Education/GH Python 7. GH Python: Custom Subdivisions - 7-1. Vertex Control 1: Offset Vertices GH file # offset each vertex randomly and create a new mesh # input type - mesh : Mesh (Item Access), depth : float (Item Access) import Rhino.Geometry as rg import random def offsetVertex(mesh): mesh2 = rg.Mesh() # create a new mesh vtx = mesh.Vertices.ToPoint3dArray() # get all vertices in a list for i in range(len(vtx)): vtx2 = vtx[i] + rg.Vector3d(mesh.Normals[i]) * depth * random.random() #offset randomly mesh2.Vertices.Add(vtx2) mesh2.Faces.AddFaces(mesh.Faces) # add all faces at once mesh2.Normals.ComputeNormals() return mesh2 a = offsetVertex(mesh) - 7-2. - 7-3. - 7-4. - 7-5. - 7-6. - 7-7. - 7-8. - 7-9. - 7-10. - 7-11. - 7-12. - 7-13. - 7-14. - 7-15. - 7-16. - 7-17. - 7-18. - 7-19. - 7-20. - 7-21. - 7-23. - 7-24. - 7-25. - 7-26. - 7-27. - 7-28. - 7-29. - 7-30. - 7-31. - 7-32. - 7-33. - 7-34. - 7-35. - 7-36.
Functions - Learn Python - Free Interactive Python Tutorial What are Functions? Functions are a convenient way to divide your code into useful blocks, allowing us to order our code, make it more readable, reuse it and save some time. Also functions are a key way to define interfaces so programmers can share their code. How do you write functions in Python? As we have seen on previous tutorials, Python makes use of blocks. A block is a area of code of written in the format of: block_head: 1st block line 2nd block line ... Session Inactive Where a block line is more Python code (even another block), and the block head is of the following format: block_keyword block_name(argument1,argument2, ...) Functions in python are defined using the block keyword "def", followed with the function's name as the block's name. def my_function(): print("Hello From My Function!") Functions may also receive arguments (variables passed from the caller to the function). def my_function_with_args(username, greeting): print("Hello, %s , From My Function!
Data Analysis for Social Scientists Course Syllabus Skip Syllabus Description Introduction to the software R with exercises. Suggested resources for learning more on the web. Introduction to the power of data and data analysis, overview of what will be covered in the course. Basics of probability and introduction to random variables. Introduction to collecting data through surveys, web scraping, and other data collection methods. Builds on the basics from module 2 to cover joint, marginal, and conditional distributions. Discussion of moments of a distribution, expectation, and variance. Discussion of properties of special distributions with several examples. Deriving and assessing estimators. Understanding randomization in the context of experimentation. In-depth discussion of the linear model and the multivariate linear model. Covariates, fixed effects, and other functional forms. Introduction to the use of machine learning for prediction. Understanding the problem of endogeneity.
How should I start learning Python? - Quora Deep Learning with Python Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. "Examples chosen are not only useful but also highlight the flexibility of deep learning in solving problems in many different fields. ~ Gustavo Patino "All of the major topics and concepts of DL are covered and well explained, using code examples and diagrams, instead of mathematical formulas." ~ Srdjan Santic
Planes in Python with Python by Dale Fugier This guide provides an overview of a RhinoScriptSytntax Plane Geometry in Python. Planes Several RhinoscriptSyntax methods either require as an argument or return as a result a plane. Planes are represented by a Plane structure. plane contains Note in a Plane, the Y axis is automatically thought as it would be 90-degrees with the original X axis. Planes can be constructed in a number of ways. 1 2 3 4 5 6 7 8 9 import rhinoscriptsyntax as rs corners = rs.GetRectangle() if corners: plane = rs.PlaneFromPoints(corners[0], corners[1], corners[3]) print plane[0] print plane[1] print plane[2] Planes can also be created using the CreatePlane(), PlaneFromFrame, PlaneFromNormal, and PlaneFromPoints functions. Plane also have a number of properties that can be used to get or set the individual values in the Point object. To change origin of a Plane simply assign a new value to the .Origin property. 1 2 for p in plane: print p Also, read the Python primer Section 8.5 Planes
Python Functions A function is a block of organized, reusable code that is used to perform a single, related action. Functions provide better modularity for your application and a high degree of code reusing. As you already know, Python gives you many built-in functions like print(), etc. but you can also create your own functions. Defining a Function You can define functions to provide the required functionality. Function blocks begin with the keyword def followed by the function name and parentheses ( ( ) ).Any input parameters or arguments should be placed within these parentheses. Syntax def functionname( parameters ): "function_docstring" function_suite return [expression] By default, parameters have a positional behavior and you need to inform them in the same order that they were defined. Example The following function takes a string as input parameter and prints it on standard screen. def printme( str ): "This prints a passed string into this function" print str return Calling a Function #! #! #! #! #! #! #!
Lecture Notes | Data Mining | Sloan School of Management | MIT OpenCourseWare A Complete Guide on Getting Started with Deep Learning in Python Deep Learning, a prominent topic in Artificial Intelligence domain, has been in the spotlight for quite some time now. It is especially known for its breakthroughs in fields like Computer Vision and Game playing (Alpha GO), surpassing human ability. Since the last survey, there has been a drastic increase in the trends. Here is what Google trends shows us: If you are interested in the topic here’s an excellent non-technical introduction. Here our aim is to provide a learning path to all those who are new to deep learning and also the ones who want to explore it further. Step 0 : Pre-requisites It is recommended that before jumping on to Deep Learning, you should know the basics of Machine Learning. If you want a shorter version, here it is: Timeline : Suggested: 2-6 months Step 1 : Setup your Machine Before going on to the next step, make sure you have the supported hardware. A good enough GPU (4+ GB), preferably NvidiaAn OK CPU (eg. If you are still unsure, go through this hardware guide.
Rhino.Python 101 with unset You’ve just opened the first edition of the Rhino Python primer. This guide was originally written by David Rutten for Rhino 4 and VBscript and has now been translated to encompass Python for Rhino 6. As always, this primer is intended to teach programming to absolute beginners, people who have tinkered with programming a bit or expert programmers looking for a quick introduction to the methods in Rhino. Rhinoscript (VBscript) has been supported for many years, with a large user group and extensive support material. As well as giving a basic introduction, this primer looks to easily transition those familiar with VBscript into the world of Rhino Python. For this reason, David Rutten’s original primer has been used extensively as the underlying framework for this Python Primer. Similar to the previous primers, we have the advantage of using geometric and visual examples to help teach programming. Good luck!
Learn Python - Free Interactive Python Tutorial This site generously supported by DataCamp. DataCamp offers online interactive Python Tutorials for Data Science. Join 575,000 other learners and get started learning Python for data science today! Welcome to the LearnPython.org interactive Python tutorial. Whether you are an experienced programmer or not, this website is intended for everyone who wishes to learn the Python programming language. You are welcome to join our group on Facebook for questions, discussions and updates. Just click on the chapter you wish to begin from, and follow the instructions. Learn the Basics Data Science Tutorials Advanced Tutorials Other Python Tutorials DataCamp has tons of great interactive Python Tutorials covering data manipulation, data visualization, statistics, machine learning, and moreRead Python Tutorials and References course from After Hours Programming Contributing Tutorials Read more here: Contributing Tutorials
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