2 Exit from Nested Loops 36

3.6 while Loops 37

3.7 if Statements 38

3.7.1 Boolean Expressions and Operators 39

3.7.2 Range Tests 39

3.8 Functions 40

3.8.1 Pass by Value and Pass by Reference 41

3.8.2 Variable Arguments 43

3.8.3 Keyword Arguments 44

3.8.4 Decorators 46

3.8.5 Type Annotation and Argument Validation 47

3.8.6 Left-Hand Side Argument Count 49

3.9 Generators 50

3.9.1 yield, next() 50

3.9.2 range() 51

3.10 Scoping Rules and Global Variables 52

3.11 Comments 54

3.11.1 Docstrings 54

3.12 Line Continuation 56

3.13 Exceptions 56

3.14 Modules and Packages 58

3.14.1 Namespace 58

3.14.2 def main() 60

3.14.3 Module Search Path 61

3.14.4 Installing New Modules 62

3.14.5 Module Dependency Conflicts and Virtual Environments 63

Chapter 4: Data Containers 65

4.1 NumPy Arrays 66

4.2 Strings 68

4.2.1 Strings, Character Arrays, and Byte Arrays 68

4.2.2 String Operations 70

4.2.3 Formatting 74

4.2.4 Separate a String into Words 75

4.2.5 Tests on Strings 76

4.2.6 String Searching, Replacing with Regular Expressions 77

4.2.7 String Templates 80

4.3 Python Lists and MATLAB Cell Arrays 81

4.3.1 Initialize an Empty List 82

4.3.2 Create a List with Given Values 83

4.3.3 Get the Length of a List 83

4.3.4 Index a List Item 84

4.3.5 Extract a Range of Items 85

4.3.6 Warning—Python Index Ranges Are Not Checked! 87

4.3.7 Append an Item 90

4.3.8 Append Another List 91

4.3.9 Preallocate an Empty List 92

4.3.10 Insert to the Beginning (or Any Other Position) of a List 93

4.3.11 Indexing Nested Containers 93

4.3.12 Membership Test: Does an Item Exist in a List? 94

4.3.13 Find the Index of an Item 95

4.3.14 Apply an Operation to All Items (List Comprehension) 97

4.3.15 Select a Subset of Items Based on a Condition 98

4.3.16 How Many Times Does an Item Occur? 99

4.3.17 Remove the First or Last (or Any Intermediate) List Item 99

4.3.18 Remove an Item by Value 100

4.3.19 Merging Multiple Lists 101

4.3.20 Unmerging Combined Lists 103

4.3.21 Sort a List 105

4.3.22 Reverse a List 106

4.4 Python Tuples 106

4.5 Python Sets and MATLAB Set Operations 109

4.6 Python Dictionaries and MATLAB Maps 111

4.6.1 Iterating over Keys 112

4.6.2 Testing for Key Existence 113

4.6.3 Iterating over Keys, Sorting by Key 115

4.6.4 Iterating over Keys, Sorting by Value 117

4.6.5 Tuples As Keys 119

4.6.6 List Values 120

4.7 Structured Data 120

4.7.1 Method 1: namedtuple 121

4.7.2 Method 2: SimpleNamespace 121

4.7.3 Method 3: Classes 122

4.7.4 Method 4: Data Classes 124

4.7.5 Enumerations 132

4.8 Caveat: “=” Copies a Reference for Nonscalars! 134

Chapter 5: Dates and Times 137

5.1 Time 137

5.1.1 Current Time 137

5.1.2 Time String Formats 138

5.1.3 tic, toc; %timeit 140

5.2 Dates 141

5.2.1 datetime Objects to and from Strings 141

5.2.2 Time Deltas 142

5.3 Timezones 144

5.3.1 UTC vs. Local Time 146

5.4 Time Conversions to and from datetime Objects 148

5.4.1 Unix Epoch Seconds 148

5.4.2 ISO 8601 Time String 149

5.4.3 Julian Date; Modified Julian Date; GPS Time 150

5.5 zoneinfo in Python >= 3.9 151

5.5.1 List Available Timezones 152

5.5.2 Date Increments Across Daylight Savings Transition 152

5.6 References 153

Chapter 6: Call Python Functions from MATLAB 155

6.1 Configure MATLAB to Recognize Python 155

6.2 Does It Work? 157

6.3 Importing (and Reloading) Python Modules 159

6.4 Configure startup.m for Python Work 159

6.5 Create Python Variables and Call Python Functions in MATLAB 161

6.5.1 Scalars 161

6.5.2 Lists and Cell Arrays 163

6.5.3 Tuples 165

6.5.4 Numeric Arrays 166

6.5.5 Dictionaries and Structs 168

6.5.6 Keyword Arguments 171

6.5.7 Python-to-MATLAB and MATLAB-to-Python Variable Converters 172

6.5.8 Traversing Generators 172

6.5.9 Traversing zip() 174

6.6 Modifying the Python Search Path Within MATLAB 174

6.6.1 Extending sys.path with an Alias 175

6.6.2 Extending sys.path with insert() 175

6.6.3 Extending sys.path with append() 176

6.7 Python Bridge Modules 177

6.8 Debugging Python Code Called by MATLAB 178

6.9 Summary of Steps to Calling Python Code from MATLAB 181

6.10 Call MATLAB from Python 182

6.10.1 Install matlab.engine 182

6.10.2 Call Functions in a New MATLAB Session 183

6.10.3 Call Functions in an Existing MATLAB Session 184

6.11 Other Mechanisms for MATLAB/Python Interaction 186

6.11.1 System Calls and File I/O 186

6.11.2 TCP/IP Exchange 188

Chapter 7: Input and Output 189

7.1 Text Files 189

7.1.1 Reading Corrupted Text Files with pathlib 191

7.1.2 Reading and Writing Numeric Data 192

7.1.3 I/O Exceptions 192

7.1.4 Parsing Text 193

7.1.5 csv 199

7.1.6 XML 203

7.1.7 YAML 212

7.1.8 JSON 213

7.1.9 ini 214

7.2 Recipe 7-1: Read YAML Files 216

7.3 Recipe 7-2: Write YAML Files 217

7.4 Recipe 7-3: Read an ini File 218

7.5 Recipe 7-4: Write an ini File 220

7.6 Binary Files 222

7.7 Excel .xls, .xlsx 225

7.7.1 Reading .xls and .xlsx Files 226

7.7.2 Writing .xlsx Files 231

7.8 Recipe 7-5: Write an .xlsx File 234

7.9 HDF5 237

7.9.1 Reading an HDF5 File 237

7.9.2 Writing an HDF5 File 239

7.9.3 Reading and Writing HDF5 Dataset Attributes 243

7.9.4 Iterating over All HDF5 Datasets 244

7.10 NetCDF4 246

7.10.1 Reading a NetCDF4 File 246

7.11 SQLite 249

7.12 Recipe 7-6: CRUD with an SQLite Database 252

7.13 Pickle Files 255

7.14 MATLAB .mat Files 257

7.14.1 Inspecting the Contents of a .mat File 258

7.14.2 Reading a .mat File 260

7.14.3 Writing a .mat File 262

7.14.4 mat Version 7.3 267

7.15 Command-Line Input 268

7.15.1 Python: sys.argv 269

7.15.2 MATLAB: Function Arguments; varargin 270

7.15.3 Python: argparse 271

7.16 Interactive Input 275

7.17 Receiving and Sending over a Network 276

7.17.1 HTTP, HTTPS 276

7.17.2 Python As a Web Server 278

7.17.3 TCP/IP 280

7.18 Recipe 7-7: TCP Server 284

7.19 Interacting with Databases 286

7.19.1 PostgreSQL 286

7.19.2 MongoDB 290

7.20 Recipe 7-8: CRUD with a PostgreSQL Database 293

7.21 Recipe 7-9: CRUD with a MongoDB Database 296

7.21.1 Read 298

7.21.2 Update 300

7.21.3 Delete 300

7.22 Recipe 7-10: Interact with Redis 300

7.23 Reference 305

Chapter 8: Interacting with the File System 307

8.1 Reading Directory Contents 308

8.2 Finding Files 309

8.3 Deleting Files 309

8.4 Creating Directories 310

8.5 Deleting Directories 310

8.6 Walking Directory Trees 311

Chapter 9: Interacting with the Operating System and External Executables 315

9.1 Reading, Setting Environment Variables 315

9.2 Calling External Executables 317

9.2.1 Checking for Failures 319

9.2.2 A Bytes-Like Object Is Required 321

9.3 Inspecting the Process Table and Process Resources 322

Chapter 10: Object-Oriented Programming 327

10.1 Classes 327

10.1.1 Private vs. Public 329

10.1.2 Custom Printers 330

10.1.3 Custom Exceptions 331

10.2 Performance Implications 333

Chapter 11: NumPy and SciPy 335

11.1 NumPy Arrays 335

11.1.1 Formatting NumPy Array Values 336

11.1.2 Differences Between NumPy Arrays and MATLAB Matrices 338

11.1.3 NumPy Data Types 341

11.1.4 Typecasting Scalars and Arrays 343

11.1.5 Hex, Binary, and Decimal Representations 344

11.1.6 Creating Arrays 345

11.1.7 Complex Scalars and Arrays 355

11.1.8 Linear Indexing 357

11.1.9 Reading/Writing Arrays to/from Text Files 359

11.1.10 Reading/Writing Arrays to/from Binary Files 360

11.1.11 Primitive Array Operations 368

11.1.12 Adding Dimensions 371

11.1.13 Array Broadcasting 373

11.1.14 Index Masks 377

11.1.15 Extracting and Updating Submatrices 380

11.1.16 Finding Terms of Interest 383

11.1.17 Object-Oriented Programming and Computational Performance 388

11.2 Linear Algebra 394

11.2.1 Linear Equations 395

11.2.2 Singular Value Decomposition 397

11.2.3 Eigenvalue Problems 398

11.3 Sparse Matrices 401

11.3.1 Sparse Matrix Creation with COO, CSC, CSR 401

11.3.2 Sparse Matrix Creation with LIL, DOK 408

11.3.3 Sparse Matrix Creation with BSR, DIA 411

11.3.4 Test Matrices 413

11.3.5 Sparse Matrix I/O 415

11.3.6 Linear Algebra 418

11.3.7 Summary of Sparse Formats and Capabilities; Recommendations 424

11.4 Interpolation 426

11.4.1 One-Dimensional Interpolation 426

11.4.2 Two-Dimensional Interpolation 429

11.4.3 Two-Dimensional Interpolation on a Grid 435

11.5 Curve Fitting 437

11.5.1 Linear Regression 437

11.5.2 Fitting Higher-Order Polynomials 438

11.5.3 Fitting to Models 439

11.6 Recipe 11-1: Curve Fitting with differential_evolution() 447

11.7 Regression 449

11.7.1 Ordinary Least Squares 449

11.7.2 Weighted Least Squares 452

11.7.3 Confidence and Prediction Intervals 453

11.8 Recipe 11-2: Weighted Least Squares in MATLAB 457

11.9 Recipe 11-3: Confidence and Prediction Intervals in MATLAB 460

11.10 Finding Roots 463

11.10.1 Univariate 463

11.10.2 Multivariate 465

11.11 Recipe 11-4: Solving Simultaneous Nonlinear Equations 466

11.12 Optimization 467

11.12.1 Linear Programming 468

11.12.2 Simulated Annealing 474

11.13 Differential Equations 479

11.14 Symbolic Mathematics 481

11.14.1 Defining Symbolic Variables 482

11.14.2 Derivatives 482

11.14.3 Integrals 483

11.14.4 Solving Equations 484

11.14.5 Linear Algebra 484

11.14.6 Series 486

11.15 Recipe 11-5: Using SymPy in MATLAB 486

11.16 Recipe 11-6: Compute Laplace Transforms 487

11.17 Unit Systems 488

11.17.1 Defining Units in pint 489

11.18 Recipe 11-7: Using pint in MATLAB 491

11.19 References 492

Chapter 12: Plotting 493

12.1 Point and Line Plots 493

12.1.1 Saving Plots to Files 496

12.1.2 Multiple Plots per Figure 497

12.1.3 Date and Time on the X Axis 499

12.1.4 Double Y Axes 500

12.1.5 Histograms 502

12.1.6 Stack Plots 504

12.2 Area Plots 505

12.2.1 imshow() 505

12.3 Animations 510

12.4 Plotting on Maps with Cartopy 514

12.4.1 Points 514

12.4.2 Lines 517

12.4.3 Area 519

12.4.4 MATLAB and Cartopy 527

12.4.5 Avoid matplotlib’s Qt Backend in MATLAB! 529

12.5 Recipe 12-1: Drawing Lines on Maps with Cartopy 529

12.6 Recipe 12-2: Overlay Contours on Globe with Cartopy 531

12.7 Recipe 12-3: Shade Map Regions by Value with Cartopy 532

12.8 Plotting on Maps with GeoPandas 535

12.9 Making Plots in Batch Mode 535

12.10 Interactive Plot Editing 536

Chapter 13: Tables and Dataframes 539

13.1 Loading Tables from Files 540

13.2 Table Summaries 543

13.2.1 Table Size, Column Names, Column Types 543

13.2.2 summary() and .info()/.describe() 545

13.2.3 groupsummary() and .value_counts() 546

13.2.4 head() and tail() 546

13.3 Cleaning Data 547

13.3.1 Renaming Columns 547

13.3.2 Changing Column Data Types 548

13.3.3 Changing Column Data 549

13.3.4 Making Timestamps from Strings 550

13.4 Creating Tables Programmatically 550

13.5 Sorting Rows 551

13.6 Table Subsets 552

13.6.1 All Rows, Selected Columns 552

13.6.2 All Columns, Selected Rows 553

13.6.3 Selected Rows, Selected Columns 553

13.6.4 Filter Rows by Conditional Operations 555

13.7 Iterating over Rows 555

13.8 Pivot Tables 556

13.8.1 Single-Level Aggregation 556

13.8.2 Multilevel Aggregation 558

13.9 Adding Columns 558

13.10 Deleting Columns 560

13.11 Joins Across Tables 561

13.12 GeoPandas 564

13.13 Recipe 13-1: Maps with GeoPandas 569

13.14 References 573

Chapter 14: High Performance Computing 575

14.1 Paths to Faster Python Code 576

14.2 Reference Problems 577

14.2.1 The Mandelbrot Set 577

14.2.2 A 2D Finite Element Solver 579

14.3 Reference Hardware and OS 581

14.4 Baseline Performance 582

14.4.1 Mandelbrot Set Performance 582

14.4.2 FE Solver Performance 582

14.5 Profiling Python Code 583

14.5.1 Scalene 584

14.5.2 Austin and FlameGraph 586

14.6 Multicore Computation with multiprocessing 589

14.7 Vectorization 591

14.8 Cython 594

14.8.1 Python Compiled with Cython 595

14.8.2 Parallel for Loops with Cython 598

14.8.3 Cython Performance 599

14.9 Pythran 599

14.9.1 Examples of Signature Comments 600

14.9.2 Python Compiled with Pythran; Parallel for Loops 601

14.9.3 Pythran Performance 603

14.10 Numba 604

14.10.1 Parallel for Loops with Numba 606

14.10.2 Numba Keyword Arguments nopython, fastmath 607

14.10.3 Numba Performance 607

14.10.4 Numba Limitations 607

14.11 f2py 608

14.12 Recipe 14-1: Accelerating MATLAB with Python on a Single Computer 612

14.12.1 Compile Python Modules in a MATLAB-Friendly Virtual Environment 612

14.12.2 MATLAB + Cython 612

14.12.3 MATLAB + Pythran 613

14.12.4 MATLAB + Numba 613

14.12.5 MATLAB + f2py 614

14.12.6 MATLAB + Python Performance Results 614

14.13 Distributed Memory Parallel Processing with Dask 615

14.13.1 Parallel MATLAB 616

14.13.2 Dask Execution Paradigm and Performance Expectations 617

14.13.3 Example 1: Sum of Prime Factors on One Computer 618

14.13.4 Setting Up a Dask Cluster on Multiple Computers 625

14.13.5 Example 2: Sum of Prime Factors on Multiple Computers 626

14.13.6 Example 3: A Gigapixel Mandelbrot Image 628

14.13.7 Example 4: Finite Element Frequency Domain Response 634

14.14 Recipe 14-2: Accelerating MATLAB with Python on Multiple Computers 646

14.14.1 Parallel Prime Sums with MATLAB 647

14.14.2 Parallel Gigapixel Mandelbrot with MATLAB 648

14.14.3 Parallel Direct Frequency Response with MATLAB 652

14.15 References 654

Chapter 15: Language Pitfalls 655

15.1 Troublesome Language Features 655

15.2 MATLAB 655

15.3 Python 658

Appendix A: MATLAB/Python Recipe Index 661

Appendix B: Generating Sample Data with Faker 663

Appendix C: Finite Element Source Listing 667

Appendix D: Python-to-MATLAB and MATLAB-to-Python Variable Converters 669

Appendix E: A Utility to Patch Cartopy to Use requests 681

Index 687

About the Author

Albert Danial

is an aerospace engineer with 30 years of experience, currently working for Northrop Grumman near Los Angeles. Before Northrop Grumman, he was a member of the NASTRAN Numerical Methods team at MSC Software and a systems analyst at SPARTA. He has a Bachelor of Aerospace Engineering degree from the Georgia Institute of Technology and master’s and Ph.D. degrees in Aeronautics and Astronautics from Purdue University. He is the author of cloc, the open source code counter.

Al has used MATLAB since 1990 and Python since 2006 for algorithm prototyping, earth science data processing, spacecraft mission planning, optimization, visualization, and countless utilities that simplify daily engineering work.

About the Technical Reviewers

Darrell Yocom

earned a master’s degree in Computer Science from USC and has over 40 years of experience programming in the aerospace industry. Through the years, he’s seen programming change from the monolithic spaghetti-Fortran programs of the 1970s to today’s object-oriented approaches. Darrell is an avid sailor and sails the Pacific weekly on his sailboat, Stargazer.

Phillip Feldman

, after receiving a PhD in Electrical Engineering from the University of Southern California in 1987, spent six years as a technology policy analyst with the RAND Corporation in Santa Monica, CA. Since then, he’s worked in the aerospace industry for over 20 years.

Much of this work involves communications satellites and related technology. Outside of work, Dr. Feldman has a broad range of interests, many of which are showcased on his website at http://phillipmfeldman.org .

..................Content has been hidden....................

You can't read the all page of ebook, please click here login for view all page.

Table of Contents for Front Matter

Create new playlist

Sign In

Sign Up

Python for MATLAB Development

Extend MATLAB with 300,000+ Modules from the Python Package Index

Table of Contents for
Front Matter