Download Hello World!: Computer Programming for Kids and Other by Warren Sande, Carter Sande PDF

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By Warren Sande, Carter Sande


A mild yet thorough advent to the area of desktop programming, it's written in language a 12-year-old can keep on with, yet someone who desires to how you can application a working laptop or computer can use it. Even adults. Written via Warren Sande and his son, Carter, and reviewed via expert educators, this e-book is kid-tested and parent-approved.

About This Book

Learn to speak for your machine in its personal language! even if you must create a online game, commence a company, or clear up an immense challenge, step one is studying to write down your individual courses. Programming is a enjoyable problem, and it's effortless to get started!

This up-to-date and revised version of hi international! introduces the realm of desktop programming in a transparent and interesting kind. Written by means of Warren Sande and his son, Carter, it's choked with examples that would get you considering and studying. Reviewed by means of specialist educators, this ebook is kid-tested and parent-approved. You don't want to know whatever approximately programming to take advantage of the ebook, simply the fundamentals of utilizing a working laptop or computer. for those who can begin a application and store a dossier, you'll be off and running!

Purchase of the print publication contains a unfastened booklet in PDF, Kindle, and ePub codecs from Manning Publications.

What'S Inside

• Explains options in transparent language
• plenty of photographs, cartoons, and enjoyable examples
• entire set of perform questions and exercises
• Illustrated in complete color
Hello World! makes use of Python, a programming language designed to be effortless to benefit. utilizing enjoyable examples, it brings to existence techniques of computing— looping, judgements, enter and output, info constructions, graphics—and many more.

About the Authors

Warren Sande is an digital platforms engineer who makes use of Python either as a "do anything" scripting language and to be able to train pcs and programming. Carter Sande is a highschool pupil who's enthusiastic about know-how. while he's no longer solving his school's community and supporting his classmates get better misplaced homework, he loves to trip his motorbike and write unfashionable video games.

Table of Contents

1. Getting Started
2. bear in mind This: reminiscence and Variables
3. uncomplicated Math
4. different types of Data
5. Input
6. GUIs—Graphical person Interfaces
7. judgements, Decisions
8. Loop the Loop
9. only for You—Comments
10. online game Time
11. Nested and Variable Loops
12. accumulating issues Together—Lists and Dictionaries
13. Functions
14. Objects
15. Modules
16. Graphics
17. Sprites and Collision Detection
18. a brand new type of Input—Events
19. Sound
20. extra GUIs
21. Print Formatting and Strings
22. dossier enter and Output
23. Take a Chance—Randomness
24. desktop Simulations
25. Skier Explained
26. Python Battle
27. What's subsequent?

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Additional info for Hello World!: Computer Programming for Kids and Other Beginners (2nd Edition)

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Py: def plot_features(im,locs,circle=False): """ Show image with features. input: im (image as array), locs (row, col, scale, orientation of each feature). 01)*2*pi x = r*cos(t) + c[0] y = r*sin(t) + c[1] plot(x,y,'b',linewidth=2) imshow(im) if circle: for p in locs: draw_circle(p[:2],p[2]) else: plot(locs[:,0],locs[:,1],'ob') axis('off') This will plot the location of the SIFT points as blue dots overlaid on the image. If the optional parameter circle is set to “True”, circles with radius equal to the scale of the feature will be drawn instead using the helper function draw_circle().

2 In the case of unit length vectors, the scalar product (without the arccos()) is equivalent to the standard Euclidean distance. 2 SIFT—Scale-Invariant Feature Transform | 41 Figure 2-5. An example of detecting and matching SIFT features between two images. 42 | Chapter 2: Local Image Descriptors Figure 2-6. An example of detecting and matching SIFT features between two images. 2 SIFT—Scale-Invariant Feature Transform | 43 (a) (b) Figure 2-7. An example of matching SIFT features between two images: (a) matches from features in the left image without using the two-sided match function; (b) the remaining matches after using the two-sided version.

Order=popularity&set=public& from=0&to=20&minx=-180&miny=-90&maxx=180&maxy=90&size=medium where minx, miny, maxx, maxy define the geographic area from which to select photos, (minimum longitude, latitude, maximum longitude and latitude, respectively), you will get the response in easy-to-parse JSON format. JSON is a common format for data 44 | Chapter 2: Local Image Descriptors transfer between web services and is more lightweight than XML and other alternatives. org/wiki/JSON. , which is usually photographed from Pennsylvania Avenue from the south side or from the north.

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